Split (2)

train · 433k rows

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
2009.13595	Forecasting Short-term load using Econometrics time series model with T-student Distribution	By significant improvements in modern electrical systems, planning for unit commitment and power dispatching of them are two big concerns between the researchers. Short-term load forecasting plays a significant role in planning and dispatching them. In recent years, numerous works have been done on Short-term load fore...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	197,780
1508.05506	Brudno's theorem for Z^d (or Z^d_+) subshifts	We generalize Brudno's theorem of $1$-dimensional shift dynamical system to $\mathbb{Z}^d$ (or $\mathbb{Z}_+^d$) subshifts. That is to say, in $\mathbb{Z}^d$ (or $\mathbb{Z}^d_+$) subshift, the Kolmogorov-Sinai entropy is equivalent to the Kolmogorov complexity density almost everywhere for an ergodic shift-invariant m...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	46,233
2210.13438	High Fidelity Neural Audio Compression	We introduce a state-of-the-art real-time, high-fidelity, audio codec leveraging neural networks. It consists in a streaming encoder-decoder architecture with quantized latent space trained in an end-to-end fashion. We simplify and speed-up the training by using a single multiscale spectrogram adversary that efficientl...	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	326,160
2312.09361	RTRA: Rapid Training of Regularization-based Approaches in Continual Learning	Catastrophic forgetting(CF) is a significant challenge in continual learning (CL). In regularization-based approaches to mitigate CF, modifications to important training parameters are penalized in subsequent tasks using an appropriate loss function. We propose the RTRA, a modification to the widely used Elastic Weight...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	415,685
2205.11733	Single-View View Synthesis in the Wild with Learned Adaptive Multiplane Images	This paper deals with the challenging task of synthesizing novel views for in-the-wild photographs. Existing methods have shown promising results leveraging monocular depth estimation and color inpainting with layered depth representations. However, these methods still have limited capability to handle scenes with comp...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	298,254
2110.12007	When to Prune? A Policy towards Early Structural Pruning	Pruning enables appealing reductions in network memory footprint and time complexity. Conventional post-training pruning techniques lean towards efficient inference while overlooking the heavy computation for training. Recent exploration of pre-training pruning at initialization hints on training cost reduction via pru...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	262,678
2501.03272	Backdoor Token Unlearning: Exposing and Defending Backdoors in Pretrained Language Models	Supervised fine-tuning has become the predominant method for adapting large pretrained models to downstream tasks. However, recent studies have revealed that these models are vulnerable to backdoor attacks, where even a small number of malicious samples can successfully embed backdoor triggers into the model. While mos...	false	false	false	false	true	false	false	false	true	false	false	false	true	false	false	false	false	false	522,820
2002.10006	On the Modularity of Hypernetworks	In the context of learning to map an input $I$ to a function $h_I:\mathcal{X}\to \mathbb{R}$, two alternative methods are compared: (i) an embedding-based method, which learns a fixed function in which $I$ is encoded as a conditioning signal $e(I)$ and the learned function takes the form $h_I(x) = q(x,e(I))$, and (ii) ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	165,250
1911.07747	DeepSat V2: Feature Augmented Convolutional Neural Nets for Satellite Image Classification	Satellite image classification is a challenging problem that lies at the crossroads of remote sensing, computer vision, and machine learning. Due to the high variability inherent in satellite data, most of the current object classification approaches are not suitable for handling satellite datasets. The progress of sat...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	153,956
2306.13660	Statistical relational learning and neuro-symbolic AI: what does first-order logic offer?	In this paper, our aim is to briefly survey and articulate the logical and philosophical foundations of using (first-order) logic to represent (probabilistic) knowledge in a non-technical fashion. Our motivation is three fold. First, for machine learning researchers unaware of why the research community cares about rel...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	375,348
1801.10190	Cell-Free Massive MIMO with Limited Backhaul	We consider a cell-free Massive multiple-input multiple-output (MIMO) system and investigate the system performance for the case when the quantized version of the estimated channel and the quantized received signal are available at the central processing unit (CPU), and the case when only the quantized version of the c...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	89,242
2105.12810	ViPTT-Net: Video pretraining of spatio-temporal model for tuberculosis type classification from chest CT scans	Pretraining has sparked groundswell of interest in deep learning workflows to learn from limited data and improve generalization. While this is common for 2D image classification tasks, its application to 3D medical imaging tasks like chest CT interpretation is limited. We explore the idea of whether pretraining a mode...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	237,103
2211.12827	Video Instance Shadow Detection Under the Sun and Sky	Instance shadow detection, crucial for applications such as photo editing and light direction estimation, has undergone significant advancements in predicting shadow instances, object instances, and their associations. The extension of this task to videos presents challenges in annotating diverse video data and address...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	332,255
2010.10151	Coherent Hierarchical Multi-Label Classification Networks	Hierarchical multi-label classification (HMC) is a challenging classification task extending standard multi-label classification problems by imposing a hierarchy constraint on the classes. In this paper, we propose C-HMCNN(h), a novel approach for HMC problems, which, given a network h for the underlying multi-label cl...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	201,798
2305.17482	Federated Empirical Risk Minimization via Second-Order Method	Many convex optimization problems with important applications in machine learning are formulated as empirical risk minimization (ERM). There are several examples: linear and logistic regression, LASSO, kernel regression, quantile regression, $p$-norm regression, support vector machines (SVM), and mean-field variational...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	368,615
2302.11362	Gradient Remedy for Multi-Task Learning in End-to-End Noise-Robust Speech Recognition	Speech enhancement (SE) is proved effective in reducing noise from noisy speech signals for downstream automatic speech recognition (ASR), where multi-task learning strategy is employed to jointly optimize these two tasks. However, the enhanced speech learned by SE objective may not always yield good ASR results. From ...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	347,186
2412.01801	SceneFactor: Factored Latent 3D Diffusion for Controllable 3D Scene Generation	We present SceneFactor, a diffusion-based approach for large-scale 3D scene generation that enables controllable generation and effortless editing. SceneFactor enables text-guided 3D scene synthesis through our factored diffusion formulation, leveraging latent semantic and geometric manifolds for generation of arbitrar...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	513,269
2207.04815	Improved Soft-aided Decoding of Product Codes with Adaptive Performance-Complexity Trade-off	We propose an improved soft-aided decoding scheme for product codes that approaches the decoding performance of conventional soft-decision TPD with only a 0.2 dB gap while keeping the complexity and internal decoder data flow similarly low as in hard decision decoders.	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	307,322
2202.13956	RouteNet-Erlang: A Graph Neural Network for Network Performance Evaluation	Network modeling is a fundamental tool in network research, design, and operation. Arguably the most popular method for modeling is Queuing Theory (QT). Its main limitation is that it imposes strong assumptions on the packet arrival process, which typically do not hold in real networks. In the field of Deep Learning, G...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	282,803
0902.0043	Cut-Simulation and Impredicativity	We investigate cut-elimination and cut-simulation in impredicative (higher-order) logics. We illustrate that adding simple axioms such as Leibniz equations to a calculus for an impredicative logic -- in our case a sequent calculus for classical type theory -- is like adding cut. The phenomenon equally applies to promin...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	3,089
2303.02342	Adaptive Predictive Portfolio Management Agent	The paper presents an advanced version of an adaptive market-making agent capable of performing experiential learning, exploiting a "try and fail" approach relying on a swarm of subordinate agents executed in a virtual environment to determine optimal strategies. The problem is treated as a "Narrow AGI" problem with th...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	true	349,317
2102.11448	MUSBO: Model-based Uncertainty Regularized and Sample Efficient Batch Optimization for Deployment Constrained Reinforcement Learning	In many contemporary applications such as healthcare, finance, robotics, and recommendation systems, continuous deployment of new policies for data collection and online learning is either cost ineffective or impractical. We consider a setting that lies between pure offline reinforcement learning (RL) and pure online R...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	221,421
1812.04439	Synergy Effect between Convolutional Neural Networks and the Multiplicity of SMILES for Improvement of Molecular Prediction	In our study, we demonstrate the synergy effect between convolutional neural networks and the multiplicity of SMILES. The model we propose, the so-called Convolutional Neural Fingerprint (CNF) model, reaches the accuracy of traditional descriptors such as Dragon (Mauri et al. [22]), RDKit (Landrum [18]), CDK2 (Willigha...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	116,220
2307.07822	Design Analysis and Experimental Validation of Relaxation Oscillator-Based Circuit for R-C Sensors	Relaxation oscillator-based circuits are widely used for interfacing various resistive and capacitive sensors. The electrical equivalent of most resistive and capacitive sensors is represented using a parallel combination of resistor and capacitor. The relaxation oscillator-based circuits are not suitable for parallel ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	379,553
2306.05089	A review of UAV Visual Detection and Tracking Methods	This paper presents a review of techniques used for the detection and tracking of UAVs or drones. There are different techniques that depend on collecting measurements of the position, velocity, and image of the UAV and then using them in detection and tracking. Hybrid detection techniques are also presented. The paper...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	372,050
2211.08184	Improved Coresets for Euclidean $k$-Means	Given a set of $n$ points in $d$ dimensions, the Euclidean $k$-means problem (resp. the Euclidean $k$-median problem) consists of finding $k$ centers such that the sum of squared distances (resp. sum of distances) from every point to its closest center is minimized. The arguably most popular way of dealing with this pr...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	330,511
2212.05948	Capacity Gains in MIMO Systems with Few-Bit ADCs Using Nonlinear Analog Circuits	Analog to Digital Converters (ADCs) are a major contributor to the power consumption of multiple-input multiple-output (MIMO) receivers with large antenna arrays operating in the millimeter wave carrier frequencies. This is especially the case in large bandwidth communication systems, due to the sudden drop in energy-e...	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	335,949
2205.01204	Multi-Task Text Classification using Graph Convolutional Networks for Large-Scale Low Resource Language	Graph Convolutional Networks (GCN) have achieved state-of-art results on single text classification tasks like sentiment analysis, emotion detection, etc. However, the performance is achieved by testing and reporting on resource-rich languages like English. Applying GCN for multi-task text classification is an unexplor...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	294,499
2303.13726	Topology-Based MPC for Automatic Footstep Placement and Contact Surface Selection	State-of-the-art approaches to footstep planning assume reduced-order dynamics when solving the combinatorial problem of selecting contact surfaces in real time. However, in exchange for computational efficiency, these approaches ignore joint torque limits and limb dynamics. In this work, we address these limitations b...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	353,791
2012.10034	Automatic detection of abnormal EEG signals using wavelet feature extraction and gradient boosting decision tree	Electroencephalography is frequently used for diagnostic evaluation of various brain-related disorders due to its excellent resolution, non-invasive nature and low cost. However, manual analysis of EEG signals could be strenuous and a time-consuming process for experts. It requires long training time for physicians to ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	212,235
2307.16555	Uncertainty-Guided Spatial Pruning Architecture for Efficient Frame Interpolation	The video frame interpolation (VFI) model applies the convolution operation to all locations, leading to redundant computations in regions with easy motion. We can use dynamic spatial pruning method to skip redundant computation, but this method cannot properly identify easy regions in VFI tasks without supervision. In...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	382,650
1905.11245	Learning by stochastic serializations	Complex structures are typical in machine learning. Tailoring learning algorithms for every structure requires an effort that may be saved by defining a generic learning procedure adaptive to any complex structure. In this paper, we propose to map any complex structure onto a generic form, called serialization, over wh...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	132,371
2204.12393	On Fragile Features and Batch Normalization in Adversarial Training	Modern deep learning architecture utilize batch normalization (BN) to stabilize training and improve accuracy. It has been shown that the BN layers alone are surprisingly expressive. In the context of robustness against adversarial examples, however, BN is argued to increase vulnerability. That is, BN helps to learn fr...	false	false	false	false	false	false	true	false	false	false	false	true	true	false	false	false	false	false	293,459
2502.01282	Rational Gaussian wavelets and corresponding model driven neural networks	In this paper we consider the continuous wavelet transform using Gaussian wavelets multiplied by an appropriate rational term. The zeros and poles of this rational modifier act as free parameters and their choice highly influences the shape of the mother wavelet. This allows the proposed construction to approximate sig...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	529,787
2102.04770	COLOGNE: Coordinated Local Graph Neighborhood Sampling	Representation learning for graphs enables the application of standard machine learning algorithms and data analysis tools to graph data. Replacing discrete unordered objects such as graph nodes by real-valued vectors is at the heart of many approaches to learning from graph data. Such vector representations, or embedd...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	219,222
2409.03664	The Kneser--Poulsen phenomena for entropy	The Kneser--Poulsen conjecture asserts that the volume of a union of balls in Euclidean space cannot be increased by bringing their centres pairwise closer. We prove that its natural information-theoretic counterpart is true. This follows from a complete answer to a question asked in arXiv:2210.12842 about Gaussian con...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	486,121
2206.13728	Boosting R-CNN: Reweighting R-CNN Samples by RPN's Error for Underwater Object Detection	Complicated underwater environments bring new challenges to object detection, such as unbalanced light conditions, low contrast, occlusion, and mimicry of aquatic organisms. Under these circumstances, the objects captured by the underwater camera will become vague, and the generic detectors often fail on these vague ob...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	305,063
2104.03767	Uppsala NLP at SemEval-2021 Task 2: Multilingual Language Models for Fine-tuning and Feature Extraction in Word-in-Context Disambiguation	We describe the Uppsala NLP submission to SemEval-2021 Task 2 on multilingual and cross-lingual word-in-context disambiguation. We explore the usefulness of three pre-trained multilingual language models, XLM-RoBERTa (XLMR), Multilingual BERT (mBERT) and multilingual distilled BERT (mDistilBERT). We compare these three...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	229,167
1409.0203	Ad Hoc Microphone Array Calibration: Euclidean Distance Matrix Completion Algorithm and Theoretical Guarantees	This paper addresses the problem of ad hoc microphone array calibration where only partial information about the distances between microphones is available. We construct a matrix consisting of the pairwise distances and propose to estimate the missing entries based on a novel Euclidean distance matrix completion algori...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	35,709
1203.5502	Exploring Text Virality in Social Networks	This paper aims to shed some light on the concept of virality - especially in social networks - and to provide new insights on its structure. We argue that: (a) virality is a phenomenon strictly connected to the nature of the content being spread, rather than to the influencers who spread it, (b) virality is a phenomen...	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	15,116
1307.2748	Self-Organized Synchronization and Voltage Stability in Networks of Synchronous Machines	The integration of renewable energy sources in the course of the energy transition is accompanied by grid decentralization and fluctuating power feed-in characteristics. This raises new challenges for power system stability and design. We intend to investigate power system stability from the viewpoint of self-organized...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	25,739
2406.10505	CroPrompt: Cross-task Interactive Prompting for Zero-shot Spoken Language Understanding	Slot filling and intent detection are two highly correlated tasks in spoken language understanding (SLU). Recent SLU research attempts to explore zero-shot prompting techniques in large language models to alleviate the data scarcity problem. Nevertheless, the existing prompting work ignores the cross-task interaction i...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	464,432
1108.0729	Estudo de Viabilidade de uma Plataforma de Baixo Custo para Data Warehouse	Often corporations need tools to improve their decision making in a competitive market. In general, these tools are based on data warehouse platforms to mange and analyze large amounts of data. However, several of these corporations do not have enough resources to buy such platforms because of the high cost. This work ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	11,549
2404.00112	An SVD-like Decomposition of Bounded-Input Bounded-Output Functions	The Singular Value Decomposition (SVD) of linear functions facilitates the calculation of their 2-induced norm and row and null spaces, hallmarks of linear control theory. In this work, we present a function representation that, similar to SVD, provides an upper bound on the 2-induced norm of bounded-input bounded-outp...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	442,765
2409.20255	PerCo (SD): Open Perceptual Compression	We introduce PerCo (SD), a perceptual image compression method based on Stable Diffusion v2.1, targeting the ultra-low bit range. PerCo (SD) serves as an open and competitive alternative to the state-of-the-art method PerCo, which relies on a proprietary variant of GLIDE and remains closed to the public. In this work, ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	493,057
2404.04942	The Spatial Structures in the Austrian COVID-19 Protest Movement: A Virtual and Geospatial Twitter User Network Analysis	The emergence of the COVID-19 pandemic, followed by policy measures to combat the virus, evoked public protest movements world-wide. These movements emerged through virtual social networks as well as local protest gatherings. Prior research has studied such movements solely in the virtual space through social network a...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	444,874
1810.06742	Assessing and Remedying Coverage for a Given Dataset	Data analysis impacts virtually every aspect of our society today. Often, this analysis is performed on an existing dataset, possibly collected through a process that the data scientists had limited control over. The existing data analyzed may not include the complete universe, but it is expected to cover the diversity...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	110,489
2001.01400	Model Predictive Control for Finite Input Systems using the D-Wave Quantum Annealer	The D-Wave quantum annealer has emerged as a novel computational architecture that is attracting significant interest, but there have been only a few practical algorithms exploiting the power of quantum annealers. Here we present a model predictive control (MPC) algorithm using a quantum annealer for a system allowing ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	159,484
2405.19272	Differentially Private Clustered Federated Learning	Federated learning (FL), which is a decentralized machine learning (ML) approach, often incorporates differential privacy (DP) to provide rigorous data privacy guarantees. Previous works attempted to address high structured data heterogeneity in vanilla FL settings through clustering clients (a.k.a clustered FL), but t...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	true	458,819
2407.17140	RT-DETRv2: Improved Baseline with Bag-of-Freebies for Real-Time Detection Transformer	In this report, we present RT-DETRv2, an improved Real-Time DEtection TRansformer (RT-DETR). RT-DETRv2 builds upon the previous state-of-the-art real-time detector, RT-DETR, and opens up a set of bag-of-freebies for flexibility and practicality, as well as optimizing the training strategy to achieve enhanced performanc...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	475,859
1602.06236	Communication Cost in Parallel Query Processing	We study the problem of computing conjunctive queries over large databases on parallel architectures without shared storage. Using the structure of such a query $q$ and the skew in the data, we study tradeoffs between the number of processors, the number of rounds of communication, and the per-processor load -- the num...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	52,344
2209.01547	Conditional Independence Testing via Latent Representation Learning	Detecting conditional independencies plays a key role in several statistical and machine learning tasks, especially in causal discovery algorithms. In this study, we introduce LCIT (Latent representation based Conditional Independence Test)-a novel non-parametric method for conditional independence testing based on rep...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	315,929
2206.06065	Deep ensemble learning for segmenting tuberculosis-consistent manifestations in chest radiographs	Automated segmentation of tuberculosis (TB)-consistent lesions in chest X-rays (CXRs) using deep learning (DL) methods can help reduce radiologist effort, supplement clinical decision-making, and potentially result in improved patient treatment. The majority of works in the literature discuss training automatic segment...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	302,240
2101.06407	ACP: Automatic Channel Pruning via Clustering and Swarm Intelligence Optimization for CNN	As the convolutional neural network (CNN) gets deeper and wider in recent years, the requirements for the amount of data and hardware resources have gradually increased. Meanwhile, CNN also reveals salient redundancy in several tasks. The existing magnitude-based pruning methods are efficient, but the performance of th...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	215,705
2005.13334	Enriched In-Order Linearization for Faster Sequence-to-Sequence Constituent Parsing	Sequence-to-sequence constituent parsing requires a linearization to represent trees as sequences. Top-down tree linearizations, which can be based on brackets or shift-reduce actions, have achieved the best accuracy to date. In this paper, we show that these results can be improved by using an in-order linearization i...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	178,986
2403.08795	Ontologia para monitorar a defici\^encia mental em seus d\'eficts no processamento da informa\c{c}\~ao por decl\'inio cognitivo e evitar agress\~oes psicol\'ogicas e f\'isicas em ambientes educacionais com ajuda da I.A*	The intention of this article is to propose the use of artificial intelligence to detect through analysis by UFO ontology the emergence of verbal and physical aggression related to psychosocial deficiencies and their provoking agents, in an attempt to prevent catastrophic consequences within school environments.	true	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	437,480
2303.17930	JobHam-place with smart recommend job options and candidate filtering options	Due to the increasing number of graduates, many applicants experience the situation about finding a job, and employers experience difficulty filtering job applicants, which might negatively impact their effectiveness. However, most job-hunting websites lack job recommendation and CV filtering or ranking functionality, ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	355,398
2205.13948	Evolution as a Service: A Privacy-Preserving Genetic Algorithm for Combinatorial Optimization	Evolutionary algorithms (EAs), such as the genetic algorithm (GA), offer an elegant way to handle combinatorial optimization problems (COPs). However, limited by expertise and resources, most users do not have enough capability to implement EAs to solve COPs. An intuitive and promising solution is to outsource evolutio...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	299,155
2112.14468	Challenges and Approaches for Mitigating Byzantine Attacks in Federated Learning	Recently emerged federated learning (FL) is an attractive distributed learning framework in which numerous wireless end-user devices can train a global model with the data remained autochthonous. Compared with the traditional machine learning framework that collects user data for centralized storage, which brings huge ...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	true	273,541
2012.02405	Applying the Chebyshev-Tau spectral method to solve the parabolic equation model of wide-angle rational approximation in ocean acoustics	Solving an acoustic wave equation using a parabolic approximation is a popular approach for many existing ocean acoustic models. Commonly used parabolic equation (PE) model programs, such as the range-dependent acoustic model (RAM), are discretized by the finite difference method (FDM). Considering the idea and theory ...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	209,755
2310.04747	Towards Dynamic and Small Objects Refinement for Unsupervised Domain Adaptative Nighttime Semantic Segmentation	Nighttime semantic segmentation plays a crucial role in practical applications, such as autonomous driving, where it frequently encounters difficulties caused by inadequate illumination conditions and the absence of well-annotated datasets. Moreover, semantic segmentation models trained on daytime datasets often face d...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	397,798
1612.05420	A Two-Phase Approach Towards Identifying Argument Structure in Natural Language	We propose a new approach for extracting argument structure from natural language texts that contain an underlying argument. Our approach comprises of two phases: Score Assignment and Structure Prediction. The Score Assignment phase trains models to classify relations between argument units (Support, Attack or Neutral)...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	65,679
1401.3148	Dynamic Topology Adaptation and Distributed Estimation for Smart Grids	This paper presents new dynamic topology adaptation strategies for distributed estimation in smart grids systems. We propose a dynamic exhaustive search--based topology adaptation algorithm and a dynamic sparsity--inspired topology adaptation algorithm, which can exploit the topology of smart grids with poor--quality l...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	29,813
1802.00136	Redundancy of unbounded memory Markov classes with continuity conditions	We study the redundancy of universally compressing strings $X_1,\dots, X_n$ generated by a binary Markov source $p$ without any bound on the memory. To better understand the connection between compression and estimation in the Markov regime, we consider a class of Markov sources restricted by a continuity condition. In...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	89,353
1205.2345	Hajj and Umrah Event Recognition Datasets	In this note, new Hajj and Umrah Event Recognition datasets (HUER) are presented. The demonstrated datasets are based on videos and images taken during 2011-2012 Hajj and Umrah seasons. HUER is the first collection of datasets covering the six types of Hajj and Umrah ritual events (rotating in Tawaf around Kabaa, perfo...	false	false	false	false	false	false	false	false	false	false	false	true	false	true	false	false	false	false	15,896
1909.05803	Self-Assembling Modular Networks for Interpretable Multi-Hop Reasoning	Multi-hop QA requires a model to connect multiple pieces of evidence scattered in a long context to answer the question. The recently proposed HotpotQA (Yang et al., 2018) dataset is comprised of questions embodying four different multi-hop reasoning paradigms (two bridge entity setups, checking multiple properties, an...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	145,213
2205.06032	D3T-GAN: Data-Dependent Domain Transfer GANs for Few-shot Image Generation	As an important and challenging problem, few-shot image generation aims at generating realistic images through training a GAN model given few samples. A typical solution for few-shot generation is to transfer a well-trained GAN model from a data-rich source domain to the data-deficient target domain. In this paper, we ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	296,124
2103.11145	Overprotective Training Environments Fall Short at Testing Time: Let Models Contribute to Their Own Training	Despite important progress, conversational systems often generate dialogues that sound unnatural to humans. We conjecture that the reason lies in their different training and testing conditions: agents are trained in a controlled "lab" setting but tested in the "wild". During training, they learn to generate an utteran...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	225,687
2406.18451	Detecting Brittle Decisions for Free: Leveraging Margin Consistency in Deep Robust Classifiers	Despite extensive research on adversarial training strategies to improve robustness, the decisions of even the most robust deep learning models can still be quite sensitive to imperceptible perturbations, creating serious risks when deploying them for high-stakes real-world applications. While detecting such cases may ...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	468,013
2202.12417	Optimal channel selection with discrete QCQP	Reducing the high computational cost of large convolutional neural networks is crucial when deploying the networks to resource-constrained environments. We first show the greedy approach of recent channel pruning methods ignores the inherent quadratic coupling between channels in the neighboring layers and cannot safel...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	282,229
2209.11916	A Simple Strategy to Provable Invariance via Orbit Mapping	Many applications require robustness, or ideally invariance, of neural networks to certain transformations of input data. Most commonly, this requirement is addressed by training data augmentation, using adversarial training, or defining network architectures that include the desired invariance by design. In this work,...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	319,348
1808.05260	Testing for Balance in Social Networks	Friendship and antipathy exist in concert with one another in real social networks. Despite the role they play in social interactions, antagonistic ties are poorly understood and infrequently measured. One important theory of negative ties that has received relatively little empirical evaluation is balance theory, the ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	105,315
2304.03718	Integrating Edge-AI in Structural Health Monitoring domain	Structural health monitoring (SHM) tasks like damage detection are crucial for decision-making regarding maintenance and deterioration. For example, crack detection in SHM is crucial for bridge maintenance as crack progression can lead to structural instability. However, most AI/ML models in the literature have low lat...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	356,918
2402.15062	Don't Just Say "I don't know"! Self-aligning Large Language Models for Responding to Unknown Questions with Explanations	Despite the remarkable abilities of Large Language Models (LLMs) to answer questions, they often display a considerable level of overconfidence even when the question does not have a definitive answer. To avoid providing hallucinated answers to these unknown questions, existing studies typically investigate approaches ...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	431,978
1606.09375	Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering	In this work, we are interested in generalizing convolutional neural networks (CNNs) from low-dimensional regular grids, where image, video and speech are represented, to high-dimensional irregular domains, such as social networks, brain connectomes or words' embedding, represented by graphs. We present a formulation o...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	57,986
2103.05712	Simple Flagellated Soft Robot for Locomotion near Air-Fluid Interface	A wide range of microorganisms, e.g. bacteria, propel themselves by rotation of soft helical tails, also known as flagella. Due to the small size of these organisms, viscous forces overwhelm inertial effects and the flow is at low Reynolds number. In this fluid-structure problem, a competition between elastic forces an...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	224,062
2003.06441	Neural Generators of Sparse Local Linear Models for Achieving both Accuracy and Interpretability	For reliability, it is important that the predictions made by machine learning methods are interpretable by human. In general, deep neural networks (DNNs) can provide accurate predictions, although it is difficult to interpret why such predictions are obtained by DNNs. On the other hand, interpretation of linear models...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	168,112
2406.07577	Structured Active Inference (Extended Abstract)	We introduce structured active inference, a large generalization and formalization of active inference using the tools of categorical systems theory. We cast generative models formally as systems "on an interface", with the latter being a compositional abstraction of the usual notion of Markov blanket; agents are then ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	463,120
2309.13007	ReConcile: Round-Table Conference Improves Reasoning via Consensus among Diverse LLMs	Large Language Models (LLMs) still struggle with natural language reasoning tasks. Motivated by the society of minds (Minsky, 1988), we propose ReConcile, a multi-model multi-agent framework designed as a round table conference among diverse LLM agents. ReConcile enhances collaborative reasoning between LLM agents via ...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	394,002
2502.13374	Task-agnostic Prompt Compression with Context-aware Sentence Embedding and Reward-guided Task Descriptor	The rise of Large Language Models (LLMs) has led to significant interest in prompt compression, a technique aimed at reducing the length of input prompts while preserving critical information. However, the prominent approaches in prompt compression often require explicit questions or handcrafted templates for compressi...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	535,337
2306.13293	Differentially Private Streaming Data Release under Temporal Correlations via Post-processing	The release of differentially private streaming data has been extensively studied, yet striking a good balance between privacy and utility on temporally correlated data in the stream remains an open problem. Existing works focus on enhancing privacy when applying differential privacy to correlated data, highlighting th...	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	true	false	375,233
1910.05791	Evaluating Load Balancing Performance in Distributed Storage with Redundancy	To facilitate load balancing, distributed systems store data redundantly. We evaluate the load balancing performance of storage schemes in which each object is stored at $d$ different nodes, and each node stores the same number of objects. In our model, the load offered for the objects is sampled uniformly at random fr...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	149,172
2003.10808	A Comprehensive Analysis of Correlated Source Compression Using Edge Computing in Distributed Systems	This paper examines the theory pertaining to lossless compression of correlated sources located at the edge of a network. Importantly, communication between nodes is prohibited. In particular, a method that combines correlated source coding and matrix partitioning is explained. This technique is then made more flexible...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	169,439
1902.03658	Word embeddings for idiolect identification	The term idiolect refers to the unique and distinctive use of language of an individual and it is the theoretical foundation of Authorship Attribution. In this paper we are focusing on learning distributed representations (embeddings) of social media users that reflect their writing style. These representations can be ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	121,169
1907.07629	On the Importance of News Content Representation in Hybrid Neural Session-based Recommender Systems	News recommender systems are designed to surface relevant information for online readers by personalizing their user experiences. A particular problem in that context is that online readers are often anonymous, which means that this personalization can only be based on the last few recorded interactions with the user, ...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	138,925
2309.11333	You can have your ensemble and run it too -- Deep Ensembles Spread Over Time	Ensembles of independently trained deep neural networks yield uncertainty estimates that rival Bayesian networks in performance. They also offer sizable improvements in terms of predictive performance over single models. However, deep ensembles are not commonly used in environments with limited computational budget -- ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	393,369
1504.04419	Wasserstein continuity of entropy and outer bounds for interference channels	It is shown that under suitable regularity conditions, differential entropy is a Lipschitz functional on the space of distributions on $n$-dimensional Euclidean space with respect to the quadratic Wasserstein distance. Under similar conditions, (discrete) Shannon entropy is shown to be Lipschitz continuous in distribut...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	42,137
2301.12503	AudioLDM: Text-to-Audio Generation with Latent Diffusion Models	Text-to-audio (TTA) system has recently gained attention for its ability to synthesize general audio based on text descriptions. However, previous studies in TTA have limited generation quality with high computational costs. In this study, we propose AudioLDM, a TTA system that is built on a latent space to learn the c...	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	342,553
1708.09496	Inferring Narrative Causality between Event Pairs in Films	To understand narrative, humans draw inferences about the underlying relations between narrative events. Cognitive theories of narrative understanding define these inferences as four different types of causality, that include pairs of events A, B where A physically causes B (X drop, X break), to pairs of events where A...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	79,795
1411.2186	Estimating Fire Weather Indices via Semantic Reasoning over Wireless Sensor Network Data Streams	Wildfires are frequent, devastating events in Australia that regularly cause significant loss of life and widespread property damage. Fire weather indices are a widely-adopted method for measuring fire danger and they play a significant role in issuing bushfire warnings and in anticipating demand for bushfire managemen...	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	37,394
2203.17020	Logit Normalization for Long-tail Object Detection	Real-world data exhibiting skewed distributions pose a serious challenge to existing object detectors. Moreover, the samplers in detectors lead to shifted training label distributions, while the tremendous proportion of background to foreground samples severely harms foreground classification. To mitigate these issues,...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	289,014
1811.06962	Exploring Gameplay With AI Agents	The process of playtesting a game is subjective, expensive and incomplete. In this paper, we present a playtesting approach that explores the game space with automated agents and collects data to answer questions posed by the designers. Rather than have agents interacting with an actual game client, this approach recre...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	113,627
2301.01148	MERLIN: Multi-agent offline and transfer learning for occupant-centric energy flexible operation of grid-interactive communities using smart meter data and CityLearn	The decarbonization of buildings presents new challenges for the reliability of the electrical grid as a result of the intermittency of renewable energy sources and increase in grid load brought about by end-use electrification. To restore reliability, grid-interactive efficient buildings can provide flexibility servic...	false	false	false	false	true	false	true	false	false	false	true	false	false	false	false	false	false	false	339,148
2009.03520	Leam: An Interactive System for In-situ Visual Text Analysis	With the increase in scale and availability of digital text generated on the web, enterprises such as online retailers and aggregators often use text analytics to mine and analyze the data to improve their services and products alike. Text data analysis is an iterative, non-linear process with diverse workflows spannin...	true	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	false	194,824
2205.01224	COMET Flows: Towards Generative Modeling of Multivariate Extremes and Tail Dependence	Normalizing flows, a popular class of deep generative models, often fail to represent extreme phenomena observed in real-world processes. In particular, existing normalizing flow architectures struggle to model multivariate extremes, characterized by heavy-tailed marginal distributions and asymmetric tail dependence am...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	294,507
1911.07034	Instance Shadow Detection	Instance shadow detection is a brand new problem, aiming to find shadow instances paired with object instances. To approach it, we first prepare a new dataset called SOBA, named after Shadow-OBject Association, with 3,623 pairs of shadow and object instances in 1,000 photos, each with individual labeled masks. Second, ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	153,706
2406.08248	Traffic Signal Cycle Control with Centralized Critic and Decentralized Actors under Varying Intervention Frequencies	Traffic congestion in urban areas is a significant problem, leading to prolonged travel times, reduced efficiency, and increased environmental concerns. Effective traffic signal control (TSC) is a key strategy for reducing congestion. Unlike most TSC systems that rely on high-frequency control, this study introduces an...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	463,406
1705.06057	Joint Learning from Earth Observation and OpenStreetMap Data to Get Faster Better Semantic Maps	In this work, we investigate the use of OpenStreetMap data for semantic labeling of Earth Observation images. Deep neural networks have been used in the past for remote sensing data classification from various sensors, including multispectral, hyperspectral, SAR and LiDAR data. While OpenStreetMap has already been used...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	73,591
2412.06946	NRSurNN3dq4: A Deep Learning Powered Numerical Relativity Surrogate for Binary Black Hole Waveforms	Gravitational wave approximants are widely used tools in gravitational-wave astronomy. They allow for dense coverage of the parameter space of binary black hole (BBH) mergers for purposes of parameter inference, or, more generally, match filtering tasks, while avoiding the computationally expensive full evolution of nu...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	515,447
2105.10909	Killing One Bird with Two Stones: Model Extraction and Attribute Inference Attacks against BERT-based APIs	The collection and availability of big data, combined with advances in pre-trained models (e.g., BERT, XLNET, etc), have revolutionized the predictive performance of modern natural language processing tasks, ranging from text classification to text generation. This allows corporations to provide machine learning as a s...	false	false	false	false	false	false	true	false	true	false	false	false	true	false	false	false	false	false	236,538

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