pwc-archive/papers-with-abstracts huggingface dataset:
IterCQR: Iterative Conversational Query Reformulation with Retrieval Guidance
Authors: Yunah Jang, Kang-il Lee, Hyunkyung Bae, Hwanhee Lee, Kyomin Jung
Date: 2023-11-16 00:00:00
Tasks: Conversational Search, Information Retrieval, Retrieval

Conversational search aims to retrieve passages containing essential information to answer queries in a multi-turn conversation. In conversational search, reformulating context-dependent conversational queries into stand-alone forms is imperative to effectively utilize off-the-shelf retrievers. Previous methodologies for conversational query reformulation frequently depend on human-annotated rewrites. However, these manually crafted queries often result in sub-optimal retrieval performance and require high collection costs. To address these challenges, we propose Iterative Conversational Query Reformulation (IterCQR), a methodology that conducts query reformulation without relying on human rewrites. IterCQR iteratively trains the conversational query reformulation (CQR) model by directly leveraging information retrieval (IR) signals as a reward. Our IterCQR training guides the CQR model such that generated queries contain necessary information from the previous dialogue context. Our proposed method shows state-of-the-art performance on two widely-used datasets, demonstrating its effectiveness on both sparse and dense retrievers. Moreover, IterCQR exhibits superior performance in challenging settings such as generalization on unseen datasets and low-resource scenarios.
Efficacy of the global protected area network is threatened by disappearing climates and potential transboundary range shifts.
Authors: Sean A Parks, Lisa M Holsinger, Caitlin E Littlefield, Solomon Z Dobrowski, Katherine A Zeller, John T Abatzoglou, Charles Besancon, Bryce L Nordgren, Joshua J Lawler
Date: 2022-04-25 00:00:00
Proceeding: Environmental Research Letters 2022 4

Protected areas are essential to conserving biodiversity, yet changing climatic conditions challenge their efficacy. For example, novel and disappearing climates within the protected area network indicate that extant species may not have suitable climate in protected areas in the future. Further, potential transboundary range shifts, those that involve movement from one country to another, are also challenging because physical (e.g. fencing) and non-physical barriers (e.g. contrasting conservation policies) may impede climate-induced movements. Through the lens of climate analogs, we examined disappearing and novel climates within the global terrestrial protected area network and the potential for transboundary range shifts among protected areas under global warming 2 degrees C above preindustrial levels. We found that globally, climates in 24% of protected lands will no longer be protected within a 500 km radius of their focal location (indicating disappearing climates within the protected area network), while 36% of protected lands will gain climates not previously protected (indicating novel climates within the protected area network). Further, we found that potential transboundary range shifts are widespread but variable; for example, 23% of protected climates in Europe and >50% of protected climates in Africa under climate change are located in a different country than the focal protected areas. As the global conservation community actively deliberates conservation frameworks (e.g. 30% by 2030), our study offers insights to reduce the prevalence of novel and disappearing climates within the global protected area network via strategic conservation actions and underscores the importance of setting and accommodating targets and strategies that transcend national boundaries.
Conditional Boundary Loss for Semantic Segmentation
Authors: Dongyue Wu, Zilin Guo, Aoyan Li, Changqian Yu, Nong Sang, Changxin Gao
Date: 2023-07-05 00:00:00
Proceeding: IEEE Transactions on Image Processing 2023 7
Tasks: Segmentation, Semantic Segmentation

Improving boundary segmentation results has recently attracted increasing attention in the field of semantic segmentation. Since existing popular methods usually exploit the long-range context, the boundary cues are obscure in the feature space, leading to poor boundary results. In this paper, we propose a novel conditional boundary loss (CBL) for semantic segmentation to improve the performance of the boundaries. The CBL creates a unique optimization goal for each boundary pixel, conditioned on its surrounding neighbors. The conditional optimization of the CBL is easy yet effective. In contrast, most previous boundary-aware methods have difficult optimization goals or may cause potential conflicts with the semantic segmentation task. Specifically, the CBL enhances the intra-class consistency and inter-class difference, by pulling each boundary pixel closer to its unique local class center and pushing it away from its different-class neighbors. Moreover, the CBL filters out noisy and incorrect information to obtain precise boundaries, since only surrounding neighbors that are correctly classified participate in the loss calculation. Our loss is a plug-and-play solution that can be used to improve the boundary segmentation performance of any semantic segmentation network. We conduct extensive experiments on ADE20K, Cityscapes, and Pascal Context, and the results show that applying the CBL to various popular segmentation networks can significantly improve the mIoU and boundary F-score performance.
Time-Transient Wireless RF Sensor with Differentiative Detecting Capability for Target Ionic Solution of Water and Dielectric Objects Introduced into Water
Authors: Sobhan Gholami, Emre Unal, Hilmi Volkan Demir
Date: 2023-11-16 00:00:00
Tasks: Sensitivity

A novel wireless microstrip-based RF sensor designed for detecting changes in ionic content of water and the addition of solid contaminant objects is proposed and demonstrated. The sensor can be installed on the exterior wall of dielectric containers and customized according to the material of the container to enable wireless sensing. Its operation within the lower microwave frequency range (670-730 MHz) serves to minimize signal attenuation in water and streamlines circuitry design. The most significant feature of this sensor is its unique design, rendering it impervious to its surrounding environment. This not only shields it from environmental noise but also maximizes its sensitivity by efficiently utilizing incoming power for sensing purposes. The sensor exhibits remarkable sensitivity, capable of detecting solute concentrations as low as 0.003125 M in water. It can also detect the insertion of foreign solid objects into the container from the exterior wirelessly and distinguish them from liquids being added. As a proof-of-concept demonstration, the sensor in this study was optimized for a porcelain wall of 10-12 mm thickness. The sensor's small size and the materials used for its fabrication make it adaptable to a wide range of applications in industries such as food, pharmaceuticals, and bathroom fixtures. The aforementioned properties position the sensor as an ideal choice for various smart bathroom applications, where accurate and reliable water use monitoring is essential for efficient water conservation.
The YOLO model that still excels in document layout analysis
Authors: Qilin Deng, Mayire Ibrayim, Askar Hamdulla, Chunhu Zhang
Date: 2023-08-22 00:00:00
Proceeding: ResearchGate 2023 8
Tasks: Diversity, Document Layout Analysis

Document layout analysis can help people better understand and use the information in a document. However, the diversity of document layouts and considerable variation in aspect ratios among document objects pose significant challenges. In this study, we designed the Multi-Convolutional Deformable Separation (MCDS) module as the main structure of the network, using the YOLO model as a baseline. Integration of this module into the Backbone and Neck layers enhances the image feature extraction process significantly. Moreover, we incorporate ParNet-Attention to direct the network's focus toward document objects through parallel networks, thereby facilitating a more exhaustive feature extraction. To optimize the model's predictive potential, the Decouple Fusion Head (DFH) is employed within the Head layer. This technique leverages multi-scale features based on the decoupled head, thereby enhancing the accuracy of predictions. Our proposed model achieves remarkable performance on three distinct public datasets with varying characteristics, namely ICDAR-POD, PubLayNet, and IIIT-AR-13K. Notably, in ICDAR-POD, both IoU$_{0.6}$ and IoU$_{0.8}$ achieve the optimal mean Average Precision (mAP), 96.2 and 94.4, respectively.
High-fidelity acoustic signal enhancement for phase-OTDR using supervised learning
Authors: Fei Jiang, 1 ZHENHAI ZHANG, 1, 5 ZIXIAO LU, 2 HONGLANG LI, 2, 6 YAHUI TIAN, 3 YIXIN ZHANG, 4 AND XUPING ZHANG4
Date: 2021-09-29 00:00:00
Proceeding: Optics Express 2021 9

Phase-measuring phase-sensitive optical time-domain reflectometry (OTDR) has
been widely used for the distributed acoustic sensing. However, the demodulated phase signals
are generally noisy due to the laser frequency drift, laser phase noise, and interference fading.
These issues are usually addressed individually. In this paper, we propose to address them
simultaneously using supervised learning. We first use numerical simulations to generate the
corresponding noisy differential phase signals for the given acoustic signals. Then we use the
generated acoustic signals and noises together with some real noise data to train an end-to-end
convolutional neutral network (CNN) for the acoustic signal enhancement. Three experiments
are conduct to evaluate the performance of the proposed signal enhancement method. After
enhancement, the average signal-to-noise ratio (SNR) of the recovered PZT vibration signals is
improved from 13.4 dB to 42.8 dB, while the average scale-invariant signal-to-distortion ratio
(SI-SDR) of the recovered speech signals is improved by 7.7 dB. The results show that, the
proposed method can well suppress the noise and signal distortion caused by the laser frequency
drift, laser phase noise, and interference fading, while recover the acoustic signals with high
fidelity.
In the Red(dit): Social Media and Stock Prices
Authors: James Baker
Date: 2023-11-14 00:00:00
Tasks: Clustering

Spearheaded by retail traders on the website reddit, the GameStop short squeeze of early 2021 shows that social media embeds information that correlates with market movements. This paper seeks to examine this relationship by using daily frequencies of classified comments and buzzwords as additional factors in a Fama-French three factor model. Comments are classified using an unsupervised clustering method, while past studies have used pretrained models that are not specific to the domains being studied.
An Innovative Tool for Uploading/Scraping Large Image Datasets on Social Networks
Authors: Nicolò Fabio Arceri, Oliver Giudice, Sebastiano Battiato
Date: 2023-11-01 00:00:00

Nowadays, people can retrieve and share digital information in an increasingly easy and fast fashion through the well-known digital platforms, including sensitive data, inappropriate or illegal content, and, in general, information that might serve as probative evidence in court. Consequently, to assess forensics issues, we need to figure out how to trace back to the posting chain of a digital evidence (e.g., a picture, an audio) throughout the involved platforms -- this is what Digital (also Forensics) Ballistics basically deals with. With the entry of Machine Learning as a tool of the trade in many research areas, the need for vast amounts of data has been dramatically increasing over the last few years. However, collecting or simply find the "right" datasets that properly enables data-driven research studies can turn out to be not trivial in some cases, if not extremely challenging, especially when it comes with highly specialized tasks, such as creating datasets analyzed to detect the source media platform of a given digital media. In this paper we propose an automated approach by means of a digital tool that we created on purpose. The tool is capable of automatically uploading an entire image dataset to the desired digital platform and then downloading all the uploaded pictures, thus shortening the overall time required to output the final dataset to be analyzed.
Neural Packing: from Visual Sensing to Reinforcement Learning
Authors: Juzhan Xu, Minglun Gong, Hao Zhang, Hui Huang, Ruizhen Hu
Date: 2023-10-17 00:00:00
Tasks: Combinatorial Optimization, Motion Planning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We present a novel learning framework to solve the transport-and-packing (TAP) problem in 3D. It constitutes a full solution pipeline from partial observations of input objects via RGBD sensing and recognition to final box placement, via robotic motion planning, to arrive at a compact packing in a target container. The technical core of our method is a neural network for TAP, trained via reinforcement learning (RL), to solve the NP-hard combinatorial optimization problem. Our network simultaneously selects an object to pack and determines the final packing location, based on a judicious encoding of the continuously evolving states of partially observed source objects and available spaces in the target container, using separate encoders both enabled with attention mechanisms. The encoded feature vectors are employed to compute the matching scores and feasibility masks of different pairings of box selection and available space configuration for packing strategy optimization. Extensive experiments, including ablation studies and physical packing execution by a real robot (Universal Robot UR5e), are conducted to evaluate our method in terms of its design choices, scalability, generalizability, and comparisons to baselines, including the most recent RL-based TAP solution. We also contribute the first benchmark for TAP which covers a variety of input settings and difficulty levels.
Using the Built-in iPhone Body Tracking System for Neurological Tests: The Example of Assessing Arm Weakness in Stroke Patients. A Preliminary Evaluation of Accuracy and Performance
Authors: Vittorio Lippi, Isabelle Daniela Walz, Tobias Heimbach, Simone Meier, Jochen Brich, Christian Haverkamp, Christoph Maurer
Date: 2023-11-16 00:00:00
Tasks: Position

Timely treatment of stroke is critical to minimize brain damage. Therefore, efforts are being made to educate the public on detecting stroke symptoms, e.g., face, arms, and speech test (FAST). In this position paper, we propose to perform the arm weakness test using the integrated video tracking from an iPhone - some general tests to assess the tracking quality and discuss potential critical points. The test has been performed on 4 stroke patients. The result is compared with the report of the clinician. Although presenting some limitations, the system proved to be able to detect arm weakness as a symptom of stroke. We envisage that introducing a portable body tracking system in such clinical tests will provide advantages in terms of objectivity, repeatability, and the possibility to record and compare groups of patients.
Robust Conformal Prediction for STL Runtime Verification under Distribution Shift
Authors: Yiqi Zhao, Bardh Hoxha, Georgios Fainekos, Jyotirmoy V. Deshmukh, Lars Lindemann
Date: 2023-11-16 00:00:00
Tasks: Conformal Prediction, Trajectory Prediction

Cyber-physical systems (CPS) designed in simulators behave differently in the real-world. Once they are deployed in the real-world, we would hence like to predict system failures during runtime. We propose robust predictive runtime verification (RPRV) algorithms under signal temporal logic (STL) tasks for general stochastic CPS. The RPRV problem faces several challenges: (1) there may not be sufficient data of the behavior of the deployed CPS, (2) predictive models are based on a distribution over system trajectories encountered during the design phase, i.e., there may be a distribution shift during deployment. To address these challenges, we assume to know an upper bound on the statistical distance (in terms of an f-divergence) between the distributions at deployment and design time, and we utilize techniques based on robust conformal prediction. Motivated by our results in [1], we construct an accurate and an interpretable RPRV algorithm. We use a trajectory prediction model to estimate the system behavior at runtime and robust conformal prediction to obtain probabilistic guarantees by accounting for distribution shifts. We precisely quantify the relationship between calibration data, desired confidence, and permissible distribution shift. To the best of our knowledge, these are the first statistically valid algorithms under distribution shift in this setting. We empirically validate our algorithms on a Franka manipulator within the NVIDIA Isaac sim environment.
Differential algebraic modeling of an alkaline electrolyzer plant
Authors: Nicola Cantisani, Josefine Dovits, John Bagterp Jørgensen
Date: 2023-11-16 00:00:00

We develop a mathematical model for dynamic simulation of an alkaline electrolyzer plant. The plant includes the stack, a water recirculation system and hydrogen storage with compressor. We model each component of the system with mass and energy balances. Our modeling strategy consists of a rigorous and systematic formulation using differential algebraic equations (DAE), along with a thermodynamic library that evaluates thermophysical properties. We perform a simulation with step power input. Dynamic modeling enables simulation and model-based optimization and control for optimal hydrogen production under varying operating conditions.
Fuel Saving Effect and Performance of Velocity Control for Modern Combustion-Powered Scooters
Authors: Jannis Kreß, Jens Rau, Hektor Hebert, Fernando Perez-Peña, Karsten Schmidt, Arturo Morgado-Estévez
Date: 2023-11-16 00:00:00

This paper investigates the performance and fuel-saving effect of a velocity control algorithm on modern 50 cc scooters (Euro 5). The European Parliament has adopted major CO$_2$ emission reductions by 2030. But modern combustion- powered scooters are inefficiently restricted and emit unnecessary amounts of CO$_2$. Replacing the original restriction method with the system presented in this paper, the engine's operating point is being improved significantly. Therefore, a Throttle-by-Wire-System senses the rider's throttle command and manipulates the throttle valve. A redundant wheel speed sensor measures the precise vehicle velocity using the magneto-resistive principle. The entire system is managed by a central ECU, executing the actual velocity control, fail-safe functions, power supply and handling inputs/outputs. For velocity control, an adaptive PI-controller has been simulated, virtually tuned and implemented, limiting the max. velocity regulated by legal constraints (45 km/h). In this way, the environmentally harmful restrictors used today can be bypassed. By implementing a human-machine interface, including a virtual dashboard, the system is capable of interfacing with the rider. For evaluation purposes a measurement box has been developed, logging vehicle orientation, system/control variables and engine parameters. A Peugeot Kisbee 50 4T (Euro 5) is serving as test vehicle. Finally, the system has been evaluated regarding performance and fuel efficiency both through simulation and road testing. Fuel savings of 13.6 % in real-world test scenarios were achieved while maintaining vehicle performance.
Load Data Valuation in Multi-Energy Systems: An End-to-End Approach
Authors: Yangze Zhou, Qingsong Wen, Jie Song, Xueyuan Cui, Yi Wang
Date: 2023-11-16 00:00:00
Tasks: Data Valuation, Load Forecasting

Accurate load forecasting serves as the foundation for the flexible operation of multi-energy systems (MES). Multi-energy loads are tightly coupled and exhibit significant uncertainties. Many works focus on enhancing forecasting accuracy by leveraging cross-sector information. However, data owners may not be motivated to share their data unless it leads to substantial benefits. Ensuring a reasonable data valuation can encourage them to share their data willingly. This paper presents an end-to-end framework to quantify multi-energy load data value by integrating forecasting and decision processes. To address optimization problems with integer variables, a two-stage end-to-end model solution is proposed. Moreover, a profit allocation strategy based on contribution to cost savings is investigated to encourage data sharing in MES. The experimental results demonstrate a significant decrease in operation costs, suggesting that the proposed valuation approach more effectively extracts the inherent data value than traditional methods. According to the proposed incentive mechanism, all sectors can benefit from data sharing by improving forecasting accuracy or receiving economic compensation.
Exploring Indoor Localization for Smart Education
Authors: Carlos S. Álvarez-Merino, Emil J. Khatib, Antonio Tarrias Muñoz, Raquel Barco
Date: 2023-11-16 00:00:00
Tasks: Indoor Localization

This comprehensive study delves into the realm of indoor positioning technologies within the domain of Smart Education (SE). Focusing on typical techniques and technologies in educational settings, the research emphasizes the importance and potential services of localization in SE. Moreover, this work explores the feasibility and limitations of these technologies, providing a detailed account of their role in educational settings. The paper also contains in an innovative Proof of Concept (PoC), demonstrating an automatic attendance control (AAC) system that integrates 5G and WiFi technologies. This PoC effectively showcases the possibilities and effectiveness of location-based services in educational surroundings even with a limited budget, setting the stage for optimizing teaching time, enhancing the quality of education.
Multi-Objective Transmission Expansion: An Offshore Wind Power Integration Case Study
Authors: Saroj Khanal, Christoph Graf, Zhirui Liang, Yury Dvorkin, Burçin Ünel
Date: 2023-11-16 00:00:00

Despite ambitious offshore wind targets in the U.S. and globally, offshore grid planning guidance remains notably scarce, contrasting with well-established frameworks for onshore grids. This gap, alongside the increasing penetration of offshore wind and other clean-energy resources in onshore grids, highlights the urgent need for a coordinated planning framework. Our paper describes a multi-objective, multistage generation, storage and transmission expansion planning model to facilitate efficient and resilient large-scale adoption of offshore wind power. Recognizing regulatory emphasis and, in some cases, requirements to consider externalities, this model explicitly accounts for negative externalities: greenhouse gas emissions and local emission-induced air pollution. Utilizing an 8-zone ISO-NE test system and a 9-zone PJM test system, we explore grid expansion sensitivities such as impacts of optimizing Points of Interconnection (POIs) versus fixed POIs, negative externalities, and consideration of extreme operational scenarios resulting from offshore wind integration. Our results indicate that accounting for negative externalities necessitates greater upfront investment in clean generation and storage (balanced by lower expected operational costs). Optimizing POIs could significantly reshape offshore topology or POIs, and lower total cost. Finally, accounting for extreme operational scenarios typically results in greater operational costs and sometimes may alter onshore line investment.