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
2109.10598	Diarisation using location tracking with agglomerative clustering	Previous works have shown that spatial location information can be complementary to speaker embeddings for a speaker diarisation task. However, the models used often assume that speakers are fairly stationary throughout a meeting. This paper proposes to relax this assumption, by explicitly modelling the movements of sp...	false	false	true	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	256,681
2408.15002	Knowledge Discovery in Optical Music Recognition: Enhancing Information Retrieval with Instance Segmentation	Optical Music Recognition (OMR) automates the transcription of musical notation from images into machine-readable formats like MusicXML, MEI, or MIDI, significantly reducing the costs and time of manual transcription. This study explores knowledge discovery in OMR by applying instance segmentation using Mask R-CNN to e...	false	false	true	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	483,761
2007.08497	Co-generation of game levels and game-playing agents	Open-endedness, primarily studied in the context of artificial life, is the ability of systems to generate potentially unbounded ontologies of increasing novelty and complexity. Engineering generative systems displaying at least some degree of this ability is a goal with clear applications to procedural content generat...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	187,646
2107.03443	BumbleBee: A Transformer for Music	We will introduce BumbleBee, a transformer model that will generate MIDI music data . We will tackle the issue of transformers applied to long sequences by implementing a longformer generative model that uses dilating sliding windows to compute the attention layers. We will compare our results to that of the music tran...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	245,160
2002.11829	Representation Learning Through Latent Canonicalizations	We seek to learn a representation on a large annotated data source that generalizes to a target domain using limited new supervision. Many prior approaches to this problem have focused on learning "disentangled" representations so that as individual factors vary in a new domain, only a portion of the representation nee...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	165,837
2307.11783	A novel integrated method of detection-grasping for specific object based on the box coordinate matching	To better care for the elderly and disabled, it is essential for service robots to have an effective fusion method of object detection and grasp estimation. However, limited research has been observed on the combination of object detection and grasp estimation. To overcome this technical difficulty, a novel integrated ...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	381,032
2112.04559	Achieving Reliable Coordination of Residential Plug-in Electric Vehicle Charging: A Pilot Study	Wide-scale electrification of the transportation sector will require careful planning and coordination with the power grid. Left unmanaged, uncoordinated charging of electric vehicles (EVs) at increased levels of penetration will amplify existing peak loads, potentially outstripping the grid's capacity to reliably meet...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	270,561
2011.14764	Binary Classification: Counterbalancing Class Imbalance by Applying Regression Models in Combination with One-Sided Label Shifts	In many real-world pattern recognition scenarios, such as in medical applications, the corresponding classification tasks can be of an imbalanced nature. In the current study, we focus on binary, imbalanced classification tasks, i.e.~binary classification tasks in which one of the two classes is under-represented (mino...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	208,886
1804.08912	Accurate 3-D Reconstruction with RGB-D Cameras using Depth Map Fusion and Pose Refinement	Depth map fusion is an essential part in both stereo and RGB-D based 3-D reconstruction pipelines. Whether produced with a passive stereo reconstruction or using an active depth sensor, such as Microsoft Kinect, the depth maps have noise and may have poor initial registration. In this paper, we introduce a method which...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	95,864
1803.08377	On LDPC Code Based Massive Random-Access Scheme for the Gaussian Multiple Access Channel	This paper deals with the problem of massive random access for Gaussian multiple access channel (MAC). We continue to investigate the coding scheme for Gaussian MAC proposed by A. Vem et al in 2017. The proposed scheme consists of four parts: (i) the data transmission is partitioned into time slots; (ii) the data, tran...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	93,246
2310.07150	Determining Winners in Elections with Absent Votes	An important question in elections is the determine whether a candidate can be a winner when some votes are absent. We study this determining winner with the absent votes (WAV) problem when the votes are top-truncated. We show that the WAV problem is NP-complete for the single transferable vote, Maximin, and Copeland, ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	398,845
2303.13511	Neural Preset for Color Style Transfer	In this paper, we present a Neural Preset technique to address the limitations of existing color style transfer methods, including visual artifacts, vast memory requirement, and slow style switching speed. Our method is based on two core designs. First, we propose Deterministic Neural Color Mapping (DNCM) to consistent...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	353,699
0905.2248	Protection against link errors and failures using network coding	We propose a network-coding based scheme to protect multiple bidirectional unicast connections against adversarial errors and failures in a network. The network consists of a set of bidirectional primary path connections that carry the uncoded traffic. The end nodes of the bidirectional connections are connected by a s...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	3,681
1210.6956	Vortexje - An Open-Source Panel Method for Co-Simulation	This paper discusses the use of the 3-dimensional panel method for dynamical system simulation. Specifically, the advantages and disadvantages of model exchange versus co-simulation of the aerodynamics and the dynamical system model are discussed. Based on a trade-off analysis, a set of recommendations for a panel meth...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	19,403
2411.19370	Machine learning the Ising transition: A comparison between discriminative and generative approaches	The detection of phase transitions is a central task in many-body physics. To automate this process, the task can be phrased as a classification problem. Classification problems can be approached in two fundamentally distinct ways: through either a discriminative or a generative method. In general, it is unclear which ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	512,214
2105.11798	Extending the Abstraction of Personality Types based on MBTI with Machine Learning and Natural Language Processing	A data-centric approach with Natural Language Processing (NLP) to predict personality types based on the MBTI (an introspective self-assessment questionnaire that indicates different psychological preferences about how people perceive the world and make decisions) through systematic enrichment of text representation, b...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	236,824
2411.00586	Improving self-training under distribution shifts via anchored confidence with theoretical guarantees	Self-training often falls short under distribution shifts due to an increased discrepancy between prediction confidence and actual accuracy. This typically necessitates computationally demanding methods such as neighborhood or ensemble-based label corrections. Drawing inspiration from insights on early learning regular...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	504,655
2201.11616	On the Role of Multi-Objective Optimization to the Transit Network Design Problem	Ongoing traffic changes, including those triggered by the COVID-19 pandemic, reveal the necessity to adapt our public transport systems to the ever-changing users' needs. This work shows that single and multi objective stances can be synergistically combined to better answer the transit network design problem (TNDP). S...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	277,342
1912.07829	Defects Mitigation in Resistive Crossbars for Analog Vector Matrix Multiplication	With storage and computation happening at the same place, computing in resistive crossbars minimizes data movement and avoids the memory bottleneck issue. It leads to ultra-high energy efficiency for data-intensive applications. However, defects in crossbars severely affect computing accuracy. Existing solutions, inclu...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	157,699
2010.11097	Privacy Preserving Set-Based Estimation Using Partially Homomorphic Encryption	The set-based estimation has gained a lot of attention due to its ability to guarantee state enclosures for safety-critical systems. However, collecting measurements from distributed sensors often requires outsourcing the set-based operations to an aggregator node, raising many privacy concerns. To address this problem...	false	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	202,123
2411.15182	Forecasting Application Counts in Talent Acquisition Platforms: Harnessing Multimodal Signals using LMs	As recruitment and talent acquisition have become more and more competitive, recruitment firms have become more sophisticated in using machine learning (ML) methodologies for optimizing their day to day activities. But, most of published ML based methodologies in this area have been limited to the tasks like candidate ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	510,473
2101.11802	Weakly Supervised Neuro-Symbolic Module Networks for Numerical Reasoning	Neural Module Networks (NMNs) have been quite successful in incorporating explicit reasoning as learnable modules in various question answering tasks, including the most generic form of numerical reasoning over text in Machine Reading Comprehension (MRC). However, to achieve this, contemporary NMNs need strong supervis...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	217,394
2205.14040	Intelligent Transportation Systems' Orchestration: Lessons Learned & Potential Opportunities	The growing deployment efforts of 5G networks globally has led to the acceleration of the businesses/services' digital transformation. This growth has led to the need for new communication technologies that will promote this transformation. 6G is being proposed as the set of technologies and architectures that will ach...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	299,189
1703.00641	Learning Mixtures of Sparse Linear Regressions Using Sparse Graph Codes	In this paper, we consider the mixture of sparse linear regressions model. Let ${\beta}^{(1)},\ldots,{\beta}^{(L)}\in\mathbb{C}^n$ be $ L $ unknown sparse parameter vectors with a total of $ K $ non-zero coefficients. Noisy linear measurements are obtained in the form $y_i={x}_i^H {\beta}^{(\ell_i)} + w_i$, each of whi...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	69,202
2209.05731	A Many-ported and Shared Memory Architecture for High-Performance ADAS SoCs	Increasing investment in computing technologies and the advancements in silicon technology has fueled rapid growth in advanced driver assistance systems (ADAS) and corresponding SoC developments. An ADAS SoC represents a heterogeneous architecture that consists of CPUs, GPUs and artificial intelligence (AI) accelerator...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	317,188
1209.6580	Testing MapReduce-Based Systems	MapReduce (MR) is the most popular solution to build applications for large-scale data processing. These applications are often deployed on large clusters of commodity machines, where failures happen constantly due to bugs, hardware problems, and outages. Testing MR-based systems is hard, since it is needed a great eff...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	18,830
1801.07194	Optimizing Prediction Intervals by Tuning Random Forest via Meta-Validation	Recent studies have shown that tuning prediction models increases prediction accuracy and that Random Forest can be used to construct prediction intervals. However, to our best knowledge, no study has investigated the need to, and the manner in which one can, tune Random Forest for optimizing prediction intervals { thi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	88,741
2302.11521	How Does In-Context Learning Help Prompt Tuning?	Fine-tuning large language models is becoming ever more impractical due to their rapidly-growing scale. This motivates the use of parameter-efficient adaptation methods such as prompt tuning (PT), which adds a small number of tunable embeddings to an otherwise frozen model, and in-context learning (ICL), in which demon...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	347,236
1811.07453	The PyTorch-Kaldi Speech Recognition Toolkit	The availability of open-source software is playing a remarkable role in the popularization of speech recognition and deep learning. Kaldi, for instance, is nowadays an established framework used to develop state-of-the-art speech recognizers. PyTorch is used to build neural networks with the Python language and has re...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	113,768
2204.05041	Pyramid Grafting Network for One-Stage High Resolution Saliency Detection	Recent salient object detection (SOD) methods based on deep neural network have achieved remarkable performance. However, most of existing SOD models designed for low-resolution input perform poorly on high-resolution images due to the contradiction between the sampling depth and the receptive field size. Aiming at res...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	290,886
2305.01366	Establishing a Learning Model for Correct Hand Hygiene Technique in a NICU	The ability of healthcare workers to learn proper hand hygiene has been an understudied area of research. Generally, hand hygiene skills are regarded as a key contributor to reduce critical infections and healthcare-associated infections. In a clinical setup, at a Neonatal Intensive Care Unit (NICU), the outcome of a m...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	361,652
2406.13778	Benchmarking Unsupervised Online IDS for Masquerade Attacks in CAN	Vehicular controller area networks (CANs) are susceptible to masquerade attacks by malicious adversaries. In masquerade attacks, adversaries silence a targeted ID and then send malicious frames with forged content at the expected timing of benign frames. As masquerade attacks could seriously harm vehicle functionality ...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	465,994
2501.13982	Attribute-based Visual Reprogramming for Image Classification with CLIP	Visual reprogramming (VR) reuses pre-trained vision models for downstream image classification tasks by adding trainable noise patterns to inputs. When applied to vision-language models (e.g., CLIP), existing VR approaches follow the same pipeline used in vision models (e.g., ResNet, ViT), where ground-truth class labe...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	526,925
2209.13862	An Operational Approach to Information Leakage via Generalized Gain Functions	We introduce a \emph{gain function} viewpoint of information leakage by proposing \emph{maximal $g$-leakage}, a rich class of operationally meaningful leakage measures that subsumes recently introduced leakage measures -- {maximal leakage} and {maximal $\alpha$-leakage}. In maximal $g$-leakage, the gain of an adversary...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	320,057
2404.04759	What Happens When Small Is Made Smaller? Exploring the Impact of Compression on Small Data Pretrained Language Models	Compression techniques have been crucial in advancing machine learning by enabling efficient training and deployment of large-scale language models. However, these techniques have received limited attention in the context of low-resource language models, which are trained on even smaller amounts of data and under compu...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	444,783
2302.06568	Comp2Comp: Open-Source Body Composition Assessment on Computed Tomography	Computed tomography (CT) is routinely used in clinical practice to evaluate a wide variety of medical conditions. While CT scans provide diagnoses, they also offer the ability to extract quantitative body composition metrics to analyze tissue volume and quality. Extracting quantitative body composition measures manuall...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	345,452
2309.13185	Visualizing Topological Importance: A Class-Driven Approach	This paper presents the first approach to visualize the importance of topological features that define classes of data. Topological features, with their ability to abstract the fundamental structure of complex data, are an integral component of visualization and analysis pipelines. Although not all topological features...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	394,093
2310.04292	Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets	Recently, pre-trained foundation models have enabled significant advancements in multiple fields. In molecular machine learning, however, where datasets are often hand-curated, and hence typically small, the lack of datasets with labeled features, and codebases to manage those datasets, has hindered the development of ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	397,595
1908.10128	TERA: the Toxicological Effect and Risk Assessment Knowledge Graph	Ecological risk assessment requires large amounts of chemical effect data from laboratory experiments. Due to experimental effort and animal welfare concerns it is desired to extrapolate data from existing sources. To cover the required chemical effect data several data sources need to be integrated to enable their int...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	143,029
1910.09664	Learning to Map Natural Language Instructions to Physical Quadcopter Control using Simulated Flight	We propose a joint simulation and real-world learning framework for mapping navigation instructions and raw first-person observations to continuous control. Our model estimates the need for environment exploration, predicts the likelihood of visiting environment positions during execution, and controls the agent to bot...	false	false	false	false	true	false	true	true	true	false	false	true	false	false	false	false	false	false	150,259
1908.05855	Distributed Edge Partitioning for Trillion-edge Graphs	We propose Distributed Neighbor Expansion (Distributed NE), a parallel and distributed graph partitioning method that can scale to trillion-edge graphs while providing high partitioning quality. Distributed NE is based on a new heuristic, called parallel expansion, where each partition is constructed in parallel by gre...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	141,835
1912.06708	A posteriori Trading-inspired Model-free Time Series Segmentation	Within the context of multivariate time series segmentation this paper proposes a method inspired by a posteriori optimal trading. After a normalization step time series are treated channel-wise as surrogate stock prices that can be traded optimally a posteriori in a virtual portfolio holding either stock or cash. Line...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	157,403
2007.05593	Attention-guided Quality Assessment for Automated Cryo-EM Grid Screening	Cryogenic electron microscopy (cryo-EM) has become an enabling technology in drug discovery and in understanding molecular bases of disease by producing near-atomic resolution (less than 0.4 nm) 3D reconstructions of biological macromolecules. The imaging process required for 3D reconstructions involves a highly iterat...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	186,720
2102.11491	Data Driven Testing of Cyber Physical Systems	Consumer grade cyber-physical systems (CPS) are becoming an integral part of our life, automatizing and simplifying everyday tasks. Indeed, due to complex interactions between hardware, networking and software, developing and testing such systems is known to be a challenging task. Various quality assurance and testing ...	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	true	false	false	221,439
1301.1223	Nearest Neighbor Decoding and Pilot-Aided Channel Estimation for Fading Channels	We study the information rates of non-coherent, stationary, Gaussian, multiple-input multiple-output (MIMO) flat-fading channels that are achievable with nearest neighbor decoding and pilot-aided channel estimation. In particular, we investigate the behavior of these achievable rates in the limit as the signal- to-nois...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	20,836
2103.14586	Understanding Robustness of Transformers for Image Classification	Deep Convolutional Neural Networks (CNNs) have long been the architecture of choice for computer vision tasks. Recently, Transformer-based architectures like Vision Transformer (ViT) have matched or even surpassed ResNets for image classification. However, details of the Transformer architecture -- such as the use of n...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	226,905
1808.10420	A concise frictional contact formulation based on surface potentials and isogeometric discretization	This work presents a concise theoretical and computational framework for the finite element formulation of frictional contact problems with arbitrarily large deformation and sliding. The aim of this work is to extend the contact theory based on surface potentials (Sauer and De Lorenzis, 2013) to account for friction. C...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	106,379
1404.1864	Sublinear algorithms for local graph centrality estimation	We study the complexity of local graph centrality estimation, with the goal of approximating the centrality score of a given target node while exploring only a sublinear number of nodes/arcs of the graph and performing a sublinear number of elementary operations. We develop a technique, that we apply to the PageRank an...	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	true	32,152
1909.02576	Deep Visual Template-Free Form Parsing	Automatic, template-free extraction of information from form images is challenging due to the variety of form layouts. This is even more challenging for historical forms due to noise and degradation. A crucial part of the extraction process is associating input text with pre-printed labels. We present a learned, templa...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	144,233
1605.06319	As Cool as a Cucumber: Towards a Corpus of Contemporary Similes in Serbian	Similes are natural language expressions used to compare unlikely things, where the comparison is not taken literally. They are often used in everyday communication and are an important part of cultural heritage. Having an up-to-date corpus of similes is challenging, as they are constantly coined and/or adapted to the ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	56,116
1707.09476	FCN-rLSTM: Deep Spatio-Temporal Neural Networks for Vehicle Counting in City Cameras	In this paper, we develop deep spatio-temporal neural networks to sequentially count vehicles from low quality videos captured by city cameras (citycams). Citycam videos have low resolution, low frame rate, high occlusion and large perspective, making most existing methods lose their efficacy. To overcome limitations o...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	78,012
2205.05321	A Survey on Applications of Cache-Aided NOMA	Contrary to orthogonal multiple-access (OMA), non-orthogonal multiple-access (NOMA) schemes can serve a pool of users without exploiting the scarce frequency or time domain resources. This is useful in meeting the sixth generation (6G) network requirements, such as, low latency, massive connectivity, users fairness, an...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	295,901
2409.01308	Representing Neural Network Layers as Linear Operations via Koopman Operator Theory	The strong performance of simple neural networks is often attributed to their nonlinear activations. However, a linear view of neural networks makes understanding and controlling networks much more approachable. We draw from a dynamical systems view of neural networks, offering a fresh perspective by using Koopman oper...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	485,287
2403.04672	Molecular Arithmetic Coding (MoAC) and Optimized Molecular Prefix Coding (MoPC) for Diffusion-Based Molecular Communication	Molecular communication (MC) enables information transfer through molecules at the nano-scale. This paper presents new and optimized source coding (data compression) methods for MC. In a recent paper, prefix source coding was introduced into the field, through an MC-adapted version of the Huffman coding. We first show ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	435,686
1911.00231	Extending Relational Query Processing with ML Inference	The broadening adoption of machine learning in the enterprise is increasing the pressure for strict governance and cost-effective performance, in particular for the common and consequential steps of model storage and inference. The RDBMS provides a natural starting point, given its mature infrastructure for fast data a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	151,770
1501.02036	Improving the Deductive System DES with Persistence by Using SQL DBMS's	This work presents how persistent predicates have been included in the in-memory deductive system DES by relying on external SQL database management systems. We introduce how persistence is supported from a user-point of view and the possible applications the system opens up, as the deductive expressive power is projec...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	true	39,148
2307.15217	Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback	Reinforcement learning from human feedback (RLHF) is a technique for training AI systems to align with human goals. RLHF has emerged as the central method used to finetune state-of-the-art large language models (LLMs). Despite this popularity, there has been relatively little public work systematizing its flaws. In thi...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	382,188
2102.06060	A fully automated method for 3D individual tooth identification and segmentation in dental CBCT	Accurate and automatic segmentation of three-dimensional (3D) individual teeth from cone-beam computerized tomography (CBCT) images is a challenging problem because of the difficulty in separating an individual tooth from adjacent teeth and its surrounding alveolar bone. Thus, this paper proposes a fully automated meth...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	219,631
2303.04315	A Threefold Review on Deep Semantic Segmentation: Efficiency-oriented, Temporal and Depth-aware design	Semantic image and video segmentation stand among the most important tasks in computer vision nowadays, since they provide a complete and meaningful representation of the environment by means of a dense classification of the pixels in a given scene. Recently, Deep Learning, and more precisely Convolutional Neural Netwo...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	350,036
2101.12175	LOME: Large Ontology Multilingual Extraction	We present LOME, a system for performing multilingual information extraction. Given a text document as input, our core system identifies spans of textual entity and event mentions with a FrameNet (Baker et al., 1998) parser. It subsequently performs coreference resolution, fine-grained entity typing, and temporal relat...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	217,508
2410.20242	Hoeffding adaptive trees for multi-label classification on data streams	Data stream learning is a very relevant paradigm because of the increasing real-world scenarios generating data at high velocities and in unbounded sequences. Stream learning aims at developing models that can process instances as they arrive, so models constantly adapt to new concepts and the temporal evolution in the...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	502,716
2103.12456	Health Status Prediction with Local-Global Heterogeneous Behavior Graph	Health management is getting increasing attention all over the world. However, existing health management mainly relies on hospital examination and treatment, which are complicated and untimely. The emerging of mobile devices provides the possibility to manage people's health status in a convenient and instant way. Est...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	226,179
1912.10231	On the Robustness of Data-Driven Controllers for Linear Systems	This paper proposes a new framework and several results to quantify the performance of data-driven state-feedback controllers for linear systems against targeted perturbations of the training data. We focus on the case where subsets of the training data are randomly corrupted by an adversary, and derive lower and upper...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	158,272
2404.03412	RADIUM: Predicting and Repairing End-to-End Robot Failures using Gradient-Accelerated Sampling	Before autonomous systems can be deployed in safety-critical applications, we must be able to understand and verify the safety of these systems. For cases where the risk or cost of real-world testing is prohibitive, we propose a simulation-based framework for a) predicting ways in which an autonomous system is likely t...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	444,244
2208.00898	Joint covariate-alignment and concept-alignment: a framework for domain generalization	In this paper, we propose a novel domain generalization (DG) framework based on a new upper bound to the risk on the unseen domain. Particularly, our framework proposes to jointly minimize both the covariate-shift as well as the concept-shift between the seen domains for a better performance on the unseen domain. While...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	311,011
1904.00327	Power Control for Wireless VBR Video Streaming: From Optimization to Reinforcement Learning	In this paper, we investigate the problem of power control for streaming variable bit rate (VBR) videos over wireless links. A system model involving a transmitter (e.g., a base station) that sends VBR video data to a receiver (e.g., a mobile user) equipped with a playout buffer is adopted, as used in dynamic adaptive ...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	125,851
2211.05235	Improved Prediction of Beta-Amyloid and Tau Burden Using Hippocampal Surface Multivariate Morphometry Statistics and Sparse Coding	Background: Beta-amyloid (A$\beta$) plaques and tau protein tangles in the brain are the defining 'A' and 'T' hallmarks of Alzheimer's disease (AD), and together with structural atrophy detectable on brain magnetic resonance imaging (MRI) scans as one of the neurodegenerative ('N') biomarkers comprise the ''ATN framewo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	329,478
1207.0170	Single parameter galaxy classification: The Principal Curve through the multi-dimensional space of galaxy properties	We propose to describe the variety of galaxies from SDSS by using only one affine parameter. To this aim, we build the Principal Curve (P-curve) passing through the spine of the data point cloud, considering the eigenspace derived from Principal Component Analysis of morphological, physical and photometric galaxy prope...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	17,143
0709.1701	Enrichment of Qualitative Beliefs for Reasoning under Uncertainty	This paper deals with enriched qualitative belief functions for reasoning under uncertainty and for combining information expressed in natural language through linguistic labels. In this work, two possible enrichments (quantitative and/or qualitative) of linguistic labels are considered and operators (addition, multipl...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	654
2412.20892	Rethinking Aleatoric and Epistemic Uncertainty	The ideas of aleatoric and epistemic uncertainty are widely used to reason about the probabilistic predictions of machine-learning models. We identify incoherence in existing discussions of these ideas and suggest this stems from the aleatoric-epistemic view being insufficiently expressive to capture all of the distinc...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	521,393
2103.16764	Research of Damped Newton Stochastic Gradient Descent Method for Neural Network Training	First-order methods like stochastic gradient descent(SGD) are recently the popular optimization method to train deep neural networks (DNNs), but second-order methods are scarcely used because of the overpriced computing cost in getting the high-order information. In this paper, we propose the Damped Newton Stochastic G...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	227,693
2203.02655	Audio-visual speech separation based on joint feature representation with cross-modal attention	Multi-modal based speech separation has exhibited a specific advantage on isolating the target character in multi-talker noisy environments. Unfortunately, most of current separation strategies prefer a straightforward fusion based on feature learning of each single modality, which is far from sufficient consideration ...	false	false	true	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	283,816
2301.01593	Multi-View MOOC Quality Evaluation via Information-Aware Graph Representation Learning	In this paper, we study the problem of MOOC quality evaluation which is essential for improving the course materials, promoting students' learning efficiency, and benefiting user services. While achieving promising performances, current works still suffer from the complicated interactions and relationships of entities ...	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	false	false	339,273
2110.08851	Unsupervised Representation Learning for Binary Networks by Joint Classifier Learning	Self-supervised learning is a promising unsupervised learning framework that has achieved success with large floating point networks. But such networks are not readily deployable to edge devices. To accelerate deployment of models with the benefit of unsupervised representation learning to such resource limited devices...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	261,578
1002.1285	The Influence of Intensity Standardization on Medical Image Registration	Acquisition-to-acquisition signal intensity variations (non-standardness) are inherent in MR images. Standardization is a post processing method for correcting inter-subject intensity variations through transforming all images from the given image gray scale into a standard gray scale wherein similar intensities achiev...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	5,636
1912.01101	Offset Sampling Improves Deep Learning based Accelerated MRI Reconstructions by Exploiting Symmetry	Deep learning approaches to accelerated MRI take a matrix of sampled Fourier-space lines as input and produce a spatial image as output. In this work we show that by careful choice of the offset used in the sampling procedure, the symmetries in k-space can be better exploited, producing higher quality reconstructions t...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	155,976
2305.18204	Kernel Density Matrices for Probabilistic Deep Learning	This paper introduces a novel approach to probabilistic deep learning, kernel density matrices, which provide a simpler yet effective mechanism for representing joint probability distributions of both continuous and discrete random variables. In quantum mechanics, a density matrix is the most general way to describe th...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	368,893
2109.00343	Exploring deep learning methods for recognizing rare diseases and their clinical manifestations from texts	Although rare diseases are characterized by low prevalence, approximately 300 million people are affected by a rare disease. The early and accurate diagnosis of these conditions is a major challenge for general practitioners, who do not have enough knowledge to identify them. In addition to this, rare diseases usually ...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	253,078
2501.04438	Effect of Information Technology on Job Creation to Support Economic: Case Studies of Graduates in Universities (2023-2024) of the KRG of Iraq	The aim of this study is to assess the impact of information technology (IT) on university graduates in terms of employment development, which will aid in economic issues. This study uses a descriptive research methodology and a quantitative approach to understand variables. The focus of this study is to ascertain how ...	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	523,225
2010.12267	Show and Speak: Directly Synthesize Spoken Description of Images	This paper proposes a new model, referred to as the show and speak (SAS) model that, for the first time, is able to directly synthesize spoken descriptions of images, bypassing the need for any text or phonemes. The basic structure of SAS is an encoder-decoder architecture that takes an image as input and predicts the ...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	202,630
1411.6667	Deletion codes in the high-noise and high-rate regimes	The noise model of deletions poses significant challenges in coding theory, with basic questions like the capacity of the binary deletion channel still being open. In this paper, we study the harder model of worst-case deletions, with a focus on constructing efficiently decodable codes for the two extreme regimes of hi...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	37,860
2104.00337	Wide-Depth-Range 6D Object Pose Estimation in Space	6D pose estimation in space poses unique challenges that are not commonly encountered in the terrestrial setting. One of the most striking differences is the lack of atmospheric scattering, allowing objects to be visible from a great distance while complicating illumination conditions. Currently available benchmark dat...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	true	227,966
2310.20518	Improving RRT for Automated Parking in Real-world Scenarios	Automated parking is a self-driving feature that has been in cars for several years. Parking assistants in currently sold cars fail to park in more complex real-world scenarios and require the driver to move the car to an expected starting position before the assistant is activated. We overcome these limitations by pro...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	404,427
2207.11676	Harmonic-Balance Based Power Flow and ZVS Analysis of a Quad-Active Bridge DC-DC Converter	The power flow control of multi-active bridge converters requires a comprehensive steady-state analysis of the converter and the determination of conditions for zero voltage switching of all switching in the converter which result in minimum switching loss. This paper aims to model and carry out the power flow and Zero...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	309,732
1908.06069	A Survey on Computational Politics	Computational Politics is the study of computational methods to analyze and moderate users' behaviors related to political activities such as election campaign persuasion, political affiliation, and opinion mining. With the rapid development and ease of access to the Internet, Information Communication Technologies (IC...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	141,900
2408.10492	Is the Lecture Engaging for Learning? Lecture Voice Sentiment Analysis for Knowledge Graph-Supported Intelligent Lecturing Assistant (ILA) System	This paper introduces an intelligent lecturing assistant (ILA) system that utilizes a knowledge graph to represent course content and optimal pedagogical strategies. The system is designed to support instructors in enhancing student learning through real-time analysis of voice, content, and teaching methods. As an init...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	481,873
2111.09093	The Faulty GPS Problem: Shortest Time Paths in Networks with Unreliable Directions	This paper optimizes motion planning when there is a known risk that the road choice suggested by a Satnav (GPS) is not on a shortest path. At every branch node of a network Q, a Satnav (GPS) points to the arc leading to the destination, or home node, H - but only with a high known probability p. Always trusting the Sa...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	266,907
1812.02640	Pathological Evidence Exploration in Deep Retinal Image Diagnosis	Though deep learning has shown successful performance in classifying the label and severity stage of certain disease, most of them give few evidence on how to make prediction. Here, we propose to exploit the interpretability of deep learning application in medical diagnosis. Inspired by Koch's Postulates, a well-known ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	115,819
2111.04389	Lattice gauge symmetry in neural networks	We review a novel neural network architecture called lattice gauge equivariant convolutional neural networks (L-CNNs), which can be applied to generic machine learning problems in lattice gauge theory while exactly preserving gauge symmetry. We discuss the concept of gauge equivariance which we use to explicitly constr...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	265,470
2310.16333	Scalable Optimal Power Management for Large-Scale Battery Energy Storage Systems	Large-scale battery energy storage systems (BESS) are helping transition the world towards sustainability with their broad use, among others, in electrified transportation, power grid, and renewables. However, optimal power management for them is often computationally formidable. To overcome this challenge, we develop ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	402,681
2207.12244	DeepFusion: Real-Time Dense 3D Reconstruction for Monocular SLAM using Single-View Depth and Gradient Predictions	While the keypoint-based maps created by sparse monocular simultaneous localisation and mapping (SLAM) systems are useful for camera tracking, dense 3D reconstructions may be desired for many robotic tasks. Solutions involving depth cameras are limited in range and to indoor spaces, and dense reconstruction systems bas...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	309,945
2404.12128	Optimizing Intensive Database Tasks Through Caching Proxy Mechanisms	Web caching is essential for the World Wide Web, saving processing power, bandwidth, and reducing latency. Many proxy caching solutions focus on buffering data from the main server, neglecting cacheable information meant for server writes. Existing systems addressing this issue are often intrusive, requiring modificati...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	447,746
1607.08472	An algorithm for motif-based network design	A determinant property of the structure of a biological network is the distribution of local connectivity patterns, i.e., network motifs. In this work, a method for creating directed, unweighted networks while promoting a certain combination of motifs is presented. This motif-based network algorithm starts with an empt...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	59,164
1307.5348	Tensor-based formulation and nuclear norm regularization for multi-energy computed tomography	The development of energy selective, photon counting X-ray detectors allows for a wide range of new possibilities in the area of computed tomographic image formation. Under the assumption of perfect energy resolution, here we propose a tensor-based iterative algorithm that simultaneously reconstructs the X-ray attenuat...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	25,938
1712.10110	Beyond Keywords and Relevance: A Personalized Ad Retrieval Framework in E-Commerce Sponsored Search	On most sponsored search platforms, advertisers bid on some keywords for their advertisements (ads). Given a search request, ad retrieval module rewrites the query into bidding keywords, and uses these keywords as keys to select Top N ads through inverted indexes. In this way, an ad will not be retrieved even if querie...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	87,453
2011.12829	All You Need is a Good Functional Prior for Bayesian Deep Learning	The Bayesian treatment of neural networks dictates that a prior distribution is specified over their weight and bias parameters. This poses a challenge because modern neural networks are characterized by a large number of parameters, and the choice of these priors has an uncontrolled effect on the induced functional pr...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	208,281
1511.06078	Learning Deep Structure-Preserving Image-Text Embeddings	This paper proposes a method for learning joint embeddings of images and text using a two-branch neural network with multiple layers of linear projections followed by nonlinearities. The network is trained using a large margin objective that combines cross-view ranking constraints with within-view neighborhood structur...	false	false	false	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	49,156
2001.08456	Ada-LISTA: Learned Solvers Adaptive to Varying Models	Neural networks that are based on unfolding of an iterative solver, such as LISTA (learned iterative soft threshold algorithm), are widely used due to their accelerated performance. Nevertheless, as opposed to non-learned solvers, these networks are trained on a certain dictionary, and therefore they are inapplicable f...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	161,297
2002.11609	ARMA Nets: Expanding Receptive Field for Dense Prediction	Global information is essential for dense prediction problems, whose goal is to compute a discrete or continuous label for each pixel in the images. Traditional convolutional layers in neural networks, initially designed for image classification, are restrictive in these problems since the filter size limits their rece...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	165,767
1708.03895	Local Large deviations for empirical locality measure of typed Random Graph Models	In this article, we prove a local large deviation principle (LLDP) for the empirical locality measure of typed random networks on $n$ nodes conditioned to have a given \emph{ empirical type measure} and \emph{ empirical link measure.} From the LLDP, we deduce a full large deviation principle for the typed random graph,...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	78,840

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