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
2302.14022	Diacritic Recognition Performance in Arabic ASR	We present an analysis of diacritic recognition performance in Arabic Automatic Speech Recognition (ASR) systems. As most existing Arabic speech corpora do not contain all diacritical marks, which represent short vowels and other phonetic information in Arabic script, current state-of-the-art ASR models do not produce ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	348,128
2204.06085	Finding Trolls Under Bridges: Preliminary Work on a Motif Detector	Motifs are distinctive recurring elements found in folklore that have significance as communicative devices in news, literature, press releases, and propaganda. Motifs concisely imply a large constellation of culturally-relevant information, and their broad usage suggests their cognitive importance as touchstones of cu...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	291,220
2104.14074	Statistical Inference with M-Estimators on Adaptively Collected Data	Bandit algorithms are increasingly used in real-world sequential decision-making problems. Associated with this is an increased desire to be able to use the resulting datasets to answer scientific questions like: Did one type of ad lead to more purchases? In which contexts is a mobile health intervention effective? How...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	232,700
2312.12705	Optimizing Distributed Training on Frontier for Large Language Models	Large language models (LLMs) have demonstrated remarkable success as foundational models, benefiting various downstream applications through fine-tuning. Recent studies on loss scaling have demonstrated the superior performance of larger LLMs compared to their smaller counterparts. Nevertheless, training LLMs with bill...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	417,065
2303.07166	Improved Tree Search for Automatic Program Synthesis	In the task of automatic program synthesis, one obtains pairs of matching inputs and outputs and generates a computer program, in a particular domain-specific language (DSL), which given each sample input returns the matching output. A key element is being able to perform an efficient search in the space of valid progr...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	351,150
2212.14612	Conformal Prediction Intervals for Remaining Useful Lifetime Estimation	The main objective of Prognostics and Health Management is to estimate the Remaining Useful Lifetime (RUL), namely, the time that a system or a piece of equipment is still in working order before starting to function incorrectly. In recent years, numerous machine learning algorithms have been proposed for RUL estimatio...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	338,668
2306.14047	Towards Optimal Pricing of Demand Response -- A Nonparametric Constrained Policy Optimization Approach	Demand response (DR) has been demonstrated to be an effective method for reducing peak load and mitigating uncertainties on both the supply and demand sides of the electricity market. One critical question for DR research is how to appropriately adjust electricity prices in order to shift electrical load from peak to o...	false	false	false	false	true	false	true	false	false	false	true	false	false	false	false	false	false	false	375,513
2101.09957	Activation Functions in Artificial Neural Networks: A Systematic Overview	Activation functions shape the outputs of artificial neurons and, therefore, are integral parts of neural networks in general and deep learning in particular. Some activation functions, such as logistic and relu, have been used for many decades. But with deep learning becoming a mainstream research topic, new activatio...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	false	false	216,769
0906.2603	Hybrid Coding for Gaussian Broadcast Channels with Gaussian Sources	This paper considers a degraded Gaussian broadcast channel over which Gaussian sources are to be communicated. When the sources are independent, this paper shows that hybrid coding achieves the optimal distortion region, the same as that of separate source and channel coding. It also shows that uncoded transmission is ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	3,879
2409.02395	Deep Brain Ultrasound Ablation Thermal Dose Modeling with in Vivo Experimental Validation	Intracorporeal needle-based therapeutic ultrasound (NBTU) is a minimally invasive option for intervening in malignant brain tumors, commonly used in thermal ablation procedures. This technique is suitable for both primary and metastatic cancers, utilizing a high-frequency alternating electric field (up to 10 MHz) to ex...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	485,685
0805.1473	A Fast Algorithm and Datalog Inexpressibility for Temporal Reasoning	We introduce a new tractable temporal constraint language, which strictly contains the Ord-Horn language of Buerkert and Nebel and the class of AND/OR precedence constraints. The algorithm we present for this language decides whether a given set of constraints is consistent in time that is quadratic in the input size. ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	1,745
2103.13630	A Survey of Quantization Methods for Efficient Neural Network Inference	As soon as abstract mathematical computations were adapted to computation on digital computers, the problem of efficient representation, manipulation, and communication of the numerical values in those computations arose. Strongly related to the problem of numerical representation is the problem of quantization: in wha...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	226,562
2109.12058	Optimized Power Normalized Cepstral Coefficients towards Robust Deep Speaker Verification	After their introduction to robust speech recognition, power normalized cepstral coefficient (PNCC) features were successfully adopted to other tasks, including speaker verification. However, as a feature extractor with long-term operations on the power spectrogram, its temporal processing and amplitude scaling steps d...	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	257,144
2312.02967	AmbiGen: Generating Ambigrams from Pre-trained Diffusion Model	Ambigrams are calligraphic designs that have different meanings depending on the viewing orientation. Creating ambigrams is a challenging task even for skilled artists, as it requires maintaining the meaning under two different viewpoints at the same time. In this work, we propose to generate ambigrams by distilling a ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	413,067
2303.12277	Stochastic Nonsmooth Convex Optimization with Heavy-Tailed Noises: High-Probability Bound, In-Expectation Rate and Initial Distance Adaptation	Recently, several studies consider the stochastic optimization problem but in a heavy-tailed noise regime, i.e., the difference between the stochastic gradient and the true gradient is assumed to have a finite $p$-th moment (say being upper bounded by $\sigma^{p}$ for some $\sigma\geq0$) where $p\in(1,2]$, which not on...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	353,198
1104.4385	Convex Approaches to Model Wavelet Sparsity Patterns	Statistical dependencies among wavelet coefficients are commonly represented by graphical models such as hidden Markov trees(HMTs). However, in linear inverse problems such as deconvolution, tomography, and compressed sensing, the presence of a sensing or observation matrix produces a linear mixing of the simple Markov...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	10,086
1703.08043	A Flexible Wideband Millimeter-Wave Channel Sounder with Local Area and NLOS to LOS Transition Measurements	This paper presents a millimeter-wave (mmWave) wideband sliding correlator channel sounder with flexibility to operate at various transmission rates. The channel sounder can transmit and receive up to 1 GHz of RF null-to-null bandwidth while measuring a 2 nanosecond multipath time resolution. The system architecture ta...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	70,509
1911.13071	Increasing Generality in Machine Learning through Procedural Content Generation	Procedural Content Generation (PCG) refers to the practice, in videogames and other games, of generating content such as levels, quests, or characters algorithmically. Motivated by the need to make games replayable, as well as to reduce authoring burden, limit storage space requirements, and enable particular aesthetic...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	155,579
2303.16938	Are Neural Architecture Search Benchmarks Well Designed? A Deeper Look Into Operation Importance	Neural Architecture Search (NAS) benchmarks significantly improved the capability of developing and comparing NAS methods while at the same time drastically reduced the computational overhead by providing meta-information about thousands of trained neural networks. However, tabular benchmarks have several drawbacks tha...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	355,045
2010.08353	On the Guaranteed Almost Equivalence between Imitation Learning from Observation and Demonstration	Imitation learning from observation (LfO) is more preferable than imitation learning from demonstration (LfD) due to the nonnecessity of expert actions when reconstructing the expert policy from the expert data. However, previous studies imply that the performance of LfO is inferior to LfD by a tremendous gap, which ma...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	201,157
2405.04707	A wearable anti-gravity supplement to therapy does not improve arm function in chronic stroke: a randomized trial	Background: Gravity confounds arm movement ability in post-stroke hemiparesis. Reducing its influence allows effective practice leading to recovery. Yet, there is a scarcity of wearable devices suitable for personalized use across diverse therapeutic activities in the clinic. Objective: In this study, we investigated t...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	452,645
2308.11551	Multi-event Video-Text Retrieval	Video-Text Retrieval (VTR) is a crucial multi-modal task in an era of massive video-text data on the Internet. A plethora of work characterized by using a two-stream Vision-Language model architecture that learns a joint representation of video-text pairs has become a prominent approach for the VTR task. However, these...	false	false	false	false	false	true	true	false	false	false	false	true	false	false	false	false	false	false	387,185
2502.09941	A Lightweight and Effective Image Tampering Localization Network with Vision Mamba	Current image tampering localization methods primarily rely on Convolutional Neural Networks (CNNs) and Transformers. While CNNs suffer from limited local receptive fields, Transformers offer global context modeling at the expense of quadratic computational complexity. Recently, the state space model Mamba has emerged ...	false	false	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	533,673
1705.05552	IAN: The Individual Aggregation Network for Person Search	Person search in real-world scenarios is a new challenging computer version task with many meaningful applications. The challenge of this task mainly comes from: (1) unavailable bounding boxes for pedestrians and the model needs to search for the person over the whole gallery images; (2) huge variance of visual appeara...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	73,513
cs/9905010	Statistical Inference and Probabilistic Modelling for Constraint-Based NLP	We present a probabilistic model for constraint-based grammars and a method for estimating the parameters of such models from incomplete, i.e., unparsed data. Whereas methods exist to estimate the parameters of probabilistic context-free grammars from incomplete data (Baum 1970), so far for probabilistic grammars invol...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	540,507
2410.13850	Influence Functions for Scalable Data Attribution in Diffusion Models	Diffusion models have led to significant advancements in generative modelling. Yet their widespread adoption poses challenges regarding data attribution and interpretability. In this paper, we aim to help address such challenges in diffusion models by developing an influence functions framework. Influence function-base...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	499,727
2103.12725	SLOE: A Faster Method for Statistical Inference in High-Dimensional Logistic Regression	Logistic regression remains one of the most widely used tools in applied statistics, machine learning and data science. However, in moderately high-dimensional problems, where the number of features $d$ is a non-negligible fraction of the sample size $n$, the logistic regression maximum likelihood estimator (MLE), and ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	226,274
1608.08823	Approximation of Continuous-Time Infinite-Horizon Optimal Control Problems Arising in Model Predictive Control - Supplementary Notes	These notes present preliminary results regarding two different approximations of linear infinite-horizon optimal control problems arising in model predictive control. Input and state trajectories are parametrized with basis functions and a finite dimensional representation of the dynamics is obtained via a Galerkin ap...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	60,399
1909.13655	A hybrid material-point spheropolygon-element method for solid and granular material interaction	Capturing the interaction between objects that have an extreme difference in Young s modulus or geometrical scale is a highly challenging topic for numerical simulation. One of the fundamental questions is how to build an accurate multi-scale method with optimal computational efficiency. In this work, we develop a mate...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	147,483
1802.06701	An Adaptive Version of Brandes' Algorithm for Betweenness Centrality	Betweenness centrality---measuring how many shortest paths pass through a vertex---is one of the most important network analysis concepts for assessing the relative importance of a vertex. The well-known algorithm of Brandes [J. Math. Sociol.~'01] computes, on an $n$-vertex and $m$-edge graph, the betweenness centralit...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	90,728
2211.07218	SA-DPSGD: Differentially Private Stochastic Gradient Descent based on Simulated Annealing	Differential privacy (DP) provides a formal privacy guarantee that prevents adversaries with access to machine learning models from extracting information about individual training points. Differentially private stochastic gradient descent (DPSGD) is the most popular training method with differential privacy in image r...	false	false	false	false	true	false	false	false	false	false	false	true	true	false	false	false	false	false	330,168
1908.08873	Predicting knee osteoarthritis severity: comparative modeling based on patient's data and plain X-ray images	Knee osteoarthritis (KOA) is a disease that impairs knee function and causes pain. A radiologist reviews knee X-ray images and grades the severity level of the impairments according to the Kellgren and Lawrence grading scheme; a five-point ordinal scale (0--4). In this study, we used Elastic Net (EN) and Random Forests...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	142,675
2209.05235	Style Variable and Irrelevant Learning for Generalizable Person Re-identification	Recently, due to the poor performance of supervised person re-identification (ReID) to an unseen domain, Domain Generalization (DG) person ReID has attracted a lot of attention which aims to learn a domain-insensitive model and can resist the influence of domain bias. In this paper, we first verify through an experimen...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	317,032
2411.11315	A Review on Machine Unlearning	Recently, an increasing number of laws have governed the useability of users' privacy. For example, Article 17 of the General Data Protection Regulation (GDPR), the right to be forgotten, requires machine learning applications to remove a portion of data from a dataset and retrain it if the user makes such a request. F...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	509,009
2211.14177	Overcoming Catastrophic Forgetting by XAI	Explaining the behaviors of deep neural networks, usually considered as black boxes, is critical especially when they are now being adopted over diverse aspects of human life. Taking the advantages of interpretable machine learning (interpretable ML), this work proposes a novel tool called Catastrophic Forgetting Disse...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	332,744
2302.10343	Non-rigid Medical Image Registration using Physics-informed Neural Networks	Biomechanical modelling of soft tissue provides a non-data-driven method for constraining medical image registration, such that the estimated spatial transformation is considered biophysically plausible. This has not only been adopted in real-world clinical applications, such as the MR-to-ultrasound registration for pr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	346,768
2002.10363	Joint Learning of Assignment and Representation for Biometric Group Membership	This paper proposes a framework for group membership protocols preventing the curious but honest server from reconstructing the enrolled biometric signatures and inferring the identity of querying clients. This framework learns the embedding parameters, group representations and assignments simultaneously. Experiments ...	false	false	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	165,379
1511.03488	Constraint-Tightening and Stability in Stochastic Model Predictive Control	Constraint tightening to non-conservatively guarantee recursive feasibility and stability in Stochastic Model Predictive Control is addressed. Stability and feasibility requirements are considered separately, highlighting the difference between existence of a solution and feasibility of a suitable, a priori known candi...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	48,762
2205.15135	Group Probability-Weighted Tree Sums for Interpretable Modeling of Heterogeneous Data	Machine learning in high-stakes domains, such as healthcare, faces two critical challenges: (1) generalizing to diverse data distributions given limited training data while (2) maintaining interpretability. To address these challenges, we propose an instance-weighted tree-sum method that effectively pools data across d...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	299,615
1906.10050	On DICE-free Smart Cities, Particulate Matter, and Feedback-Enabled Access Control	The link between transport related emissions and human health is a major issue for city municipalities worldwide. PM emissions from exhaust and non-exhaust sources are one of the main worrying contributors to air-pollution. In this paper, we challenge the notion that a ban on internal combustion engine vehicles will re...	false	false	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	136,344
2005.12494	Towards Fine-grained Human Pose Transfer with Detail Replenishing Network	Human pose transfer (HPT) is an emerging research topic with huge potential in fashion design, media production, online advertising and virtual reality. For these applications, the visual realism of fine-grained appearance details is crucial for production quality and user engagement. However, existing HPT methods ofte...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	178,742
2409.19750	AstroMLab 2: AstroLLaMA-2-70B Model and Benchmarking Specialised LLMs for Astronomy	Continual pretraining of large language models on domain-specific data has been proposed to enhance performance on downstream tasks. In astronomy, the previous absence of astronomy-focused benchmarks has hindered objective evaluation of these specialized LLM models. Leveraging a recent initiative to curate high-quality...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	492,834
2405.00566	NumLLM: Numeric-Sensitive Large Language Model for Chinese Finance	Recently, many works have proposed various financial large language models (FinLLMs) by pre-training from scratch or fine-tuning open-sourced LLMs on financial corpora. However, existing FinLLMs exhibit unsatisfactory performance in understanding financial text when numeric variables are involved in questions. In this ...	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	450,968
1906.01507	A numerical measure of the instability of Mapper-type algorithms	Mapper is an unsupervised machine learning algorithm generalising the notion of clustering to obtain a geometric description of a dataset. The procedure splits the data into possibly overlapping bins which are then clustered. The output of the algorithm is a graph where nodes represent clusters and edges represent the ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	133,727
1904.07619	Compressed Indexes for Fast Search of Semantic Data	The sheer increase in volume of RDF data demands efficient solutions for the triple indexing problem, that is devising a compressed data structure to compactly represent RDF triples by guaranteeing, at the same time, fast pattern matching operations. This problem lies at the heart of delivering good practical performan...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	127,845
2408.08990	Adaptive Uncertainty Quantification for Generative AI	This work is concerned with conformal prediction in contemporary applications (including generative AI) where a black-box model has been trained on data that are not accessible to the user. Mirroring split-conformal inference, we design a wrapper around a black-box algorithm which calibrates conformity scores. This cal...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	481,239
2305.10072	Infinite-dimensional observers for high order boundary-controlled port-Hamiltonian systems	This letter investigates the design of a class of infinite-dimensional observers for one dimensional (1D) boundary controlled port-Hamiltonian systems (BC-PHS) defined by differential operators of order $N \geq 1$. The convergence of the proposed observer depends on the number and location of available boundary measure...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	364,899
2404.19288	Training-free Graph Neural Networks and the Power of Labels as Features	We propose training-free graph neural networks (TFGNNs), which can be used without training and can also be improved with optional training, for transductive node classification. We first advocate labels as features (LaF), which is an admissible but not explored technique. We show that LaF provably enhances the express...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	450,583
2306.17207	A Fast Fourier Convolutional Deep Neural Network For Accurate and Explainable Discrimination Of Wheat Yellow Rust And Nitrogen Deficiency From Sentinel-2 Time-Series Data	Accurate and timely detection of plant stress is essential for yield protection, allowing better-targeted intervention strategies. Recent advances in remote sensing and deep learning have shown great potential for rapid non-invasive detection of plant stress in a fully automated and reproducible manner. However, the ex...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	376,640
1710.10006	Deep Learning for Accelerated Ultrasound Imaging	In portable, 3-D, or ultra-fast ultrasound (US) imaging systems, there is an increasing demand to reconstruct high quality images from limited number of data. However, the existing solutions require either hardware changes or computationally expansive algorithms. To overcome these limitations, here we propose a novel d...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	83,299
2203.07825	SPA-VAE: Similar-Parts-Assignment for Unsupervised 3D Point Cloud Generation	This paper addresses the problem of unsupervised parts-aware point cloud generation with learned parts-based self-similarity. Our SPA-VAE infers a set of latent canonical candidate shapes for any given object, along with a set of rigid body transformations for each such candidate shape to one or more locations within t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	285,574
1709.06518	Identifying Retweetable Tweets with a Personalized Global Classifier	In this paper we present a method to identify tweets that a user may find interesting enough to retweet. The method is based on a global, but personalized classifier, which is trained on data from several users, represented in terms of user-specific features. Thus, the method is trained on a sufficient volume of data, ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	81,118
1804.01256	NegPSpan: efficient extraction of negative sequential patterns with embedding constraints	Mining frequent sequential patterns consists in extracting recurrent behaviors, modeled as patterns, in a big sequence dataset. Such patterns inform about which events are frequently observed in sequences, i.e. what does really happen. Sometimes, knowing that some specific event does not happen is more informative than...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	true	94,200
2401.16348	Improving the TENOR of Labeling: Re-evaluating Topic Models for Content Analysis	Topic models are a popular tool for understanding text collections, but their evaluation has been a point of contention. Automated evaluation metrics such as coherence are often used, however, their validity has been questioned for neural topic models (NTMs) and can overlook a models benefits in real world applications...	true	false	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	424,787
2404.02845	Cross-Modal Conditioned Reconstruction for Language-guided Medical Image Segmentation	Recent developments underscore the potential of textual information in enhancing learning models for a deeper understanding of medical visual semantics. However, language-guided medical image segmentation still faces a challenging issue. Previous works employ implicit and ambiguous architectures to embed textual inform...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	444,022
1007.4767	Formalization of Psychological Knowledge in Answer Set Programming and its Application	In this paper we explore the use of Answer Set Programming (ASP) to formalize, and reason about, psychological knowledge. In the field of psychology, a considerable amount of knowledge is still expressed using only natural language. This lack of a formalization complicates accurate studies, comparisons, and verificatio...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	7,131
2108.02667	Adaptive Normalized Representation Learning for Generalizable Face Anti-Spoofing	With various face presentation attacks arising under unseen scenarios, face anti-spoofing (FAS) based on domain generalization (DG) has drawn growing attention due to its robustness. Most existing methods utilize DG frameworks to align the features to seek a compact and generalized feature space. However, little attent...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	249,408
1912.12636	Training of Quantized Deep Neural Networks using a Magnetic Tunnel Junction-Based Synapse	Quantized neural networks (QNNs) are being actively researched as a solution for the computational complexity and memory intensity of deep neural networks. This has sparked efforts to develop algorithms that support both inference and training with quantized weight and activation values, without sacrificing accuracy. A...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	true	158,895
2410.12861	Scaled and Inter-token Relation Enhanced Transformer for Sample-restricted Residential NILM	Transformers have demonstrated exceptional performance across various domains due to their self-attention mechanism, which captures complex relationships in data. However, training on smaller datasets poses challenges, as standard attention mechanisms can over-smooth attention scores and overly prioritize intra-token r...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	499,248
1411.3787	Asymmetric Minwise Hashing	Minwise hashing (Minhash) is a widely popular indexing scheme in practice. Minhash is designed for estimating set resemblance and is known to be suboptimal in many applications where the desired measure is set overlap (i.e., inner product between binary vectors) or set containment. Minhash has inherent bias towards sma...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	true	true	37,535
1307.4038	An alternative Gospel of structure: order, composition, processes	We survey some basic mathematical structures, which arguably are more primitive than the structures taught at school. These structures are orders, with or without composition, and (symmetric) monoidal categories. We list several `real life' incarnations of each of these. This paper also serves as an introduction to the...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	25,852
1807.11766	Remote sensing image regression for heterogeneous change detection	Change detection in heterogeneous multitemporal satellite images is an emerging topic in remote sensing. In this paper we propose a framework, based on image regression, to perform change detection in heterogeneous multitemporal satellite images, which has become a main topic in remote sensing. Our method learns a tran...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	104,249
2212.10678	Causally Testing Gender Bias in LLMs: A Case Study on Occupational Bias	Generated texts from large language models (LLMs) have been shown to exhibit a variety of harmful, human-like biases against various demographics. These findings motivate research efforts aiming to understand and measure such effects. This paper introduces a causal formulation for bias measurement in generative languag...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	337,553
2202.08712	Mining On Alzheimer's Diseases Related Knowledge Graph to Identity Potential AD-related Semantic Triples for Drug Repurposing	To date, there are no effective treatments for most neurodegenerative diseases. Knowledge graphs can provide comprehensive and semantic representation for heterogeneous data, and have been successfully leveraged in many biomedical applications including drug repurposing. Our objective is to construct a knowledge graph ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	280,967
2002.07600	Three-dimensional convolutional neural network (3D-CNN) for heterogeneous material homogenization	Homogenization is a technique commonly used in multiscale computational science and engineering for predicting collective response of heterogeneous materials and extracting effective mechanical properties. In this paper, a three-dimensional deep convolutional neural network (3D-CNN) is proposed to predict the effective...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	164,515
2409.09207	FB-HyDON: Parameter-Efficient Physics-Informed Operator Learning of Complex PDEs via Hypernetwork and Finite Basis Domain Decomposition	Deep operator networks (DeepONet) and neural operators have gained significant attention for their ability to map infinite-dimensional function spaces and perform zero-shot super-resolution. However, these models often require large datasets for effective training. While physics-informed operators offer a data-agnostic...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	488,217
2402.12806	SymBa: Symbolic Backward Chaining for Structured Natural Language Reasoning	To improve the performance and explainability of LLM-based natural language reasoning, structured reasoning can be applied to generate explicitly structured proofs. Among different methods for structured reasoning, we specifically focus on backward chaining, where the proof goal is recursively decomposed to subgoals by...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	431,003
1304.5974	Dynamic stochastic blockmodels: Statistical models for time-evolving networks	Significant efforts have gone into the development of statistical models for analyzing data in the form of networks, such as social networks. Most existing work has focused on modeling static networks, which represent either a single time snapshot or an aggregate view over time. There has been recent interest in statis...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	24,136
2412.21161	Open RAN-Enabled Deep Learning-Assisted Mobility Management for Connected Vehicles	Connected Vehicles (CVs) can leverage the unique features of 5G and future 6G/NextG networks to enhance Intelligent Transportation System (ITS) services. However, even with advancements in cellular network generations, CV applications may experience communication interruptions in high-mobility scenarios due to frequent...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	521,471
2102.01004	Hybrid Information-driven Multi-agent Reinforcement Learning	Information theoretic sensor management approaches are an ideal solution to state estimation problems when considering the optimal control of multi-agent systems, however they are too computationally intensive for large state spaces, especially when considering the limited computational resources typical of large-scale...	false	false	false	false	true	false	false	false	false	true	false	false	false	false	true	false	false	false	217,975
2308.08448	Implementing Quantum Generative Adversarial Network (qGAN) and QCBM in Finance	Quantum machine learning (QML) is a cross-disciplinary subject made up of two of the most exciting research areas: quantum computing and classical machine learning (ML), with ML and artificial intelligence (AI) being projected as the first fields that will be impacted by the rise of quantum machines. Quantum computers ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	385,905
1611.09203	Computational Mapping of the Ground Reflectivity with Laser Scanners	In this investigation we focus on the problem of mapping the ground reflectivity with multiple laser scanners mounted on mobile robots/vehicles. The problem originates because regions of the ground become populated with a varying number of reflectivity measurements whose value depends on the observer and its correspond...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	64,636
1309.6727	Genie Tree and Degrees of Freedom of the Symmetric MIMO Interfering Broadcast Channel	In this paper, we study the information theoretic maximal degrees of freedom (DoF) for the symmetric multi-input-multi-output (MIMO) interfering broadcast channel (IBC) with arbitrary antenna configurations. For the G-cell K-user MXN MIMO-IBC network, we find that the information theoretic maximal DoF per user are rela...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	27,268
2304.09702	Losing Focus: Can It Be Useful in Robotic Laser Surgery?	This paper proposes a method to regulate the tissue temperature during laser surgery by robotically controlling the laser focus. Laser-tissue interactions are generally considered hard to control due to the inherent inhomogeneity of biological tissue, which can create significant variability in its thermal response to ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	359,140
1712.10207	Dense Pooling layers in Fully Convolutional Network for Skin Lesion Segmentation	One of the essential tasks in medical image analysis is segmentation and accurate detection of borders. Lesion segmentation in skin images is an essential step in the computerized detection of skin cancer. However, many of the state-of-the-art segmentation methods have deficiencies in their border detection phase. In t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	87,469
2103.08759	A Transition-based Parser for Unscoped Episodic Logical Forms	"Episodic Logic:Unscoped Logical Form" (EL-ULF) is a semantic representation capturing predicate-argument structure as well as more challenging aspects of language within the Episodic Logic formalism. We present the first learned approach for parsing sentences into ULFs, using a growing set of annotated examples. The r...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	224,978
1903.10145	Cyclical Annealing Schedule: A Simple Approach to Mitigating KL Vanishing	Variational autoencoders (VAEs) with an auto-regressive decoder have been applied for many natural language processing (NLP) tasks. The VAE objective consists of two terms, (i) reconstruction and (ii) KL regularization, balanced by a weighting hyper-parameter \beta. One notorious training difficulty is that the KL term...	false	false	false	false	true	false	true	false	true	false	false	true	false	false	false	false	false	false	125,212
2406.18859	Two-Pronged Human Evaluation of ChatGPT Self-Correction in Radiology Report Simplification	Radiology reports are highly technical documents aimed primarily at doctor-doctor communication. There has been an increasing interest in sharing those reports with patients, necessitating providing them patient-friendly simplifications of the original reports. This study explores the suitability of large language mode...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	468,205
2310.14512	CorefPrompt: Prompt-based Event Coreference Resolution by Measuring Event Type and Argument Compatibilities	Event coreference resolution (ECR) aims to group event mentions referring to the same real-world event into clusters. Most previous studies adopt the "encoding first, then scoring" framework, making the coreference judgment rely on event encoding. Furthermore, current methods struggle to leverage human-summarized ECR r...	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	false	false	401,889
2405.01588	Towards Unbiased Evaluation of Detecting Unanswerable Questions in EHRSQL	Incorporating unanswerable questions into EHR QA systems is crucial for testing the trustworthiness of a system, as providing non-existent responses can mislead doctors in their diagnoses. The EHRSQL dataset stands out as a promising benchmark because it is the only dataset that incorporates unanswerable questions in t...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	451,420
2406.16655	Large Language Models Are Cross-Lingual Knowledge-Free Reasoners	Large Language Models have demonstrated impressive reasoning capabilities across multiple languages. However, the relationship between capabilities in different languages is less explored. In this work, we decompose the process of reasoning tasks into two separated components: knowledge retrieval and knowledge-free rea...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	467,212
2405.07282	Branching Narratives: Character Decision Points Detection	This paper presents the Character Decision Points Detection (CHADPOD) task, a task of identification of points within narratives where characters make decisions that may significantly influence the story's direction. We propose a novel dataset based on CYOA-like games graphs to be used as a benchmark for such a task. W...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	453,648
2304.14844	Using Large Language Models for Interpreting Autonomous Robots Behaviors	The deployment of autonomous robots in various domains has raised significant concerns about their trustworthiness and accountability. This study explores the potential of Large Language Models (LLMs) in analyzing ROS 2 logs generated by autonomous robots and proposes a framework for log analysis that categorizes log f...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	361,116
2407.09717	Deep-TEMPEST: Using Deep Learning to Eavesdrop on HDMI from its Unintended Electromagnetic Emanations	In this work, we address the problem of eavesdropping on digital video displays by analyzing the electromagnetic waves that unintentionally emanate from the cables and connectors, particularly HDMI. This problem is known as TEMPEST. Compared to the analog case (VGA), the digital case is harder due to a 10-bit encoding ...	false	false	false	false	false	false	true	false	false	false	false	true	true	false	false	false	false	false	472,691
2112.05839	ANA: Ant Nesting Algorithm for Optimizing Real-World Problems	In this paper, a novel swarm intelligent algorithm is proposed called ant nesting algorithm (ANA). The algorithm is inspired by Leptothorax ants and mimics the behavior of ants searching for positions to deposit grains while building a new nest. Although the algorithm is inspired by the swarming behavior of ants, it do...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	270,970
2311.06534	Translating Legalese: Enhancing Public Understanding of Court Opinions with Legal Summarizers	Judicial opinions are written to be persuasive and could build public trust in court decisions, yet they can be difficult for non-experts to understand. We present a pipeline for using an AI assistant to generate simplified summaries of judicial opinions. Compared to existing expert-written summaries, these AI-generate...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	406,976
1710.06525	Near-Optimal Adversarial Policy Switching for Decentralized Asynchronous Multi-Agent Systems	A key challenge in multi-robot and multi-agent systems is generating solutions that are robust to other self-interested or even adversarial parties who actively try to prevent the agents from achieving their goals. The practicality of existing works addressing this challenge is limited to only small-scale synchronous d...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	82,790
1505.01927	A simpler sublinear algorithm for approximating the triangle count	A recent result of Eden, Levi, and Ron (ECCC 2015) provides a sublinear time algorithm to estimate the number of triangles in a graph. Given an undirected graph $G$, one can query the degree of a vertex, the existence of an edge between vertices, and the $i$th neighbor of a vertex. Suppose the graph has $n$ vertices, $...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	42,898
2303.14244	Implicit Balancing and Regularization: Generalization and Convergence Guarantees for Overparameterized Asymmetric Matrix Sensing	Recently, there has been significant progress in understanding the convergence and generalization properties of gradient-based methods for training overparameterized learning models. However, many aspects including the role of small random initialization and how the various parameters of the model are coupled during gr...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	354,000
1903.03425	The Ethics of AI Ethics -- An Evaluation of Guidelines	Current advances in research, development and application of artificial intelligence (AI) systems have yielded a far-reaching discourse on AI ethics. In consequence, a number of ethics guidelines have been released in recent years. These guidelines comprise normative principles and recommendations aimed to harness the ...	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	false	false	123,731
1406.0909	Improvement Tracking Dynamic Programming using Replication Function for Continuous Sign Language Recognition	In this paper we used a Replication Function (R. F.)for improvement tracking with dynamic programming. The R. F. transforms values of gray level [0 255] to [0 1]. The resulting images of R. F. are more striking and visible in skin regions. The R. F. improves Dynamic Programming (D. P.) in overlapping hand and face. Res...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	33,580
2209.10732	In Differential Privacy, There is Truth: On Vote Leakage in Ensemble Private Learning	When learning from sensitive data, care must be taken to ensure that training algorithms address privacy concerns. The canonical Private Aggregation of Teacher Ensembles, or PATE, computes output labels by aggregating the predictions of a (possibly distributed) collection of teacher models via a voting mechanism. The m...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	318,952
2404.02372	Obfuscated Malware Detection: Investigating Real-world Scenarios through Memory Analysis	In the era of the internet and smart devices, the detection of malware has become crucial for system security. Malware authors increasingly employ obfuscation techniques to evade advanced security solutions, making it challenging to detect and eliminate threats. Obfuscated malware, adept at hiding itself, poses a signi...	false	false	false	false	false	false	true	false	true	false	false	false	true	false	false	false	false	false	443,827
2409.02438	Non-target Divergence Hypothesis: Toward Understanding Domain Gaps in Cross-Modal Knowledge Distillation	Compared to single-modal knowledge distillation, cross-modal knowledge distillation faces more severe challenges due to domain gaps between modalities. Although various methods have proposed various solutions to overcome these challenges, there is still limited research on how domain gaps affect cross-modal knowledge d...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	485,705
2411.04274	Effective Capacity of a Battery Energy Storage System Captive to a Wind Farm	Wind energy's role in the global electric grid is set to expand significantly. New York State alone anticipates offshore wind farms (WFs) contributing 9GW by 2035. Integration of energy storage emerges as crucial for this advancement. In this study, we focus on a WF paired with a captive battery energy storage system (...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	506,201
1908.08162	Globally optimal registration of noisy point clouds	Registration of 3D point clouds is a fundamental task in several applications of robotics and computer vision. While registration methods such as iterative closest point and variants are very popular, they are only locally optimal. There has been some recent work on globally optimal registration, but they perform poorl...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	142,475
2209.08498	LATITUDE: Robotic Global Localization with Truncated Dynamic Low-pass Filter in City-scale NeRF	Neural Radiance Fields (NeRFs) have made great success in representing complex 3D scenes with high-resolution details and efficient memory. Nevertheless, current NeRF-based pose estimators have no initial pose prediction and are prone to local optima during optimization. In this paper, we present LATITUDE: Global Local...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	318,149
2305.05136	Localisation of Mammographic masses by Greedy Backtracking of Activations in the Stacked Auto-Encoders	Mammographic image analysis requires accurate localisation of salient mammographic masses. In mammographic computer-aided diagnosis, mass or Region of Interest (ROI) is often marked by physicians and features are extracted from the marked ROI. In this paper, we present a novel mammographic mass localisation framework, ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	363,021
1609.01987	CHSalign: A Web Server That Builds upon Junction-Explorer and RNAJAG for Pairwise Alignment of RNA Secondary Structures with Coaxial Helical Stacking	RNA junctions are important structural elements of RNA molecules. They are formed when three or more helices come together in three-dimensional space. Recent studies have focused on the annotation and prediction of coaxial helical stacking (CHS) motifs within junctions. Here we exploit such predictions to develop an ef...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	60,666
1706.02427	Content-Based Table Retrieval for Web Queries	Understanding the connections between unstructured text and semi-structured table is an important yet neglected problem in natural language processing. In this work, we focus on content-based table retrieval. Given a query, the task is to find the most relevant table from a collection of tables. Further progress toward...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	74,975

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