pwc-archive/papers-with-abstracts huggingface dataset:
Physics Sensor Based Deep Learning Fall Detection System
Authors: Zeyuan Qu, Tiange Huang, Yuxin Ji, Yongjun Li
Date: 2024-02-29 00:00:00
Tasks: Deep Learning, motion prediction

Fall detection based on embedded sensor is a practical and popular research direction in recent years. In terms of a specific application: fall detection methods based upon physics sensors such as [gyroscope and accelerator] have been exploited using traditional hand crafted features and feed them in machine learning models like Markov chain or just threshold based classification methods. In this paper, we build a complete system named TSFallDetect including data receiving device based on embedded sensor, mobile deep-learning model deploying platform, and a simple server, which will be used to gather models and data for future expansion. On the other hand, we exploit the sequential deep-learning methods to address this falling motion prediction problem based on data collected by inertial and film pressure sensors. We make a empirical study based on existing datasets and our datasets collected from our system separately, which shows that the deep-learning model has more potential advantage than other traditional methods, and we proposed a new deep-learning model based on the time series data to predict the fall, and it may be superior to other sequential models in this particular field.
Exact algorithms and heuristics for capacitated covering salesman problems
Authors: Lucas Porto Maziero, Fábio Luiz Usberti, Celso Cavellucci
Date: 2024-03-03 00:00:00

This paper introduces the Capacitated Covering Salesman Problem (CCSP), approaching the notion of service by coverage in capacitated vehicle routing problems. In CCSP, locations where vehicles can transit are provided, some of which have customers with demands. The objective is to service customers through a fleet of vehicles based in a depot, minimizing the total distance traversed by the vehicles. CCSP is unique in the sense that customers, to be serviced, do not need to be visited by a vehicle. Instead, they can be serviced if they are within a coverage area of the vehicle. This assumption is motivated by applications in which some customers are unreachable (e.g., forbidden access to vehicles) or visiting every customer is impractical. In this work, optimization methodologies are proposed for the CCSP based on ILP (Integer Linear Programming) and BRKGA (Biased Random-Key Genetic Algorithm) metaheuristic. Computational experiments conducted on a benchmark of instances for the CCSP evaluate the performance of the methodologies with respect to primal bounds. Furthermore, our ILP formulation is extended in order to create a novel MILP (Mixed Integer Linear Programming) for the Multi-Depot Covering Tour Vehicle Routing Problem (MDCTVRP). Computational experiments show that the extended MILP formulation outperformed the previous state-of-the-art exact approach with respect to optimality gaps. In particular, optimal solutions were obtained for several previously unsolved instances.
On the stochastics of human and artificial creativity
Authors: Solve Sæbø, Helge Brovold
Date: 2024-03-03 00:00:00
Tasks: Philosophy

What constitutes human creativity, and is it possible for computers to exhibit genuine creativity? We argue that achieving human-level intelligence in computers, or so-called Artificial General Intelligence, necessitates attaining also human-level creativity. We contribute to this discussion by developing a statistical representation of human creativity, incorporating prior insights from stochastic theory, psychology, philosophy, neuroscience, and chaos theory. This highlights the stochastic nature of the human creative process, which includes both a bias guided, random proposal step, and an evaluation step depending on a flexible or transformable bias structure. The acquired representation of human creativity is subsequently used to assess the creativity levels of various contemporary AI systems. Our analysis includes modern AI algorithms such as reinforcement learning, diffusion models, and large language models, addressing to what extent they measure up to human level creativity. We conclude that these technologies currently lack the capability for autonomous creative action at a human level.
On Globular T-Spherical Fuzzy (G-TSF) Sets with Application to G-TSF Multi-Criteria Group Decision-Making
Authors: Miin-Shen Yang, Yasir Akhtar, Mehboob Ali
Date: 2024-03-09 00:00:00
Tasks: Decision Making

In this paper, we give the concept of Globular T-Spherical Fuzzy (G-TSF) Sets (G-TSFSs) as an innovative extension of T-Spherical Fuzzy Sets (TSFSs) and Circular Spherical Fuzzy Sets (C-SFSs). G-TSFSs represent membership, indeterminacy, and non-membership degrees using a globular/sphere bound that can offer a more accurate portrayal of vague, ambiguous, and imprecise information. By employing a structured representation of data points on a sphere with a specific center and radius, this model enhances decision-making processes by enabling a more comprehensive evaluation of objects within a flexible region. Following the newly defined G-TSFSs, we establish some basic set operations and introduce fundamental algebraic operations for G-TSF Values (G-TSFVs). These operations expand the evaluative capabilities of decision-makers, facilitating more sensitive decision-making processes in a broader region. To quantify a similarity measure (SM) between GTSFVs, the SM is defined based on the radius of G-TSFSs. Additionally, Hamming distance and Euclidean distance are introduced for G-TSFSs. We also present theorems and examples to elucidate computational mechanisms. Furthermore, we give the G-TSF Weighted Average (G-TSFWA) and G-TSF Weighted Geometric (G-TSFWG) operators. Leveraging our proposed SM, a Multi-Criteria Group Decision-Making (MCGDM) scheme for G-TSFSs, named G-TSF MCGDM (G-TSFMCGDM), is developed to address group decision-making problems. The applicability and effectiveness of the proposed G-TSFMCGDM method are demonstrated by applying it to solve the selection problem of the best venue for professional development training sessions in a firm. The analysis results affirm the suitability and utility of the proposed method for resolving MCGDM problems, establishing its effectiveness in practical decision-making scenarios.
Circuit as Set of Points
Authors: Jialv Zou, Xinggang Wang, Jiahao Guo, Wenyu Liu, Qian Zhang, Chang Huang
Date: 2023-10-26 00:00:00
Proceeding: NeurIPS 2023 11

As the size of circuit designs continues to grow rapidly, artificial intelligence technologies are being extensively used in Electronic Design Automation (EDA) to assist with circuit design. Placement and routing are the most time-consuming parts of the physical design process, and how to quickly evaluate the placement has become a hot research topic. Prior works either transformed circuit designs into images using hand-crafted methods and then used Convolutional Neural Networks (CNN) to extract features, which are limited by the quality of the hand-crafted methods and could not achieve end-to-end training, or treated the circuit design as a graph structure and used Graph Neural Networks (GNN) to extract features, which require time-consuming preprocessing. In our work, we propose a novel perspective for circuit design by treating circuit components as point clouds and using Transformer-based point cloud perception methods to extract features from the circuit. This approach enables direct feature extraction from raw data without any preprocessing, allows for end-to-end training, and results in high performance. Experimental results show that our method achieves state-of-the-art performance in congestion prediction tasks on both the CircuitNet and ISPD2015 datasets, as well as in design rule check (DRC) violation prediction tasks on the CircuitNet dataset. Our method establishes a bridge between the relatively mature point cloud perception methods and the fast-developing EDA algorithms, enabling us to leverage more collective intelligence to solve this task. To facilitate the research of open EDA design, source codes and pre-trained models are released at https://github.com/hustvl/circuitformer.
Ecsnet: Spatio-temporal feature learning for event camera
Authors: Zhiwen Chen, Jinjian Wu, Junhui Hou, Leida Li, Weisheng Dong, Guangming Shi
Date: 2022-08-29 00:00:00
Proceeding: IEEE Transactions on Circuits and Systems for Video Technology 2022 8
Tasks: Action Recognition, Event-based vision, Event data classification, Gesture Generation

The neuromorphic event cameras can efficiently sense the latent geometric structures and motion clues of a scene by generating asynchronous and sparse event signals. Due to the irregular layout of the event signals, how to leverage their plentiful spatio-temporal information for recognition tasks remains a significant challenge. Existing methods tend
to treat events as dense image-like or point-serie representations. However, they either suffer from severe destruction on
the sparsity of event data or fail to encode robust spatial cues. To fully exploit their inherent sparsity with reconciling
the spatio-temporal information, we introduce a compact event representation, namely 2D-1T event cloud sequence (2D-1T ECS). We couple this representation with a novel light-weight spatiotemporal learning framework (ECSNet) that accommodates both object classification and action recognition tasks. The core of our framework is a hierarchical spatial relation module. Equipped with specially designed surface-event-based sampling unit and local event normalization unit to enhance the inter-event relation encoding, this module learns robust geometric features from the 2D event clouds. And we propose a motion attention module for efficiently capturing long-term temporal context evolving with the 1T cloud sequence. Empirically, the experiments show that our framework achieves par or even better state-of-the-art
performance. Importantly, our approach cooperates well with the sparsity of event data without any sophisticated operations, hence leading to low computational costs and prominent inference speeds.
Modeling non-stationarity in intensity, duration and frequency of extreme rainfall over India
Authors: Arpita Mondal, P.P. Mujumdar
Date: 2014-11-24 00:00:00
Proceeding: Elsevier 2014 11

Significant changes are reported in extreme rainfall characteristics over India in recent studies though
there are disagreements on the spatial uniformity and causes of trends. Based on recent theoretical
advancements in the Extreme Value Theory (EVT), we analyze changes in extreme rainfall characteristics
over India using a high-resolution daily gridded (1 latitude  1 longitude) dataset. Intensity, duration
and frequency of excess rain over a high threshold in the summer monsoon season are modeled by
non-stationary distributions whose parameters vary with physical covariates like the El-Nino Southern
Oscillation index (ENSO-index) which is an indicator of large-scale natural variability, global average
temperature which is an indicator of human-induced global warming and local mean temperatures
which possibly indicate more localized changes. Each non-stationary model considers one physical covar￾iate and the best chosen statistical model at each rainfall grid gives the most significant physical driver
for each extreme rainfall characteristic at that grid. Intensity, duration and frequency of extreme rainfall
exhibit non-stationarity due to different drivers and no spatially uniform pattern is observed in the
changes in them across the country. At most of the locations, duration of extreme rainfall spells is found
to be stationary, while non-stationary associations between intensity and frequency and local changes in
temperature are detected at a large number of locations. This study presents the first application of non￾stationary statistical modeling of intensity, duration and frequency of extreme rainfall over India. The
developed models are further used for rainfall frequency analysis to show changes in the 100-year
extreme rainfall event. Our findings indicate the varying nature of each extreme rainfall characteristic
and their drivers and emphasize the necessity of a comprehensive framework to assess resulting risks
of precipitation induced flooding.
A Novel Steganography Technique for Digital Images Using the Least Significant Bit Substitution Method
Authors: SHAHID RAHMAN1, JAMAL UDDIN 1, HABIB ULLAH KHAN 2, HAMEED HUSSAIN3, AYAZ ALI KHAN 4, AND MUHAMMAD ZAKARYA 5, (Senior Member, IEEE)
Date: 2022-11-24 00:00:00
Proceeding: 24 November 2022 11
Tasks: Image Steganography

Communication has become a lot easier in this era of technology, development of high-speed
computer networks, and the inexpensive uses of Internet. Therefore, data transmission has become vulnerable
to and unsafe from different external attacks. Every communication body wants to secure their data
while communicating over the Internet. The internet has various benets but the main demerit is the
privacy and security and the transmission of data over insecure network or channel may happen. Various
techniques used for secure communication in order to address these issues, steganography plays an important
role. Steganography is the process of obfuscation that makes something incomprehensible and unclear.
Different image steganography research methods are proposed recently but each has their advantages and
disadvantages and still have necessity to develop some better image steganography mechanisms to achieve
the reliability between the basics criteria of image steganography. Therefore, the proposedwork, in this paper,
is based on the Least Signicant Bit (LSB) substitution method. The LSB substitution method can minimize
the error rate in embedding process and can achieve greater reliability in criteria, using novel algorithm
based on value difference. In this paper, we proposed a novel technique in steganography within the digital
images such is RGB, Gray Scale, Texture, Aerial images to achieve higher security, imperceptibility, capacity,
and robustness as compared with existing methods. The experimental outcomes of the suggested approach
prove further developed strength and justify the feasibility of our research. Through numerical simulations,
we observed that the proposed strategy outperformed the next-best current methodology by 5.561 percent in
terms of PSNR Correlation score. Additionally, the proposed approach achieved a 6.43 percent better score
in PSNR with a variable measure of code inserted in similar images with distinct dimensions. Furthermore,
encrypting the same amount of information in images of varying sizes resulted in approximately 6.77 percent
improvements. Embedding different sizes of a particular secret message in a different image (such as Gray,
Texture, Aerial and RGB images) came out with about 5.466 percent of better score.
Finite-Time Consensus for High -Ordered Disturbed Multiagent Systems with Bounded Control Input
Authors: Xiao Wen, Wencheng Zou, Zhengrong Xiang
Date: 2023-08-07 00:00:00
Proceeding: IEEE SYSTEMS JOURNAL, VOL. 17, NO. 4, DECEMBER2023 2023 8

In this study, the finite-time consensus tracking prob
lem for a class of high-order multiagent systems with the external
 disturbances and bounded control input is investigated, and a
 continuous finite-time consensus protocol via a class of distributed
 observers is proposed. The protocol is designed by adopting a
 power integrator technique and the super-twisting algorithm with
 saturation constraint. Despite the presence of disturbances, the
 proposed protocol enables the states of all agents to track the states
 of the leader andthetracking errors converge to zero in finite time.
 Besides, the input signal is bounded and its upper bound can be
 calculated. Some sufficient conditions on realizing the consensus
 of multiagent systems in finite time are deduced by employing the
 inductive analysis and the Lyapunov-based technology. Finally, a
 numerical example is provided for demonstrating the validity of
 developed protocols.
Physics-informed generative model for drug-like molecule conformers
Authors: David C. Williams, Neil Inala
Date: 2024-02-29 00:00:00

We present a diffusion-based, generative model for conformer generation. Our model is focused on the reproduction of bonded structure and is constructed from the associated terms traditionally found in classical force fields to ensure a physically relevant representation. Techniques in deep learning are used to infer atom typing and geometric parameters from a training set. Conformer sampling is achieved by taking advantage of recent advancements in diffusion-based generation. By training on large, synthetic data sets of diverse, drug-like molecules optimized with the semiempirical GFN2-xTB method, high accuracy is achieved for bonded parameters, exceeding that of conventional, knowledge-based methods. Results are also compared to experimental structures from the Protein Databank (PDB) and Cambridge Structural Database (CSD).
Do Deep Neural Network Solutions Form a Star Domain?
Authors: Ankit Sonthalia, Alexander Rubinstein, Ehsan Abbasnejad, Seong Joon Oh
Date: 2024-03-12 00:00:00
Tasks: Form

It has recently been conjectured that neural network solution sets reachable via stochastic gradient descent (SGD) are convex, considering permutation invariances (Entezari et al., 2022). This means that a linear path can connect two independent solutions with low loss, given the weights of one of the models are appropriately permuted. However, current methods to test this theory often require very wide networks to succeed. In this work, we conjecture that more generally, the SGD solution set is a "star domain" that contains a "star model" that is linearly connected to all the other solutions via paths with low loss values, modulo permutations. We propose the Starlight algorithm that finds a star model of a given learning task. We validate our claim by showing that this star model is linearly connected with other independently found solutions. As an additional benefit of our study, we demonstrate better uncertainty estimates on the Bayesian Model Averaging over the obtained star domain. Further, we demonstrate star models as potential substitutes for model ensembles. Our code is available at https://github.com/aktsonthalia/starlight.
FastMAC: Stochastic Spectral Sampling of Correspondence Graph
Authors: Yifei Zhang, Hao Zhao, Hongyang Li, Siheng Chen
Date: 2024-03-13 00:00:00
Proceeding: CVPR 2024 1
Tasks: Point Cloud Registration

3D correspondence, i.e., a pair of 3D points, is a fundamental concept in computer vision. A set of 3D correspondences, when equipped with compatibility edges, forms a correspondence graph. This graph is a critical component in several state-of-the-art 3D point cloud registration approaches, e.g., the one based on maximal cliques (MAC). However, its properties have not been well understood. So we present the first study that introduces graph signal processing into the domain of correspondence graph. We exploit the generalized degree signal on correspondence graph and pursue sampling strategies that preserve high-frequency components of this signal. To address time-consuming singular value decomposition in deterministic sampling, we resort to a stochastic approximate sampling strategy. As such, the core of our method is the stochastic spectral sampling of correspondence graph. As an application, we build a complete 3D registration algorithm termed as FastMAC, that reaches real-time speed while leading to little to none performance drop. Through extensive experiments, we validate that FastMAC works for both indoor and outdoor benchmarks. For example, FastMAC can accelerate MAC by 80 times while maintaining high registration success rate on KITTI. Codes are publicly available at https://github.com/Forrest-110/FastMAC.
Consistent Prompting for Rehearsal-Free Continual Learning
Authors: Zhanxin Gao, Jun Cen, Xiaobin Chang
Date: 2024-03-13 00:00:00
Proceeding: CVPR 2024 1
Tasks: Continual Learning

Continual learning empowers models to adapt autonomously to the ever-changing environment or data streams without forgetting old knowledge. Prompt-based approaches are built on frozen pre-trained models to learn the task-specific prompts and classifiers efficiently. Existing prompt-based methods are inconsistent between training and testing, limiting their effectiveness. Two types of inconsistency are revealed. Test predictions are made from all classifiers while training only focuses on the current task classifier without holistic alignment, leading to Classifier inconsistency. Prompt inconsistency indicates that the prompt selected during testing may not correspond to the one associated with this task during training. In this paper, we propose a novel prompt-based method, Consistent Prompting (CPrompt), for more aligned training and testing. Specifically, all existing classifiers are exposed to prompt training, resulting in classifier consistency learning. In addition, prompt consistency learning is proposed to enhance prediction robustness and boost prompt selection accuracy. Our Consistent Prompting surpasses its prompt-based counterparts and achieves state-of-the-art performance on multiple continual learning benchmarks. Detailed analysis shows that improvements come from more consistent training and testing.
CleanAgent: Automating Data Standardization with LLM-based Agents
Authors: Danrui Qi, Zhengjie Miao, Jiannan Wang
Date: 2024-03-13 00:00:00
Tasks: Code Generation, Natural Language Understanding

Data standardization is a crucial part of the data science life cycle. While tools like Pandas offer robust functionalities, their complexity and the manual effort required for customizing code to diverse column types pose significant challenges. Although large language models (LLMs) like ChatGPT have shown promise in automating this process through natural language understanding and code generation, it still demands expert-level programming knowledge and continuous interaction for prompt refinement. To solve these challenges, our key idea is to propose a Python library with declarative, unified APIs for standardizing different column types, simplifying the LLM's code generation with concise API calls. We first propose Dataprep.Clean, a component of the Dataprep Python Library, significantly reduces the coding complexity by enabling the standardization of specific column types with a single line of code. Then, we introduce the CleanAgent framework integrating Dataprep.Clean and LLM-based agents to automate the data standardization process. With CleanAgent, data scientists only need to provide their requirements once, allowing for a hands-free process. To demonstrate the practical utility of CleanAgent, we developed a user-friendly web application, allowing users to interact with it using real-world datasets.