id stringlengths 9 16 | title stringlengths 4 278 | abstract stringlengths 3 4.08k | cs.HC bool 2 classes | cs.CE bool 2 classes | cs.SD bool 2 classes | cs.SI bool 2 classes | cs.AI bool 2 classes | cs.IR bool 2 classes | cs.LG bool 2 classes | cs.RO bool 2 classes | cs.CL bool 2 classes | cs.IT bool 2 classes | cs.SY bool 2 classes | cs.CV bool 2 classes | cs.CR bool 2 classes | cs.CY bool 2 classes | cs.MA bool 2 classes | cs.NE bool 2 classes | cs.DB bool 2 classes | Other bool 2 classes | __index_level_0__ int64 0 541k |
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2007.10712 | On Analyzing Antisocial Behaviors Amid COVID-19 Pandemic | The COVID-19 pandemic has developed to be more than a bio-crisis as global news has reported a sharp rise in xenophobia and discrimination in both online and offline communities. Such toxic behaviors take a heavy toll on society, especially during these daunting times. Despite the gravity of the issue, very few studies have studied online antisocial behaviors amid the COVID-19 pandemic. In this paper, we fill the research gap by collecting and annotating a large dataset of over 40 million COVID-19 related tweets. Specially, we propose an annotation framework to annotate the antisocial behavior tweets automatically. We also conduct an empirical analysis of our annotated dataset and found that new abusive lexicons are introduced amid the COVID-19 pandemic. Our study also identified the vulnerable targets of antisocial behaviors and the factors that influence the spreading of online antisocial content. | false | false | false | true | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 188,349 |
2012.02998 | Fusing Optical and SAR time series for LAI gap filling with multioutput
Gaussian processes | The availability of satellite optical information is often hampered by the natural presence of clouds, which can be problematic for many applications. Persistent clouds over agricultural fields can mask key stages of crop growth, leading to unreliable yield predictions. Synthetic Aperture Radar (SAR) provides all-weather imagery which can potentially overcome this limitation, but given its high and distinct sensitivity to different surface properties, the fusion of SAR and optical data still remains an open challenge. In this work, we propose the use of Multi-Output Gaussian Process (MOGP) regression, a machine learning technique that learns automatically the statistical relationships among multisensor time series, to detect vegetated areas over which the synergy between SAR-optical imageries is profitable. For this purpose, we use the Sentinel-1 Radar Vegetation Index (RVI) and Sentinel-2 Leaf Area Index (LAI) time series over a study area in north west of the Iberian peninsula. Through a physical interpretation of MOGP trained models, we show its ability to provide estimations of LAI even over cloudy periods using the information shared with RVI, which guarantees the solution keeps always tied to real measurements. Results demonstrate the advantage of MOGP especially for long data gaps, where optical-based methods notoriously fail. The leave-one-image-out assessment technique applied to the whole vegetation cover shows MOGP predictions improve standard GP estimations over short-time gaps (R$^2$ of 74\% vs 68\%, RMSE of 0.4 vs 0.44 $[m^2m^{-2}]$) and especially over long-time gaps (R$^2$ of 33\% vs 12\%, RMSE of 0.5 vs 1.09 $[m^2m^{-2}]$). | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 209,944 |
1905.02463 | Adaptive Generation of Unrestricted Adversarial Inputs | Neural networks are vulnerable to adversarially-constructed perturbations of their inputs. Most research so far has considered perturbations of a fixed magnitude under some $l_p$ norm. Although studying these attacks is valuable, there has been increasing interest in the construction of (and robustness to) unrestricted attacks, which are not constrained to a small and rather artificial subset of all possible adversarial inputs. We introduce a novel algorithm for generating such unrestricted adversarial inputs which, unlike prior work, is adaptive: it is able to tune its attacks to the classifier being targeted. It also offers a 400-2,000x speedup over the existing state of the art. We demonstrate our approach by generating unrestricted adversarial inputs that fool classifiers robust to perturbation-based attacks. We also show that, by virtue of being adaptive and unrestricted, our attack is able to defeat adversarial training against it. | false | false | false | false | false | false | true | false | false | false | false | true | true | false | false | false | false | false | 129,980 |
1410.7922 | Extended Dynamic Programming and Fast Multidimensional Search Algorithm
for Energy Minization in Stereo and Motion | This paper presents a novel extended dynamic programming approach for energy minimization (EDP) to solve the correspondence problem for stereo and motion. A significant speedup is achieved using a recursive minimum search strategy (RMS). The mentioned speedup is particularly important if the disparity space is 2D as well as 3D. The proposed RMS can also be applied in the well-known dynamic programming (DP) approach for stereo and motion. In this case, the general 2D problem of the global discrete energy minimization is reduced to several mutually independent sub-problems of the one-dimensional minimization. The EDP method is used when the approximation of the general 2D discrete energy minimization problem is considered. Then the RMS algorithm is an essential part of the EDP method. Using the EDP algorithm we obtain a lower energy bound than the graph cuts (GC) expansion technique on stereo and motion problems. The proposed calculation scheme possesses natural parallelism and can be realized on graphics processing unit (GPU) platforms, and can be potentially restricted further by the number of scanlines in the image plane. Furthermore, the RMS and EDP methods can be used in any optimization problem where the objective function meets specific conditions in the smoothness term. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 37,117 |
2107.06217 | What classifiers know what they don't? | Being uncertain when facing the unknown is key to intelligent decision making. However, machine learning algorithms lack reliable estimates about their predictive uncertainty. This leads to wrong and overly-confident decisions when encountering classes unseen during training. Despite the importance of equipping classifiers with uncertainty estimates ready for the real world, prior work has focused on small datasets and little or no class discrepancy between training and testing data. To close this gap, we introduce UIMNET: a realistic, ImageNet-scale test-bed to evaluate predictive uncertainty estimates for deep image classifiers. Our benchmark provides implementations of eight state-of-the-art algorithms, six uncertainty measures, four in-domain metrics, three out-domain metrics, and a fully automated pipeline to train, calibrate, ensemble, select, and evaluate models. Our test-bed is open-source and all of our results are reproducible from a fixed commit in our repository. Adding new datasets, algorithms, measures, or metrics is a matter of a few lines of code-in so hoping that UIMNET becomes a stepping stone towards realistic, rigorous, and reproducible research in uncertainty estimation. Our results show that ensembles of ERM classifiers as well as single MIMO classifiers are the two best alternatives currently available to measure uncertainty about both in-domain and out-domain classes. | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | false | false | 246,027 |
1908.07448 | Evaluating Contextualized Embeddings on 54 Languages in POS Tagging,
Lemmatization and Dependency Parsing | We present an extensive evaluation of three recently proposed methods for contextualized embeddings on 89 corpora in 54 languages of the Universal Dependencies 2.3 in three tasks: POS tagging, lemmatization, and dependency parsing. Employing the BERT, Flair and ELMo as pretrained embedding inputs in a strong baseline of UDPipe 2.0, one of the best-performing systems of the CoNLL 2018 Shared Task and an overall winner of the EPE 2018, we present a one-to-one comparison of the three contextualized word embedding methods, as well as a comparison with word2vec-like pretrained embeddings and with end-to-end character-level word embeddings. We report state-of-the-art results in all three tasks as compared to results on UD 2.2 in the CoNLL 2018 Shared Task. | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 142,298 |
2101.00784 | WearMask: Fast In-browser Face Mask Detection with Serverless Edge
Computing for COVID-19 | The COVID-19 epidemic has been a significant healthcare challenge in the United States. According to the Centers for Disease Control and Prevention (CDC), COVID-19 infection is transmitted predominately by respiratory droplets generated when people breathe, talk, cough, or sneeze. Wearing a mask is the primary, effective, and convenient method of blocking 80% of all respiratory infections. Therefore, many face mask detection and monitoring systems have been developed to provide effective supervision for hospitals, airports, publication transportation, sports venues, and retail locations. However, the current commercial face mask detection systems are typically bundled with specific software or hardware, impeding public accessibility. In this paper, we propose an in-browser serverless edge-computing based face mask detection solution, called Web-based efficient AI recognition of masks (WearMask), which can be deployed on any common devices (e.g., cell phones, tablets, computers) that have internet connections using web browsers, without installing any software. The serverless edge-computing design minimizes the extra hardware costs (e.g., specific devices or cloud computing servers). The contribution of the proposed method is to provide a holistic edge-computing framework of integrating (1) deep learning models (YOLO), (2) high-performance neural network inference computing framework (NCNN), and (3) a stack-based virtual machine (WebAssembly). For end-users, our web-based solution has advantages of (1) serverless edge-computing design with minimal device limitation and privacy risk, (2) installation free deployment, (3) low computing requirements, and (4) high detection speed. Our WearMask application has been launched with public access at facemask-detection.com. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 214,202 |
2104.07551 | HIVE-COTE 2.0: a new meta ensemble for time series classification | The Hierarchical Vote Collective of Transformation-based Ensembles (HIVE-COTE) is a heterogeneous meta ensemble for time series classification. HIVE-COTE forms its ensemble from classifiers of multiple domains, including phase-independent shapelets, bag-of-words based dictionaries and phase-dependent intervals. Since it was first proposed in 2016, the algorithm has remained state of the art for accuracy on the UCR time series classification archive. Over time it has been incrementally updated, culminating in its current state, HIVE-COTE 1.0. During this time a number of algorithms have been proposed which match the accuracy of HIVE-COTE. We propose comprehensive changes to the HIVE-COTE algorithm which significantly improve its accuracy and usability, presenting this upgrade as HIVE-COTE 2.0. We introduce two novel classifiers, the Temporal Dictionary Ensemble (TDE) and Diverse Representation Canonical Interval Forest (DrCIF), which replace existing ensemble members. Additionally, we introduce the Arsenal, an ensemble of ROCKET classifiers as a new HIVE-COTE 2.0 constituent. We demonstrate that HIVE-COTE 2.0 is significantly more accurate than the current state of the art on 112 univariate UCR archive datasets and 26 multivariate UEA archive datasets. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 230,461 |
2404.00798 | On Difficulties of Attention Factorization through Shared Memory | Transformers have revolutionized deep learning in numerous fields, including natural language processing, computer vision, and audio processing. Their strength lies in their attention mechanism, which allows for the discovering of complex input relationships. However, this mechanism's quadratic time and memory complexity pose challenges for larger inputs. Researchers are now investigating models like Linear Unified Nested Attention (Luna) or Memory Augmented Transformer, which leverage external learnable memory to either reduce the attention computation complexity down to linear, or to propagate information between chunks in chunk-wise processing. Our findings challenge the conventional thinking on these models, revealing that interfacing with the memory directly through an attention operation is suboptimal, and that the performance may be considerably improved by filtering the input signal before communicating with memory. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 443,108 |
1903.05994 | Can Adversarial Network Attack be Defended? | Machine learning has been successfully applied to complex network analysis in various areas, and graph neural networks (GNNs) based methods outperform others. Recently, adversarial attack on networks has attracted special attention since carefully crafted adversarial networks with slight perturbations on clean network may invalid lots of network applications, such as node classification, link prediction, and community detection etc. Such attacks are easily constructed with serious security threat to various analyze methods, including traditional methods and deep models. To the best of our knowledge, it is the first time that defense method against network adversarial attack is discussed. In this paper, we are interested in the possibility of defense against adversarial attack on network, and propose defense strategies for GNNs against attacks. First, we propose novel adversarial training strategies to improve GNNs' defensibility against attacks. Then, we analytically investigate the robustness properties for GNNs granted by the use of smooth defense, and propose two special smooth defense strategies: smoothing distillation and smoothing cross-entropy loss function. Both of them are capable of smoothing gradient of GNNs, and consequently reduce the amplitude of adversarial gradients, which benefits gradient masking from attackers. The comprehensive experiments show that our proposed strategies have great defensibility against different adversarial attacks on four real-world networks in different network analyze tasks. | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | false | 124,283 |
1912.02292 | Deep Double Descent: Where Bigger Models and More Data Hurt | We show that a variety of modern deep learning tasks exhibit a "double-descent" phenomenon where, as we increase model size, performance first gets worse and then gets better. Moreover, we show that double descent occurs not just as a function of model size, but also as a function of the number of training epochs. We unify the above phenomena by defining a new complexity measure we call the effective model complexity and conjecture a generalized double descent with respect to this measure. Furthermore, our notion of model complexity allows us to identify certain regimes where increasing (even quadrupling) the number of train samples actually hurts test performance. | false | false | false | false | false | false | true | false | false | false | false | true | false | false | false | true | false | false | 156,308 |
2007.05113 | FC2RN: A Fully Convolutional Corner Refinement Network for Accurate
Multi-Oriented Scene Text Detection | Recent scene text detection works mainly focus on curve text detection. However, in real applications, the curve texts are more scarce than the multi-oriented ones. Accurate detection of multi-oriented text with large variations of scales, orientations, and aspect ratios is of great significance. Among the multi-oriented detection methods, direct regression for the geometry of scene text shares a simple yet powerful pipeline and gets popular in academic and industrial communities, but it may produce imperfect detections, especially for long texts due to the limitation of the receptive field. In this work, we aim to improve this while keeping the pipeline simple. A fully convolutional corner refinement network (FC2RN) is proposed for accurate multi-oriented text detection, in which an initial corner prediction and a refined corner prediction are obtained at one pass. With a novel quadrilateral RoI convolution operation tailed for multi-oriented scene text, the initial quadrilateral prediction is encoded into the feature maps which can be further used to predict offset between the initial prediction and the ground-truth as well as output a refined confidence score. Experimental results on four public datasets including MSRA-TD500, ICDAR2017-RCTW, ICDAR2015, and COCO-Text demonstrate that FC2RN can outperform the state-of-the-art methods. The ablation study shows the effectiveness of corner refinement and scoring for accurate text localization. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 186,571 |
2006.00070 | Refined Reliability Combining for Binary Message Passing Decoding of
Product Codes | We propose a novel soft-aided iterative decoding algorithm for product codes (PCs). The proposed algorithm, named iterative bounded distance decoding with combined reliability (iBDD-CR), enhances the conventional iterative bounded distance decoding (iBDD) of PCs by exploiting some level of soft information. In particular, iBDD-CR can be seen as a modification of iBDD where the hard decisions of the row and column decoders are made based on a reliability estimate of the BDD outputs. The reliability estimates are derived using extrinsic message passing for generalized low-density-parity check (GLDPC) ensembles, which encompass PCs. We perform a density evolution analysis of iBDD-CR for transmission over the additive white Gaussian noise channel for the GLDPC ensemble. We consider both binary transmission and bit-interleaved coded modulation with quadrature amplitude modulation.We show that iBDD-CR achieves performance gains up to $0.51$ dB compared to iBDD with the same internal decoder data flow. This makes the algorithm an attractive solution for very high-throughput applications such as fiber-optic communications. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 179,357 |
2304.14094 | Categorical Foundations of Explainable AI: A Unifying Theory | Explainable AI (XAI) aims to address the human need for safe and reliable AI systems. However, numerous surveys emphasize the absence of a sound mathematical formalization of key XAI notions -- remarkably including the term "explanation" which still lacks a precise definition. To bridge this gap, this paper presents the first mathematically rigorous definitions of key XAI notions and processes, using the well-funded formalism of Category theory. We show that our categorical framework allows to: (i) model existing learning schemes and architectures, (ii) formally define the term "explanation", (iii) establish a theoretical basis for XAI taxonomies, and (iv) analyze commonly overlooked aspects of explaining methods. As a consequence, our categorical framework promotes the ethical and secure deployment of AI technologies as it represents a significant step towards a sound theoretical foundation of explainable AI. | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | false | false | 360,823 |
2310.19914 | Efficient entanglement purification based on noise guessing decoding | In this paper, we propose a novel bipartite entanglement purification protocol built upon hashing and upon the guessing random additive noise decoding (GRAND) approach recently devised for classical error correction codes. Our protocol offers substantial advantages over existing hashing protocols, requiring fewer qubits for purification, achieving higher fidelities, and delivering better yields with reduced computational costs. We provide numerical and semi-analytical results to corroborate our findings and provide a detailed comparison with the hashing protocol of Bennet et al. Although that pioneering work devised performance bounds, it did not offer an explicit construction for implementation. The present work fills that gap, offering both an explicit and more efficient purification method. We demonstrate that our protocol is capable of purifying states with noise on the order of 10% per Bell pair even with a small ensemble of 16 pairs. The work explores a measurement-based implementation of the protocol to address practical setups with noise. This work opens the path to practical and efficient entanglement purification using hashing-based methods with feasible computational costs. Compared to the original hashing protocol, the proposed method can achieve some desired fidelity with a number of initial resources up to one hundred times smaller. Therefore, the proposed method seems well-fit for future quantum networks with a limited number of resources and entails a relatively low computational overhead. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 404,181 |
1904.01814 | Deep Neural Networks for Rotation-Invariance Approximation and Learning | Based on the tree architecture, the objective of this paper is to design deep neural networks with two or more hidden layers (called deep nets) for realization of radial functions so as to enable rotational invariance for near-optimal function approximation in an arbitrarily high dimensional Euclidian space. It is shown that deep nets have much better performance than shallow nets (with only one hidden layer) in terms of approximation accuracy and learning capabilities. In particular, for learning radial functions, it is shown that near-optimal rate can be achieved by deep nets but not by shallow nets. Our results illustrate the necessity of depth in neural network design for realization of rotation-invariance target functions. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 126,259 |
2005.10019 | Every Colour You Are: Stance Prediction and Turnaround in Controversial
Issues | Web platforms have allowed political manifestation and debate for decades. Technology changes have brought new opportunities for expression, and the availability of longitudinal data of these debates entice new questions regarding who participates, and who updates their opinion. The aim of this work is to provide a methodology to measure these phenomena, and to test this methodology on a specific topic, abortion, as observed on one of the most popular micro-blogging platforms. To do so, we followed the discussion on Twitter about abortion in two Spanish-speaking countries from 2015 to 2018. Our main insights are two fold. On the one hand, people adopted new technologies to express their stances, particularly colored variations of heart emojis ([green heart] & [purple heart]) in a way that mirrored physical manifestations on abortion. On the other hand, even on issues with strong opinions, opinions can change, and these changes show differences in demographic groups. These findings imply that debate on the Web embraces new ways of stance adherence, and that changes of opinion can be measured and characterized. | false | false | false | true | false | false | false | false | false | false | false | false | false | true | false | false | false | false | 178,066 |
2206.11939 | Measuring Representational Robustness of Neural Networks Through Shared
Invariances | A major challenge in studying robustness in deep learning is defining the set of ``meaningless'' perturbations to which a given Neural Network (NN) should be invariant. Most work on robustness implicitly uses a human as the reference model to define such perturbations. Our work offers a new view on robustness by using another reference NN to define the set of perturbations a given NN should be invariant to, thus generalizing the reliance on a reference ``human NN'' to any NN. This makes measuring robustness equivalent to measuring the extent to which two NNs share invariances, for which we propose a measure called STIR. STIR re-purposes existing representation similarity measures to make them suitable for measuring shared invariances. Using our measure, we are able to gain insights into how shared invariances vary with changes in weight initialization, architecture, loss functions, and training dataset. Our implementation is available at: \url{https://github.com/nvedant07/STIR}. | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | false | false | 304,422 |
1410.0507 | Generating functionals for computational intelligence: the Fisher
information as an objective function for self-limiting Hebbian learning rules | Generating functionals may guide the evolution of a dynamical system and constitute a possible route for handling the complexity of neural networks as relevant for computational intelligence. We propose and explore a new objective function, which allows to obtain plasticity rules for the afferent synaptic weights. The adaption rules are Hebbian, self-limiting, and result from the minimization of the Fisher information with respect to the synaptic flux. We perform a series of simulations examining the behavior of the new learning rules in various circumstances. The vector of synaptic weights aligns with the principal direction of input activities, whenever one is present. A linear discrimination is performed when there are two or more principal directions; directions having bimodal firing-rate distributions, being characterized by a negative excess kurtosis, are preferred. We find robust performance and full homeostatic adaption of the synaptic weights results as a by-product of the synaptic flux minimization. This self-limiting behavior allows for stable online learning for arbitrary durations. The neuron acquires new information when the statistics of input activities is changed at a certain point of the simulation, showing however, a distinct resilience to unlearn previously acquired knowledge. Learning is fast when starting with randomly drawn synaptic weights and substantially slower when the synaptic weights are already fully adapted. | false | false | false | false | false | false | false | false | false | false | false | false | false | false | false | true | false | false | 36,476 |
1712.05927 | SRPGAN: Perceptual Generative Adversarial Network for Single Image Super
Resolution | Single image super resolution (SISR) is to reconstruct a high resolution image from a single low resolution image. The SISR task has been a very attractive research topic over the last two decades. In recent years, convolutional neural network (CNN) based models have achieved great performance on SISR task. Despite the breakthroughs achieved by using CNN models, there are still some problems remaining unsolved, such as how to recover high frequency details of high resolution images. Previous CNN based models always use a pixel wise loss, such as l2 loss. Although the high resolution images constructed by these models have high peak signal-to-noise ratio (PSNR), they often tend to be blurry and lack high-frequency details, especially at a large scaling factor. In this paper, we build a super resolution perceptual generative adversarial network (SRPGAN) framework for SISR tasks. In the framework, we propose a robust perceptual loss based on the discriminator of the built SRPGAN model. We use the Charbonnier loss function to build the content loss and combine it with the proposed perceptual loss and the adversarial loss. Compared with other state-of-the-art methods, our method has demonstrated great ability to construct images with sharp edges and rich details. We also evaluate our method on different benchmarks and compare it with previous CNN based methods. The results show that our method can achieve much higher structural similarity index (SSIM) scores on most of the benchmarks than the previous state-of-art methods. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 86,796 |
1709.09723 | Estimating a Separably-Markov Random Field (SMuRF) from Binary
Observations | A fundamental problem in neuroscience is to characterize the dynamics of spiking from the neurons in a circuit that is involved in learning about a stimulus or a contingency. A key limitation of current methods to analyze neural spiking data is the need to collapse neural activity over time or trials, which may cause the loss of information pertinent to understanding the function of a neuron or circuit. We introduce a new method that can determine not only the trial-to-trial dynamics that accompany the learning of a contingency by a neuron, but also the latency of this learning with respect to the onset of a conditioned stimulus. The backbone of the method is a separable two-dimensional (2D) random field (RF) model of neural spike rasters, in which the joint conditional intensity function of a neuron over time and trials depends on two latent Markovian state sequences that evolve separately but in parallel. Classical tools to estimate state-space models cannot be applied readily to our 2D separable RF model. We develop efficient statistical and computational tools to estimate the parameters of the separable 2D RF model. We apply these to data collected from neurons in the pre-frontal cortex (PFC) in an experiment designed to characterize the neural underpinnings of the associative learning of fear in mice. Overall, the separable 2D RF model provides a detailed, interpretable, characterization of the dynamics of neural spiking that accompany the learning of a contingency. | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | false | false | false | 81,673 |
2404.15328 | Time topological analysis of EEG using signature theory | Anomaly detection in multivariate signals is a task of paramount importance in many disciplines (epidemiology, finance, cognitive sciences and neurosciences, oncology, etc.). In this perspective, Topological Data Analysis (TDA) offers a battery of "shape" invariants that can be exploited for the implementation of an effective detection scheme. Our contribution consists of extending the constructions presented in \cite{chretienleveraging} on the construction of simplicial complexes from the Signatures of signals and their predictive capacities, rather than the use of a generic distance as in \cite{petri2014homological}. Signature theory is a new theme in Machine Learning arXiv:1603.03788 stemming from recent work on the notions of Rough Paths developed by Terry Lyons and his team \cite{lyons2002system} based on the formalism introduced by Chen \cite{chen1957integration}. We explore in particular the detection of changes in topology, based on tracking the evolution of homological persistence and the Betti numbers associated with the complex introduced in \cite{chretienleveraging}. We apply our tools for the analysis of brain signals such as EEG to detect precursor phenomena to epileptic seizures. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 449,047 |
1911.11552 | Network Intrusion Detection based on LSTM and Feature Embedding | Growing number of network devices and services have led to increasing demand for protective measures as hackers launch attacks to paralyze or steal information from victim systems. Intrusion Detection System (IDS) is one of the essential elements of network perimeter security which detects the attacks by inspecting network traffic packets or operating system logs. While existing works demonstrated effectiveness of various machine learning techniques, only few of them utilized the time-series information of network traffic data. Also, categorical information has not been included in neural network based approaches. In this paper, we propose network intrusion detection models based on sequential information using long short-term memory (LSTM) network and categorical information using the embedding technique. We have experimented the models with UNSW-NB15, which is a comprehensive network traffic dataset. The experiment results confirm that the proposed method improve the performance, observing binary classification accuracy of 99.72\%. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | true | 155,161 |
1304.3098 | Evidential Reasoning in Parallel Hierarchical Vision Programs | This paper presents an efficient adaptation and application of the Dempster-Shafer theory of evidence, one that can be used effectively in a massively parallel hierarchical system for visual pattern perception. It describes the techniques used, and shows in an extended example how they serve to improve the system's performance as it applies a multiple-level set of processes. | false | false | false | false | true | false | false | false | false | false | false | true | false | false | false | false | false | false | 23,814 |
2212.04606 | The R-algebra of Quasiknowledge and Convex Optimization | This article develops a convex description of a classical or quantum learner's or agent's state of knowledge about its environment, presented as a convex subset of a commutative R-algebra. With caveats, this leads to a generalization of certain semidefinite programs in quantum information (such as those describing the universal query algorithm dual to the quantum adversary bound, related to optimal learning or control of the environment) to the classical and faulty-quantum setting, which would not be possible with a naive description via joint probability distributions over environment and internal memory. More philosophically, it also makes an interpretation of the set of reduced density matrices as "states of knowledge" of an observer of its environment, related to these techniques, more explicit. As another example, I describe and solve a formal differential equation of states of knowledge in that algebra, where an agent obtains experimental data in a Poissonian process, and its state of knowledge evolves as an exponential power series. However, this framework currently lacks impressive applications, and I post it in part to solicit feedback and collaboration on those. In particular, it may be possible to develop it into a new framework for the design of experiments, e.g. the problem of finding maximally informative questions to ask human labelers or the environment in machine-learning problems. The parts of the article not related to quantum information don't assume knowledge of it. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 335,500 |
2302.11705 | ACE: Zero-Shot Image to Image Translation via Pretrained
Auto-Contrastive-Encoder | Image-to-image translation is a fundamental task in computer vision. It transforms images from one domain to images in another domain so that they have particular domain-specific characteristics. Most prior works train a generative model to learn the mapping from a source domain to a target domain. However, learning such mapping between domains is challenging because data from different domains can be highly unbalanced in terms of both quality and quantity. To address this problem, we propose a new approach to extract image features by learning the similarities and differences of samples within the same data distribution via a novel contrastive learning framework, which we call Auto-Contrastive-Encoder (ACE). ACE learns the content code as the similarity between samples with the same content information and different style perturbations. The design of ACE enables us to achieve zero-shot image-to-image translation with no training on image translation tasks for the first time. Moreover, our learning method can learn the style features of images on different domains effectively. Consequently, our model achieves competitive results on multimodal image translation tasks with zero-shot learning as well. Additionally, we demonstrate the potential of our method in transfer learning. With fine-tuning, the quality of translated images improves in unseen domains. Even though we use contrastive learning, all of our training can be performed on a single GPU with the batch size of 8. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 347,284 |
2102.00668 | Graphs of Joint Types, Noninteractive Simulation, and Stronger
Hypercontractivity | In this paper, we study the type graph, namely, a bipartite graph induced by a joint type. We investigate the maximum edge density of induced bipartite subgraphs of this graph having a number of vertices on each side on an exponential scale in the length $n$ of the type. This can be seen as an isoperimetric problem. We provide asymptotically sharp bounds for the exponent of the maximum edge density as the length of the type goes to infinity. We also study the biclique rate region of the type graph, which is defined as the set of $(R_{1},R_{2})$ such that there exists a biclique of the type graph which has respectively $2^{nR_{1}}$ and $2^{nR_{2}}$ vertices on the two sides. We provide asymptotically sharp bounds for the biclique rate region as well. We then discuss the connections of these results to noninteractive simulation and hypercontractivity inequalities. Furthermore, as an application of our results, a new outer bound for the zero-error capacity region of the binary adder channel is provided, which improves the previously best known bound, due to Austrin, Kaski, Koivisto, and Nederlof. Our proofs in this paper are based on the method of types and linear algebra. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 217,872 |
2008.07524 | Reinforcement Learning with Quantum Variational Circuits | The development of quantum computational techniques has advanced greatly in recent years, parallel to the advancements in techniques for deep reinforcement learning. This work explores the potential for quantum computing to facilitate reinforcement learning problems. Quantum computing approaches offer important potential improvements in time and space complexity over traditional algorithms because of its ability to exploit the quantum phenomena of superposition and entanglement. Specifically, we investigate the use of quantum variational circuits, a form of quantum machine learning. We present our techniques for encoding classical data for a quantum variational circuit, we further explore pure and hybrid quantum algorithms for DQN and Double DQN. Our results indicate both hybrid and pure quantum variational circuit have the ability to solve reinforcement learning tasks with a smaller parameter space. These comparison are conducted with two OpenAI Gym environments: CartPole and Blackjack, The success of this work is indicative of a strong future relationship between quantum machine learning and deep reinforcement learning. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 192,136 |
2409.19471 | SELP: Generating Safe and Efficient Task Plans for Robot Agents with
Large Language Models | Despite significant advancements in large language models (LLMs) that enhance robot agents' understanding and execution of natural language (NL) commands, ensuring the agents adhere to user-specified constraints remains challenging, particularly for complex commands and long-horizon tasks. To address this challenge, we present three key insights, equivalence voting, constrained decoding, and domain-specific fine-tuning, which significantly enhance LLM planners' capability in handling complex tasks. Equivalence voting ensures consistency by generating and sampling multiple Linear Temporal Logic (LTL) formulas from NL commands, grouping equivalent LTL formulas, and selecting the majority group of formulas as the final LTL formula. Constrained decoding then uses the generated LTL formula to enforce the autoregressive inference of plans, ensuring the generated plans conform to the LTL. Domain-specific fine-tuning customizes LLMs to produce safe and efficient plans within specific task domains. Our approach, Safe Efficient LLM Planner (SELP), combines these insights to create LLM planners to generate plans adhering to user commands with high confidence. We demonstrate the effectiveness and generalizability of SELP across different robot agents and tasks, including drone navigation and robot manipulation. For drone navigation tasks, SELP outperforms state-of-the-art planners by 10.8% in safety rate (i.e., finishing tasks conforming to NL commands) and by 19.8% in plan efficiency. For robot manipulation tasks, SELP achieves 20.4% improvement in safety rate. Our datasets for evaluating NL-to-LTL and robot task planning will be released in github.com/lt-asset/selp. | false | false | false | false | true | false | false | true | true | false | false | false | false | false | false | false | false | true | 492,696 |
2409.06727 | Data-driven methods for computational mechanics: A fair comparison
between neural networks based and model-free approaches | We present a comparison between two approaches to modelling hyperelastic material behaviour using data. The first approach is a novel approach based on Data-driven Computational Mechanics (DDCM) that completely bypasses the definition of a material model by using only data from simulations or real-life experiments to perform computations. The second is a neural network (NN) based approach, where a neural network is used as a constitutive model. It is trained on data to learn the underlying material behaviour and is implemented in the same way as conventional models. The DDCM approach has been extended to include strategies for recovering isotropic behaviour and local smoothing of data. These have proven to be critical in certain cases and increase accuracy in most cases. The NN approach contains certain elements to enforce principles such as material symmetry, thermodynamic consistency, and convexity. In order to provide a fair comparison between the approaches, they use the same data and solve the same numerical problems with a selection of problems highlighting the advantages and disadvantages of each approach. Both the DDCM and the NNs have shown acceptable performance. The DDCM performed better when applied to cases similar to those from which the data is gathered from, albeit at the expense of generality, whereas NN models were more advantageous when applied to wider range of applications. | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | false | false | false | 487,252 |
2311.06602 | BizBench: A Quantitative Reasoning Benchmark for Business and Finance | Answering questions within business and finance requires reasoning, precision, and a wide-breadth of technical knowledge. Together, these requirements make this domain difficult for large language models (LLMs). We introduce BizBench, a benchmark for evaluating models' ability to reason about realistic financial problems. BizBench comprises eight quantitative reasoning tasks, focusing on question-answering (QA) over financial data via program synthesis. We include three financially-themed code-generation tasks from newly collected and augmented QA data. Additionally, we isolate the reasoning capabilities required for financial QA: reading comprehension of financial text and tables for extracting intermediate values, and understanding financial concepts and formulas needed to calculate complex solutions. Collectively, these tasks evaluate a model's financial background knowledge, ability to parse financial documents, and capacity to solve problems with code. We conduct an in-depth evaluation of open-source and commercial LLMs, comparing and contrasting the behavior of code-focused and language-focused models. We demonstrate that the current bottleneck in performance is due to LLMs' limited business and financial understanding, highlighting the value of a challenging benchmark for quantitative reasoning within this domain. | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 407,002 |
1407.3068 | Deep Networks with Internal Selective Attention through Feedback
Connections | Traditional convolutional neural networks (CNN) are stationary and feedforward. They neither change their parameters during evaluation nor use feedback from higher to lower layers. Real brains, however, do. So does our Deep Attention Selective Network (dasNet) architecture. DasNets feedback structure can dynamically alter its convolutional filter sensitivities during classification. It harnesses the power of sequential processing to improve classification performance, by allowing the network to iteratively focus its internal attention on some of its convolutional filters. Feedback is trained through direct policy search in a huge million-dimensional parameter space, through scalable natural evolution strategies (SNES). On the CIFAR-10 and CIFAR-100 datasets, dasNet outperforms the previous state-of-the-art model. | false | false | false | false | false | false | true | false | false | false | false | true | false | false | false | true | false | false | 34,593 |
2211.10265 | Context Variance Evaluation of Pretrained Language Models for
Prompt-based Biomedical Knowledge Probing | Pretrained language models (PLMs) have motivated research on what kinds of knowledge these models learn. Fill-in-the-blanks problem (e.g., cloze tests) is a natural approach for gauging such knowledge. BioLAMA generates prompts for biomedical factual knowledge triples and uses the Top-k accuracy metric to evaluate different PLMs' knowledge. However, existing research has shown that such prompt-based knowledge probing methods can only probe a lower bound of knowledge. Many factors like prompt-based probing biases make the LAMA benchmark unreliable and unstable. This problem is more prominent in BioLAMA. The severe long-tailed distribution in vocabulary and large-N-M relation make the performance gap between LAMA and BioLAMA remain notable. To address these, we introduce context variance into the prompt generation and propose a new rank-change-based evaluation metric. Different from the previous known-unknown evaluation criteria, we propose the concept of "Misunderstand" in LAMA for the first time. Through experiments on 12 PLMs, our context variance prompts and Understand-Confuse-Misunderstand (UCM) metric makes BioLAMA more friendly to large-N-M relations and rare relations. We also conducted a set of control experiments to disentangle "understand" from just "read and copy". | false | false | false | false | true | false | false | false | true | false | false | false | false | false | false | false | false | false | 331,253 |
2205.13124 | PixelGame: Infrared small target segmentation as a Nash equilibrium | A key challenge of infrared small target segmentation (ISTS) is to balance false negative pixels (FNs) and false positive pixels (FPs). Traditional methods combine FNs and FPs into a single objective by weighted sum, and the optimization process is decided by one actor. Minimizing FNs and FPs with the same strategy leads to antagonistic decisions. To address this problem, we propose a competitive game framework (pixelGame) from a novel perspective for ISTS. In pixelGame, FNs and FPs are controlled by different player whose goal is to minimize their own utility function. FNs-player and FPs-player are designed with different strategies: One is to minimize FNs and the other is to minimize FPs. The utility function drives the evolution of the two participants in competition. We consider the Nash equilibrium of pixelGame as the optimal solution. In addition, we propose maximum information modulation (MIM) to highlight the tar-get information. MIM effectively focuses on the salient region including small targets. Extensive experiments on two standard public datasets prove the effectiveness of our method. Compared with other state-of-the-art methods, our method achieves better performance in terms of F1-measure (F1) and the intersection of union (IoU). | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 298,816 |
1808.10379 | Asymptotic analysis of the Friedkin-Johnsen model when the matrix of the
susceptibility weights approaches the identity matrix | In this paper we analyze the Friedkin-Johnsen model of opinions when the coefficients weighting the agent susceptibilities to interpersonal influence approach 1. We will show that in this case, under suitable assumptions, the model converges to a quasi-consensus condition among the agents. In general the achieved consensus value will be different to the one obtained by the corresponding DeGroot model | false | false | false | true | false | false | false | false | false | false | true | false | false | false | true | false | false | false | 106,371 |
1610.00017 | On Optimal Latency of Communications | In this paper we investigate the optimal latency of communications. Focusing on fixed rate communication without any feedback channel, this paper encompasses low-latency strategies with which one hop and multi-hop communication issues are treated from an information theoretic perspective. By defining the latency as the time required to make decisions, we prove that if short messages can be transmitted in parallel Gaussian channels, for example, via orthogonal frequency-division multiplexing (OFDM)-like signals, there exists an optimal low-latency strategy for every code. This can be achieved via early-detection schemes or asynchronous detections. We first provide the optimal achievable latency in additive white Gaussian noise (AWGN) channels for every channel code given a probability block error $\epsilon$. This can be obtained via sequential ratio tests or a "genie" aided, \textit{e.g}. error-detecting codes. Results demonstrate the effectiveness of the approach. Next, we show how early-detection can be effective with OFDM signals while maintaining its spectral efficiency via random coding or pre-coding random matrices. Finally, we explore the optimal low-latency strategy in multi-hop relaying schemes. For amplify-and-forward (AF) and decode-and-forward (DF) relaying schemes there exist an optimal achievable latency. In particular, we first show that there exist a better low-latency strategy, for which AF relays could transmit while receiving. This can be achieved by using amplify and forward combined with early detection. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 61,779 |
1606.01314 | Optimal Storage Allocation for Wireless Cloud Caching Systems with a
Limited Sum Storage Capacity | In wireless cloud storage systems, the recovery failure probability depends on not only wireless channel conditions but also storage size of each distributed storage node. For an efficient utilization of limited storage capacity and the performance characterization of allocation strategies, we asymptotically analyze the recovery failure probability of a wireless cloud storage system with a sum storage capacity constraint for both high SNR regime and low SNR regime. Then, we find the optimal storage allocation strategy across distributed storage nodes in terms of the asymptotic recovery failure probability. Our analysis reveals that the maximal symmetric allocation is optimal for high SNR regime and the minimal allocation (with $\lfloor T\rfloor$ complete storage nodes and an incomplete storage node) is optimal for low SNR regime, where $T$ is the sum storage capacity. Based on the numerical investigation, we also show that in intermediate SNR regime, a balance allocation between the minimal allocation and the maximal symmetric allocation would not be required if we select one between them according to SNR. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 56,790 |
2104.11549 | Antenna Efficiency in Massive MIMO Detection | In this paper, we consider the multi-user detection problem in a multiple-input multiple-output (MIMO) system, where the number of receive antennas at the base station (BS) grows infinitely large. We propose a new performance metric, called antenna efficiency, to characterize how fast the vector error probability (VEP) decreases as the number of receive antennas increases in the large system limit. We analyze the optimal maximum-likelihood (ML) detector and the simple zero-forcing (ZF) detector and prove that their antenna efficiency admits a simple closed form, which quantifies the impacts of the user-to-antenna ratio, the signal-to-noise ratio (SNR), and the constellation set on the VEP. Numerical results show that our analysis can well describe the empirical detection error performance in a realistic massive MIMO system. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 231,944 |
2409.01382 | Automatic Detection of LLM-generated Code: A Case Study of Claude 3
Haiku | Using Large Language Models (LLMs) has gained popularity among software developers for generating source code. However, the use of LLM-generated code can introduce risks of adding suboptimal, defective, and vulnerable code. This makes it necessary to devise methods for the accurate detection of LLM-generated code. Toward this goal, we perform a case study of Claude 3 Haiku (or Claude 3 for brevity) on CodeSearchNet dataset. We divide our analyses into two parts: function-level and class-level. We extract 22 software metric features, such as Code Lines and Cyclomatic Complexity, for each level of granularity. We then analyze code snippets generated by Claude 3 and their human-authored counterparts using the extracted features to understand how unique the code generated by Claude 3 is. In the following step, we use the unique characteristics of Claude 3-generated code to build Machine Learning (ML) models and identify which features of the code snippets make them more detectable by ML models. Our results indicate that Claude 3 tends to generate longer functions, but shorter classes than humans, and this characteristic can be used to detect Claude 3-generated code with ML models with 82% and 66% accuracies for function-level and class-level snippets, respectively. | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | false | true | 485,315 |
2009.08633 | fastHan: A BERT-based Multi-Task Toolkit for Chinese NLP | We present fastHan, an open-source toolkit for four basic tasks in Chinese natural language processing: Chinese word segmentation (CWS), Part-of-Speech (POS) tagging, named entity recognition (NER), and dependency parsing. The backbone of fastHan is a multi-task model based on a pruned BERT, which uses the first 8 layers in BERT. We also provide a 4-layer base model compressed from the 8-layer model. The joint-model is trained and evaluated on 13 corpora of four tasks, yielding near state-of-the-art (SOTA) performance in dependency parsing and NER, achieving SOTA performance in CWS and POS. Besides, fastHan's transferability is also strong, performing much better than popular segmentation tools on a non-training corpus. To better meet the need of practical application, we allow users to use their own labeled data to further fine-tune fastHan. In addition to its small size and excellent performance, fastHan is user-friendly. Implemented as a python package, fastHan isolates users from the internal technical details and is convenient to use. The project is released on Github. | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 196,300 |
2002.12507 | Decentralized Federated Learning via SGD over Wireless D2D Networks | Federated Learning (FL), an emerging paradigm for fast intelligent acquisition at the network edge, enables joint training of a machine learning model over distributed data sets and computing resources with limited disclosure of local data. Communication is a critical enabler of large-scale FL due to significant amount of model information exchanged among edge devices. In this paper, we consider a network of wireless devices sharing a common fading wireless channel for the deployment of FL. Each device holds a generally distinct training set, and communication typically takes place in a Device-to-Device (D2D) manner. In the ideal case in which all devices within communication range can communicate simultaneously and noiselessly, a standard protocol that is guaranteed to converge to an optimal solution of the global empirical risk minimization problem under convexity and connectivity assumptions is Decentralized Stochastic Gradient Descent (DSGD). DSGD integrates local SGD steps with periodic consensus averages that require communication between neighboring devices. In this paper, wireless protocols are proposed that implement DSGD by accounting for the presence of path loss, fading, blockages, and mutual interference. The proposed protocols are based on graph coloring for scheduling and on both digital and analog transmission strategies at the physical layer, with the latter leveraging over-the-air computing via sparsity-based recovery. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | true | 166,056 |
2401.07543 | Must: Maximizing Latent Capacity of Spatial Transcriptomics Data | Spatial transcriptomics (ST) technologies have revolutionized the study of gene expression patterns in tissues by providing multimodality data in transcriptomic, spatial, and morphological, offering opportunities for understanding tissue biology beyond transcriptomics. However, we identify the modality bias phenomenon in ST data species, i.e., the inconsistent contribution of different modalities to the labels leads to a tendency for the analysis methods to retain the information of the dominant modality. How to mitigate the adverse effects of modality bias to satisfy various downstream tasks remains a fundamental challenge. This paper introduces Multiple-modality Structure Transformation, named MuST, a novel methodology to tackle the challenge. MuST integrates the multi-modality information contained in the ST data effectively into a uniform latent space to provide a foundation for all the downstream tasks. It learns intrinsic local structures by topology discovery strategy and topology fusion loss function to solve the inconsistencies among different modalities. Thus, these topology-based and deep learning techniques provide a solid foundation for a variety of analytical tasks while coordinating different modalities. The effectiveness of MuST is assessed by performance metrics and biological significance. The results show that it outperforms existing state-of-the-art methods with clear advantages in the precision of identifying and preserving structures of tissues and biomarkers. MuST offers a versatile toolkit for the intricate analysis of complex biological systems. | false | true | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | 421,584 |
1611.05181 | Graph Learning from Data under Structural and Laplacian Constraints | Graphs are fundamental mathematical structures used in various fields to represent data, signals and processes. In this paper, we propose a novel framework for learning/estimating graphs from data. The proposed framework includes (i) formulation of various graph learning problems, (ii) their probabilistic interpretations and (iii) associated algorithms. Specifically, graph learning problems are posed as estimation of graph Laplacian matrices from some observed data under given structural constraints (e.g., graph connectivity and sparsity level). From a probabilistic perspective, the problems of interest correspond to maximum a posteriori (MAP) parameter estimation of Gaussian-Markov random field (GMRF) models, whose precision (inverse covariance) is a graph Laplacian matrix. For the proposed graph learning problems, specialized algorithms are developed by incorporating the graph Laplacian and structural constraints. The experimental results demonstrate that the proposed algorithms outperform the current state-of-the-art methods in terms of accuracy and computational efficiency. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 63,969 |
2203.14085 | Near-Infrared Depth-Independent Image Dehazing using Haar Wavelets | We propose a fusion algorithm for haze removal that combines color information from an RGB image and edge information extracted from its corresponding NIR image using Haar wavelets. The proposed algorithm is based on the key observation that NIR edge features are more prominent in the hazy regions of the image than the RGB edge features in those same regions. To combine the color and edge information, we introduce a haze-weight map which proportionately distributes the color and edge information during the fusion process. Because NIR images are, intrinsically, nearly haze-free, our work makes no assumptions like existing works that rely on a scattering model and essentially designing a depth-independent method. This helps in minimizing artifacts and gives a more realistic sense to the restored haze-free image. Extensive experiments show that the proposed algorithm is both qualitatively and quantitatively better on several key metrics when compared to existing state-of-the-art methods. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 287,871 |
1805.10702 | Study of Unique-Word Based GFDM Transmission Systems | In this paper, we propose the use of a deterministic sequence, known as unique word (UW), instead of the cyclic prefix (CP) in generalized frequency division multiplexing (GFDM) systems. The UW consists of known sequences that, if not null, can be used advantageously for synchronization and channel estimation purposes. In addition, UW allows the application of a highly efficient linear minimum mean squared error (LMMSE) smoother for noise reduction at the receiver. To avoid the conditions of non-orthogonality caused by the insertion of the UW and performance degradation in time varying frequency-selective channels, we use frequency-shift offset quadrature amplitude modulation (FS-OQAM). We present a signal model of a UW-GFDM system considering a single and multiple UWs. We then develop an LMMSE receive filter for signal reception of the proposed UW-GFDM system. Simulations show that the proposed UW-GFDM system outperforms prior work. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 98,746 |
1309.7430 | Pilot Beam Pattern Design for Channel Estimation in Massive MIMO Systems | In this paper, the problem of pilot beam pattern design for channel estimation in massive multiple-input multiple-output systems with a large number of transmit antennas at the base station is considered, and a new algorithm for pilot beam pattern design for optimal channel estimation is proposed under the assumption that the channel is a stationary Gauss-Markov random process. The proposed algorithm designs the pilot beam pattern sequentially by exploiting the properties of Kalman filtering and the associated prediction error covariance matrices and also the channel statistics such as spatial and temporal channel correlation. The resulting design generates a sequentially-optimal sequence of pilot beam patterns with low complexity for a given set of system parameters. Numerical results show the effectiveness of the proposed algorithm. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 27,371 |
2401.11881 | Modelling the Dynamics of Identity and Fairness in Ultimatum Game | Allocation games are zero-sum games that model the distribution of resources among multiple agents. In this paper, we explore the interplay between an \textit{subjective identity} and its impact on notions of fairness in allocation. The sense of identity in agents is known to lead to responsible decision-making in non-cooperative, non-zero-sum games like Prisoners' Dilemma, and is a desirable feature to add into agent models. However, when it comes to allocation, the sense of identity can be shown to exacerbate inequities in allocation, giving no rational incentive for agents to act fairly towards one another. This lead us to introduce a sense of fairness as an innate characteristic of autonomous agency. For this, we implement the well-known Ultimatum Game between two agents, where their sense of identity association and their sense of fairness are both varied. We study the points at which agents find it no longer rational to identify with the other agent, and uphold their sense of fairness, and vice versa. Such a study also helps us discern the subtle difference between responsibility and fairness when it comes to autonomous agency. | false | false | false | false | false | false | false | false | false | false | false | false | false | false | true | false | false | true | 423,190 |
2102.10457 | Development of the first Portuguese radar tracking sensor for Space
Debris | Currently, space debris represents a threat for satellites and space-based operations, both in-orbit and during the launching process. The yearly increase in space debris represents a serious concern to major space agencies leading to the development of dedicated space programs to deal with this issue. Ground-based radars can detect Earth orbiting debris down to a few square centimeters and therefore constitute a major building block of a space debris monitoring system. New radar sensors are required in Europe to enhance capabilities and availability of its small radar network capable of tracking and surveying space objects and to respond to the debris increase expected from the New Space economy activities. This article presents ATLAS, a new tracking radar system for debris detection located in Portugal. It starts by an extensive technical description of all the system components followed by a study that estimates its future performance. A section dedicated to waveform design is also presented, since the system allows the usage of several types of pulse modulation schemes such as LFM and phase coded modulations while enabling the development and testing of more advanced ones. By presenting an architecture that is highly modular with fully digital signal processing, ATLAS establishes a platform for fast and easy development, research and innovation. The system follows the use of Commercial-Off-The-Shelf technologies and Open Systems which is unique among current radar systems. | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | 221,099 |
2308.02226 | Learning to Paraphrase Sentences to Different Complexity Levels | While sentence simplification is an active research topic in NLP, its adjacent tasks of sentence complexification and same-level paraphrasing are not. To train models on all three tasks, we present two new unsupervised datasets. We compare these datasets, one labeled by a weak classifier and the other by a rule-based approach, with a single supervised dataset. Using these three datasets for training, we perform extensive experiments on both multitasking and prompting strategies. Compared to other systems trained on unsupervised parallel data, models trained on our weak classifier labeled dataset achieve state-of-the-art performance on the ASSET simplification benchmark. Our models also outperform previous work on sentence level targeting. Finally, we establish how a handful of Large Language Models perform on these tasks under a zero-shot setting. | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 383,545 |
2411.18369 | G3Flow: Generative 3D Semantic Flow for Pose-aware and Generalizable
Object Manipulation | Recent advances in imitation learning for 3D robotic manipulation have shown promising results with diffusion-based policies. However, achieving human-level dexterity requires seamless integration of geometric precision and semantic understanding. We present G3Flow, a novel framework that constructs real-time semantic flow, a dynamic, object-centric 3D semantic representation by leveraging foundation models. Our approach uniquely combines 3D generative models for digital twin creation, vision foundation models for semantic feature extraction, and robust pose tracking for continuous semantic flow updates. This integration enables complete semantic understanding even under occlusions while eliminating manual annotation requirements. By incorporating semantic flow into diffusion policies, we demonstrate significant improvements in both terminal-constrained manipulation and cross-object generalization. Extensive experiments across five simulation tasks show that G3Flow consistently outperforms existing approaches, achieving up to 68.3% and 50.1% average success rates on terminal-constrained manipulation and cross-object generalization tasks respectively. Our results demonstrate the effectiveness of G3Flow in enhancing real-time dynamic semantic feature understanding for robotic manipulation policies. | false | false | false | false | true | false | false | true | false | false | true | true | false | false | false | false | false | false | 511,844 |
2003.05438 | Un-Mix: Rethinking Image Mixtures for Unsupervised Visual Representation
Learning | The recently advanced unsupervised learning approaches use the siamese-like framework to compare two "views" from the same image for learning representations. Making the two views distinctive is a core to guarantee that unsupervised methods can learn meaningful information. However, such frameworks are sometimes fragile on overfitting if the augmentations used for generating two views are not strong enough, causing the over-confident issue on the training data. This drawback hinders the model from learning subtle variance and fine-grained information. To address this, in this work we aim to involve the distance concept on label space in the unsupervised learning and let the model be aware of the soft degree of similarity between positive or negative pairs through mixing the input data space, to further work collaboratively for the input and loss spaces. Despite its conceptual simplicity, we show empirically that with the solution -- Unsupervised image mixtures (Un-Mix), we can learn subtler, more robust and generalized representations from the transformed input and corresponding new label space. Extensive experiments are conducted on CIFAR-10, CIFAR-100, STL-10, Tiny ImageNet and standard ImageNet with popular unsupervised methods SimCLR, BYOL, MoCo V1&V2, SwAV, etc. Our proposed image mixture and label assignment strategy can obtain consistent improvement by 1~3% following exactly the same hyperparameters and training procedures of the base methods. Code is publicly available at https://github.com/szq0214/Un-Mix. | false | false | false | false | false | false | true | false | false | false | false | true | false | false | false | false | false | false | 167,856 |
2310.18382 | From Generative AI to Generative Internet of Things: Fundamentals,
Framework, and Outlooks | Generative Artificial Intelligence (GAI) possesses the capabilities of generating realistic data and facilitating advanced decision-making. By integrating GAI into modern Internet of Things (IoT), Generative Internet of Things (GIoT) is emerging and holds immense potential to revolutionize various aspects of society, enabling more efficient and intelligent IoT applications, such as smart surveillance and voice assistants. In this article, we present the concept of GIoT and conduct an exploration of its potential prospects. Specifically, we first overview four GAI techniques and investigate promising GIoT applications. Then, we elaborate on the main challenges in enabling GIoT and propose a general GAI-based secure incentive mechanism framework to address them, in which we adopt Generative Diffusion Models (GDMs) for incentive mechanism designs and apply blockchain technologies for secure GIoT management. Moreover, we conduct a case study on modern Internet of Vehicle traffic monitoring, which utilizes GDMs to generate effective contracts for incentivizing users to contribute sensing data with high quality. Finally, we suggest several open directions worth investigating for the future popularity of GIoT. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | true | 403,522 |
2201.04387 | Maximizing Self-supervision from Thermal Image for Effective
Self-supervised Learning of Depth and Ego-motion | Recently, self-supervised learning of depth and ego-motion from thermal images shows strong robustness and reliability under challenging scenarios. However, the inherent thermal image properties such as weak contrast, blurry edges, and noise hinder to generate effective self-supervision from thermal images. Therefore, most research relies on additional self-supervision sources such as well-lit RGB images, generative models, and Lidar information. In this paper, we conduct an in-depth analysis of thermal image characteristics that degenerates self-supervision from thermal images. Based on the analysis, we propose an effective thermal image mapping method that significantly increases image information, such as overall structure, contrast, and details, while preserving temporal consistency. The proposed method shows outperformed depth and pose results than previous state-of-the-art networks without leveraging additional RGB guidance. | false | false | false | false | false | false | false | true | false | false | false | true | false | false | false | false | false | false | 275,087 |
2312.15246 | A Theory of Non-Acyclic Generative Flow Networks | GFlowNets is a novel flow-based method for learning a stochastic policy to generate objects via a sequence of actions and with probability proportional to a given positive reward. We contribute to relaxing hypotheses limiting the application range of GFlowNets, in particular: acyclicity (or lack thereof). To this end, we extend the theory of GFlowNets on measurable spaces which includes continuous state spaces without cycle restrictions, and provide a generalization of cycles in this generalized context. We show that losses used so far push flows to get stuck into cycles and we define a family of losses solving this issue. Experiments on graphs and continuous tasks validate those principles. | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | false | true | 417,939 |
2110.03900 | Neural Strokes: Stylized Line Drawing of 3D Shapes | This paper introduces a model for producing stylized line drawings from 3D shapes. The model takes a 3D shape and a viewpoint as input, and outputs a drawing with textured strokes, with variations in stroke thickness, deformation, and color learned from an artist's style. The model is fully differentiable. We train its parameters from a single training drawing of another 3D shape. We show that, in contrast to previous image-based methods, the use of a geometric representation of 3D shape and 2D strokes allows the model to transfer important aspects of shape and texture style while preserving contours. Our method outputs the resulting drawing in a vector representation, enabling richer downstream analysis or editing in interactive applications. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | true | 259,673 |
1909.08297 | Transferable Feature Representation for Visible-to-Infrared
Cross-Dataset Human Action Recognition | Recently, infrared human action recognition has attracted increasing attention for it has many advantages over visible light, that is, being robust to illumination change and shadows. However, the infrared action data is limited until now, which degrades the performance of infrared action recognition. Motivated by the idea of transfer learning, an infrared human action recognition framework using auxiliary data from visible light is proposed to solve the problem of limited infrared action data. In the proposed framework, we first construct a novel Cross-Dataset Feature Alignment and Generalization (CDFAG) framework to map the infrared data and visible light data into a common feature space, where Kernel Manifold Alignment (KEMA) and a dual alignedto-generalized encoders (AGE) model are employed to represent the feature. Then, a support vector machine (SVM) is trained, using both the infrared data and visible light data, and can classify the features derived from infrared data. The proposed method is evaluated on InfAR, which is a publicly available infrared human action dataset. To build up auxiliary data, we set up a novel visible light action dataset XD145. Experimental results show that the proposed method can achieve state-of-the-art performance compared with several transfer learning and domain adaptation methods. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 145,949 |
2209.14937 | NAG-GS: Semi-Implicit, Accelerated and Robust Stochastic Optimizer | Classical machine learning models such as deep neural networks are usually trained by using Stochastic Gradient Descent-based (SGD) algorithms. The classical SGD can be interpreted as a discretization of the stochastic gradient flow. In this paper we propose a novel, robust and accelerated stochastic optimizer that relies on two key elements: (1) an accelerated Nesterov-like Stochastic Differential Equation (SDE) and (2) its semi-implicit Gauss-Seidel type discretization. The convergence and stability of the obtained method, referred to as NAG-GS, are first studied extensively in the case of the minimization of a quadratic function. This analysis allows us to come up with an optimal learning rate in terms of the convergence rate while ensuring the stability of NAG-GS. This is achieved by the careful analysis of the spectral radius of the iteration matrix and the covariance matrix at stationarity with respect to all hyperparameters of our method. Further, we show that NAG- GS is competitive with state-of-the-art methods such as momentum SGD with weight decay and AdamW for the training of machine learning models such as the logistic regression model, the residual networks models on standard computer vision datasets, Transformers in the frame of the GLUE benchmark and the recent Vision Transformers. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | true | 320,404 |
2304.03843 | Why think step by step? Reasoning emerges from the locality of
experience | Humans have a powerful and mysterious capacity to reason. Working through a set of mental steps enables us to make inferences we would not be capable of making directly even though we get no additional data from the world. Similarly, when large language models generate intermediate steps (a chain of thought) before answering a question, they often produce better answers than they would directly. We investigate why and how chain-of-thought reasoning is useful in language models, testing the hypothesis that reasoning is effective when training data consists of overlapping local clusters of variables that influence each other strongly. These training conditions enable the chaining of accurate local inferences to estimate relationships between variables that were not seen together in training. We prove that there will exist a "reasoning gap", where reasoning through intermediate variables reduces bias, for the simple case of an autoregressive density estimator trained on local samples from a chain-structured probabilistic model. We then test our hypothesis experimentally in more complex models, training an autoregressive language model on samples from Bayes nets but only including a subset of variables in each sample. We test language models' ability to match conditional probabilities with and without intermediate reasoning steps, finding that intermediate steps are only helpful when the training data is locally structured with respect to dependencies between variables. The combination of locally structured observations and reasoning is much more data-efficient than training on all variables. Our results illustrate how the effectiveness of reasoning step by step is rooted in the local statistical structure of the training data. | false | false | false | false | true | false | true | false | true | false | false | false | false | false | false | false | false | false | 356,961 |
2006.06730 | Is deep learning necessary for simple classification tasks? | Automated machine learning (AutoML) and deep learning (DL) are two cutting-edge paradigms used to solve a myriad of inductive learning tasks. In spite of their successes, little guidance exists for when to choose one approach over the other in the context of specific real-world problems. Furthermore, relatively few tools exist that allow the integration of both AutoML and DL in the same analysis to yield results combining both of their strengths. Here, we seek to address both of these issues, by (1.) providing a head-to-head comparison of AutoML and DL in the context of binary classification on 6 well-characterized public datasets, and (2.) evaluating a new tool for genetic programming-based AutoML that incorporates deep estimators. Our observations suggest that AutoML outperforms simple DL classifiers when trained on similar datasets for binary classification but integrating DL into AutoML improves classification performance even further. However, the substantial time needed to train AutoML+DL pipelines will likely outweigh performance advantages in many applications. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | true | false | false | 181,532 |
2310.11486 | End-to-End real time tracking of children's reading with pointer network | In this work, we explore how a real time reading tracker can be built efficiently for children's voices. While previously proposed reading trackers focused on ASR-based cascaded approaches, we propose a fully end-to-end model making it less prone to lags in voice tracking. We employ a pointer network that directly learns to predict positions in the ground truth text conditioned on the streaming speech. To train this pointer network, we generate ground truth training signals by using forced alignment between the read speech and the text being read on the training set. Exploring different forced alignment models, we find a neural attention based model is at least as close in alignment accuracy to the Montreal Forced Aligner, but surprisingly is a better training signal for the pointer network. Our results are reported on one adult speech data (TIMIT) and two children's speech datasets (CMU Kids and Reading Races). Our best model can accurately track adult speech with 87.8% accuracy and the much harder and disfluent children's speech with 77.1% accuracy on CMU Kids data and a 65.3% accuracy on the Reading Races dataset. | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | false | false | 400,662 |
2204.02446 | Detecting Cloud-Based Phishing Attacks by Combining Deep Learning Models | Web-based phishing attacks nowadays exploit popular cloud web hosting services and apps such as Google Sites and Typeform for hosting their attacks. Since these attacks originate from reputable domains and IP addresses of the cloud services, traditional phishing detection methods such as IP reputation monitoring and blacklisting are not very effective. Here we investigate the effectiveness of deep learning models in detecting this class of cloud-based phishing attacks. Specifically, we evaluate deep learning models for three phishing detection methods--LSTM model for URL analysis, YOLOv2 model for logo analysis, and triplet network model for visual similarity analysis. We train the models using well-known datasets and test their performance on cloud-based phishing attacks in the wild. Our results qualitatively explain why the models succeed or fail. Furthermore, our results highlight how combining results from the individual models can improve the effectiveness of detecting cloud-based phishing attacks. | false | false | false | false | true | false | false | false | false | false | false | true | true | false | false | false | false | false | 289,943 |
1802.07954 | The State of the Art in Integrating Machine Learning into Visual
Analytics | Visual analytics systems combine machine learning or other analytic techniques with interactive data visualization to promote sensemaking and analytical reasoning. It is through such techniques that people can make sense of large, complex data. While progress has been made, the tactful combination of machine learning and data visualization is still under-explored. This state-of-the-art report presents a summary of the progress that has been made by highlighting and synthesizing select research advances. Further, it presents opportunities and challenges to enhance the synergy between machine learning and visual analytics for impactful future research directions. | true | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 91,005 |
2104.05196 | StylePTB: A Compositional Benchmark for Fine-grained Controllable Text
Style Transfer | Text style transfer aims to controllably generate text with targeted stylistic changes while maintaining core meaning from the source sentence constant. Many of the existing style transfer benchmarks primarily focus on individual high-level semantic changes (e.g. positive to negative), which enable controllability at a high level but do not offer fine-grained control involving sentence structure, emphasis, and content of the sentence. In this paper, we introduce a large-scale benchmark, StylePTB, with (1) paired sentences undergoing 21 fine-grained stylistic changes spanning atomic lexical, syntactic, semantic, and thematic transfers of text, as well as (2) compositions of multiple transfers which allow modeling of fine-grained stylistic changes as building blocks for more complex, high-level transfers. By benchmarking existing methods on StylePTB, we find that they struggle to model fine-grained changes and have an even more difficult time composing multiple styles. As a result, StylePTB brings novel challenges that we hope will encourage future research in controllable text style transfer, compositional models, and learning disentangled representations. Solving these challenges would present important steps towards controllable text generation. | false | false | false | false | true | false | true | false | true | false | false | false | false | false | false | false | false | false | 229,632 |
2208.03083 | Neural Network Verification using Residual Reasoning | With the increasing integration of neural networks as components in mission-critical systems, there is an increasing need to ensure that they satisfy various safety and liveness requirements. In recent years, numerous sound and complete verification methods have been proposed towards that end, but these typically suffer from severe scalability limitations. Recent work has proposed enhancing such verification techniques with abstraction-refinement capabilities, which have been shown to boost scalability: instead of verifying a large and complex network, the verifier constructs and then verifies a much smaller network, whose correctness implies the correctness of the original network. A shortcoming of such a scheme is that if verifying the smaller network fails, the verifier needs to perform a refinement step that increases the size of the network being verified, and then start verifying the new network from scratch - effectively "wasting" its earlier work on verifying the smaller network. In this paper, we present an enhancement to abstraction-based verification of neural networks, by using residual reasoning: the process of utilizing information acquired when verifying an abstract network, in order to expedite the verification of a refined network. In essence, the method allows the verifier to store information about parts of the search space in which the refined network is guaranteed to behave correctly, and allows it to focus on areas where bugs might be discovered. We implemented our approach as an extension to the Marabou verifier, and obtained promising results. | false | false | false | false | false | false | false | false | false | false | false | false | false | false | false | true | false | false | 311,671 |
2112.01830 | Table2Vec: Automated Universal Representation Learning to Encode
All-round Data DNA for Benchmarkable and Explainable Enterprise Data Science | Enterprise data typically involves multiple heterogeneous data sources and external data that respectively record business activities, transactions, customer demographics, status, behaviors, interactions and communications with the enterprise, and the consumption and feedback of its products, services, production, marketing, operations, and management, etc. A critical challenge in enterprise data science is to enable an effective whole-of-enterprise data understanding and data-driven discovery and decision-making on all-round enterprise DNA. We introduce a neural encoder Table2Vec for automated universal representation learning of entities such as customers from all-round enterprise DNA with automated data characteristics analysis and data quality augmentation. The learned universal representations serve as representative and benchmarkable enterprise data genomes and can be used for enterprise-wide and domain-specific learning tasks. Table2Vec integrates automated universal representation learning on low-quality enterprise data and downstream learning tasks. We illustrate Table2Vec in characterizing all-round customer data DNA in an enterprise on complex heterogeneous multi-relational big tables to build universal customer vector representations. The learned universal representation of each customer is all-round, representative and benchmarkable to support both enterprise-wide and domain-specific learning goals and tasks in enterprise data science. Table2Vec significantly outperforms the existing shallow, boosting and deep learning methods typically used for enterprise analytics. We further discuss the research opportunities, directions and applications of automated universal enterprise representation and learning and the learned enterprise data DNA for automated, all-purpose, whole-of-enterprise and ethical machine learning and data science. | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | false | false | 269,629 |
2405.19019 | Physics-Aware Neural Implicit Solvers for multiscale, parametric PDEs
with applications in heterogeneous media | We propose Physics-Aware Neural Implicit Solvers (PANIS), a novel, data-driven framework for learning surrogates for parametrized Partial Differential Equations (PDEs). It consists of a probabilistic, learning objective in which weighted residuals are used to probe the PDE and provide a source of {\em virtual} data i.e. the actual PDE never needs to be solved. This is combined with a physics-aware implicit solver that consists of a much coarser, discretized version of the original PDE, which provides the requisite information bottleneck for high-dimensional problems and enables generalization in out-of-distribution settings (e.g. different boundary conditions). We demonstrate its capability in the context of random heterogeneous materials where the input parameters represent the material microstructure. We extend the framework to multiscale problems and show that a surrogate can be learned for the effective (homogenized) solution without ever solving the reference problem. We further demonstrate how the proposed framework can accommodate and generalize several existing learning objectives and architectures while yielding probabilistic surrogates that can quantify predictive uncertainty. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 458,715 |
2405.00824 | Efficient and Responsible Adaptation of Large Language Models for Robust
Top-k Recommendations | Conventional recommendation systems (RSs) are typically optimized to enhance performance metrics uniformly across all training samples. This makes it hard for data-driven RSs to cater to a diverse set of users due to the varying properties of these users. The performance disparity among various populations can harm the model's robustness with respect to sub-populations. While recent works have shown promising results in adapting large language models (LLMs) for recommendation to address hard samples, long user queries from millions of users can degrade the performance of LLMs and elevate costs, processing times and inference latency. This challenges the practical applicability of LLMs for recommendations. To address this, we propose a hybrid task allocation framework that utilizes the capabilities of both LLMs and traditional RSs. By adopting a two-phase approach to improve robustness to sub-populations, we promote a strategic assignment of tasks for efficient and responsible adaptation of LLMs. Our strategy works by first identifying the weak and inactive users that receive a suboptimal ranking performance by RSs. Next, we use an in-context learning approach for such users, wherein each user interaction history is contextualized as a distinct ranking task and given to an LLM. We test our hybrid framework by incorporating various recommendation algorithms -- collaborative filtering and learning-to-rank recommendation models -- and two LLMs -- both open and close-sourced. Our results on three real-world datasets show a significant reduction in weak users and improved robustness of RSs to sub-populations $(\approx12\%)$ and overall performance without disproportionately escalating costs. | true | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | 451,095 |
1104.1389 | Generalizing the Markov and covariance interpolation problem using
input-to-state filters | In the Markov and covariance interpolation problem a transfer function $W$ is sought that match the first coefficients in the expansion of $W$ around zero and the first coefficients of the Laurent expansion of the corresponding spectral density $WW^\star$. Here we solve an interpolation problem where the matched parameters are the coefficients of expansions of $W$ and $WW^\star$ around various points in the disc. The solution is derived using input-to-state filters and is determined by simple calculations such as solving Lyapunov equations and generalized eigenvalue problems. | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | 9,907 |
2308.01752 | Quantifying Retrospective Human Responsibility in Intelligent Systems | Intelligent systems have become a major part of our lives. Human responsibility for outcomes becomes unclear in the interaction with these systems, as parts of information acquisition, decision-making, and action implementation may be carried out jointly by humans and systems. Determining human causal responsibility with intelligent systems is particularly important in events that end with adverse outcomes. We developed three measures of retrospective human causal responsibility when using intelligent systems. The first measure concerns repetitive human interactions with a system. Using information theory, it quantifies the average human's unique contribution to the outcomes of past events. The second and third measures concern human causal responsibility in a single past interaction with an intelligent system. They quantify, respectively, the unique human contribution in forming the information used for decision-making and the reasonability of the actions that the human carried out. The results show that human retrospective responsibility depends on the combined effects of system design and its reliability, the human's role and authority, and probabilistic factors related to the system and the environment. The new responsibility measures can serve to investigate and analyze past events involving intelligent systems. They may aid the judgment of human responsibility and ethical and legal discussions, providing a novel quantitative perspective. | true | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | 383,353 |
1903.07837 | Turing-Completeness of Dynamics in Abstract Persuasion Argumentation | Abstract Persuasion Argumentation (APA) is a dynamic argumentation formalism that extends Dung argumentation with persuasion relations. In this work, we show through two-counter Minsky machine encoding that APA dynamics is Turing-complete. | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | true | 124,716 |
1606.02433 | Training Design and Two-stage Channel Estimation for Correlated Two-way
MIMO Relay Systems | This paper addresses the training signal design for the channel estimation in two-way multiple-input-and-multipleoutput (MIMO) relay systems, where the channels are correlated. We first derive the backward channel estimator with the optimal training signal sent by the relay node. Given the estimated backward channels and the probabilistic knowledge of the estimation error, we mainly focus on the forward channel estimation and the related training signal design. We further propose a novel training signal. The design criterion is to minimize the relaxation of the total mean square error (MSE) of the forward channel estimators, which is conditioned on the estimated backward channels. Finally, the numerical results show that the proposed training signal can improve the MSE performance. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | true | 56,959 |
2312.00660 | Resource-constrained knowledge diffusion processes inspired by human
peer learning | We consider a setting where a population of artificial learners is given, and the objective is to optimize aggregate measures of performance, under constraints on training resources. The problem is motivated by the study of peer learning in human educational systems. In this context, we study natural knowledge diffusion processes in networks of interacting artificial learners. By `natural', we mean processes that reflect human peer learning where the students' internal state and learning process is mostly opaque, and the main degree of freedom lies in the formation of peer learning groups by a coordinator who can potentially evaluate the learners before assigning them to peer groups. Among else, we empirically show that such processes indeed make effective use of the training resources, and enable the design of modular neural models that have the capacity to generalize without being prone to overfitting noisy labels. | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | false | false | 412,126 |
1907.10931 | Closing the Gap between Deep and Conventional Image Registration using
Probabilistic Dense Displacement Networks | Nonlinear image registration continues to be a fundamentally important tool in medical image analysis. Diagnostic tasks, image-guided surgery and radiotherapy as well as motion analysis all rely heavily on accurate intra-patient alignment. Furthermore, inter-patient registration enables atlas-based segmentation or landmark localisation and shape analysis. When labelled scans are scarce and anatomical differences large, conventional registration has often remained superior to deep learning methods that have so far mainly dealt with relatively small or low-complexity deformations. We address this shortcoming by leveraging ideas from probabilistic dense displacement optimisation that has excelled in many registration tasks with large deformations. We propose to design a network with approximate min-convolutions and mean field inference for differentiable displacement regularisation within a discrete weakly-supervised registration setting. By employing these meaningful and theoretically proven constraints, our learnable registration algorithm contains very few trainable weights (primarily for feature extraction) and is easier to train with few labelled scans. It is very fast in training and inference and achieves state-of-the-art accuracies for the challenging inter-patient registration of abdominal CT outperforming previous deep learning approaches by 15% Dice overlap. | false | false | false | false | false | false | true | false | false | false | false | true | false | false | false | false | false | false | 139,741 |
2404.03434 | Learning From Simplicial Data Based on Random Walks and 1D Convolutions | Triggered by limitations of graph-based deep learning methods in terms of computational expressivity and model flexibility, recent years have seen a surge of interest in computational models that operate on higher-order topological domains such as hypergraphs and simplicial complexes. While the increased expressivity of these models can indeed lead to a better classification performance and a more faithful representation of the underlying system, the computational cost of these higher-order models can increase dramatically. To this end, we here explore a simplicial complex neural network learning architecture based on random walks and fast 1D convolutions (SCRaWl), in which we can adjust the increase in computational cost by varying the length and number of random walks considered while accounting for higher-order relationships. Importantly, due to the random walk-based design, the expressivity of the proposed architecture is provably incomparable to that of existing message-passing simplicial neural networks. We empirically evaluate SCRaWl on real-world datasets and show that it outperforms other simplicial neural networks. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 444,258 |
physics/0308041 | Ensembles of Protein Molecules as Statistical Analog Computers | A class of analog computers built from large numbers of microscopic probabilistic machines is discussed. It is postulated that such computers are implemented in biological systems as ensembles of protein molecules. The formalism is based on an abstract computational model referred to as Protein Molecule Machine (PMM). A PMM is a continuous-time first-order Markov system with real input and output vectors, a finite set of discrete states, and the input-dependent conditional probability densities of state transitions. The output of a PMM is a function of its input and state. The components of input vector, called generalized potentials, can be interpreted as membrane potential, and concentrations of neurotransmitters. The components of output vector, called generalized currents, can represent ion currents, and the flows of second messengers. An Ensemble of PMMs (EPMM) is a set of independent identical PMMs with the same input vector, and the output vector equal to the sum of output vectors of individual PMMs. The paper suggests that biological neurons have much more sophisticated computational resources than the presently popular models of artificial neurons. | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | true | false | false | 540,811 |
2304.02163 | GINA-3D: Learning to Generate Implicit Neural Assets in the Wild | Modeling the 3D world from sensor data for simulation is a scalable way of developing testing and validation environments for robotic learning problems such as autonomous driving. However, manually creating or re-creating real-world-like environments is difficult, expensive, and not scalable. Recent generative model techniques have shown promising progress to address such challenges by learning 3D assets using only plentiful 2D images -- but still suffer limitations as they leverage either human-curated image datasets or renderings from manually-created synthetic 3D environments. In this paper, we introduce GINA-3D, a generative model that uses real-world driving data from camera and LiDAR sensors to create realistic 3D implicit neural assets of diverse vehicles and pedestrians. Compared to the existing image datasets, the real-world driving setting poses new challenges due to occlusions, lighting-variations and long-tail distributions. GINA-3D tackles these challenges by decoupling representation learning and generative modeling into two stages with a learned tri-plane latent structure, inspired by recent advances in generative modeling of images. To evaluate our approach, we construct a large-scale object-centric dataset containing over 1.2M images of vehicles and pedestrians from the Waymo Open Dataset, and a new set of 80K images of long-tail instances such as construction equipment, garbage trucks, and cable cars. We compare our model with existing approaches and demonstrate that it achieves state-of-the-art performance in quality and diversity for both generated images and geometries. | false | false | false | false | true | false | false | true | false | false | false | true | false | false | false | false | false | true | 356,338 |
2009.03665 | GPU-based Self-Organizing Maps for Post-Labeled Few-Shot Unsupervised
Learning | Few-shot classification is a challenge in machine learning where the goal is to train a classifier using a very limited number of labeled examples. This scenario is likely to occur frequently in real life, for example when data acquisition or labeling is expensive. In this work, we consider the problem of post-labeled few-shot unsupervised learning, a classification task where representations are learned in an unsupervised fashion, to be later labeled using very few annotated examples. We argue that this problem is very likely to occur on the edge, when the embedded device directly acquires the data, and the expert needed to perform labeling cannot be prompted often. To address this problem, we consider an algorithm consisting of the concatenation of transfer learning with clustering using Self-Organizing Maps (SOMs). We introduce a TensorFlow-based implementation to speed-up the process in multi-core CPUs and GPUs. Finally, we demonstrate the effectiveness of the method using standard off-the-shelf few-shot classification benchmarks. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | true | false | false | 194,855 |
2105.03123 | AI in (and for) Games | This chapter outlines the relation between artificial intelligence (AI) / machine learning (ML) algorithms and digital games. This relation is two-fold: on one hand, AI/ML researchers can generate large, in-the-wild datasets of human affective activity, player behaviour (i.e. actions within the game world), commercial behaviour, interaction with graphical user interface elements or messaging with other players, while games can utilise intelligent algorithms to automate testing of game levels, generate content, develop intelligent and responsive non-player characters (NPCs) or predict and respond player behaviour across a wide variety of player cultures. In this work, we discuss some of the most common and widely accepted uses of AI/ML in games and how intelligent systems can benefit from those, elaborating on estimating player experience based on expressivity and performance, and on generating proper and interesting content for a language learning game. | true | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | 234,046 |
2405.16386 | Variational Offline Multi-agent Skill Discovery | Skills are effective temporal abstractions established for sequential decision making, which enable efficient hierarchical learning for long-horizon tasks and facilitate multi-task learning through their transferability. Despite extensive research, research gaps remain in multi-agent scenarios, particularly for automatically extracting subgroup coordination patterns in a multi-agent task. In this case, we propose two novel auto-encoder schemes: VO-MASD-3D and VO-MASD-Hier, to simultaneously capture subgroup- and temporal-level abstractions and form multi-agent skills, which firstly solves the aforementioned challenge. An essential algorithm component of these schemes is a dynamic grouping function that can automatically detect latent subgroups based on agent interactions in a task. Our method can be applied to offline multi-task data, and the discovered subgroup skills can be transferred across relevant tasks without retraining. Empirical evaluations on StarCraft tasks indicate that our approach significantly outperforms existing hierarchical multi-agent reinforcement learning (MARL) methods. Moreover, skills discovered using our method can effectively reduce the learning difficulty in MARL scenarios with delayed and sparse reward signals. | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | false | false | 457,391 |
1412.6913 | The weak core and the structure of elites in social multiplex networks | Recent approaches on elite identification highlighted the important role of {\em intermediaries}, by means of a new definition of the core of a multiplex network, the {\em generalised} $K$-core. This newly introduced core subgraph crucially incorporates those individuals who, in spite of not being very connected, maintain the cohesiveness and plasticity of the core. Interestingly, it has been shown that the performance on elite identification of the generalised $K$-core is sensibly better that the standard $K$-core. Here we go further: Over a multiplex social system, we isolate the community structure of the generalised $K$-core and we identify the weakly connected regions acting as bridges between core communities, ensuring the cohesiveness and connectivity of the core region. This gluing region is the {\em Weak core} of the multiplex system. We test the suitability of our method on data from the society of 420.000 players of the Massive Multiplayer Online Game {\em Pardus}. Results show that the generalised $K$-core displays a clearly identifiable community structure and that the weak core gluing the core communities shows very low connectivity and clustering. Nonetheless, despite its low connectivity, the weak core forms a unique, cohesive structure. In addition, we find that members populating the weak core have the best scores on social performance, when compared to the other elements of the generalised $K$-core. The weak core provides a new angle on understanding the social structure of elites, highlighting those subgroups of individuals whose role is to glue different communities in the core. | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | false | false | 38,734 |
1701.06659 | DSSD : Deconvolutional Single Shot Detector | The main contribution of this paper is an approach for introducing additional context into state-of-the-art general object detection. To achieve this we first combine a state-of-the-art classifier (Residual-101[14]) with a fast detection framework (SSD[18]). We then augment SSD+Residual-101 with deconvolution layers to introduce additional large-scale context in object detection and improve accuracy, especially for small objects, calling our resulting system DSSD for deconvolutional single shot detector. While these two contributions are easily described at a high-level, a naive implementation does not succeed. Instead we show that carefully adding additional stages of learned transformations, specifically a module for feed-forward connections in deconvolution and a new output module, enables this new approach and forms a potential way forward for further detection research. Results are shown on both PASCAL VOC and COCO detection. Our DSSD with $513 \times 513$ input achieves 81.5% mAP on VOC2007 test, 80.0% mAP on VOC2012 test, and 33.2% mAP on COCO, outperforming a state-of-the-art method R-FCN[3] on each dataset. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 67,173 |
2412.07592 | Complexity of inversion of functions on the reals | We study the complexity of deterministic and probabilistic inversions of partial computable functions on the reals. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 515,728 |
2304.09797 | Progressive-Hint Prompting Improves Reasoning in Large Language Models | The performance of Large Language Models (LLMs) in reasoning tasks depends heavily on prompt design, with Chain-of-Thought (CoT) and self-consistency being critical methods that enhance this ability. However, these methods do not fully exploit the answers generated by the LLM to guide subsequent responses. This paper proposes a new prompting method, named Progressive-Hint Prompting (PHP), that enables automatic multiple interactions between users and LLMs by using previously generated answers as hints to progressively guide toward the correct answers. PHP is orthogonal to CoT and self-consistency, making it easy to combine with state-of-the-art techniques to further improve performance. We conducted extensive and comprehensive experiments on seven benchmarks. The results show that PHP significantly improves accuracy while remaining highly efficient. For instance, with text-davinci-003, we observed a 4.2% improvement on GSM8K with greedy decoding compared to Complex CoT, and a 46.17% reduction in sample paths with self-consistency. With GPT-4 and PHP, we achieve state-of-the-art performances on SVAMP (89.1% -> 91.9%), GSM8K (92% -> 95.5%), AQuA (76.4% -> 79.9%) and MATH (50.3% -> 53.9%). | false | false | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | 359,175 |
2010.09919 | Optimal Decision Lists using SAT | Decision lists are one of the most easily explainable machine learning models. Given the renewed emphasis on explainable machine learning decisions, this machine learning model is increasingly attractive, combining small size and clear explainability. In this paper, we show for the first time how to construct optimal "perfect" decision lists which are perfectly accurate on the training data, and minimal in size, making use of modern SAT solving technology. We also give a new method for determining optimal sparse decision lists, which trade off size and accuracy. We contrast the size and test accuracy of optimal decisions lists versus optimal decision sets, as well as other state-of-the-art methods for determining optimal decision lists. We also examine the size of average explanations generated by decision sets and decision lists. | false | false | false | false | true | false | true | false | false | false | false | false | false | false | false | false | false | true | 201,703 |
2101.02595 | A Clinical Evaluation of a Low-Cost Strain Gauge Respiration Belt and
Machine Learning to Detect Sleep Apnea | Sleep apnea is a serious and severely under-diagnosed sleep-related respiration disorder characterized by repeated disrupted breathing events during sleep. It is diagnosed via polysomnography which is an expensive test conducted in a sleep lab requiring sleep experts to manually score the recorded data. Since the symptoms of sleep apnea are often ambiguous, it is difficult for a physician to decide whether to prescribe polysomnography. In this study, we investigate whether helpful information can be obtained by collecting and automatically analysing sleep data using a smartphone and an inexpensive strain gauge respiration belt. We evaluate how accurately we can detect sleep apnea with wide variety of machine learning techniques with data from a clinical study with 49 overnight sleep recordings. With less than one hour of training, we can distinguish between normal and apneic minutes with an accuracy, sensitivity, and specificity of 0.7609, 0.7833, and 0.7217, respectively. These results can be achieved even if we train only on high-quality data from an entirely separate, clinically certified sensor, which has the potential to substantially reduce the cost of data collection. Data from a complete night can be analyzed in about one second on a smartphone. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 214,680 |
2203.06692 | Towards Semantic Communications: A Paradigm Shift | The last seventy years have witnessed the transition of communication from Shannon's theoretical concept to current high-efficient practical systems. Classical communication systems address the capability-deficiency issue mainly by module-stacking and technique-densification with ever-increasing complexity. In such a traditional viewpoint, classical source coding only uses explicit probabilistic models to compress data, regardless of the meaning of transmitted source messages. Also, channel coded transmission does not identify the source content. In this sense, state-of-the-art communication systems work merely at the technical level as summarized by Weaver. Unlike the traditional system design philosophy, this article proposes a new route to boost the system capabilities towards intelligence-endogenous and primitive-concise communications. The communication paradigm upgrades to the semantic level, which is radically different since all the key techniques imply the use of meanings of transmitted data, thus deeply changing the design of the communication system. This paradigm shifting unveils a promising direction due to its ability to offer an identical quality of service with much lower data transmission requirement. Different from other similar works, this article constitutes a brief tutorial on the framework of semantic communications, its gain analyzed from the information theory perspective, a method to calculate the semantic compression bound, and an exemplary use case of semantic communications. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 285,201 |
2210.06682 | Application-Driven AI Paradigm for Hand-Held Action Detection | In practical applications especially with safety requirement, some hand-held actions need to be monitored closely, including smoking cigarettes, dialing, eating, etc. Taking smoking cigarettes as example, existing smoke detection algorithms usually detect the cigarette or cigarette with hand as the target object only, which leads to low accuracy. In this paper, we propose an application-driven AI paradigm for hand-held action detection based on hierarchical object detection. It is a coarse-to-fine hierarchical detection framework composed of two modules. The first one is a coarse detection module with the human pose consisting of the whole hand, cigarette and head as target object. The followed second one is a fine detection module with the fingers holding cigarette, mouth area and the whole cigarette as target. Some experiments are done with the dataset collected from real-world scenarios, and the results show that the proposed framework achieve higher detection rate with good adaptation and robustness in complex environments. | false | false | false | false | true | false | false | false | false | false | false | true | false | false | false | false | false | false | 323,392 |
2203.00843 | X-Trans2Cap: Cross-Modal Knowledge Transfer using Transformer for 3D
Dense Captioning | 3D dense captioning aims to describe individual objects by natural language in 3D scenes, where 3D scenes are usually represented as RGB-D scans or point clouds. However, only exploiting single modal information, e.g., point cloud, previous approaches fail to produce faithful descriptions. Though aggregating 2D features into point clouds may be beneficial, it introduces an extra computational burden, especially in inference phases. In this study, we investigate a cross-modal knowledge transfer using Transformer for 3D dense captioning, X-Trans2Cap, to effectively boost the performance of single-modal 3D caption through knowledge distillation using a teacher-student framework. In practice, during the training phase, the teacher network exploits auxiliary 2D modality and guides the student network that only takes point clouds as input through the feature consistency constraints. Owing to the well-designed cross-modal feature fusion module and the feature alignment in the training phase, X-Trans2Cap acquires rich appearance information embedded in 2D images with ease. Thus, a more faithful caption can be generated only using point clouds during the inference. Qualitative and quantitative results confirm that X-Trans2Cap outperforms previous state-of-the-art by a large margin, i.e., about +21 and about +16 absolute CIDEr score on ScanRefer and Nr3D datasets, respectively. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 283,142 |
2412.15208 | OpenEMMA: Open-Source Multimodal Model for End-to-End Autonomous Driving | Since the advent of Multimodal Large Language Models (MLLMs), they have made a significant impact across a wide range of real-world applications, particularly in Autonomous Driving (AD). Their ability to process complex visual data and reason about intricate driving scenarios has paved the way for a new paradigm in end-to-end AD systems. However, the progress of developing end-to-end models for AD has been slow, as existing fine-tuning methods demand substantial resources, including extensive computational power, large-scale datasets, and significant funding. Drawing inspiration from recent advancements in inference computing, we propose OpenEMMA, an open-source end-to-end framework based on MLLMs. By incorporating the Chain-of-Thought reasoning process, OpenEMMA achieves significant improvements compared to the baseline when leveraging a diverse range of MLLMs. Furthermore, OpenEMMA demonstrates effectiveness, generalizability, and robustness across a variety of challenging driving scenarios, offering a more efficient and effective approach to autonomous driving. We release all the codes in https://github.com/taco-group/OpenEMMA. | false | false | false | false | false | false | true | true | false | false | false | true | false | false | false | false | false | false | 518,982 |
2312.11861 | A Proximal Gradient Method With Probabilistic Multi-Gossip
Communications for Decentralized Composite Optimization | Decentralized optimization methods with local updates have recently gained attention for their provable ability to communication acceleration. In these methods, nodes perform several iterations of local computations between the communication rounds. Nevertheless, this capability is effective only when the loss function is smooth and the network is sufficiently well-connected. In this paper, we propose a communication-efficient method MG-Skip with probabilistic local updates and multi-gossip communications for decentralized composite (smooth + nonsmooth) optimization, whose stepsize is independent of the number of local updates and the network topology. Without any additional condition for network connectivity, MG-Skip allows for the multi-gossip communications to be skipped in most iterations in the strongly convex setting, while its iteration complexity is $\mathcal{O}\left(\kappa \log \frac{1}{\epsilon}\right)$ and communication complexity is only $\mathcal{O}\left(\sqrt{\frac{\kappa}{(1-\rho)}} \log \frac{1}{\epsilon}\right)$, where $\kappa$ is the condition number of the loss function, $\rho$ reflects the connectivity of the network topology, and $\epsilon$ is the target accuracy. The theoretical results demonstrate that MG-Skip achieves the optimal communication complexity and confirm the benefits of local updates in the nonsmooth setup. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 416,749 |
2204.00179 | GraftNet: Towards Domain Generalized Stereo Matching with a
Broad-Spectrum and Task-Oriented Feature | Although supervised deep stereo matching networks have made impressive achievements, the poor generalization ability caused by the domain gap prevents them from being applied to real-life scenarios. In this paper, we propose to leverage the feature of a model trained on large-scale datasets to deal with the domain shift since it has seen various styles of images. With the cosine similarity based cost volume as a bridge, the feature will be grafted to an ordinary cost aggregation module. Despite the broad-spectrum representation, such a low-level feature contains much general information which is not aimed at stereo matching. To recover more task-specific information, the grafted feature is further input into a shallow network to be transformed before calculating the cost. Extensive experiments show that the model generalization ability can be improved significantly with this broad-spectrum and task-oriented feature. Specifically, based on two well-known architectures PSMNet and GANet, our methods are superior to other robust algorithms when transferring from SceneFlow to KITTI 2015, KITTI 2012, and Middlebury. Code is available at https://github.com/SpadeLiu/Graft-PSMNet. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 289,169 |
2311.14532 | Digital Twin-Native AI-Driven Service Architecture for Industrial
Networks | The dramatic increase in the connectivity demand results in an excessive amount of Internet of Things (IoT) sensors. To meet the management needs of these large-scale networks, such as accurate monitoring and learning capabilities, Digital Twin (DT) is the key enabler. However, current attempts regarding DT implementations remain insufficient due to the perpetual connectivity requirements of IoT networks. Furthermore, the sensor data streaming in IoT networks cause higher processing time than traditional methods. In addition to these, the current intelligent mechanisms cannot perform well due to the spatiotemporal changes in the implemented IoT network scenario. To handle these challenges, we propose a DT-native AI-driven service architecture in support of the concept of IoT networks. Within the proposed DT-native architecture, we implement a TCP-based data flow pipeline and a Reinforcement Learning (RL)-based learner model. We apply the proposed architecture to one of the broad concepts of IoT networks, the Internet of Vehicles (IoV). We measure the efficiency of our proposed architecture and note ~30% processing time-saving thanks to the TCP-based data flow pipeline. Moreover, we test the performance of the learner model by applying several learning rate combinations for actor and critic networks and highlight the most successive model. | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | false | true | 410,141 |
2007.00210 | Review of Learning-Assisted Power System Optimization | With dramatic breakthroughs in recent years, machine learning is showing great potential to upgrade the toolbox for power system optimization. Understanding the strength and limitation of machine learning approaches is crucial to decide when and how to deploy them to boost the optimization performance. This paper pays special attention to the coordination between machine learning approaches and optimization models, and carefully evaluates how such data-driven analysis may improve the rule-based optimization. The typical references are selected and categorized into four groups: the boundary parameter improvement, the optimization option selection, the surrogate model, and the hybrid model. This taxonomy provides a novel perspective to elaborate the latest research progress and development. We further compare the design patterns of different categories, and discuss several key challenges and opportunities as well. Deep integration between machine learning approaches and optimization models is expected to become the most promising technical trend. | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | 185,046 |
2002.00325 | Polar decreasing monomial-Cartesian codes | We prove that families of polar codes with multiple kernels over certain symmetric channels can be viewed as polar decreasing monomial-Cartesian codes, offering a unified treatment for such codes, over any finite field. We define decreasing monomial-Cartesian codes as the evaluation of a set of monomials closed under divisibility over a Cartesian product. Polar decreasing monomial-Cartesian codes are decreasing monomial-Cartesian codes whose sets of monomials are closed respect a partial order inspired by the recent work of Bardet, Dragoi, Otmani, and Tillich ["Algebraic properties of polar codes from a new polynomial formalism," 2016 IEEE International Symposium on Information Theory (ISIT)]. Extending the main theorem of Mori and Tanaka ["Source and Channel Polarization Over Finite Fields and Reed-Solomon Matrices," in IEEE Transactions on Information Theory, vol. 60, no. 5, pp. 2720--2736, May 2014], we prove that any sequence of invertible matrices over an arbitrary field satisfying certain conditions polarizes any symmetric over the field channel. In addition, we prove that the dual of a decreasing monomial-Cartesian code is monomially equivalent to a decreasing monomial-Cartesian code. Defining the minimal generating set for a set of monomials, we use it to describe the length, dimension and minimum distance of a decreasing monomial-Cartesian code. | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | 162,325 |
2011.08298 | Facebook AI's WMT20 News Translation Task Submission | This paper describes Facebook AI's submission to WMT20 shared news translation task. We focus on the low resource setting and participate in two language pairs, Tamil <-> English and Inuktitut <-> English, where there are limited out-of-domain bitext and monolingual data. We approach the low resource problem using two main strategies, leveraging all available data and adapting the system to the target news domain. We explore techniques that leverage bitext and monolingual data from all languages, such as self-supervised model pretraining, multilingual models, data augmentation, and reranking. To better adapt the translation system to the test domain, we explore dataset tagging and fine-tuning on in-domain data. We observe that different techniques provide varied improvements based on the available data of the language pair. Based on the finding, we integrate these techniques into one training pipeline. For En->Ta, we explore an unconstrained setup with additional Tamil bitext and monolingual data and show that further improvement can be obtained. On the test set, our best submitted systems achieve 21.5 and 13.7 BLEU for Ta->En and En->Ta respectively, and 27.9 and 13.0 for Iu->En and En->Iu respectively. | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | 206,823 |
2410.01212 | Absolute State-wise Constrained Policy Optimization: High-Probability
State-wise Constraints Satisfaction | Enforcing state-wise safety constraints is critical for the application of reinforcement learning (RL) in real-world problems, such as autonomous driving and robot manipulation. However, existing safe RL methods only enforce state-wise constraints in expectation or enforce hard state-wise constraints with strong assumptions. The former does not exclude the probability of safety violations, while the latter is impractical. Our insight is that although it is intractable to guarantee hard state-wise constraints in a model-free setting, we can enforce state-wise safety with high probability while excluding strong assumptions. To accomplish the goal, we propose Absolute State-wise Constrained Policy Optimization (ASCPO), a novel general-purpose policy search algorithm that guarantees high-probability state-wise constraint satisfaction for stochastic systems. We demonstrate the effectiveness of our approach by training neural network policies for extensive robot locomotion tasks, where the agent must adhere to various state-wise safety constraints. Our results show that ASCPO significantly outperforms existing methods in handling state-wise constraints across challenging continuous control tasks, highlighting its potential for real-world applications. | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | false | false | false | 493,657 |
1501.07648 | Improved Adaptive Sparse Channel Estimation Using Re-Weighted L1-norm
Normalized Least Mean Fourth Algorithm | In next-generation wireless communications systems, accurate sparse channel estimation (SCE) is required for coherent detection. This paper studies SCE in terms of adaptive filtering theory, which is often termed as adaptive channel estimation (ACE). Theoretically, estimation accuracy could be improved by either exploiting sparsity or adopting suitable error criterion. It motivates us to develop effective adaptive sparse channel estimation (ASCE) methods to improve estimation performance. In our previous research, two ASCE methods have been proposed by combining forth-order error criterion based normalized least mean fourth (NLMF) and L1-norm penalized functions, i.e., zero-attracting NLMF (ZA-NLMF) algorithm and reweighted ZA-NLMF (RZA-NLMF) algorithm. Motivated by compressive sensing theory, an improved ASCE method is proposed by using reweighted L1-norm NLMF (RL1-NLMF) algorithm where RL1 can exploit more sparsity information than ZA and RZA. Specifically, we construct the cost function of RL1-NLMF and hereafter derive its update equation. In addition, intuitive figure is also given to verify that RL1 is more efficient than conventional two sparsity constraints. Finally, simulation results are provided to confirm this study. | false | false | false | false | false | false | false | false | false | true | true | false | false | false | false | false | false | false | 39,726 |
2008.07960 | Dataset Bias in Few-shot Image Recognition | The goal of few-shot image recognition (FSIR) is to identify novel categories with a small number of annotated samples by exploiting transferable knowledge from training data (base categories). Most current studies assume that the transferable knowledge can be well used to identify novel categories. However, such transferable capability may be impacted by the dataset bias, and this problem has rarely been investigated before. Besides, most of few-shot learning methods are biased to different datasets, which is also an important issue that needs to be investigated deeply. In this paper, we first investigate the impact of transferable capabilities learned from base categories. Specifically, we use the relevance to measure relationships between base categories and novel categories. Distributions of base categories are depicted via the instance density and category diversity. The FSIR model learns better transferable knowledge from relevant training data. In the relevant data, dense instances or diverse categories can further enrich the learned knowledge. Experimental results on different sub-datasets of ImagNet demonstrate category relevance, instance density and category diversity can depict transferable bias from base categories. Second, we investigate performance differences on different datasets from dataset structures and different few-shot learning methods. Specifically, we introduce image complexity, intra-concept visual consistency, and inter-concept visual similarity to quantify characteristics of dataset structures. We use these quantitative characteristics and four few-shot learning methods to analyze performance differences on five different datasets. Based on the experimental analysis, some insightful observations are obtained from the perspective of both dataset structures and few-shot learning methods. We hope these observations are useful to guide future FSIR research. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 192,268 |
2007.06279 | Dual-Teacher: Integrating Intra-domain and Inter-domain Teachers for
Annotation-efficient Cardiac Segmentation | Medical image annotations are prohibitively time-consuming and expensive to obtain. To alleviate annotation scarcity, many approaches have been developed to efficiently utilize extra information, e.g.,semi-supervised learning further exploring plentiful unlabeled data, domain adaptation including multi-modality learning and unsupervised domain adaptation resorting to the prior knowledge from additional modality. In this paper, we aim to investigate the feasibility of simultaneously leveraging abundant unlabeled data and well-established cross-modality data for annotation-efficient medical image segmentation. To this end, we propose a novel semi-supervised domain adaptation approach, namely Dual-Teacher, where the student model not only learns from labeled target data (e.g., CT), but also explores unlabeled target data and labeled source data (e.g., MR) by two teacher models. Specifically, the student model learns the knowledge of unlabeled target data from intra-domain teacher by encouraging prediction consistency, as well as the shape priors embedded in labeled source data from inter-domain teacher via knowledge distillation. Consequently, the student model can effectively exploit the information from all three data resources and comprehensively integrate them to achieve improved performance. We conduct extensive experiments on MM-WHS 2017 dataset and demonstrate that our approach is able to concurrently utilize unlabeled data and cross-modality data with superior performance, outperforming semi-supervised learning and domain adaptation methods with a large margin. | false | false | false | false | false | false | false | false | false | false | false | true | false | false | false | false | false | false | 186,964 |
1511.06841 | Online Sequence Training of Recurrent Neural Networks with Connectionist
Temporal Classification | Connectionist temporal classification (CTC) based supervised sequence training of recurrent neural networks (RNNs) has shown great success in many machine learning areas including end-to-end speech and handwritten character recognition. For the CTC training, however, it is required to unroll (or unfold) the RNN by the length of an input sequence. This unrolling requires a lot of memory and hinders a small footprint implementation of online learning or adaptation. Furthermore, the length of training sequences is usually not uniform, which makes parallel training with multiple sequences inefficient on shared memory models such as graphics processing units (GPUs). In this work, we introduce an expectation-maximization (EM) based online CTC algorithm that enables unidirectional RNNs to learn sequences that are longer than the amount of unrolling. The RNNs can also be trained to process an infinitely long input sequence without pre-segmentation or external reset. Moreover, the proposed approach allows efficient parallel training on GPUs. For evaluation, phoneme recognition and end-to-end speech recognition examples are presented on the TIMIT and Wall Street Journal (WSJ) corpora, respectively. Our online model achieves 20.7% phoneme error rate (PER) on the very long input sequence that is generated by concatenating all 192 utterances in the TIMIT core test set. On WSJ, a network can be trained with only 64 times of unrolling while sacrificing 4.5% relative word error rate (WER). | false | false | false | false | false | false | true | false | false | false | false | false | false | false | false | true | false | false | 49,334 |
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