pwc-archive/papers-with-abstracts huggingface dataset:
Adapting Standard Retrieval Benchmarks to Evaluate Generated Answers
Authors: Negar Arabzadeh, Amin Bigdeli, Charles L. A. Clarke
Date: 2024-01-09 00:00:00
Tasks: Information Retrieval, Retrieval

Large language models can now directly generate answers to many factual questions without referencing external sources. Unfortunately, relatively little attention has been paid to methods for evaluating the quality and correctness of these answers, for comparing the performance of one model to another, or for comparing one prompt to another. In addition, the quality of generated answers are rarely directly compared to the quality of retrieved answers. As models evolve and prompts are modified, we have no systematic way to measure improvements without resorting to expensive human judgments. To address this problem we adapt standard retrieval benchmarks to evaluate answers generated by large language models. Inspired by the BERTScore metric for summarization, we explore two approaches. In the first, we base our evaluation on the benchmark relevance judgments. We empirically run experiments on how information retrieval relevance judgments can be utilized as an anchor to evaluating the generated answers. In the second, we compare generated answers to the top results retrieved by a diverse set of retrieval models, ranging from traditional approaches to advanced methods, allowing us to measure improvements without human judgments. In both cases, we measure the similarity between an embedded representation of the generated answer and an embedded representation of a known, or assumed, relevant passage from the retrieval benchmark.
A case study of Generative AI in MSX Sales Copilot: Improving seller productivity with a real-time question-answering system for content recommendation
Authors: Manpreet Singh, Ravdeep Pasricha, Nitish Singh, Ravi Prasad Kondapalli, Manoj R, Kiran R, Laurent Boué
Date: 2024-01-04 00:00:00
Tasks: Question Answering, Recommendation Systems

In this paper, we design a real-time question-answering system specifically targeted for helping sellers get relevant material/documentation they can share live with their customers or refer to during a call. Taking the Seismic content repository as a relatively large scale example of a diverse dataset of sales material, we demonstrate how LLM embeddings of sellers' queries can be matched with the relevant content. We achieve this by engineering prompts in an elaborate fashion that makes use of the rich set of meta-features available for documents and sellers. Using a bi-encoder with cross-encoder re-ranker architecture, we show how the solution returns the most relevant content recommendations in just a few seconds even for large datasets. Our recommender system is deployed as an AML endpoint for real-time inferencing and has been integrated into a Copilot interface that is now deployed in the production version of the Dynamics CRM, known as MSX, used daily by Microsoft sellers.
HEProto: A Hierarchical Enhancing ProtoNet based on Multi-Task Learning for Few-shot Named Entity Recognition
Authors: Wei Chen, Lili Zhao, Pengfei Luo, Tong Xu, Yi Zheng, Enhong Chen
Date: 2023-10-21 00:00:00
Proceeding: CIKM 2023 10
Tasks: Contrastive Learning, Few-shot NER, Multi-Task Learning, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

Few-shot Named Entity Recognition (NER) task, which aims to identify and classify entities from different domains with limited training samples, has long been treated as a basic step for knowledge graph (KG) construction. Great efforts have been made on this task with competitive performance, however, they usually treat the two subtasks, namely span detection and type classification, as mutually independent, and the integrity and correlation between subtasks have been largely ignored. Moreover, prior arts may fail to absorb the coarse-grained features of entities, resulting in a semantic-insufficient representation of entity types. To that end, in this paper, we propose a Hierarchical Enhancing ProtoNet (HEProto) based on multi-task learning, which is utilized to jointly learn these two subtasks and model their correlation. Specifically, we adopt contrastive learning to enhance the span boundary information and the type semantic representations in these two subtasks. Then, the hierarchical prototypical network is designed to leverage the coarse-grained information of entities in the type classification stage, which could help the model to better learn the fine-grained semantic representations. Along this line, we construct a similarity margin loss to reduce the similarity between fine-grained entities and other irrelevant coarse-grained prototypes. Finally, extensive experiments on the Few-NERD dataset prove that our solution outperforms competitive baseline methods. The source code of HEProto is available at https://github.com/fanshu6hao/HEProto.
DeepSpaceYoloDataset: Annotated Astronomical Images Captured with Smart Telescopes
Authors: Parisot, Olivier
Date: 2024-01-10 00:00:00
Proceeding: Data 2024 1

Recent smart telescopes allow the automatic collection of a large quantity of data for specific portions of the night sky—with the goal of capturing images of deep sky objects (nebula, galaxies, globular clusters). Nevertheless, human verification is still required afterwards to check whether celestial targets are effectively visible in the images produced by these instruments. Depending on the magnitude of deep sky objects, the observation conditions and the cumulative time of data acquisition, it is possible that only stars are present in the images. In addition, unfavorable external conditions (light pollution, bright moon, etc.) can make capture difficult. In this paper, we describe DeepSpaceYoloDataset, a set of 4696 RGB astronomical images captured by two smart telescopes and annotated with the positions of deep sky objects that are effectively in the images. This dataset can be used to train detection models on this type of image, enabling the better control of the duration of capture sessions, but also to detect unexpected celestial events such as supernova.
Self-supervised speech representation and contextual text embedding for match-mismatch classification with EEG recording
Authors: Bo wang, Xiran Xu, Zechen Zhang, Haolin Zhu, Yujie Yan, Xihong Wu, Jing Chen
Date: 2024-01-10 00:00:00
Tasks: Contrastive Learning, EEG

Relating speech to EEG holds considerable importance but is challenging. In this study, a deep convolutional network was employed to extract spatiotemporal features from EEG data. Self-supervised speech representation and contextual text embedding were used as speech features. Contrastive learning was used to relate EEG features to speech features. The experimental results demonstrate the benefits of using self-supervised speech representation and contextual text embedding. Through feature fusion and model ensemble, an accuracy of 60.29% was achieved, and the performance was ranked as No.2 in Task 1 of the Auditory EEG Challenge (ICASSP 2024). The code to implement our work is available on Github: https://github.com/bobwangPKU/EEG-Stimulus-Match-Mismatch.
Closed-loop Identification of a MSW Grate Incinerator using Bayesian Optimization for Selecting Model Inputs and Structure
Authors: Johannes Lips, Stefan DeYoung, Max Schönsteiner, Hendrik Lens
Date: 2024-01-10 00:00:00
Tasks: Bayesian Optimization, FLUE

The creation of low-order dynamic models for complex industrial systems is complicated by disturbances and limited sensor accuracy. This work presents a system identification procedure that uses machine learning methods and process knowledge to robustly identify a low-order closed-loop model of a municipal solid waste (MSW) grate incineration plant. These types of plants are known for their strong disturbances coming from fuel composition fluctuations. Using Bayesian optimization, the algorithm ranks and selects inputs from the available sensor data and chooses the model structure. This results in accurate models with low complexity while avoiding overfitting. The method is applied and validated using data of an industrial MSW incineration plant. The obtained models give excellent predictions and confidence intervals for the steam capacity and intermediate quantities such as supply air flow and flue gas temperature. The identified continuous-time models are fully given, and their step-response dynamics are discussed. The models can be used to develop model-based unit control schemes for grate incineration plants. The presented method shows great potential for the identification of over-actuated systems or disturbed systems with many sensors.
TRTAR: Transmissive RIS-assisted Through-the-wall Human Activity Recognition
Authors: Junshuo Liu, Yunlong Huang, Jianan Zhang, Rujing Xiong, Robert Caiming Qiu
Date: 2024-01-10 00:00:00
Tasks: Activity Recognition, Human Activity Recognition

Device-free human activity recognition plays a pivotal role in wireless sensing. However, current systems often fail to accommodate signal transmission through walls or necessitate dedicated noise removal algorithms. To overcome these limitations, we introduce TRTAR: a device-free passive human activity recognition system integrated with a transmissive reconfigurable intelligent surface (RIS). TRTAR eliminates the necessity for dedicated devices or noise removal algorithms, while specifically addressing signal propagation through walls. Unlike existing approaches, TRTAR solely employs a transmissive RIS at the transmitter or receiver without modifying the inherent hardware structure. Experimental results demonstrate that TRTAR attains an average accuracy of 98.13% when signals traverse concrete walls.
Sampled-Data Primal-Dual Gradient Dynamics in Model Predictive Control
Authors: Ryuta Moriyasu, Sho Kawaguchi, Kenji Kashima
Date: 2024-01-10 00:00:00
Tasks: Model Predictive Control

Model Predictive Control (MPC) is a versatile approach capable of accommodating diverse control requirements that holds significant promise for a broad spectrum of industrial applications. Noteworthy challenges associated with MPC include the substantial computational burden, which is sometimes considered excessive even for linear systems. Recently, a rapid computation method that guides the input toward convergence with the optimal control problem solution by employing primal-dual gradient (PDG) dynamics as a controller has been proposed for linear MPCs. However, stability has been ensured under the assumption that the controller is a continuous-time system, leading to potential instability when the controller undergoes discretization and is implemented as a sampled-data system. In this paper, we propose a discrete-time dynamical controller, incorporating specific modifications to the PDG approach, and present stability conditions relevant to the resulting sampled-data system. Additionally, we introduce an extension designed to enhance control performance, that was traded off in the original. Numerical examples substantiate that our proposed method, which can be executed in only 1 $\mu$s in a standard laptop, not only ensures stability with considering sampled-data implementation but also effectively enhances control performance.
Occupancy Prediction for Building Energy Systems with Latent Force Models
Authors: Thore Wietzke, Jan Gall, Knut Graichen
Date: 2024-01-10 00:00:00
Tasks: Model Predictive Control

This paper presents a new approach to predict the occupancy for building energy systems (BES). A Gaussian Process (GP) is used to model the occupancy and is represented as a state space model that is equivalent to the full GP if Kalman filtering and smoothing is used. The combination of GPs and mechanistic models is called Latent Force Model (LFM). An LFM-based model predictive control (MPC) concept for BES is presented that benefits from the extrapolation capability of mechanistic models and the learning ability of GPs to predict the occupancy within the building. Simulations with EnergyPlus and a comparison with real-world data from the Bosch Research Campus in Renningen show that a reduced energy demand and thermal discomfort can be obtained with the LFM-based MPC scheme by accounting for the predicted stochastic occupancy.
Some Critical Thinking on EV Battery Reliability: from Enhancement to Optimization -- comprehensive perspectives, lifecycle innovation, system cognation, and strategic insights
Authors: Jing, Lin, Christofer Silfvenius
Date: 2024-01-10 00:00:00

In the era of sustainable transportation, the significance of electric vehicles (EVs) and their battery technology is becoming increasingly paramount. This study addresses the critical aspect of EV battery reliability, an essential factor in the vehicles' sustainability, performance, and longevity. Current efforts to enhance EV battery reliability tend to focus on isolated areas, often missing the broader, interconnected challenges within the system. This research investigates these challenges across micro, meso, and macro levels, presenting a novel lifecycle framework that includes "Zero"-Life reliability and phases such as use, reuse, repurpose, and recycling. By adopting a holistic approach and delving into system cognition, the study aims to bridge the gap between isolated improvements and comprehensive system optimization, aligning with global sustainability goals and contributing to the advancement of sustainable transportation and EV technology.
Toward Microgravity Mass Gauging with Electrical Capacitance Volume Sensing: Sensor Design and Experiment
Authors: M. A. Charleston, S. M. Chowdhury, Q. M. Marashdeh, B. J. Straiton, F. L. Teixeira
Date: 2024-01-10 00:00:00

The use of capacitance sensors for fuel mass gauging has been in consideration since the early days of manned space flight. However, certain difficulties arise when considering tanks in microgravity environments. Surface tension effects lead to fluid wetting of the interior surface of the tank, leaving large interior voids, while thrust/settling effects can lead to dispersed two-phase mixtures. With the exception of capacitance-based sensing, few sensing technologies are well suited for measuring annular, stratified, and dispersed fluid configurations. Two modalities of capacitance measurement are compared: Electrical Capacitance Volume Tomography (ECVT) and Electrical Capacitance Volume Sensing (ECVS). ECVT is a non-invasive imaging modality first introduced in 2006. ECVS is a measurement modality introduced in this paper that is derived from ECVT technology but does not reconstruct an image as part of the mass measurement. To optimize the design of future capacitance-based spherical tank mass gauging sensors, different electrode plate layouts are evaluated in a mass gauging context. Prototype sensors are constructed, and experiments are conducted with fluid fills in various orientations. The plate layouts and their effect on the performance of the sensor as a fuel mass gauge are analyzed through the use of imaging and averaging techniques.
Optimization-based Framework for Selecting Under-frequency Load Shedding Parameters
Authors: Waheed Owonikoko, Mazen Elsaadany, Amritanshu Pandey, Mads R. Almassalkhi
Date: 2024-01-09 00:00:00

High penetration of renewable resources results in a power system with lower inertia and higher frequency sensitivity to power imbalances. Such systems are becoming increasingly susceptible to frequency collapse during extreme disturbances. Under-Frequency Load Shedding (UFLS) is a last-resort protection scheme and acts as an emergency brake by shedding load to arrest frequency decline. Current and emerging efforts to optimize UFLS settings and frequency thresholds are mostly network agnostic, ignoring network spatial information. With the prevalence of Distributed Energy Resources (DERs) in the high-renewable paradigm, the power grid is becoming more bidirectional, making some locations in the network less effective for UFLS action than others. This work proposes a Mixed Integer Linear Program that optimizes the UFLS setpoints (prioritizing one location over another) to minimize frequency deviation and load-shed for a given disturbance. The formulation considers system information and DER generation mix at different network locations, increasing model fidelity. The formulation also captures the discrete nature and practical time delays and deadbands associated with UFLS using a minimal set of binary variables, reducing problem complexity. We empirically validate the optimization approach on the dynamic IEEE 39-bus system for performance metrics, including frequency nadir, steady-state frequency and total load shed.
Towards a BMS2 Design Framework: Adaptive Data-driven State-of-health Estimation for Second-Life Batteries with BIBO Stability Guarantees
Authors: Xiaofan Cui, Muhammad Aadil Khan, Surinder Singh, Ratnesh Sharma, Simona Onori
Date: 2024-01-08 00:00:00
Tasks: Management

A key challenge that is currently hindering the widespread use of retired electric vehicle (EV) batteries for second-life (SL) applications is the ability to accurately estimate and monitor their state of health (SOH). Second-life battery systems can be sourced from different battery packs with lack of knowledge of their historical usage. To tackle the in-the-field use of SL batteries, this paper introduces an online adaptive health estimation approach with guaranteed bounded-input-bounded-output (BIBO) stability. This method relies exclusively on operational data that can be accessed in real time from SL batteries. The effectiveness of the proposed approach is shown on a laboratory aged experimental data set of retired EV batteries. The estimator gains are dynamically adapted to accommodate the distinct characteristics of each individual cell, making it a promising candidate for future SL battery management systems (BMS2).
The Rise of Data-Driven Microscopy powered by Machine Learning
Authors: Leonor Morgado, Estibaliz Gómez-de-Mariscal, Hannah S. Heil, Ricardo Henriques
Date: 2024-01-10 00:00:00

Optical microscopy is an indispensable tool in life sciences research, but conventional techniques require compromises between imaging parameters like speed, resolution, field-of-view, and phototoxicity. To overcome these limitations, data-driven microscopes incorporate feedback loops between data acquisition and analysis. This review overviews how machine learning enables automated image analysis to optimise microscopy in real-time. We first introduce key data-driven microscopy concepts and machine learning methods relevant to microscopy image analysis. Subsequently, we highlight pioneering works and recent advances in integrating machine learning into microscopy acquisition workflows, including optimising illumination, switching modalities and acquisition rates, and triggering targeted experiments. We then discuss the remaining challenges and future outlook. Overall, intelligent microscopes that can sense, analyse, and adapt promise to transform optical imaging by opening new experimental possibilities.
Tailoring Frictional Properties of Surfaces Using Diffusion Models
Authors: Even Marius Nordhagen, Henrik Andersen Sveinsson, Anders Malthe-Sørenssen
Date: 2024-01-05 00:00:00
Tasks: Denoising, Friction

This Letter introduces an approach for precisely designing surface friction properties using a conditional generative machine learning model, specifically a diffusion denoising probabilistic model (DDPM). We created a dataset of synthetic surfaces with frictional properties determined by molecular dynamics simulations, which trained the DDPM to predict surface structures from desired frictional outcomes. Unlike traditional trial-and-error and numerical optimization methods, our approach directly yields surface designs meeting specified frictional criteria with high accuracy and efficiency. This advancement in material surface engineering demonstrates the potential of machine learning in reducing the iterative nature of surface design processes. Our findings not only provide a new pathway for precise surface property tailoring but also suggest broader applications in material science where surface characteristics are critical.
Clinical Applications of Plantar Pressure Measurement
Authors: Kelsey Detels, David Shin, Harrison Wilson, Shanni Zhou, Andrew Chen, Jessica Rosendorf, Atta Taseh, Bardiya Akhbari, Joseph H. Schwab, Hamid Ghaednia
Date: 2024-01-09 00:00:00

Plantar pressure measurements can provide valuable insight into various health characteristics in patients. In this study, we describe different plantar pressure devices available on the market and their clinical relevance. Current devices are either platform-based or wearable and consist of a variety of sensor technologies: resistive, capacitive, piezoelectric, and optical. The measurements collected from any of these sensors can be utilized for a range of clinical applications including patients with diabetes, trauma, deformity and cerebral palsy, stroke, cervical myelopathy, ankle instability, sports injuries, and Parkinsons disease. However, the proper technology should be selected based on the clinical need and the type of tests being performed on the device. In this review we provide the reader with a simple overview of the existing technologies their advantages and disadvantages and provide application examples for each. Moreover, we suggest new areas in orthopaedic that plantar pressure mapping technology can be utilized for increased quality of care.
Principal Component Analysis in Application to Brillouin Microscopy Data
Authors: Hadi Mahmodi, Christopher G. Poulton, Mathew N. Lesley, Glenn Oldham, Hui Xin Ong, Steven J. Langford, Irina V. Kabakova
Date: 2024-01-09 00:00:00

Brillouin microscopy has recently emerged as a new bio-imaging modality that provides information on the micromechanical properties of biological materials, cells and tissues. The data collected in a typical Brillouin microscopy experiment represents the high-dimensional set of spectral information. Its analysis requires non-trivial approaches due to subtlety in spectral variations as well as spatial and spectral overlaps of measured features. This article offers a guide to the application of Principal Component Analysis (PCA) for processing Brillouin imaging data. Being unsupervised multivariate analysis, PCA is well-suited to tackle processing of complex Brillouin spectra from heterogeneous biological samples with minimal a priori information requirements. We point out the importance of data pre-processing steps in order to improve outcomes of PCA. We also present a strategy where PCA combined with k-means clustering method can provide a working solution to data reconstruction and deeper insights into sample composition, structure and mechanics.
IV Estimation of Panel Data Tobit Models with Normal Errors
Authors: Bo E. Honore
Date: 2024-01-09 00:00:00
Tasks: regression

Amemiya (1973) proposed a ``consistent initial estimator'' for the parameters in a censored regression model with normal errors. This paper demonstrates that a similar approach can be used to construct moment conditions for fixed--effects versions of the model considered by Amemiya. This result suggests estimators for models that have not previously been considered.