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
2402.14925	Efficient Unbiased Sparsification	An unbiased $m$-sparsification of a vector $p\in \mathbb{R}^n$ is a random vector $Q\in \mathbb{R}^n$ with mean $p$ that has at most $m<n$ nonzero coordinates. Unbiased sparsification compresses the original vector without introducing bias; it arises in various contexts, such as in federated learning and sampling spars...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	431,915
2412.08647	SegFace: Face Segmentation of Long-Tail Classes	Face parsing refers to the semantic segmentation of human faces into key facial regions such as eyes, nose, hair, etc. It serves as a prerequisite for various advanced applications, including face editing, face swapping, and facial makeup, which often require segmentation masks for classes like eyeglasses, hats, earrin...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	516,187
2304.08660	(LC)$^2$: LiDAR-Camera Loop Constraints For Cross-Modal Place Recognition	Localization has been a challenging task for autonomous navigation. A loop detection algorithm must overcome environmental changes for the place recognition and re-localization of robots. Therefore, deep learning has been extensively studied for the consistent transformation of measurements into localization descriptor...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	358,780
2106.08686	Do Acoustic Word Embeddings Capture Phonological Similarity? An Empirical Study	Several variants of deep neural networks have been successfully employed for building parametric models that project variable-duration spoken word segments onto fixed-size vector representations, or acoustic word embeddings (AWEs). However, it remains unclear to what degree we can rely on the distance in the emerging A...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	241,382
1005.2263	Context models on sequences of covers	We present a class of models that, via a simple construction, enables exact, incremental, non-parametric, polynomial-time, Bayesian inference of conditional measures. The approach relies upon creating a sequence of covers on the conditioning variable and maintaining a different model for each set within a cover. Infere...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	6,475
2301.04452	Uncertainty Estimation based on Geometric Separation	In machine learning, accurately predicting the probability that a specific input is correct is crucial for risk management. This process, known as uncertainty (or confidence) estimation, is particularly important in mission-critical applications such as autonomous driving. In this work, we put forward a novel geometric...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	340,068
1902.01328	Optimization-based Feedback Manipulation Through an Array of Ultrasonic Transducers	In this paper we document a novel laboratory experimental platform for non-contact planar manipulation (positioning) of millimeter-scale objects using acoustic pressure. The manipulated objects are either floating on a water surface or rolling on a solid surface. The pressure field is shaped in real time through an 8-b...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	120,627
1302.4954	Probabilistic Temporal Reasoning with Endogenous Change	This paper presents a probabilistic model for reasoning about the state of a system as it changes over time, both due to exogenous and endogenous influences. Our target domain is a class of medical prediction problems that are neither so urgent as to preclude careful diagnosis nor progress so slowly as to allow arbitra...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	22,228
2410.03943	Oscillatory State-Space Models	We propose Linear Oscillatory State-Space models (LinOSS) for efficiently learning on long sequences. Inspired by cortical dynamics of biological neural networks, we base our proposed LinOSS model on a system of forced harmonic oscillators. A stable discretization, integrated over time using fast associative parallel s...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	495,054
2303.04025	DeepSeeColor: Realtime Adaptive Color Correction for Autonomous Underwater Vehicles via Deep Learning Methods	Successful applications of complex vision-based behaviours underwater have lagged behind progress in terrestrial and aerial domains. This is largely due to the degraded image quality resulting from the physical phenomena involved in underwater image formation. Spectrally-selective light attenuation drains some colors f...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	349,937
2410.20532	Search Wide, Focus Deep: Automated Fetal Brain Extraction with Sparse Training Data	Automated fetal brain extraction from full-uterus MRI is a challenging task due to variable head sizes, orientations, complex anatomy, and prevalent artifacts. While deep-learning (DL) models trained on synthetic images have been successful in adult brain extraction, adapting these networks for fetal MRI is difficult d...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	502,850
2207.12061	Balancing Stability and Plasticity through Advanced Null Space in Continual Learning	Continual learning is a learning paradigm that learns tasks sequentially with resources constraints, in which the key challenge is stability-plasticity dilemma, i.e., it is uneasy to simultaneously have the stability to prevent catastrophic forgetting of old tasks and the plasticity to learn new tasks well. In this pap...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	309,878
2411.11515	Cascaded Diffusion Models for 2D and 3D Microscopy Image Synthesis to Enhance Cell Segmentation	Automated cell segmentation in microscopy images is essential for biomedical research, yet conventional methods are labor-intensive and prone to error. While deep learning-based approaches have proven effective, they often require large annotated datasets, which are scarce due to the challenges of manual annotation. To...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	509,085
2408.10624	WRIM-Net: Wide-Ranging Information Mining Network for Visible-Infrared Person Re-Identification	For the visible-infrared person re-identification (VI-ReID) task, one of the primary challenges lies in significant cross-modality discrepancy. Existing methods struggle to conduct modality-invariant information mining. They often focus solely on mining singular dimensions like spatial or channel, and overlook the extr...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	481,947
2410.12289	AI-Aided Kalman Filters	The Kalman filter (KF) and its variants are among the most celebrated algorithms in signal processing. These methods are used for state estimation of dynamic systems by relying on mathematical representations in the form of simple state-space (SS) models, which may be crude and inaccurate descriptions of the underlying...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	498,953
2002.05603	Performance Analysis of Intelligent Reflective Surfaces for Wireless Communication	A statistical characterization of the fundamental performance bounds of an intelligent reflective surface (IRS) intended for aiding wireless communications is presented. To this end, the outage probability, average symbol error probability, and achievable rate bounds are derived in closed-form. By virtue of asymptotic ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	163,944
2410.03151	Media Framing through the Lens of Event-Centric Narratives	From a communications perspective, a frame defines the packaging of the language used in such a way as to encourage certain interpretations and to discourage others. For example, a news article can frame immigration as either a boost or a drain on the economy, and thus communicate very different interpretations of the ...	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	494,649
2312.06979	On the notion of Hallucinations from the lens of Bias and Validity in Synthetic CXR Images	Medical imaging has revolutionized disease diagnosis, yet the potential is hampered by limited access to diverse and privacy-conscious datasets. Open-source medical datasets, while valuable, suffer from data quality and clinical information disparities. Generative models, such as diffusion models, aim to mitigate these...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	414,750
2011.11751	Multimodal dynamics modeling for off-road autonomous vehicles	Dynamics modeling in outdoor and unstructured environments is difficult because different elements in the environment interact with the robot in ways that can be hard to predict. Leveraging multiple sensors to perceive maximal information about the robot's environment is thus crucial when building a model to perform pr...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	207,925
2412.12641	Lagrangian Index Policy for Restless Bandits with Average Reward	We study the Lagrangian Index Policy (LIP) for restless multi-armed bandits with long-run average reward. In particular, we compare the performance of LIP with the performance of the Whittle Index Policy (WIP), both heuristic policies known to be asymptotically optimal under certain natural conditions. Even though in m...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	517,961
2206.01272	Data-Driven Linear Koopman Embedding for Networked Systems: Model-Predictive Grid Control	This paper presents a data-learned linear Koopman embedding of nonlinear networked dynamics and uses it to enable real-time model predictive emergency voltage control in a power network. The approach involves a novel data-driven ``basis-dictionary free" lifting of the system dynamics into a higher dimensional linear sp...	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	300,406
2004.01483	Cascade Extended State Observer for Active Disturbance Rejection Control Applications under Measurement Noise	The extended state observer (ESO) plays an important role in the design of feedback control for nonlinear systems. However, its high-gain nature creates a challenge in engineering practice in cases where the output measurement is corrupted by non-negligible, high-frequency noise. The presence of such noise puts a const...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	170,923
1410.7357	Statistical models for cores decomposition of an undirected random graph	The $k$-core decomposition is a widely studied summary statistic that describes a graph's global connectivity structure. In this paper, we move beyond using $k$-core decomposition as a tool to summarize a graph and propose using $k$-core decomposition as a tool to model random graphs. We propose using the shell distrib...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	37,067
2402.14832	Integrating Simulation Budget Management into Drum-Buffer-Rope: A Study on Parametrization and Reducing Computational Effort	In manufacturing, a bottleneck workstation frequently emerges, complicating production planning and escalating costs. To address this, Drum-Buffer-Rope (DBR) is a widely recognized production planning and control method that focuses on centralizing the bottleneck workstation, thereby improving production system perform...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	431,856
2003.08272	Unsupervised Pidgin Text Generation By Pivoting English Data and Self-Training	West African Pidgin English is a language that is significantly spoken in West Africa, consisting of at least 75 million speakers. Nevertheless, proper machine translation systems and relevant NLP datasets for pidgin English are virtually absent. In this work, we develop techniques targeted at bridging the gap between ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	168,678
2110.08465	Heterogeneous Graph-Based Multimodal Brain Network Learning	Graph neural networks (GNNs) provide powerful insights for brain neuroimaging technology from the view of graphical networks. However, most existing GNN-based models assume that the neuroimaging-produced brain connectome network is a homogeneous graph with single types of nodes and edges. In fact, emerging studies have...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	261,413
2501.11883	An Improved Lower Bound on Oblivious Transfer Capacity Using Polarization and Interaction	We consider the oblivious transfer (OT) capacities of noisy channels against the passive adversary; this problem has not been solved even for the binary symmetric channel (BSC). In the literature, the general construction of OT has been known only for generalized erasure channels (GECs); for the BSC, we convert the cha...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	526,077
2006.00290	Spatial Distribution of the Mean Peak Age of Information in Wireless Networks	This paper considers a large-scale wireless network consisting of source-destination (SD) pairs, where the sources send time-sensitive information, termed status updates, to their corresponding destinations in a time-slotted fashion. We employ Age of information (AoI) for quantifying the freshness of the status updates...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	179,428
1905.05350	Understanding Pedestrian-Vehicle Interactions with Vehicle Mounted Vision: An LSTM Model and Empirical Analysis	Pedestrians and vehicles often share the road in complex inner city traffic. This leads to interactions between the vehicle and pedestrians, with each affecting the other's motion. In order to create robust methods to reason about pedestrian behavior and to design interfaces of communication between self-driving cars a...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	130,697
1909.04455	Learning review representations from user and product level information for spam detection	Opinion spam has become a widespread problem in social media, where hired spammers write deceptive reviews to promote or demote products to mislead the consumers for profit or fame. Existing works mainly focus on manually designing discrete textual or behavior features, which cannot capture complex semantics of reviews...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	144,811
2206.05712	Graph-based Spatial Transformer with Memory Replay for Multi-future Pedestrian Trajectory Prediction	Pedestrian trajectory prediction is an essential and challenging task for a variety of real-life applications such as autonomous driving and robotic motion planning. Besides generating a single future path, predicting multiple plausible future paths is becoming popular in some recent work on trajectory prediction. Howe...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	302,107
2103.11594	Deep Neural Networks Learn Meta-Structures from Noisy Labels in Semantic Segmentation	How deep neural networks (DNNs) learn from noisy labels has been studied extensively in image classification but much less in image segmentation. So far, our understanding of the learning behavior of DNNs trained by noisy segmentation labels remains limited. In this study, we address this deficiency in both binary segm...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	225,864
2410.02917	Deep image-based Adaptive BRDF Measure	Efficient and accurate measurement of the bi-directional reflectance distribution function (BRDF) plays a key role in high quality image rendering and physically accurate sensor simulation. However, obtaining the reflectance properties of a material is both time-consuming and challenging. This paper presents a novel me...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	494,523
2402.18490	TAMM: TriAdapter Multi-Modal Learning for 3D Shape Understanding	The limited scale of current 3D shape datasets hinders the advancements in 3D shape understanding, and motivates multi-modal learning approaches which transfer learned knowledge from data-abundant 2D image and language modalities to 3D shapes. However, even though the image and language representations have been aligne...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	433,448
1907.13120	An Experiment on Measurement of Pavement Roughness via Android-Based Smartphones	The study focuses on the experiment of using three different smartphones to collect acceleration data from vibration for the road roughness detection. The Android operating system is used in the application. The study takes place on asphaltic pavement of the expressway system of Thailand, with 9 km distance. The run ve...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	140,302
2005.13101	State Estimation-Based Robust Optimal Control of Influenza Epidemics in an Interactive Human Society	This paper presents a state estimation-based robust optimal control strategy for influenza epidemics in an interactive human society in the presence of modeling uncertainties. Interactive society is influenced by the random entrance of individuals from other human societies whose effects can be modeled as a non-Gaussia...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	178,901
2003.11723	Learning transferable and discriminative features for unsupervised domain adaptation	Although achieving remarkable progress, it is very difficult to induce a supervised classifier without any labeled data. Unsupervised domain adaptation is able to overcome this challenge by transferring knowledge from a labeled source domain to an unlabeled target domain. Transferability and discriminability are two ke...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	169,708
2011.04106	Ensemble Knowledge Distillation for CTR Prediction	Recently, deep learning-based models have been widely studied for click-through rate (CTR) prediction and lead to improved prediction accuracy in many industrial applications. However, current research focuses primarily on building complex network architectures to better capture sophisticated feature interactions and d...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	205,463
2207.02000	Disentangling private classes through regularization	Deep learning models are nowadays broadly deployed to solve an incredibly large variety of tasks. However, little attention has been devoted to connected legal aspects. In 2016, the European Union approved the General Data Protection Regulation which entered into force in 2018. Its main rationale was to protect the pri...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	false	306,369
2104.07381	On the Assessment of Benchmark Suites for Algorithm Comparison	Benchmark suites, i.e. a collection of benchmark functions, are widely used in the comparison of black-box optimization algorithms. Over the years, research has identified many desired qualities for benchmark suites, such as diverse topology, different difficulties, scalability, representativeness of real-world problem...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	true	230,395
2404.00885	Modeling Output-Level Task Relatedness in Multi-Task Learning with Feedback Mechanism	Multi-task learning (MTL) is a paradigm that simultaneously learns multiple tasks by sharing information at different levels, enhancing the performance of each individual task. While previous research has primarily focused on feature-level or parameter-level task relatedness, and proposed various model architectures an...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	443,148
2305.03327	FlowText: Synthesizing Realistic Scene Text Video with Optical Flow Estimation	Current video text spotting methods can achieve preferable performance, powered with sufficient labeled training data. However, labeling data manually is time-consuming and labor-intensive. To overcome this, using low-cost synthetic data is a promising alternative. This paper introduces a novel video text synthesis tec...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	362,355
1609.06277	Design of Admissible Heuristics for Kinodynamic Motion Planning via Sum-of-Squares Programming	How does one obtain an admissible heuristic for a kinodynamic motion planning problem? This paper develops the analytical tools and techniques to answer this question. A sufficient condition for the admissibility of a heuristic is presented which can be checked directly from the problem data. This condition is also use...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	61,264
2011.09456	The Effect of Modern Traffic Information on Braess' Paradox	Braess' paradox has been shown to appear rather generically in many systems of transport on networks. It is especially relevant for vehicular traffic where it shows that in certain situations building a new road in an urban or highway network can lead to increased average travel times for all users. Here we address the...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	207,193
2205.01921	Second Order Path Variationals in Non-Stationary Online Learning	We consider the problem of universal dynamic regret minimization under exp-concave and smooth losses. We show that appropriately designed Strongly Adaptive algorithms achieve a dynamic regret of $\tilde O(d^2 n^{1/5} C_n^{2/5} \vee d^2)$, where $n$ is the time horizon and $C_n$ a path variational based on second order ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	294,768
1712.04332	Scaling Limit: Exact and Tractable Analysis of Online Learning Algorithms with Applications to Regularized Regression and PCA	We present a framework for analyzing the exact dynamics of a class of online learning algorithms in the high-dimensional scaling limit. Our results are applied to two concrete examples: online regularized linear regression and principal component analysis. As the ambient dimension tends to infinity, and with proper tim...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	86,590
0809.5191	A Coded Bit-Loading Linear Precoded Discrete Multitone Solution for Power Line Communication	Linear precoded discrete multitone modulation (LP-DMT) system has been already proved advantageous with adaptive resource allocation algorithm in a power line communication (PLC) context. In this paper, we investigate the bit and energy allocation algorithm of an adaptive LP-DMT system taking into account the channel c...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	2,431
1406.6323	Dense Correspondences Across Scenes and Scales	We seek a practical method for establishing dense correspondences between two images with similar content, but possibly different 3D scenes. One of the challenges in designing such a system is the local scale differences of objects appearing in the two images. Previous methods often considered only small subsets of ima...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	34,112
1904.05519	Efficient and Robust Registration on the 3D Special Euclidean Group	We present an accurate, robust and fast method for registration of 3D scans. Our motion estimation optimizes a robust cost function on the intrinsic representation of rigid motions, i.e., the Special Euclidean group $\mathbb{SE}(3)$. We exploit the geometric properties of Lie groups as well as the robustness afforded b...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	127,337
1612.07157	New Convolutional Codes Derived from Algebraic Geometry Codes	In this paper, we construct new families of convolutional codes. Such codes are obtained by means of algebraic geometry codes. Additionally, more families of convolutional codes are constructed by means of puncturing, extending, expanding and by the direct product code construction applied to algebraic geometry codes. ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	65,908
2110.13048	Nonuniform Negative Sampling and Log Odds Correction with Rare Events Data	We investigate the issue of parameter estimation with nonuniform negative sampling for imbalanced data. We first prove that, with imbalanced data, the available information about unknown parameters is only tied to the relatively small number of positive instances, which justifies the usage of negative sampling. However...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	263,053
2201.05545	Multimodal registration of FISH and nanoSIMS images using convolutional neural network models	Nanoscale secondary ion mass spectrometry (nanoSIMS) and fluorescence in situ hybridization (FISH) microscopy provide high-resolution, multimodal image representations of the identity and cell activity respectively of targeted microbial communities in microbiological research. Despite its importance to microbiologists,...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	275,419
2107.06970	Identifying Competition and Mutualism Between Online Groups	Platforms often host multiple online groups with overlapping topics and members. How can researchers and designers understand how related groups affect each other? Inspired by population ecology, prior research in social computing and human-computer interaction has studied related groups by correlating group size with ...	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	246,256
1611.00228	Application Specific Instrumentation (ASIN): A Bio-inspired Paradigm to Instrumentation using recognition before detection	In this paper we present a new scheme for instrumentation, which has been inspired by the way small mammals sense their environment. We call this scheme Application Specific Instrumentation (ASIN). A conventional instrumentation system focuses on gathering as much information about the scene as possible. This, usually,...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	63,190
2111.01396	Boundary Distribution Estimation for Precise Object Detection	In the field of state-of-the-art object detection, the task of object localization is typically accomplished through a dedicated subnet that emphasizes bounding box regression. This subnet traditionally predicts the object's position by regressing the box's center position and scaling factors. Despite the widespread ad...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	264,546
2107.09577	How Does Cell-Free Massive MIMO Support Multiple Federated Learning Groups?	Federated learning (FL) has been considered as a promising learning framework for future machine learning systems due to its privacy preservation and communication efficiency. In beyond-5G/6G systems, it is likely to have multiple FL groups with different learning purposes. This scenario leads to a question: How does a...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	247,066
2405.09789	LeMeViT: Efficient Vision Transformer with Learnable Meta Tokens for Remote Sensing Image Interpretation	Due to spatial redundancy in remote sensing images, sparse tokens containing rich information are usually involved in self-attention (SA) to reduce the overall token numbers within the calculation, avoiding the high computational cost issue in Vision Transformers. However, such methods usually obtain sparse tokens by h...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	454,529
1404.1978	An Abrupt Change Detection Heuristic with Applications to Cyber Data Attacks on Power Systems	We present an analysis of a heuristic for abrupt change detection of systems with bounded state variations. The proposed analysis is based on the Singular Value Decomposition (SVD) of a history matrix built from system observations. We show that monitoring the largest singular value of the history matrix can be used as...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	32,161
2411.07954	Learning Memory Mechanisms for Decision Making through Demonstrations	In Partially Observable Markov Decision Processes, integrating an agent's history into memory poses a significant challenge for decision-making. Traditional imitation learning, relying on observation-action pairs for expert demonstrations, fails to capture the expert's memory mechanisms used in decision-making. To capt...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	507,730
2402.16888	Chaotic attractor reconstruction using small reservoirs -- the influence of topology	Forecasting timeseries based upon measured data is needed in a wide range of applications and has been the subject of extensive research. A particularly challenging task is the forecasting of timeseries generated by chaotic dynamics. In recent years reservoir computing has been shown to be an effective method of foreca...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	432,750
1406.5273	Identifiability of the Simplex Volume Minimization Criterion for Blind Hyperspectral Unmixing: The No Pure-Pixel Case	In blind hyperspectral unmixing (HU), the pure-pixel assumption is well-known to be powerful in enabling simple and effective blind HU solutions. However, the pure-pixel assumption is not always satisfied in an exact sense, especially for scenarios where pixels are heavily mixed. In the no pure-pixel case, a good blind...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	34,012
2108.05516	Text Anchor Based Metric Learning for Small-footprint Keyword Spotting	Keyword Spotting (KWS) remains challenging to achieve the trade-off between small footprint and high accuracy. Recently proposed metric learning approaches improved the generalizability of models for the KWS task, and 1D-CNN based KWS models have achieved the state-of-the-arts (SOTA) in terms of model size. However, fo...	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	250,319
2309.13939	The Time Traveler's Guide to Semantic Web Research: Analyzing Fictitious Research Themes in the ESWC "Next 20 Years" Track	What will Semantic Web research focus on in 20 years from now? We asked this question to the community and collected their visions in the "Next 20 years" track of ESWC 2023. We challenged the participants to submit "future" research papers, as if they were submitting to the 2043 edition of the conference. The submissio...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	394,411
2104.12953	Exploring Uncertainty in Deep Learning for Construction of Prediction Intervals	Deep learning has achieved impressive performance on many tasks in recent years. However, it has been found that it is still not enough for deep neural networks to provide only point estimates. For high-risk tasks, we need to assess the reliability of the model predictions. This requires us to quantify the uncertainty ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	232,366
2001.07136	Sampling Graphlets of Multi-layer Networks: A Restricted Random Walk Approach	Graphlets are induced subgraph patterns that are crucial to the understanding of the structure and function of a large network. A lot of efforts have been devoted to calculating graphlet statistics where random walk based approaches are commonly used to access restricted graphs through the available application program...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	160,974
1206.1402	A New Greedy Algorithm for Multiple Sparse Regression	This paper proposes a new algorithm for multiple sparse regression in high dimensions, where the task is to estimate the support and values of several (typically related) sparse vectors from a few noisy linear measurements. Our algorithm is a "forward-backward" greedy procedure that -- uniquely -- operates on two disti...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	16,365
2009.14404	Learning to Reflect and to Beamform for Intelligent Reflecting Surface with Implicit Channel Estimation	Intelligent reflecting surface (IRS), which consists of a large number of tunable reflective elements, is capable of enhancing the wireless propagation environment in a cellular network by intelligently reflecting the electromagnetic waves from the base-station (BS) toward the users. The optimal tuning of the phase shi...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	198,017
2411.04480	CFPNet: Improving Lightweight ToF Depth Completion via Cross-zone Feature Propagation	Depth completion using lightweight time-of-flight (ToF) depth sensors is attractive due to their low cost. However, lightweight ToF sensors usually have a limited field of view (FOV) compared with cameras. Thus, only pixels in the zone area of the image can be associated with depth signals. Previous methods fail to pro...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	506,289
2307.12981	3D-LLM: Injecting the 3D World into Large Language Models	Large language models (LLMs) and Vision-Language Models (VLMs) have been proven to excel at multiple tasks, such as commonsense reasoning. Powerful as these models can be, they are not grounded in the 3D physical world, which involves richer concepts such as spatial relationships, affordances, physics, layout, and so o...	false	false	false	false	true	false	true	true	true	false	false	true	false	false	false	false	false	false	381,448
2304.04343	Certifiable Black-Box Attacks with Randomized Adversarial Examples: Breaking Defenses with Provable Confidence	Black-box adversarial attacks have demonstrated strong potential to compromise machine learning models by iteratively querying the target model or leveraging transferability from a local surrogate model. Recently, such attacks can be effectively mitigated by state-of-the-art (SOTA) defenses, e.g., detection via the pat...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	357,188
2212.11235	Machine Learning Assisted Inertia Estimation using Ambient Measurements	With the increasing penetration of converter-based renewable resources, different types of dynamics have been introduced to the power system. Due to the complexity and high order of the modern power system, mathematical model-based inertia estimation method becomes more difficult. This paper proposes two novel machine ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	337,735
2110.00687	Investigating Robustness of Dialog Models to Popular Figurative Language Constructs	Humans often employ figurative language use in communication, including during interactions with dialog systems. Thus, it is important for real-world dialog systems to be able to handle popular figurative language constructs like metaphor and simile. In this work, we analyze the performance of existing dialog models in...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	258,490
1804.02767	YOLOv3: An Incremental Improvement	We present some updates to YOLO! We made a bunch of little design changes to make it better. We also trained this new network that's pretty swell. It's a little bigger than last time but more accurate. It's still fast though, don't worry. At 320x320 YOLOv3 runs in 22 ms at 28.2 mAP, as accurate as SSD but three times f...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	94,488
2406.18906	Sonnet or Not, Bot? Poetry Evaluation for Large Models and Datasets	Large language models (LLMs) can now generate and recognize poetry. But what do LLMs really know about poetry? We develop a task to evaluate how well LLMs recognize one aspect of English-language poetry--poetic form--which captures many different poetic features, including rhyme scheme, meter, and word or line repetiti...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	468,224
2501.04597	FrontierNet: Learning Visual Cues to Explore	Exploration of unknown environments is crucial for autonomous robots; it allows them to actively reason and decide on what new data to acquire for tasks such as mapping, object discovery, and environmental assessment. Existing methods, such as frontier-based methods, rely heavily on 3D map operations, which are limited...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	523,277
2211.13416	Data Origin Inference in Machine Learning	It is a growing direction to utilize unintended memorization in ML models to benefit real-world applications, with recent efforts like user auditing, dataset ownership inference and forgotten data measurement. Standing on the point of ML model development, we introduce a process named data origin inference, to assist M...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	true	false	332,464
2311.06279	A novel method of restoration path optimization for the AC-DC bulk power grid after a major blackout	The restoration control of the modern alternating current-direct current (AC-DC) hybrid power grid after a major blackout is difficult and complex. Taking into account the interaction between the line-commutated converter high-voltage direct current (LCC-HVDC) and the AC power grid, this paper proposes a novel optimiza...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	406,879
1903.11680	Gradient Descent with Early Stopping is Provably Robust to Label Noise for Overparameterized Neural Networks	Modern neural networks are typically trained in an over-parameterized regime where the parameters of the model far exceed the size of the training data. Such neural networks in principle have the capacity to (over)fit any set of labels including pure noise. Despite this, somewhat paradoxically, neural network models tr...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	125,555
1102.4272	Bounds on the Achievable Rate for the Fading Relay Channel with Finite Input Constellations	We consider the wireless Rayleigh fading relay channel with finite complex input constellations. Assuming global knowledge of the channel state information and perfect synchronization, upper and lower bounds on the achievable rate, for the full-duplex relay, as well as the more practical half-duplex relay (in which the...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	9,304
1905.12561	Anti-efficient encoding in emergent communication	Despite renewed interest in emergent language simulations with neural networks, little is known about the basic properties of the induced code, and how they compare to human language. One fundamental characteristic of the latter, known as Zipf's Law of Abbreviation (ZLA), is that more frequent words are efficiently ass...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	true	false	false	false	132,797
2102.02509	An Analysis of International Use of Robots for COVID-19	This article analyses data collected on 338 instances of robots used explicitly in response to COVID-19 from 24 Jan, 2020, to 23 Jan, 2021, in 48 countries. The analysis was guided by four overarching questions: 1) What were robots used for in the COVID-19 response? 2) When were they used? 3) How did different countrie...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	218,431
1907.00263	A Power Efficient Artificial Neuron Using Superconducting Nanowires	With the rising societal demand for more information-processing capacity with lower power consumption, alternative architectures inspired by the parallelism and robustness of the human brain have recently emerged as possible solutions. In particular, spiking neural networks (SNNs) offer a bio-realistic approach, relyin...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	136,984
1912.00862	ICD Coding from Clinical Text Using Multi-Filter Residual Convolutional Neural Network	Automated ICD coding, which assigns the International Classification of Disease codes to patient visits, has attracted much research attention since it can save time and labor for billing. The previous state-of-the-art model utilized one convolutional layer to build document representations for predicting ICD codes. Ho...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	155,915
2101.10399	Anchor Distance for 3D Multi-Object Distance Estimation from 2D Single Shot	Visual perception of the objects in a 3D environment is a key to successful performance in autonomous driving and simultaneous localization and mapping (SLAM). In this paper, we present a real time approach for estimating the distances to multiple objects in a scene using only a single-shot image. Given a 2D Bounding B...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	216,915
2311.03146	Enabling In-Situ Resources Utilisation by leveraging collaborative robotics and astronaut-robot interaction	Space exploration and establishing human presence on other planets demand advanced technology and effective collaboration between robots and astronauts. Efficient space resource utilization is also vital for extraterrestrial settlements. The Collaborative In-Situ Resources Utilisation (CISRU) project has developed a so...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	405,732
2412.03600	Social Media Informatics for Sustainable Cities and Societies: An Overview of the Applications, associated Challenges, and Potential Solutions	In the modern world, our cities and societies face several technological and societal challenges, such as rapid urbanization, global warming & climate change, the digital divide, and social inequalities, increasing the need for more sustainable cities and societies. Addressing these challenges requires a multifaceted a...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	514,031
2209.13428	LitCovid in 2022: an information resource for the COVID-19 literature	LitCovid (https://www.ncbi.nlm.nih.gov/research/coronavirus/), first launched in February 2020, is a first-of-its-kind literature hub for tracking up-to-date published research on COVID-19. The number of articles in LitCovid has increased from 55,000 to ~300,000 over the past two and half years, with a consistent growt...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	319,889
2409.00014	DivDiff: A Conditional Diffusion Model for Diverse Human Motion Prediction	Diverse human motion prediction (HMP) aims to predict multiple plausible future motions given an observed human motion sequence. It is a challenging task due to the diversity of potential human motions while ensuring an accurate description of future human motions. Current solutions are either low-diversity or limited ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	484,716
2401.05583	Diffusion Priors for Dynamic View Synthesis from Monocular Videos	Dynamic novel view synthesis aims to capture the temporal evolution of visual content within videos. Existing methods struggle to distinguishing between motion and structure, particularly in scenarios where camera poses are either unknown or constrained compared to object motion. Furthermore, with information solely fr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	420,840
2305.09579	Private Everlasting Prediction	A private learner is trained on a sample of labeled points and generates a hypothesis that can be used for predicting the labels of newly sampled points while protecting the privacy of the training set [Kasiviswannathan et al., FOCS 2008]. Research uncovered that private learners may need to exhibit significantly highe...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	true	364,691
2501.10808	Optimizing MACD Trading Strategies A Dance of Finance, Wavelets, and Genetics	In today's financial markets, quantitative trading has become an essential trading method, with the MACD indicator widely employed in quantitative trading strategies. This paper begins by screening and cleaning the dataset, establishing a model that adheres to the basic buy and sell rules of the MACD, and calculating k...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	525,667
2406.14744	Training Next Generation AI Users and Developers at NCSA	This article focuses on training work carried out in artificial intelligence (AI) at the National Center for Supercomputing Applications (NCSA) at the University of Illinois Urbana-Champaign via a research experience for undergraduates (REU) program named FoDOMMaT. It also describes why we are interested in AI, and con...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	466,441
1909.11193	Scaling-Translation-Equivariant Networks with Decomposed Convolutional Filters	Encoding the scale information explicitly into the representation learned by a convolutional neural network (CNN) is beneficial for many computer vision tasks especially when dealing with multiscale inputs. We study, in this paper, a scaling-translation-equivariant (ST-equivariant) CNN with joint convolutions across th...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	146,731
1904.11742	Capacity per Unit-Energy of Gaussian Many-Access Channels	We consider a Gaussian multiple-access channel where the number of transmitters grows with the blocklength $n$. For this setup, the maximum number of bits that can be transmitted reliably per unit-energy is analyzed. We show that if the number of users is of an order strictly above $n/\log n$, then the users cannot ach...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	128,938
2007.03311	An Accelerated DFO Algorithm for Finite-sum Convex Functions	Derivative-free optimization (DFO) has recently gained a lot of momentum in machine learning, spawning interest in the community to design faster methods for problems where gradients are not accessible. While some attention has been given to the concept of acceleration in the DFO literature, existing stochastic algorit...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	186,023
1612.09434	Data driven estimation of Laplace-Beltrami operator	Approximations of Laplace-Beltrami operators on manifolds through graph Lapla-cians have become popular tools in data analysis and machine learning. These discretized operators usually depend on bandwidth parameters whose tuning remains a theoretical and practical problem. In this paper, we address this problem for the...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	66,188
2407.03194	Prediction Instability in Machine Learning Ensembles	In machine learning ensembles predictions from multiple models are aggregated. Despite widespread use and strong performance of ensembles in applied problems little is known about the mathematical properties of aggregating models and associated consequences for safe, explainable use of such models. In this paper we pro...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	470,066
2412.01657	PassionNet: An Innovative Framework for Duplicate and Conflicting Requirements Identification	Early detection and resolution of duplicate and conflicting requirements can significantly enhance project efficiency and overall software quality. Researchers have developed various computational predictors by leveraging Artificial Intelligence (AI) potential to detect duplicate and conflicting requirements. However, ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	513,214
1901.03264	The Capacity Achieving Distribution for the Amplitude Constrained Additive Gaussian Channel: An Upper Bound on the Number of Mass Points	This paper studies an $n$-dimensional additive Gaussian noise channel with a peak-power-constrained input. It is well known that, in this case, when $n=1$ the capacity-achieving input distribution is discrete with finitely many mass points, and when $n>1$ the capacity-achieving input distribution is supported on fini...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	118,371
2310.10925	Path Following Control of Automated Vehicle Considering Uncertainties and Disturbances with Parametric Varying	Automated Vehicle Path Following Control (PFC) is an advanced control system that can regulate the vehicle into a collision-free region in the presence of other objects on the road. Common collision avoidance functions, such as forward collision warning and automatic emergency braking, have recently been developed and ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	400,435

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