pwc-archive/papers-with-abstracts huggingface dataset:
Deep Generative Quantile Bayes
Authors: Jungeum Kim, Percy S. Zhai, Veronika Ročková
Date: 2024-10-10 00:00:00

We develop a multivariate posterior sampling procedure through deep generative quantile learning. Simulation proceeds implicitly through a push-forward mapping that can transform i.i.d. random vector samples from the posterior. We utilize Monge-Kantorovich depth in multivariate quantiles to directly sample from Bayesian credible sets, a unique feature not offered by typical posterior sampling methods. To enhance the training of the quantile mapping, we design a neural network that automatically performs summary statistic extraction. This additional neural network structure has performance benefits, including support shrinkage (i.e., contraction of our posterior approximation) as the observation sample size increases. We demonstrate the usefulness of our approach on several examples where the absence of likelihood renders classical MCMC infeasible. Finally, we provide the following frequentist theoretical justifications for our quantile learning framework: {consistency of the estimated vector quantile, of the recovered posterior distribution, and of the corresponding Bayesian credible sets.
A Methodology for Evaluating RAG Systems: A Case Study On Configuration Dependency Validation
Authors: Sebastian Simon, Alina Mailach, Johannes Dorn, Norbert Siegmund
Date: 2024-10-11 00:00:00
Tasks: Hallucination, RAG, Retrieval-augmented Generation

Retrieval-augmented generation (RAG) is an umbrella of different components, design decisions, and domain-specific adaptations to enhance the capabilities of large language models and counter their limitations regarding hallucination and outdated and missing knowledge. Since it is unclear which design decisions lead to a satisfactory performance, developing RAG systems is often experimental and needs to follow a systematic and sound methodology to gain sound and reliable results. However, there is currently no generally accepted methodology for RAG evaluation despite a growing interest in this technology. In this paper, we propose a first blueprint of a methodology for a sound and reliable evaluation of RAG systems and demonstrate its applicability on a real-world software engineering research task: the validation of configuration dependencies across software technologies. In summary, we make two novel contributions: (i) A novel, reusable methodological design for evaluating RAG systems, including a demonstration that represents a guideline, and (ii) a RAG system, which has been developed following this methodology, that achieves the highest accuracy in the field of dependency validation. For the blueprint's demonstration, the key insights are the crucial role of choosing appropriate baselines and metrics, the necessity for systematic RAG refinements derived from qualitative failure analysis, as well as the reporting practices of key design decision to foster replication and evaluation.
Meta-analysis of the relationship between quality management practices and firm performance—implications for quality management theory development
Authors: Anand Nair
Date: 2005-12-13 00:00:00
Proceeding: Journal of Operations Management 2005 12
Tasks: Attribute, Management

Quality management (QM) has received a high degree of attention in extant literature. Several research papers attribute superior
firm performance to adoption of QM practices. The availability of a large number of research papers that investigate the impact of
QM practices on performance provide an ideal setting for theory extension and refinement using meta-analysis techniques. In this
paper a meta-analytic study is presented that fulfills two objectives. First, the paper formalizes performance implications of
adopting QM practices and present hypothesized relationship between QM practices and performance. Second, a meta-analysis of
correlation (Hunter and Schmidt, 1990) approach is used to examine the empirical research in QM to determine which QM practices
are positively related to improved performance. The study also examines the presence of moderating factors in the association
between QM practices and performance. The results support many hypothesized relationships and also point towards the presence
of moderating factors in almost all QM practice–performance relationships. A discussion of the findings is presented and directions
for further development of QM theory are proposed
Robust Variable-Horizon MPC with Adaptive Terminal Constraints
Authors: Renato Quartullo, Gianni Bianchini, Andrea Garulli, Antonio Giannitrapani
Date: 2024-10-11 00:00:00
Tasks: Model Predictive Control

This paper presents a novel robust variable-horizon model predictive control scheme designed to intercept a target moving along a known trajectory, in finite time. Linear discrete-time systems affected by bounded process disturbances are considered and a tube-based MPC approach is adopted. The main contribution is an adaptive mechanism for choosing the terminal constraint set sequence in the MPC optimization problem. This mechanism is designed to ensure recursive feasibility while promoting minimization of the final distance to the target. Finite-time convergence of the proposed control scheme is proven. In order to evaluate its effectiveness, the designed control law is tested through numerical simulations, including a case study involving orbital rendezvous of a satellite with a tumbling object. The results indicate a significant reduction in conservatism compared to existing state-of-the-art methods using a fixed terminal set sequence.
Unsupervised Learning of Spatio-Temporal Patterns in Spiking Neuronal Networks
Authors: Florian Feiler, Emre Neftci, Younes Bouhadjar
Date: 2024-10-11 00:00:00
Tasks: Management, Weather Forecasting

The ability to predict future events or patterns based on previous experience is crucial for many applications such as traffic control, weather forecasting, or supply chain management. While modern supervised Machine Learning approaches excel at such sequential tasks, they are computationally expensive and require large training data. A previous work presented a biologically plausible sequence learning model, developed through a bottom-up approach, consisting of a spiking neural network and unsupervised local learning rules. The model in its original formulation identifies only a specific type of sequence elements composed of synchronous spikes by activating a subset of neurons with identical stimulus preference. In this work, we extend the model to detect and learn sequences of various spatio-temporal patterns (STPs) by incorporating plastic connections in the input synapses. We showcase that the model is able to learn and predict high-order sequences. We further study the robustness of the model against different input settings and parameters.
SHARON: Secure and Efficient Cross-shard Transaction Processing via Shard Rotation
Authors: Shan Jiang, Jiannong Cao, Cheung Leong Tung, Yuqin Wang, Shan Wang
Date: 2024-05-20 00:00:00
Proceeding: INFOCOM 2024 5
Tasks: Scheduling

Recently, sharding has become a popular direction
to scale out blockchain systems by dividing the network into
shards that process transactions in parallel. However, secure
and efficient cross-shard transaction processing remains a vital
and unaddressed challenge. Existing work handles a cross-
shard transaction via transaction division: dividing it into sub-
transactions, processing them separately, and combing the pro-
cessing results. Such an approach is unfavorable for decentralized
blockchain due to its reliance on trustworthy parties, e.g., the
client or a reference node, to perform the transaction division
and result combination. Furthermore, the processing result of one
transaction can affect another, violating the important property
of transaction isolation. In this work, we propose Sharon, a novel
sharding protocol that processes cross-shard transactions via
shard rotation rather than transaction division. In Sharon, shards
rotate to merge pairwisely and process cross-shard transactions
when merged. Sharon eliminates reliance on trustworthy parties
and provides transaction isolation in nature because transactions
are no longer divided. Nevertheless, it poses a scientific question
of when and how to merge the shards to improve system per-
formance. To answer the question, we formally define the shard
scheduling problem to minimize transaction confirmation latency
and propose a novel construction algorithm. The proposed
algorithm is proven optimal and runs in polynomial time. We
conduct extensive experiments on Amazon EC2 instances using
Bitcoin and Ethereum data. The results indicate that Sharon
achieves nearly linear scalability, improves the system throughput
by 139%, and saves the transaction processing latency by 72.4%
compared with state-of-the-art approaches.
No Tick-Size Too Small: A General Method for Modelling Small Tick Limit Order Books
Authors: Konark Jain, Jean-François Muzy, Jonathan Kochems, Emmanuel Bacry
Date: 2024-10-11 00:00:00

Tick sizes not only influence the granularity of the price formation process but also affect market agents' behavior. We investigate the disparity in the microstructural properties of the Limit Order Book (LOB) across different relative tick sizes. A key contribution of this study is the identification of several stylized facts, which are used to differentiate between large, medium, and small tick stocks, along with clear metrics for their measurement. We provide cross-asset visualizations to illustrate how these attributes vary with relative tick size. Further, we propose a Hawkes Process model that accounts for sparsity, multi-tick level price moves, and the shape of the book in small-tick stocks. Through simulation studies, we demonstrate the universality of the model and identify key variables that determine whether a simulated LOB resembles a large-tick or small-tick stock. Our tests show that stylized facts like sparsity, shape, and relative returns distribution can be smoothly transitioned from a large-tick to a small-tick asset using our model. We test this model's assumptions, showcase its challenges and propose questions for further directions in this area of research.
Embedding an ANN-Based Crystal Plasticity Model into the Finite Element Framework using an ABAQUS User-Material Subroutine
Authors: Yuqing He, Yousef Heider, Bernd Markert
Date: 2024-09-25 00:00:00

This manuscript presents a practical method for incorporating trained Neural Networks (NNs) into the Finite Element (FE) framework using a user material (UMAT) subroutine. The work exemplifies crystal plasticity, a complex inelastic non-linear path-dependent material response, with a wide range of applications in ABAQUS UMAT. However, this approach can be extended to other material behaviors and FE tools. The use of a UMAT subroutine serves two main purposes: (1) it predicts and updates the stress or other mechanical properties of interest directly from the strain history; (2) it computes the Jacobian matrix either through backpropagation or numerical differentiation, which plays an essential role in the solution convergence. By implementing NNs in a UMAT subroutine, a trained machine learning model can be employed as a data-driven constitutive law within the FEM framework, preserving multiscale information that conventional constitutive laws often neglect or average. The versatility of this method makes it a powerful tool for integrating machine learning into mechanical simulation. While this approach is expected to provide higher accuracy in reproducing realistic material behavior, the reliability of the solution process and the convergence conditions must be paid special attention. While the theory of the model is explained in [Heider et al. 2020], exemplary source code is also made available for interested readers [https://doi.org/10.25835/6n5uu50y]
Learning Bipedal Walking for Humanoid Robots in Challenging Environments with Obstacle Avoidance
Authors: Marwan Hamze, Mitsuharu Morisawa, Eiichi Yoshida
Date: 2024-09-25 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning

Deep reinforcement learning has seen successful implementations on humanoid robots to achieve dynamic walking. However, these implementations have been so far successful in simple environments void of obstacles. In this paper, we aim to achieve bipedal locomotion in an environment where obstacles are present using a policy-based reinforcement learning. By adding simple distance reward terms to a state of art reward function that can achieve basic bipedal locomotion, the trained policy succeeds in navigating the robot towards the desired destination without colliding with the obstacles along the way.
A Visual-Analytical Approach for Automatic Detection of Cyclonic Events in Satellite Observations
Authors: Akash Agrawal, Mayesh Mohapatra, Abhinav Raja, Paritosh Tiwari, Vishwajeet Pattanaik, Neeru Jaiswal, Arpit Agarwal, Punit Rathore
Date: 2024-09-25 00:00:00

Estimating the location and intensity of tropical cyclones holds crucial significance for predicting catastrophic weather events. In this study, we approach this task as a detection and regression challenge, specifically over the North Indian Ocean (NIO) region where best tracks location and wind speed information serve as the labels. The current process for cyclone detection and intensity estimation involves physics-based simulation studies which are time-consuming, only using image features will automate the process for significantly faster and more accurate predictions. While conventional methods typically necessitate substantial prior knowledge for training, we are exploring alternative approaches to enhance efficiency. This research aims to focus specifically on cyclone detection, intensity estimation and related aspects using only image input and data-driven approaches and will lead to faster inference time and automate the process as opposed to current NWP models being utilized at SAC. In context to algorithm development, a novel two stage detection and intensity estimation module is proposed. In the first level detection we try to localize the cyclone over an entire image as captured by INSAT3D over the NIO (North Indian Ocean). For the intensity estimation task, we propose a CNN-LSTM network, which works on the cyclone centered images, utilizing a ResNet-18 backbone, by which we are able to capture both temporal and spatial characteristics.
Variational Source-Channel Coding for Semantic Communication
Authors: Yulong Feng, Jing Xu, Liujun Hu, Guanghui Yu, Xiangyang Duan
Date: 2024-09-26 00:00:00
Tasks: Semantic Communication, SSIM, Variational Inference

Semantic communication technology emerges as a pivotal bridge connecting AI with classical communication. The current semantic communication systems are generally modeled as an Auto-Encoder (AE). AE lacks a deep integration of AI principles with communication strategies due to its inability to effectively capture channel dynamics. This gap makes it difficult to justify the need for joint source-channel coding (JSCC) and to explain why performance improves. This paper begins by exploring lossless and lossy communication, highlighting that the inclusion of data distortion distinguishes semantic communication from classical communication. It breaks the conditions for the separation theorem to hold and explains why the amount of data transferred by semantic communication is less. Therefore, employing JSCC becomes imperative for achieving optimal semantic communication. Moreover, a Variational Source-Channel Coding (VSCC) method is proposed for constructing semantic communication systems based on data distortion theory, integrating variational inference and channel characteristics. Using a deep learning network, we develop a semantic communication system employing the VSCC method and demonstrate its capability for semantic transmission. We also establish semantic communication systems of equivalent complexity employing the AE method and the VAE method. Experimental results reveal that the VSCC model offers superior interpretability compared to AE model, as it clearly captures the semantic features of the transmitted data, represented as the variance of latent variables in our experiments. In addition, VSCC model exhibits superior semantic transmission capabilities compared to VAE model. At the same level of data distortion evaluated by PSNR, VSCC model exhibits stronger human interpretability, which can be partially assessed by SSIM.
A Survey of Spatio-Temporal EEG data Analysis: from Models to Applications
Authors: Pengfei Wang, Huanran Zheng, Silong Dai, Yiqiao Wang, Xiaotian Gu, Yuanbin Wu, Xiaoling Wang
Date: 2024-09-26 00:00:00
Tasks: EEG, Self-Supervised Learning, Survey

In recent years, the field of electroencephalography (EEG) analysis has witnessed remarkable advancements, driven by the integration of machine learning and artificial intelligence. This survey aims to encapsulate the latest developments, focusing on emerging methods and technologies that are poised to transform our comprehension and interpretation of brain activity. We delve into self-supervised learning methods that enable the robust representation of brain signals, which are fundamental for a variety of downstream applications. We also explore emerging discriminative methods, including graph neural networks (GNN), foundation models, and large language models (LLMs)-based approaches. Furthermore, we examine generative technologies that harness EEG data to produce images or text, offering novel perspectives on brain activity visualization and interpretation. The survey provides an extensive overview of these cutting-edge techniques, their current applications, and the profound implications they hold for future research and clinical practice. The relevant literature and open-source materials have been compiled and are consistently being refreshed at \url{https://github.com/wpf535236337/LLMs4TS}
EarthquakeNPP: Benchmark Datasets for Earthquake Forecasting with Neural Point Processes
Authors: Samuel Stockman, Daniel Lawson, Maximilian Werner
Date: 2024-09-27 00:00:00
Tasks: Benchmarking, Dataset Generation, Point Processes

Classical point process models, such as the epidemic-type aftershock sequence (ETAS) model, have been widely used for forecasting the event times and locations of earthquakes for decades. Recent advances have led to Neural Point Processes (NPPs), which promise greater flexibility and improvements over classical models. However, the currently-used benchmark dataset for NPPs does not represent an up-to-date challenge in the seismological community since it lacks a key earthquake sequence from the region and improperly splits training and testing data. Furthermore, initial earthquake forecast benchmarking lacks a comparison to state-of-the-art earthquake forecasting models typically used by the seismological community. To address these gaps, we introduce EarthquakeNPP: a collection of benchmark datasets to facilitate testing of NPPs on earthquake data, accompanied by a credible implementation of the ETAS model. The datasets cover a range of small to large target regions within California, dating from 1971 to 2021, and include different methodologies for dataset generation. In a benchmarking experiment, we compare three spatio-temporal NPPs against ETAS and find that none outperform ETAS in either spatial or temporal log-likelihood. These results indicate that current NPP implementations are not yet suitable for practical earthquake forecasting. However, EarthquakeNPP will serve as a platform for collaboration between the seismology and machine learning communities with the goal of improving earthquake predictability.
Multi-Atlas Brain Network Classification through Consistency Distillation and Complementary Information Fusion
Authors: Jiaxing Xu, Mengcheng Lan, Xia Dong, Kai He, Wei zhang, Qingtian Bian, Yiping Ke
Date: 2024-09-28 00:00:00

In the realm of neuroscience, identifying distinctive patterns associated with neurological disorders via brain networks is crucial. Resting-state functional magnetic resonance imaging (fMRI) serves as a primary tool for mapping these networks by correlating blood-oxygen-level-dependent (BOLD) signals across different brain regions, defined as regions of interest (ROIs). Constructing these brain networks involves using atlases to parcellate the brain into ROIs based on various hypotheses of brain division. However, there is no standard atlas for brain network classification, leading to limitations in detecting abnormalities in disorders. Some recent methods have proposed utilizing multiple atlases, but they neglect consistency across atlases and lack ROI-level information exchange. To tackle these limitations, we propose an Atlas-Integrated Distillation and Fusion network (AIDFusion) to improve brain network classification using fMRI data. AIDFusion addresses the challenge of utilizing multiple atlases by employing a disentangle Transformer to filter out inconsistent atlas-specific information and distill distinguishable connections across atlases. It also incorporates subject- and population-level consistency constraints to enhance cross-atlas consistency. Additionally, AIDFusion employs an inter-atlas message-passing mechanism to fuse complementary information across brain regions. Experimental results on four datasets of different diseases demonstrate the effectiveness and efficiency of AIDFusion compared to state-of-the-art methods. A case study illustrates AIDFusion extract patterns that are both interpretable and consistent with established neuroscience findings.
Finetuning YOLOv9 for Vehicle Detection: Deep Learning for Intelligent Transportation Systems in Dhaka, Bangladesh
Authors: Shahriar Ahmad Fahim
Date: 2024-09-29 00:00:00
Tasks: vehicle detection

Rapid urbanization in megacities around the world, like Dhaka, has caused numerous transportation challenges that need to be addressed. Emerging technologies of deep learning and artificial intelligence can help us solve these problems to move towards Intelligent Transportation Systems (ITS) in the city. The government of Bangladesh recognizes the integration of ITS to ensure smart mobility as a vital step towards the development plan "Smart Bangladesh Vision 2041", but faces challenges in understanding ITS, its effects, and directions to implement. A vehicle detection system can pave the way to understanding traffic congestion, finding mobility patterns, and ensuring traffic surveillance. So, this paper proposes a fine-tuned object detector, the YOLOv9 model to detect native vehicles trained on a Bangladesh-based dataset. Results show that the fine-tuned YOLOv9 model achieved a mean Average Precision (mAP) of 0.934 at the Intersection over Union (IoU) threshold of 0.5, achieving state-of-the-art performance over past studies on Bangladesh-based datasets, shown through a comparison. Later, by suggesting the model to be deployed on CCTVs (closed circuit television) on the roads, a conceptual technique is proposed to process the vehicle detection model output data in a graph structure creating a vehicle detection system in the city. Finally, applications of such vehicle detection system are discussed showing a framework on how it can solve further ITS research questions, to provide a rationale for policymakers to implement the proposed vehicle detection system in the city.