pwc-archive/papers-with-abstracts huggingface dataset:
A semiconcavity approach to stability of entropic plans and exponential convergence of Sinkhorn's algorithm
Authors: Alberto Chiarini, Giovanni Conforti, Giacomo Greco, Luca Tamanini
Date: 2024-12-12 00:00:00

We study stability of optimizers and convergence of Sinkhorn's algorithm in the framework of entropic optimal transport. We show entropic stability for optimal plans in terms of the Wasserstein distance between their marginals under a semiconcavity assumption on the sum of the cost and one of the two entropic potentials. When employed in the analysis of Sinkhorn's algorithm, this result gives a natural sufficient condition for its exponential convergence, which does not require the ground cost to be bounded. By controlling from above the Hessians of Sinkhorn potentials in examples of interest, we obtain new exponential convergence results. For instance, for the first time we obtain exponential convergence for log-concave marginals and quadratic costs for all values of the regularization parameter. Moreover, the convergence rate has a linear dependence on the regularization: this behavior is sharp and had only been previously obtained for compact distributions arXiv:2407.01202. Other interesting new applications include subspace elastic costs [Cuturi et al. PMLR 202(2023)], weakly log-concave marginals, marginals with light tails, where, under reinforced assumptions, we manage to improve the rates obtained in arXiv:2311.04041, the case of unbounded Lipschitz costs, and compact Riemannian manifolds.
On the number of modes of Gaussian kernel density estimators
Authors: Borjan Geshkovski, Philippe Rigollet, Yihang Sun
Date: 2024-12-12 00:00:00

We consider the Gaussian kernel density estimator with bandwidth $\beta^{-\frac12}$ of $n$ iid Gaussian samples. Using the Kac-Rice formula and an Edgeworth expansion, we prove that the expected number of modes on the real line scales as $\Theta(\sqrt{\beta\log\beta})$ as $\beta,n\to\infty$ provided $n^c\lesssim \beta\lesssim n^{2-c}$ for some constant $c>0$. An impetus behind this investigation is to determine the number of clusters to which Transformers are drawn in a metastable state.
MIMO Detection via Gaussian Mixture Expectation Propagation: A Bayesian Machine Learning Approach for High-Order High-Dimensional MIMO Systems
Authors: Shachar Shayovitz, Doron Ezri, Yoav Levinbook
Date: 2024-12-12 00:00:00

MIMO systems can simultaneously transmit multiple data streams within the same frequency band, thus exploiting the spatial dimension to enhance performance. MIMO detection poses considerable challenges due to the interference and noise introduced by the concurrent transmission of multiple streams. Efficient Uplink (UL) MIMO detection algorithms are crucial for decoding these signals accurately and ensuring robust communication. In this paper a MIMO detection algorithm is proposed which improves over the Expectation Propagation (EP) algorithm. The proposed algorithm is based on a Gaussian Mixture Model (GMM) approximation for Belief Propagation (BP) and EP messages. The GMM messages better approximate the data prior when EP fails to do so and thus improve detection. This algorithm outperforms state of the art detection algorithms while maintaining low computational complexity.
On improving generalization in a class of learning problems with the method of small parameters for weakly-controlled optimal gradient systems
Authors: Getachew K. Befekadu
Date: 2024-12-11 00:00:00

In this paper, we provide a mathematical framework for improving generalization in a class of learning problems which is related to point estimations for modeling of high-dimensional nonlinear functions. In particular, we consider a variational problem for a weakly-controlled gradient system, whose control input enters into the system dynamics as a coefficient to a nonlinear term which is scaled by a small parameter. Here, the optimization problem consists of a cost functional, which is associated with how to gauge the quality of the estimated model parameters at a certain fixed final time w.r.t. the model validating dataset, while the weakly-controlled gradient system, whose the time-evolution is guided by the model training dataset and its perturbed version with small random noise. Using the perturbation theory, we provide results that will allow us to solve a sequence of optimization problems, i.e., a set of decomposed optimization problems, so as to aggregate the corresponding approximate optimal solutions that are reasonably sufficient for improving generalization in such a class of learning problems. Moreover, we also provide an estimate for the rate of convergence for such approximate optimal solutions. Finally, we present some numerical results for a typical case of nonlinear regression problem.
SPRec: Leveraging Self-Play to Debias Preference Alignment for Large Language Model-based Recommendations
Authors: Chongming Gao, Ruijun Chen, Shuai Yuan, Kexin Huang, Yuanqing Yu, Xiangnan He
Date: 2024-12-12 00:00:00
Tasks: Fairness, Language Modeling, Language Modelling, Large Language Model, Recommendation Systems

Large language models (LLMs) have attracted significant attention in recommendation systems. Current LLM-based recommender systems primarily rely on supervised fine-tuning (SFT) to train the model for recommendation tasks. However, relying solely on positive samples limits the model's ability to align with user satisfaction and expectations. To address this, researchers have introduced Direct Preference Optimization (DPO), which explicitly aligns recommendations with user preferences using offline preference ranking data. Despite its advantages, our theoretical analysis reveals that DPO inherently biases the model towards a few items, exacerbating the filter bubble issue and ultimately degrading user experience. In this paper, we propose SPRec, a novel self-play recommendation framework designed to mitigate over-recommendation and improve fairness without requiring additional data or manual intervention. In each self-play iteration, the model undergoes an SFT step followed by a DPO step, treating offline interaction data as positive samples and the predicted outputs from the previous iteration as negative samples. This effectively re-weights the DPO loss function using the model's logits, adaptively suppressing biased items. Extensive experiments on multiple real-world datasets demonstrate SPRec's effectiveness in enhancing recommendation accuracy and addressing fairness concerns. The implementation is available via https://github.com/RegionCh/SPRec
HyKo: A Spectral Dataset for Scene Understanding
Authors: Christian Winkens, Florian Sattler, Veronika Adams, Dietrich Paulus
Date: 2017-10-22 00:00:00
Proceeding: IEEE International Conference on Computer Vision Workshops (ICCVW) 2017 10
Tasks: Autonomous Driving, Scene Understanding

We present datasets containing urban traffic and rural road scenes recorded using hyperspectral snap-shot sensors mounted on a moving car. The novel hyperspectral cameras used can capture whole spectral cubes at up to 15 Hz. This emerging new sensor modality enables hyperspectral scene analysis for autonomous driving tasks. Up to the best of the author's knowledge no such dataset has been published so far. The datasets contain synchronized 3-D laser, spectrometer and hyperspectral data. Dense ground truth annotations are provided as semantic labels, material and traversability. The hyperspectral data ranges from visible to near infrared wavelengths. We explain our recoding platform and method, the associated data format along with a code library for easy data consumption. The datasets are publicly available for download.
Isogeometric Analysis for the Pricing of Financial Derivatives with Nonlinear Models: Convertible Bonds and Options
Authors: Rakhymzhan Kazbek, Yogi Erlangga, Yerlan Amanbek, Dongming Wei
Date: 2024-12-12 00:00:00
Tasks: Computational Efficiency

Computational efficiency is essential for enhancing the accuracy and practicality of pricing complex financial derivatives. In this paper, we discuss Isogeometric Analysis (IGA) for valuing financial derivatives, modeled by two nonlinear Black-Scholes PDEs: the Leland model for European call with transaction costs and the AFV model for convertible bonds with default options. We compare the solutions of IGA with finite difference methods (FDM) and finite element methods (FEM). In particular, very accurate solutions can be numerically calculated on far less mesh (knots) than FDM or FEM, by using non-uniform knots and weighted cubic NURBS, which in turn reduces the computational time significantly.
Subspace tracking for online system identification
Authors: András Sasfi, Alberto Padoan, Ivan Markovsky, Florian Dörfler
Date: 2024-12-12 00:00:00
Tasks: Uncertainty Quantification

This paper introduces an online approach for identifying time-varying subspaces defined by linear dynamical systems, leveraging optimization on the Grassmannian manifold leading to the Grassmannian Recursive Algorithm for Tracking (GREAT) method. The approach of representing linear systems by non-parametric subspace models has received significant interest in the field of data-driven control recently. We view subspaces as points on the Grassmannian manifold, and therefore, tracking is achieved by performing optimization on the manifold. At each time step, a single measurement from the current subspace corrupted by a bounded error is available. The subspace estimate is updated online using Grassmannian gradient descent on a cost function incorporating a window of the most recent data. Under suitable assumptions on the signal-to-noise ratio of the online data and the subspace's rate of change, we establish theoretical guarantees for the resulting algorithm. More specifically, we prove an exponential convergence rate and provide a consistent uncertainty quantification of the estimates in terms of an upper bound on their distance to the true subspace. The applicability of the proposed algorithm is demonstrated by means of numerical examples, and it is shown to compare favorably with competing parametric system identification methods.
Hardware implementation of timely reliable Bayesian decision-making using memristors
Authors: Lekai Song, Pengyu Liu, Yang Liu, Jingfang Pei, Wenyu Cui, Songwei Liu, Yingyi Wen, Teng Ma, Kong-Pang Pun, Leonard W. T. Ng, Guohua Hu
Date: 2024-12-07 00:00:00
Tasks: Bayesian Inference, Decision Making, Scene Parsing

Brains perform decision-making by Bayes theorem. The theorem quantifies events as probabilities and, based on probability rules, renders the decisions. Learning from this, Bayes theorem can be applied to enable efficient user-scene interactions. However, given the probabilistic nature, implementing Bayes theorem in hardware using conventional deterministic computing can incur excessive computational cost and decision latency. Though challenging, here we present a probabilistic computing approach based on memristors to implement the Bayes theorem. We integrate memristors with Boolean logics and, by exploiting the volatile stochastic switching of the memristors, realise probabilistic logic operations, key for hardware Bayes theorem implementation. To empirically validate the efficacy of the hardware Bayes theorem in user-scene interactions, we develop lightweight Bayesian inference and fusion hardware operators using the probabilistic logics and apply the operators in road scene parsing for self-driving, including route planning and obstacle detection. The results show our operators can achieve reliable decisions in less than 0.4 ms (or equivalently 2,500 fps), outperforming human decision-making and the existing driving assistance systems.
IMPACT:InMemory ComPuting Architecture Based on Y-FlAsh Technology for Coalesced Tsetlin Machine Inference
Authors: Omar Ghazal, Wei Wang, Shahar Kvatinsky, Farhad Merchant, Alex Yakovlev, Rishad Shafik
Date: 2024-12-04 00:00:00

The increasing demand for processing large volumes of data for machine learning models has pushed data bandwidth requirements beyond the capability of traditional von Neumann architecture. In-memory computing (IMC) has recently emerged as a promising solution to address this gap by enabling distributed data storage and processing at the micro-architectural level, significantly reducing both latency and energy. In this paper, we present the IMPACT: InMemory ComPuting Architecture Based on Y-FlAsh Technology for Coalesced Tsetlin Machine Inference, underpinned on a cutting-edge memory device, Y-Flash, fabricated on a 180 nm CMOS process. Y-Flash devices have recently been demonstrated for digital and analog memory applications, offering high yield, non-volatility, and low power consumption. The IMPACT leverages the Y-Flash array to implement the inference of a novel machine learning algorithm: coalesced Tsetlin machine (CoTM) based on propositional logic. CoTM utilizes Tsetlin automata (TA) to create Boolean feature selections stochastically across parallel clauses. The IMPACT is organized into two computational crossbars for storing the TA and weights. Through validation on the MNIST dataset, IMPACT achieved 96.3% accuracy. The IMPACT demonstrated improvements in energy efficiency, e.g., 2.23X over CNN-based ReRAM, 2.46X over Neuromorphic using NOR-Flash, and 2.06X over DNN-based PCM, suited for modern ML inference applications.
DRC-Coder: Automated DRC Checker Code Generation Using LLM Autonomous Agent
Authors: Chen-Chia Chang, Chia-Tung Ho, Yaguang Li, Yiran Chen, Haoxing Ren
Date: 2024-11-28 00:00:00
Tasks: Code Generation

In the advanced technology nodes, the integrated design rule checker (DRC) is often utilized in place and route tools for fast optimization loops for power-performance-area. Implementing integrated DRC checkers to meet the standard of commercial DRC tools demands extensive human expertise to interpret foundry specifications, analyze layouts, and debug code iteratively. However, this labor-intensive process, requiring to be repeated by every update of technology nodes, prolongs the turnaround time of designing circuits. In this paper, we present DRC-Coder, a multi-agent framework with vision capabilities for automated DRC code generation. By incorporating vision language models and large language models (LLM), DRC-Coder can effectively process textual, visual, and layout information to perform rule interpretation and coding by two specialized LLMs. We also design an auto-evaluation function for LLMs to enable DRC code debugging. Experimental results show that targeting on a sub-3nm technology node for a state-of-the-art standard cell layout tool, DRC-Coder achieves perfect F1 score 1.000 in generating DRC codes for meeting the standard of a commercial DRC tool, highly outperforming standard prompting techniques (F1=0.631). DRC-Coder can generate code for each design rule within four minutes on average, which significantly accelerates technology advancement and reduces engineering costs.
DocEDA: Automated Extraction and Design of Analog Circuits from Documents with Large Language Model
Authors: Hong Cai Chen, Longchang Wu, Ming Gao, Lingrui Shen, Jiarui Zhong, Yipin Xu
Date: 2024-11-25 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

Efficient and accurate extraction of electrical parameters from circuit datasheets and design documents is critical for accelerating circuit design in Electronic Design Automation (EDA). Traditional workflows often rely on engineers manually searching and extracting these parameters, which is time-consuming, and prone to human error. To address these challenges, we introduce DocEDA, an automated system that leverages advanced computer vision techniques and Large Language Models (LLMs) to extract electrical parameters seamlessly from documents. The layout analysis model specifically designed for datasheet is proposed to classify documents into circuit-related parts. Utilizing the inherent Chain-of-Thought reasoning capabilities of LLMs, DocEDA automates the extraction of electronic component parameters from documents. For circuit diagrams parsing, an improved GAM-YOLO model is hybrid with topology identification to transform diagrams into circuit netlists. Then, a space mapping enhanced optimization framework is evoked for optimization the layout in the document. Experimental evaluations demonstrate that DocEDA significantly enhances the efficiency of processing circuit design documents and the accuracy of electrical parameter extraction. It exhibits adaptability to various circuit design scenarios and document formats, offering a novel solution for EDA with the potential to transform traditional methodologies.
evS2CP: Real-time Simultaneous Speed and Charging Planner for Connected Electric Vehicles
Authors: Minwoo Gwon, Jiwon Kim, Seungjun Yoo, Kwang-Ki K. Kim
Date: 2024-12-12 00:00:00
Tasks: Autonomous Vehicles, Computational Efficiency

This paper presents evS2CP, an optimization-based framework for simultaneous speed and charging planning designed for connected electric vehicles (EVs). With EVs emerging as competitive alternatives to internal combustion engine vehicles, overcoming challenges such as limited charging infrastructure is crucial. evS2CP addresses these issues by minimizing the travel time, charging time, and energy consumption, providing practical solutions for both human-operated and autonomous vehicles. This framework leverages V2X communication to integrate essential EV planning data, including route geometry, real-time traffic conditions, and charging station availability, while simulating dynamic driving environments using open-web API services. The speed and charging planning problem was initially formulated as a nonlinear programming model, which was then convexified into a quadratic programming model without charging-stop constraints. Additionally, a mixed-integer programming approach was employed to optimize charging station selection and minimize the frequency of charging events. A mixed-integer quadratic programming implementation exhibited exceptional computational efficiency and scalability, effectively solving trip plans over distances exceeding 700 km in a few seconds. Simulations conducted using open-source and commercial solvers validated the framework's near-global optimality, demonstrating its robustness and feasibility for real-world applications in connected EV ecosystems.
Advancing Operational Efficiency: Airspace Users' Perspective on Trajectory-Based Operations
Authors: Pablo Costas, Italo Romani de Oliveira
Date: 2024-12-11 00:00:00
Tasks: Management

This work explores the evolution of the Flight Operations Center (FOC) and flight trajectory exchange tools within Trajectory-Based Operations (TBO), emphasizing the benefits of the ICAO's Flight and Flow Information for a Collaborative Environment (FF-ICE) messaging framework and Electronic Flight Bags (EFBs). It highlights the collaborative management of four-dimensional flight trajectories, serving as a common reference for decision-making among stakeholders, including Air Navigation Service Providers (ANSPs), airspace users, and airport operators. Key enabling technologies such as Performance Based Navigation (PBN), data communications, and System-wide Information Management (SWIM) are discussed, showcasing their roles in rapid information exchange and trajectory optimization. A live flight case study demonstrates TBO concepts through international collaboration, indicating significant improvements in safety, efficiency, and sustainability. The paper presents results from TBO prototype implementations, including enhanced trajectory accuracy, improved flight path efficiency, and real-time adjustments based on evolving conditions. The integration of advanced trajectory optimization engines and automation within the FOC has led to more effective flight planning, allowing airlines to negotiate trajectory changes dynamically and optimize operations throughout the flight lifecycle. Findings suggest that TBO can enhance operational predictability, flexibility, and strategic planning while reducing uncertainty and improving alignment between strategic and tactical actions. Key conclusions include: TBO is feasible with most currently flying commercial aircraft; full TBO implementation can lead to a greener, more efficient aviation industry with widespread benefits; and continued collaboration among stakeholders is essential for the further development and realization of TBO.
Distributed Intrusion Detection System using Semantic-based Rules for SCADA in Smart Grid
Authors: Sathya Narayana Mohan, Gelli Ravikumar, Manimaran Govindarasu
Date: 2024-12-10 00:00:00
Tasks: Intrusion Detection

Cyber-physical system (CPS) security for the smart grid enables secure communication for the SCADA and wide-area measurement system data. Power utilities world-wide use various SCADA protocols, namely DNP3, Modbus, and IEC 61850, for the data exchanges across substation field devices, remote terminal units (RTUs), and control center applications. Adversaries may exploit compromised SCADA protocols for the reconnaissance, data exfiltration, vulnerability assessment, and injection of stealthy cyberattacks to affect power system operation. In this paper, we propose an efficient algorithm to generate robust rule sets. We integrate the rule sets into an intrusion detection system (IDS), which continuously monitors the DNP3 data traffic at a substation network and detects intrusions and anomalies in real-time. To enable CPS-aware wide-area situational awareness, we integrated the methodology into an open-source distributed-IDS (D-IDS) framework. The D-IDS facilitates central monitoring of the detected anomalies from the geographically distributed substations and to the control center. The proposed algorithm provides an optimal solution to detect network intrusions and abnormal behavior. Different types of IDS rules based on packet payload, packet flow, and time threshold are generated. Further, IDS testing and evaluation is performed with a set of rules in different sequences. The detection time is measured for different IDS rules, and the results are plotted. All the experiments are conducted at Power Cyber Lab, Iowa State University, for multiple power grid models. After successful testing and evaluation, knowledge and implementation are transferred to field deployment.
Chatbots im Schulunterricht: Wir testen das Fobizz-Tool zur automatischen Bewertung von Hausaufgaben
Authors: Rainer Muehlhoff, Marte Henningsen
Date: 2024-12-09 00:00:00
Tasks: Marketing

This study examines the AI-powered grading tool "AI Grading Assistant" by the German company Fobizz, designed to support teachers in evaluating and providing feedback on student assignments. Against the societal backdrop of an overburdened education system and rising expectations for artificial intelligence as a solution to these challenges, the investigation evaluates the tool's functional suitability through two test series. The results reveal significant shortcomings: The tool's numerical grades and qualitative feedback are often random and do not improve even when its suggestions are incorporated. The highest ratings are achievable only with texts generated by ChatGPT. False claims and nonsensical submissions frequently go undetected, while the implementation of some grading criteria is unreliable and opaque. Since these deficiencies stem from the inherent limitations of large language models (LLMs), fundamental improvements to this or similar tools are not immediately foreseeable. The study critiques the broader trend of adopting AI as a quick fix for systemic problems in education, concluding that Fobizz's marketing of the tool as an objective and time-saving solution is misleading and irresponsible. Finally, the study calls for systematic evaluation and subject-specific pedagogical scrutiny of the use of AI tools in educational contexts.