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
2102.03980	Grab the Reins of Crowds: Estimating the Effects of Crowd Movement Guidance Using Causal Inference	Crowd movement guidance has been a fascinating problem in various fields, such as easing traffic congestion in unusual events and evacuating people from an emergency-affected area. To grab the reins of crowds, there has been considerable demand for a decision support system that can answer a typical question: ``what wi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	218,942
2308.08746	SurgicalSAM: Efficient Class Promptable Surgical Instrument Segmentation	The Segment Anything Model (SAM) is a powerful foundation model that has revolutionised image segmentation. To apply SAM to surgical instrument segmentation, a common approach is to locate precise points or boxes of instruments and then use them as prompts for SAM in a zero-shot manner. However, we observe two problems...	false	false	false	false	true	false	false	true	false	false	false	true	false	false	false	false	false	false	386,015
1809.03758	Threshold-Based Heuristics for Trust Inference in a Social Network	Trust among the users of a social network plays a pivotal role in item recommendation, particularly for the cold start users. Due to the sparse nature of these networks, trust information between any two users may not be always available. To infer the missing trust values, one well-known approach is path based trust es...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	107,402
2206.02782	Towards Job-Transition-Tag Graph for a Better Job Title Representation Learning	Works on learning job title representation are mainly based on \textit{Job-Transition Graph}, built from the working history of talents. However, since these records are usually messy, this graph is very sparse, which affects the quality of the learned representation and hinders further analysis. To address this specif...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	301,028
1104.1880	Approximative Covariance Interpolation	When methods of moments are used for identification of power spectral densities, a model is matched to estimated second order statistics such as, e.g., covariance estimates. If the estimates are good there is an infinite family of power spectra consistent with such an estimate and in applications, such as identificatio...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	9,935
2107.14571	Observer-based switched-linear system identification	In this paper, we present a methodology to identify discrete-time state-space switched linear systems (SLSs) from input-output measurements. Continuous-state is not assumed to be measured. The key step is a deadbeat observer based transformation to a switched auto-regressive with exogenous input (SARX) model. This tran...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	248,499
2303.15663	Predicting Thermoelectric Power Factor of Bismuth Telluride During Laser Powder Bed Fusion Additive Manufacturing	An additive manufacturing (AM) process, like laser powder bed fusion, allows for the fabrication of objects by spreading and melting powder in layers until a freeform part shape is created. In order to improve the properties of the material involved in the AM process, it is important to predict the material characteriz...	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	354,571
2404.00964	S2RC-GCN: A Spatial-Spectral Reliable Contrastive Graph Convolutional Network for Complex Land Cover Classification Using Hyperspectral Images	Spatial correlations between different ground objects are an important feature of mining land cover research. Graph Convolutional Networks (GCNs) can effectively capture such spatial feature representations and have demonstrated promising results in performing hyperspectral imagery (HSI) classification tasks of complex...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	443,190
1804.04412	Unsupervised Discovery of Object Landmarks as Structural Representations	Deep neural networks can model images with rich latent representations, but they cannot naturally conceptualize structures of object categories in a human-perceptible way. This paper addresses the problem of learning object structures in an image modeling process without supervision. We propose an autoencoding formulat...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	94,849
1810.03046	MeetupNet Dublin: Discovering Communities in Dublin's Meetup Network	Meetup.com is a global online platform which facilitates the organisation of meetups in different parts of the world. A meetup group typically focuses on one specific topic of interest, such as sports, music, language, or technology. However, many users of this platform attend multiple meetups. On this basis, we can co...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	109,720
2301.11414	A Simple Algorithm For Scaling Up Kernel Methods	The recent discovery of the equivalence between infinitely wide neural networks (NNs) in the lazy training regime and Neural Tangent Kernels (NTKs) (Jacot et al., 2018) has revived interest in kernel methods. However, conventional wisdom suggests kernel methods are unsuitable for large samples due to their computationa...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	342,129
2412.01091	DuoCast: Duo-Probabilistic Meteorology-Aware Model for Extended Precipitation Nowcasting	Recently, extended short-term precipitation nowcasting struggles with decreasing precision because of insufficient consideration of meteorological knowledge, such as weather fronts which significantly influence precipitation intensity, duration, and spatial distribution. Therefore, in this paper, we present DuoCast, a ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	512,945
2107.05819	Multitask Identity-Aware Image Steganography via Minimax Optimization	High-capacity image steganography, aimed at concealing a secret image in a cover image, is a technique to preserve sensitive data, e.g., faces and fingerprints. Previous methods focus on the security during transmission and subsequently run a risk of privacy leakage after the restoration of secret images at the receivi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	245,896
1912.12397	Natural language processing of MIMIC-III clinical notes for identifying diagnosis and procedures with neural networks	Coding diagnosis and procedures in medical records is a crucial process in the healthcare industry, which includes the creation of accurate billings, receiving reimbursements from payers, and creating standardized patient care records. In the United States, Billing and Insurance related activities cost around $471 bill...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	158,835
2106.05830	A Template-guided Hybrid Pointer Network for Knowledge-basedTask-oriented Dialogue Systems	Most existing neural network based task-oriented dialogue systems follow encoder-decoder paradigm, where the decoder purely depends on the source texts to generate a sequence of words, usually suffering from instability and poor readability. Inspired by the traditional template-based generation approaches, we propose a...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	240,240
1606.01735	Integrated perception with recurrent multi-task neural networks	Modern discriminative predictors have been shown to match natural intelligences in specific perceptual tasks in image classification, object and part detection, boundary extraction, etc. However, a major advantage that natural intelligences still have is that they work well for "all" perceptual problems together, solvi...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	56,854
2404.16907	Season combinatorial intervention predictions with Salt & Peper	Interventions play a pivotal role in the study of complex biological systems. In drug discovery, genetic interventions (such as CRISPR base editing) have become central to both identifying potential therapeutic targets and understanding a drug's mechanism of action. With the advancement of CRISPR and the proliferation ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	449,672
2002.11661	Data Structures & Algorithms for Exact Inference in Hierarchical Clustering	Hierarchical clustering is a fundamental task often used to discover meaningful structures in data, such as phylogenetic trees, taxonomies of concepts, subtypes of cancer, and cascades of particle decays in particle physics. Typically approximate algorithms are used for inference due to the combinatorial number of poss...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	165,792
2010.04712	Gaussian Process (GP)-based Learning Control of Selective Laser Melting Process	Selective laser melting (SLM) is one of emerging processes for effective metal additive manufacturing. Due to complex heat exchange and material phase changes, it is challenging to accurately model the SLM dynamics and design robust control of SLM process. In this paper, we first present a data-driven Gaussian process ...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	199,840
2012.03731	Computing flood probabilities using Twitter: application to the Houston urban area during Harvey	In this paper, we investigate the conversion of a Twitter corpus into geo-referenced raster cells holding the probability of the associated geographical areas of being flooded. We describe a baseline approach that combines a density ratio function, aggregation using a spatio-temporal Gaussian kernel function, and TFIDF...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	210,229
1907.04967	Diverse Trajectory Forecasting with Determinantal Point Processes	The ability to forecast a set of likely yet diverse possible future behaviors of an agent (e.g., future trajectories of a pedestrian) is essential for safety-critical perception systems (e.g., autonomous vehicles). In particular, a set of possible future behaviors generated by the system must be diverse to account for ...	false	false	false	false	false	false	true	true	false	false	false	true	false	false	false	false	false	false	138,244
2409.06377	Enhancing Sequential Recommendations through Multi-Perspective Reflections and Iteration	Sequence recommendation (SeqRec) aims to predict the next item a user will interact with by understanding user intentions and leveraging collaborative filtering information. Large language models (LLMs) have shown great promise in recommendation tasks through prompt-based, fixed reflection libraries, and fine-tuning te...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	487,108
2212.14731	UBIWEAR: An end-to-end, data-driven framework for intelligent physical activity prediction to empower mHealth interventions	It is indisputable that physical activity is vital for an individual's health and wellness. However, a global prevalence of physical inactivity has induced significant personal and socioeconomic implications. In recent years, a significant amount of work has showcased the capabilities of self-tracking technology to cre...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	338,721
2208.03566	Towards Robust Deep Learning using Entropic Losses	Current deep learning solutions are well known for not informing whether they can reliably classify an example during inference. One of the most effective ways to build more reliable deep learning solutions is to improve their performance in the so-called out-of-distribution detection task, which essentially consists o...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	311,825
1606.06041	Bandit-Based Random Mutation Hill-Climbing	The Random Mutation Hill-Climbing algorithm is a direct search technique mostly used in discrete domains. It repeats the process of randomly selecting a neighbour of a best-so-far solution and accepts the neighbour if it is better than or equal to it. In this work, we propose to use a novel method to select the neighbo...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	57,515
cs/0203023	Agent trade servers in financial exchange systems	New services based on the best-effort paradigm could complement the current deterministic services of an electronic financial exchange. Four crucial aspects of such systems would benefit from a hybrid stance: proper use of processing resources, bandwidth management, fault tolerance, and exception handling. We argue tha...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	537,528
1911.01921	DLA: Dense-Layer-Analysis for Adversarial Example Detection	In recent years Deep Neural Networks (DNNs) have achieved remarkable results and even showed super-human capabilities in a broad range of domains. This led people to trust in DNNs' classifications and resulting actions even in security-sensitive environments like autonomous driving. Despite their impressive achieveme...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	152,235
2004.10629	Amortized Bayesian model comparison with evidential deep learning	Comparing competing mathematical models of complex natural processes is a shared goal among many branches of science. The Bayesian probabilistic framework offers a principled way to perform model comparison and extract useful metrics for guiding decisions. However, many interesting models are intractable with standard ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	173,686
2403.04197	Large Language Models are In-Context Molecule Learners	Large Language Models (LLMs) have demonstrated exceptional performance in biochemical tasks, especially the molecule caption translation task, which aims to bridge the gap between molecules and natural language texts. However, previous methods in adapting LLMs to the molecule-caption translation task required extra dom...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	435,502
1301.3220	A Low-Complexity Encoding of Quasi-Cyclic Codes Based on Galois Fourier Transform	The encoding complexity of a general (en,ek) quasi-cyclic code is O[(e^2)(n-k)k]. This paper presents a novel low-complexity encoding algorithm for quasi-cyclic (QC) codes based on matrix transformation. First, a message vector is encoded into a transformed codeword in the transform domain. Then, the transmitted codewo...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	21,071
2006.08437	Depth Uncertainty in Neural Networks	Existing methods for estimating uncertainty in deep learning tend to require multiple forward passes, making them unsuitable for applications where computational resources are limited. To solve this, we perform probabilistic reasoning over the depth of neural networks. Different depths correspond to subnetworks which s...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	182,193
1101.3755	Transductive-Inductive Cluster Approximation Via Multivariate Chebyshev Inequality	Approximating adequate number of clusters in multidimensional data is an open area of research, given a level of compromise made on the quality of acceptable results. The manuscript addresses the issue by formulating a transductive inductive learning algorithm which uses multivariate Chebyshev inequality. Considering c...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	8,861
2305.09024	Scalable Adaptive Traffic Light Control Over a Traffic Network Including Transit Delays	We study the Traffic Light Control (TLC) problem for a traffic network with multiple intersections in an artery, including the effect of transit delays for vehicles moving from one intersection to the next. The goal is to minimize the overall mean waiting time and improve the ``green wave'' properties in such systems. ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	364,483
2403.10380	BirdSet: A Large-Scale Dataset for Audio Classification in Avian Bioacoustics	Deep learning (DL) has greatly advanced audio classification, yet the field is limited by the scarcity of large-scale benchmark datasets that have propelled progress in other domains. While AudioSet is a pivotal step to bridge this gap as a universal-domain dataset, its restricted accessibility and limited range of eva...	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	438,176
1802.03079	Hole Filling with Multiple Reference Views in DIBR View Synthesis	Depth-image-based rendering (DIBR) oriented view synthesis has been widely employed in the current depth-based 3D video systems by synthesizing a virtual view from an arbitrary viewpoint. However, holes may appear in the synthesized view due to disocclusion, thus significantly degrading the quality. Consequently, effor...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	89,892
0904.2953	Towards an Intelligent System for Risk Prevention and Management	Making a decision in a changeable and dynamic environment is an arduous task owing to the lack of information, their uncertainties and the unawareness of planners about the future evolution of incidents. The use of a decision support system is an efficient solution of this issue. Such a system can help emergency planne...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	3,560
1905.10836	OOGAN: Disentangling GAN with One-Hot Sampling and Orthogonal Regularization	Exploring the potential of GANs for unsupervised disentanglement learning, this paper proposes a novel GAN-based disentanglement framework with One-Hot Sampling and Orthogonal Regularization (OOGAN). While previous works mostly attempt to tackle disentanglement learning through VAE and seek to implicitly minimize the T...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	132,214
2411.04156	Crystal: Illuminating LLM Abilities on Language and Code	Large Language Models (LLMs) specializing in code generation (which are also often referred to as code LLMs), e.g., StarCoder and Code Llama, play increasingly critical roles in various software development scenarios. It is also crucial for code LLMs to possess both code generation and natural language abilities for ma...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	true	506,172
2009.05938	Coding Facial Expressions with Gabor Wavelets (IVC Special Issue)	We present a method for extracting information about facial expressions from digital images. The method codes facial expression images using a multi-orientation, multi-resolution set of Gabor filters that are topographically ordered and approximately aligned with the face. A similarity space derived from this code is c...	true	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	195,479
2210.00503	DARE: A large-scale handwritten date recognition system	Handwritten text recognition for historical documents is an important task but it remains difficult due to a lack of sufficient training data in combination with a large variability of writing styles and degradation of historical documents. While recurrent neural network architectures are commonly used for handwritten ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	320,889
2502.00234	Fast Solvers for Discrete Diffusion Models: Theory and Applications of High-Order Algorithms	Discrete diffusion models have emerged as a powerful generative modeling framework for discrete data with successful applications spanning from text generation to image synthesis. However, their deployment faces challenges due to the high dimensionality of the state space, necessitating the development of efficient inf...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	529,271
2402.15650	Uniformly Safe RL with Objective Suppression for Multi-Constraint Safety-Critical Applications	Safe reinforcement learning tasks are a challenging domain despite being very common in the real world. The widely adopted CMDP model constrains the risks in expectation, which makes room for dangerous behaviors in long-tail states. In safety-critical domains, such behaviors could lead to disastrous outcomes. To addres...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	432,226
2306.07229	MRS Drone: A Modular Platform for Real-World Deployment of Aerial Multi-Robot Systems	This paper presents a modular autonomous Unmanned Aerial Vehicle (UAV) platform called the Multi-robot Systems (MRS) Drone that can be used in a large range of indoor and outdoor applications. The MRS Drone features unique modularity with respect to changes in actuators, frames, and sensory configuration. As the name s...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	372,935
1910.06934	Human Action Recognition with Multi-Laplacian Graph Convolutional Networks	Convolutional neural networks are nowadays witnessing a major success in different pattern recognition problems. These learning models were basically designed to handle vectorial data such as images but their extension to non-vectorial and semi-structured data (namely graphs with variable sizes, topology, etc.) remains...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	149,482
1209.6012	Minimum Weight Dynamo and Fast Opinion Spreading	We consider the following multi--level opinion spreading model on networks. Initially, each node gets a weight from the set [0..k-1], where such a weight stands for the individuals conviction of a new idea or product. Then, by proceeding to rounds, each node updates its weight according to the weights of its neighbors....	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	18,786
1912.03980	Hybrid Physical-Deep Learning Model for Astronomical Inverse Problems	We present a Bayesian machine learning architecture that combines a physically motivated parametrization and an analytic error model for the likelihood with a deep generative model providing a powerful data-driven prior for complex signals. This combination yields an interpretable and differentiable generative model, a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	156,734
2403.18886	Self-Expansion of Pre-trained Models with Mixture of Adapters for Continual Learning	Continual learning (CL) aims to continually accumulate knowledge from a non-stationary data stream without catastrophic forgetting of learned knowledge, requiring a balance between stability and adaptability. Relying on the generalizable representation in pre-trained models (PTMs), PTM-based CL methods perform effectiv...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	442,119
2408.07427	Beyond Inter-Item Relations: Dynamic Adaption for Enhancing LLM-Based Sequential Recommendation	Sequential recommender systems (SRS) predict the next items that users may prefer based on user historical interaction sequences. Inspired by the rise of large language models (LLMs) in various AI applications, there is a surge of work on LLM-based SRS. Despite their attractive performance, existing LLM-based SRS still...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	480,578
2308.15918	Physics-Informed DeepMRI: Bridging the Gap from Heat Diffusion to k-Space Interpolation	In the field of parallel imaging (PI), alongside image-domain regularization methods, substantial research has been dedicated to exploring $k$-space interpolation. However, the interpretability of these methods remains an unresolved issue. Furthermore, these approaches currently face acceleration limitations that are c...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	388,848
2403.05139	Improving Diffusion Models for Authentic Virtual Try-on in the Wild	This paper considers image-based virtual try-on, which renders an image of a person wearing a curated garment, given a pair of images depicting the person and the garment, respectively. Previous works adapt existing exemplar-based inpainting diffusion models for virtual try-on to improve the naturalness of the generate...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	435,894
1908.03176	Defending Against Adversarial Iris Examples Using Wavelet Decomposition	Deep neural networks have presented impressive performance in biometric applications. However, their performance is highly at risk when facing carefully crafted input samples known as adversarial examples. In this paper, we present three defense strategies to detect adversarial iris examples. These defense strategies a...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	141,178
2107.01402	Cell-Free Massive MIMO-OFDM Transmission over Frequency-Selective Fading Channels	This letter presents and analyzes orthogonal frequency-division multiplexing (OFDM)-based multi-carrier transmission for cell-free massive multi-input multi-output (CFmMIMO) over frequency-selective fading channels. Frequency-domain conjugate beamforming, pilot assignment, and user-specific resource allocation are prop...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	244,475
1607.06198	Supervised Adverse Drug Reaction Signalling Framework Imitating Bradford Hill's Causality Considerations	Big longitudinal observational medical data potentially hold a wealth of information and have been recognised as potential sources for gaining new drug safety knowledge. Unfortunately there are many complexities and underlying issues when analysing longitudinal observational data. Due to these complexities, existing me...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	58,856
1404.4468	On Independence Atoms and Keys	Uniqueness and independence are two fundamental properties of data. Their enforcement in database systems can lead to higher quality data, faster data service response time, better data-driven decision making and knowledge discovery from data. The applications can be effectively unlocked by providing efficient solution...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	32,404
1501.02344	Fitting the Log Skew Normal to the Sum of Independent Lognormals Distribution	Sums of lognormal random variables (RVs) occur in many important problems in wireless communications especially in interferences calculation. Several methods have been proposed to approximate the lognormal sum distribution. Most of them requires lengthy Monte Carlo simulations, or advanced slowly converging numerical i...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	39,169
1308.3548	Distributed Ranging and Localization for Wireless Networks via Compressed Sensing	Location-based services in a wireless network require nodes to know their locations accurately. Conventional solutions rely on contention-based medium access, where only one node can successfully transmit at any time in any neighborhood. In this paper, a novel, complete, distributed ranging and localization solution is...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	26,479
1709.05750	Adaptive Laplace Mechanism: Differential Privacy Preservation in Deep Learning	In this paper, we focus on developing a novel mechanism to preserve differential privacy in deep neural networks, such that: (1) The privacy budget consumption is totally independent of the number of training steps; (2) It has the ability to adaptively inject noise into features based on the contribution of each to the...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	80,948
2307.15568	We are all Individuals: The Role of Robot Personality and Human Traits in Trustworthy Interaction	As robots take on roles in our society, it is important that their appearance, behaviour and personality are appropriate for the job they are given and are perceived favourably by the people with whom they interact. Here, we provide an extensive quantitative and qualitative study exploring robot personality but, import...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	382,308
2108.09105	Airbert: In-domain Pretraining for Vision-and-Language Navigation	Vision-and-language navigation (VLN) aims to enable embodied agents to navigate in realistic environments using natural language instructions. Given the scarcity of domain-specific training data and the high diversity of image and language inputs, the generalization of VLN agents to unseen environments remains challeng...	true	false	false	false	true	false	true	false	true	false	false	true	false	false	false	false	false	false	251,496
2405.08599	The distributed biased min-consensus protocol revisited: pre-specified finite time control strategies and small-gain based analysis	Unlike the classical distributed consensus protocols enabling the group of agents as a whole to reach an agreement regarding a certain quantity of interest in a distributed fashion, the distributed biased min-consensus protocol (DBMC) has been proven to generate advanced complexity pertaining to solving the shortest pa...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	454,152
2412.01721	BroadTrack: Broadcast Camera Tracking for Soccer	Camera calibration and localization, sometimes simply named camera calibration, enables many applications in the context of soccer broadcasting, for instance regarding the interpretation and analysis of the game, or the insertion of augmented reality graphics for storytelling or refereeing purposes. To contribute to su...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	513,234
2105.04547	Highly Efficient Memory Failure Prediction using Mcelog-based Data Mining and Machine Learning	In the data center, unexpected downtime caused by memory failures can lead to a decline in the stability of the server and even the entire information technology infrastructure, which harms the business. Therefore, whether the memory failure can be accurately predicted in advance has become one of the most important is...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	true	234,540
2402.04797	Offline Deep Model Predictive Control (MPC) for Visual Navigation	In this paper, we propose a new visual navigation method based on a single RGB perspective camera. Using the Visual Teach & Repeat (VT&R) methodology, the robot acquires a visual trajectory consisting of multiple subgoal images in the teaching step. In the repeat step, we propose two network architectures, namely ViewN...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	427,593
2302.12444	On the Training Instability of Shuffling SGD with Batch Normalization	We uncover how SGD interacts with batch normalization and can exhibit undesirable training dynamics such as divergence. More precisely, we study how Single Shuffle (SS) and Random Reshuffle (RR) -- two widely used variants of SGD -- interact surprisingly differently in the presence of batch normalization: RR leads to m...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	347,571
2111.01257	Implicit Model Specialization through DAG-based Decentralized Federated Learning	Federated learning allows a group of distributed clients to train a common machine learning model on private data. The exchange of model updates is managed either by a central entity or in a decentralized way, e.g. by a blockchain. However, the strong generalization across all clients makes these approaches unsuited fo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	264,498
1503.04996	On Extreme Pruning of Random Forest Ensembles for Real-time Predictive Applications	Random Forest (RF) is an ensemble supervised machine learning technique that was developed by Breiman over a decade ago. Compared with other ensemble techniques, it has proved its accuracy and superiority. Many researchers, however, believe that there is still room for enhancing and improving its performance accuracy. ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	41,201
1506.08670	Automatic Channel Network Extraction from Remotely Sensed Images by Singularity Analysis	Quantitative analysis of channel networks plays an important role in river studies. To provide a quantitative representation of channel networks, we propose a new method that extracts channels from remotely sensed images and estimates their widths. Our fully automated method is based on a recently proposed Multiscale S...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	44,643
2102.06548	Is Q-Learning Minimax Optimal? A Tight Sample Complexity Analysis	Q-learning, which seeks to learn the optimal Q-function of a Markov decision process (MDP) in a model-free fashion, lies at the heart of reinforcement learning. When it comes to the synchronous setting (such that independent samples for all state-action pairs are drawn from a generative model in each iteration), substa...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	219,788
2304.14152	Spiking Neural Network Decision Feedback Equalization for IM/DD Systems	A spiking neural network (SNN) equalizer with a decision feedback structure is applied to an IM/DD link with various parameters. The SNN outperforms linear and artificial neural network (ANN) based equalizers.	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	360,844
2103.07491	Private Cross-Silo Federated Learning for Extracting Vaccine Adverse Event Mentions	Federated Learning (FL) is quickly becoming a goto distributed training paradigm for users to jointly train a global model without physically sharing their data. Users can indirectly contribute to, and directly benefit from a much larger aggregate data corpus used to train the global model. However, literature on succe...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	false	224,601
2003.08550	Detecting Lane and Road Markings at A Distance with Perspective Transformer Layers	Accurate detection of lane and road markings is a task of great importance for intelligent vehicles. In existing approaches, the detection accuracy often degrades with the increasing distance. This is due to the fact that distant lane and road markings occupy a small number of pixels in the image, and scales of lane an...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	168,771
2101.04281	Temporally Guided Articulated Hand Pose Tracking in Surgical Videos	Articulated hand pose tracking is an under-explored problem that carries the potential for use in an extensive number of applications, especially in the medical domain. With a robust and accurate tracking system on surgical videos, the motion dynamics and movement patterns of the hands can be captured and analyzed for ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	215,106
2301.02200	Impact, Attention, Influence: Early Assessment of Autonomous Driving Datasets	Autonomous Driving (AD), the area of robotics with the greatest potential impact on society, has gained a lot of momentum in the last decade. As a result of this, the number of datasets in AD has increased rapidly. Creators and users of datasets can benefit from a better understanding of developments in the field. Whil...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	339,440
2002.01587	Deep Learning Tubes for Tube MPC	Learning-based control aims to construct models of a system to use for planning or trajectory optimization, e.g. in model-based reinforcement learning. In order to obtain guarantees of safety in this context, uncertainty must be accurately quantified. This uncertainty may come from errors in learning (due to a lack of ...	false	false	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	162,688
2407.08558	ST-Mamba: Spatial-Temporal Mamba for Traffic Flow Estimation Recovery using Limited Data	Traffic flow estimation (TFE) is crucial for urban intelligent traffic systems. While traditional on-road detectors are hindered by limited coverage and high costs, cloud computing and data mining of vehicular network data, such as driving speeds and GPS coordinates, present a promising and cost-effective alternative. ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	472,212
1403.3339	Capacity of a Nonlinear Optical Channel with Finite Memory	The channel capacity of a nonlinear, dispersive fiber-optic link is revisited. To this end, the popular Gaussian noise (GN) model is extended with a parameter to account for the finite memory of realistic fiber channels. This finite-memory model is harder to analyze mathematically but, in contrast to previous models, i...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	31,562
2306.10028	Graph Based Long-Term And Short-Term Interest Model for Click-Through Rate Prediction	Click-through rate (CTR) prediction aims to predict the probability that the user will click an item, which has been one of the key tasks in online recommender and advertising systems. In such systems, rich user behavior (viz. long- and short-term) has been proved to be of great value in capturing user interests. Both ...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	374,062
2206.03353	Improving Adversarial Robustness by Putting More Regularizations on Less Robust Samples	Adversarial training, which is to enhance robustness against adversarial attacks, has received much attention because it is easy to generate human-imperceptible perturbations of data to deceive a given deep neural network. In this paper, we propose a new adversarial training algorithm that is theoretically well motivat...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	301,246
2112.07159	Birds Eye View Social Distancing Analysis System	Social distancing can reduce the infection rates in respiratory pandemics such as COVID-19. Traffic intersections are particularly suitable for monitoring and evaluation of social distancing behavior in metropolises. We propose and evaluate a privacy-preserving social distancing analysis system (B-SDA), which uses bird...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	true	271,392
2312.01916	PEACE: Prototype lEarning Augmented transferable framework for Cross-domain rEcommendation	To help merchants/customers to provide/access a variety of services through miniapps, online service platforms have occupied a critical position in the effective content delivery, in which how to recommend items in the new domain launched by the service provider for customers has become more urgent. However, the non-ne...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	412,630
1603.00663	Unsupervised Watertight Mesh Generation for Physics Simulation Applications Using Growing Neural Gas on Noisy Free-Form Object Models	We present a framework to generate watertight mesh representations in an unsupervised manner from noisy point clouds of complex, heterogeneous objects with free-form surfaces. The resulting meshes are ready to use in applications like kinematics and dynamics simulation where watertightness and fast processing are the m...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	52,805
2104.13225	Visually grounded models of spoken language: A survey of datasets, architectures and evaluation techniques	This survey provides an overview of the evolution of visually grounded models of spoken language over the last 20 years. Such models are inspired by the observation that when children pick up a language, they rely on a wide range of indirect and noisy clues, crucially including signals from the visual modality co-occur...	false	false	true	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	232,442
1711.04481	An Automatic Diagnosis Method of Facial Acne Vulgaris Based on Convolutional Neural Network	In this paper, we present a new automatic diagnosis method of facial acne vulgaris based on convolutional neural network. This method is proposed to overcome the shortcoming of classification types in previous methods. The core of our method is to extract features of images based on convolutional neural network and ach...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	84,399
2412.04945	HOLa: HoloLens Object Labeling	In the context of medical Augmented Reality (AR) applications, object tracking is a key challenge and requires a significant amount of annotation masks. As segmentation foundation models like the Segment Anything Model (SAM) begin to emerge, zero-shot segmentation requires only minimal human participation obtaining hig...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	514,641
2410.09567	Timeseria: an object-oriented time series processing library	Timeseria is an object-oriented time series processing library implemented in Python, which aims at making it easier to manipulate time series data and to build statistical and machine learning models on top of it. Unlike common data analysis frameworks, it builds up from well defined and reusable logical units (object...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	497,655
2404.17284	Machine Learning based prediction of Vanadium Redox Flow Battery temperature rise under different charge-discharge conditions	Accurate prediction of battery temperature rise is very essential for designing an efficient thermal management scheme. In this paper, machine learning (ML) based prediction of Vanadium Redox Flow Battery (VRFB) thermal behavior during charge-discharge operation has been demonstrated for the first time. Considering dif...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	449,807
1611.09143	Rate Adaptation for Secure HARQ Protocols	This paper investigates the incremental-redundancy hybrid-automatic repeat request (IR-HARQ) transmission over independent block-fading channels in the presence of an eavesdropper, where the secrecy of the transmission is ensured via introduction of dummy-messages. Since the encoder only knows the statistics of the cha...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	64,624
2111.00526	FinEAS: Financial Embedding Analysis of Sentiment	We introduce a new language representation model in finance called Financial Embedding Analysis of Sentiment (FinEAS). In financial markets, news and investor sentiment are significant drivers of security prices. Thus, leveraging the capabilities of modern NLP approaches for financial sentiment analysis is a crucial co...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	264,246
1808.09058	Quantum enhanced cross-validation for near-optimal neural networks architecture selection	This paper proposes a quantum-classical algorithm to evaluate and select classical artificial neural networks architectures. The proposed algorithm is based on a probabilistic quantum memory and the possibility to train artificial neural networks in superposition. We obtain an exponential quantum speedup in the evaluat...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	106,101
2411.10595	FedAli: Personalized Federated Learning with Aligned Prototypes through Optimal Transport	Federated Learning (FL) enables collaborative, personalized model training across multiple devices without sharing raw data, making it ideal for pervasive computing applications that optimize user-centric performances in diverse environments. However, data heterogeneity among clients poses a significant challenge, lead...	false	false	false	false	false	false	true	false	false	false	false	true	true	false	false	false	false	false	508,707
2311.11013	Implicit Event-RGBD Neural SLAM	Implicit neural SLAM has achieved remarkable progress recently. Nevertheless, existing methods face significant challenges in non-ideal scenarios, such as motion blur or lighting variation, which often leads to issues like convergence failures, localization drifts, and distorted mapping. To address these challenges, we...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	408,757
2212.07768	A scalable framework for annotating photovoltaic cell defects in electroluminescence images	The correct functioning of photovoltaic (PV) cells is critical to ensuring the optimal performance of a solar plant. Anomaly detection techniques for PV cells can result in significant cost savings in operation and maintenance (O&M). Recent research has focused on deep learning techniques for automatically detecting an...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	336,516
2102.13455	Inverse deformation analysis: an experimental and numerical assessment using the FEniCS Project	In this paper, we develop a framework for solving inverse deformation problems using the FEniCS Project finite element software. We validate our approach with experimental imaging data acquired from a soft silicone beam under gravity. In contrast with inverse iterative algorithms that require multiple solutions of a st...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	222,068
2312.06152	Improving the performance of weak supervision searches using transfer and meta-learning	Weak supervision searches have in principle the advantages of both being able to train on experimental data and being able to learn distinctive signal properties. However, the practical applicability of such searches is limited by the fact that successfully training a neural network via weak supervision can require a l...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	414,393
1606.08942	Predicting risky behavior in social communities	Predicting risk profiles of individuals in networks (e.g.~susceptibility to a particular disease, or likelihood of smoking) is challenging for a variety of reasons. For one, `local' features (such as an individual's demographic information) may lack sufficient information to make informative predictions; this is especi...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	57,925
2107.00689	Aerial Map-Based Navigation Using Semantic Segmentation and Pattern Matching	This paper proposes a novel approach to map-based navigation system for unmanned aircraft. The proposed system attempts label-to-label matching, not image-to-image matching, between aerial images and a map database. The ground objects can be labelled by deep learning approaches and the configuration of the objects is u...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	244,237
1506.00481	Robust Face Recognition with Structural Binary Gradient Patterns	This paper presents a computationally efficient yet powerful binary framework for robust facial representation based on image gradients. It is termed as structural binary gradient patterns (SBGP). To discover underlying local structures in the gradient domain, we compute image gradients from multiple directions and sim...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	43,672
2211.07005	Quantifying syntax similarity with a polynomial representation of dependency trees	We introduce a graph polynomial that distinguishes tree structures to represent dependency grammar and a measure based on the polynomial representation to quantify syntax similarity. The polynomial encodes accurate and comprehensive information about the dependency structure and dependency relations of words in a sente...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	330,096
2212.05478	Mul-GAD: a semi-supervised graph anomaly detection framework via aggregating multi-view information	Anomaly detection is defined as discovering patterns that do not conform to the expected behavior. Previously, anomaly detection was mostly conducted using traditional shallow learning techniques, but with little improvement. As the emergence of graph neural networks (GNN), graph anomaly detection has been greatly deve...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	335,799
2107.09881	How Do Pedophiles Tweet? Investigating the Writing Styles and Online Personas of Child Cybersex Traffickers in the Philippines	One of the most important humanitarian responsibility of every individual is to protect the future of our children. This entails not only protection of physical welfare but also from ill events that can potentially affect the mental well-being of a child such as sexual coercion and abuse which, in worst-case scenarios,...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	247,148

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