pwc-archive/papers-with-abstracts huggingface dataset:
Solving Long-run Average Reward Robust MDPs via Stochastic Games
Authors: Krishnendu Chatterjee, Ehsan Kafshdar Goharshady, Mehrdad Karrabi, Petr Novotný, Đorđe Žikelić
Date: 2023-12-21 00:00:00
Tasks: Decision Making, Decision Making Under Uncertainty, Sequential Decision Making

Markov decision processes (MDPs) provide a standard framework for sequential decision making under uncertainty. However, MDPs do not take uncertainty in transition probabilities into account. Robust Markov decision processes (RMDPs) address this shortcoming of MDPs by assigning to each transition an uncertainty set rather than a single probability value. In this work, we consider polytopic RMDPs in which all uncertainty sets are polytopes and study the problem of solving long-run average reward polytopic RMDPs. We present a novel perspective on this problem and show that it can be reduced to solving long-run average reward turn-based stochastic games with finite state and action spaces. This reduction allows us to derive several important consequences that were hitherto not known to hold for polytopic RMDPs. First, we derive new computational complexity bounds for solving long-run average reward polytopic RMDPs, showing for the first time that the threshold decision problem for them is in $NP \cap coNP$ and that they admit a randomized algorithm with sub-exponential expected runtime. Second, we present Robust Polytopic Policy Iteration (RPPI), a novel policy iteration algorithm for solving long-run average reward polytopic RMDPs. Our experimental evaluation shows that RPPI is much more efficient in solving long-run average reward polytopic RMDPs compared to state-of-the-art methods based on value iteration.
EfficientPPS: Part-aware Panoptic Segmentation of Transparent Objects for Robotic Manipulation
Authors: Benjamin Alt, Minh Dang Nguyen, Andreas Hermann, Darko Katic, Rainer Jäkel, Rüdiger Dillmann, Eric Sax
Date: 2023-12-21 00:00:00
Tasks: Panoptic Segmentation, Part-aware Panoptic Segmentation, Transparent objects

The use of autonomous robots for assistance tasks in hospitals has the potential to free up qualified staff and im-prove patient care. However, the ubiquity of deformable and transparent objects in hospital settings poses signif-icant challenges to vision-based perception systems. We present EfficientPPS, a neural architecture for part-aware panoptic segmentation that provides robots with semantically rich visual information for grasping and ma-nipulation tasks. We also present an unsupervised data collection and labelling method to reduce the need for human involvement in the training process. EfficientPPS is evaluated on a dataset containing real-world hospital objects and demonstrated to be robust and efficient in grasping transparent transfusion bags with a collaborative robot arm.
Domain-Specific Fine-Tuning of Large Language Models for Interactive Robot Programming
Authors: Benjamin Alt, Urs Keßner, Aleksandar Taranovic, Darko Katic, Andreas Hermann, Rainer Jäkel, Gerhard Neumann
Date: 2023-12-21 00:00:00
Tasks: Industrial Robots

Industrial robots are applied in a widening range of industries, but robot programming mostly remains a task limited to programming experts. We propose a natural language-based assistant for programming of advanced, industrial robotic applications and investigate strategies for domain-specific fine-tuning of foundation models with limited data and compute.
Understanding Inter-Session Intentions via Complex Logical Reasoning
Authors: Jiaxin Bai, Chen Luo, Zheng Li, Qingyu Yin, Yangqiu Song
Date: 2023-12-21 00:00:00
Tasks: Attribute, Complex Query Answering, Logical Reasoning

Understanding user intentions is essential for improving product recommendations, navigation suggestions, and query reformulations. However, user intentions can be intricate, involving multiple sessions and attribute requirements connected by logical operators such as And, Or, and Not. For instance, a user may search for Nike or Adidas running shoes across various sessions, with a preference for purple. In another example, a user may have purchased a mattress in a previous session and is now looking for a matching bed frame without intending to buy another mattress. Existing research on session understanding has not adequately addressed making product or attribute recommendations for such complex intentions. In this paper, we present the task of logical session complex query answering (LS-CQA), where sessions are treated as hyperedges of items, and we frame the problem of complex intention understanding as an LS-CQA task on an aggregated hypergraph of sessions, items, and attributes. This is a unique complex query answering task with sessions as ordered hyperedges. We also introduce a new model, the Logical Session Graph Transformer (LSGT), which captures interactions among items across different sessions and their logical connections using a transformer structure. We analyze the expressiveness of LSGT and prove the permutation invariance of the inputs for the logical operators. By evaluating LSGT on three datasets, we demonstrate that it achieves state-of-the-art results.
Image Clustering using Restricted Boltzman Machine
Authors: Abraham Woubie, Enoch Solomon, Eyael Solomon Emiru
Date: 2023-12-21 00:00:00
Tasks: Clustering, Image Clustering

In various verification systems, Restricted Boltzmann Machines (RBMs) have demonstrated their efficacy in both front-end and back-end processes. In this work, we propose the use of RBMs to the image clustering tasks. RBMs are trained to convert images into image embeddings. We employ the conventional bottom-up Agglomerative Hierarchical Clustering (AHC) technique. To address the challenge of limited test face image data, we introduce Agglomerative Hierarchical Clustering based Method for Image Clustering using Restricted Boltzmann Machine (AHC-RBM) with two major steps. Initially, a universal RBM model is trained using all available training dataset. Subsequently, we train an adapted RBM model using the data from each test image. Finally, RBM vectors which is the embedding vector is generated by concatenating the visible-to-hidden weight matrices of these adapted models, and the bias vectors. These vectors effectively preserve class-specific information and are utilized in image clustering tasks. Our experimental results, conducted on two benchmark image datasets (MS-Celeb-1M and DeepFashion), demonstrate that our proposed approach surpasses well-known clustering algorithms such as k-means, spectral clustering, and approximate Rank-order.
Team Flow at DRC2023: Building Common Ground and Text-based Turn-taking in a Travel Agent Spoken Dialogue System
Authors: Ryu Hirai, Shinya Iizuka, Haruhisa Iseno, Ao Guo, Jingjing Jiang, Atsumoto Ohashi, Ryuichiro Higashinaka
Date: 2023-12-21 00:00:00

At the Dialogue Robot Competition 2023 (DRC2023), which was held to improve the capability of dialogue robots, our team developed a system that could build common ground and take more natural turns based on user utterance texts. Our system generated queries for sightseeing spot searches using the common ground and engaged in dialogue while waiting for user comprehension.
Sparse Training for Federated Learning with Regularized Error Correction
Authors: Ran Greidi, Kobi Cohen
Date: 2023-12-21 00:00:00
Tasks: Federated Learning

Federated Learning (FL) has attracted much interest due to the significant advantages it brings to training deep neural network (DNN) models. However, since communications and computation resources are limited, training DNN models in FL systems face challenges such as elevated computational and communication costs in complex tasks. Sparse training schemes gain increasing attention in order to scale down the dimensionality of each client (i.e., node) transmission. Specifically, sparsification with error correction methods is a promising technique, where only important updates are sent to the parameter server (PS) and the rest are accumulated locally. While error correction methods have shown to achieve a significant sparsification level of the client-to-PS message without harming convergence, pushing sparsity further remains unresolved due to the staleness effect. In this paper, we propose a novel algorithm, dubbed Federated Learning with Accumulated Regularized Embeddings (FLARE), to overcome this challenge. FLARE presents a novel sparse training approach via accumulated pulling of the updated models with regularization on the embeddings in the FL process, providing a powerful solution to the staleness effect, and pushing sparsity to an exceptional level. The performance of FLARE is validated through extensive experiments on diverse and complex models, achieving a remarkable sparsity level (10 times and more beyond the current state-of-the-art) along with significantly improved accuracy. Additionally, an open-source software package has been developed for the benefit of researchers and developers in related fields.
Few Shot Part Segmentation Reveals Compositional Logic for Industrial Anomaly Detection
Authors: Soopil Kim, Sion An, Philip Chikontwe, Myeongkyun Kang, Ehsan Adeli, Kilian M. Pohl, Sang Hyun Park
Date: 2023-12-21 00:00:00
Tasks: Anomaly Detection, Segmentation, Semantic Segmentation

Logical anomalies (LA) refer to data violating underlying logical constraints e.g., the quantity, arrangement, or composition of components within an image. Detecting accurately such anomalies requires models to reason about various component types through segmentation. However, curation of pixel-level annotations for semantic segmentation is both time-consuming and expensive. Although there are some prior few-shot or unsupervised co-part segmentation algorithms, they often fail on images with industrial object. These images have components with similar textures and shapes, and a precise differentiation proves challenging. In this study, we introduce a novel component segmentation model for LA detection that leverages a few labeled samples and unlabeled images sharing logical constraints. To ensure consistent segmentation across unlabeled images, we employ a histogram matching loss in conjunction with an entropy loss. As segmentation predictions play a crucial role, we propose to enhance both local and global sample validity detection by capturing key aspects from visual semantics via three memory banks: class histograms, component composition embeddings and patch-level representations. For effective LA detection, we propose an adaptive scaling strategy to standardize anomaly scores from different memory banks in inference. Extensive experiments on the public benchmark MVTec LOCO AD reveal our method achieves 98.1% AUROC in LA detection vs. 89.6% from competing methods.
On Task Performance and Model Calibration with Supervised and Self-Ensembled In-Context Learning
Authors: Chengzu Li, Han Zhou, Goran Glavaš, Anna Korhonen, Ivan Vulić
Date: 2023-12-21 00:00:00
Tasks: In-Context Learning

Following the standard supervised fine-tuning (SFT) paradigm, in-context learning (ICL) has become an efficient approach propelled by the recent advancements in large language models (LLMs), yielding promising performance across various tasks in few-shot data setups. However, both paradigms are prone to suffer from the critical problem of overconfidence (i.e., miscalibration), especially in such limited data setups. In this work, we deliver an in-depth analysis of the behavior across different choices of learning methods from the perspective of both performance and calibration, as well as their interplay. Through extensive controlled experiments, we find that simultaneous gains for both task performance and calibration are difficult to achieve, and the problem of miscalibration exists across all learning methods in low-resource scenarios. To address this challenging trade-off between performance and calibration, we then investigate the potential of self-ensembling techniques applied at different modeling stages (e.g., variations of in-context examples or variations in prompts or different ensembling strategies). We justify the feasibility of self-ensembling on SFT in addition to ICL, to make the predictions more calibrated and have comparable or even better performance. Our work sheds light on which learning paradigm to choose and how to enhance both task performance and calibration of LLMs.
Team Irisapu Project Description for DRC2023
Authors: Reon Ohashi, Shinjitsu Agatsuma, Kazuya Tsubokura, Yurie Iribe
Date: 2023-12-21 00:00:00

This paper describes the dialog robot system designed by Team Irisapu for the preliminary round of the Dialogue Robot Competition 2023 (DRC2023). In order to generate dialogue responses flexibly while adhering to predetermined scenarios, we attempted to generate dialogue response sentences using OpenAI's GPT-3. We aimed to create a system that can appropriately respond to users by dividing the dialogue scenario into five sub-scenarios, and creating prompts for each sub-scenario. Also, we incorporated a recovery strategy that can handle dialogue breakdowns flexibly. Our research group has been working on research related to dialogue breakdown detection, and we incorporated our findings to date in this competition. As a result of the preliminary round, a bug in our system affected the outcome and we were not able to achieve a satisfactory result. However, in the evaluation category of "reliability of provided information", we ranked third among all teams.
Cross-Layer Optimization for Fault-Tolerant Deep Learning
Authors: Qing Zhang, Cheng Liu, Bo Liu, Haitong Huang, Ying Wang, Huawei Li, Xiaowei Li
Date: 2023-12-21 00:00:00
Tasks: Bayesian Optimization, Deep Learning, Quantization

Fault-tolerant deep learning accelerator is the basis for highly reliable deep learning processing and critical to deploy deep learning in safety-critical applications such as avionics and robotics. Since deep learning is known to be computing- and memory-intensive, traditional fault-tolerant approaches based on redundant computing will incur substantial overhead including power consumption and chip area. To this end, we propose to characterize deep learning vulnerability difference across both neurons and bits of each neuron, and leverage the vulnerability difference to enable selective protection of the deep learning processing components from the perspective of architecture layer and circuit layer respectively. At the same time, we observe the correlation between model quantization and bit protection overhead of the underlying processing elements of deep learning accelerators, and propose to reduce the bit protection overhead by adding additional quantization constrain without compromising the model accuracy. Finally, we employ Bayesian optimization strategy to co-optimize the correlated cross-layer design parameters at algorithm layer, architecture layer, and circuit layer to minimize the hardware resource consumption while fulfilling multiple user constraints including reliability, accuracy, and performance of the deep learning processing at the same time.
Hunting imaging biomarkers in pulmonary fibrosis: Benchmarks of the AIIB23 challenge
Authors: Yang Nan, Xiaodan Xing, Shiyi Wang, Zeyu Tang, Federico N Felder, Sheng Zhang, Roberta Eufrasia Ledda, Xiaoliu Ding, Ruiqi Yu, Weiping Liu, Feng Shi, Tianyang Sun, Zehong Cao, Minghui Zhang, Yun Gu, Hanxiao Zhang, Jian Gao, Pingyu Wang, Wen Tang, Pengxin Yu, Han Kang, Junqiang Chen, Xing Lu, Boyu Zhang, Michail Mamalakis, Francesco Prinzi, Gianluca Carlini, Lisa Cuneo, Abhirup Banerjee, Zhaohu Xing, Lei Zhu, Zacharia Mesbah, Dhruv Jain, Tsiry Mayet, Hongyu Yuan, Qing Lyu, Abdul Qayyum, Moona Mazher, Athol Wells, Simon LF Walsh, Guang Yang
Date: 2023-12-21 00:00:00
Tasks: Mortality Prediction, Prognosis

Airway-related quantitative imaging biomarkers are crucial for examination, diagnosis, and prognosis in pulmonary diseases. However, the manual delineation of airway trees remains prohibitively time-consuming. While significant efforts have been made towards enhancing airway modelling, current public-available datasets concentrate on lung diseases with moderate morphological variations. The intricate honeycombing patterns present in the lung tissues of fibrotic lung disease patients exacerbate the challenges, often leading to various prediction errors. To address this issue, the 'Airway-Informed Quantitative CT Imaging Biomarker for Fibrotic Lung Disease 2023' (AIIB23) competition was organized in conjunction with the official 2023 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). The airway structures were meticulously annotated by three experienced radiologists. Competitors were encouraged to develop automatic airway segmentation models with high robustness and generalization abilities, followed by exploring the most correlated QIB of mortality prediction. A training set of 120 high-resolution computerised tomography (HRCT) scans were publicly released with expert annotations and mortality status. The online validation set incorporated 52 HRCT scans from patients with fibrotic lung disease and the offline test set included 140 cases from fibrosis and COVID-19 patients. The results have shown that the capacity of extracting airway trees from patients with fibrotic lung disease could be enhanced by introducing voxel-wise weighted general union loss and continuity loss. In addition to the competitive image biomarkers for prognosis, a strong airway-derived biomarker (Hazard ratio>1.5, p<0.0001) was revealed for survival prognostication compared with existing clinical measurements, clinician assessment and AI-based biomarkers.
Critic-Guided Decision Transformer for Offline Reinforcement Learning
Authors: Yuanfu Wang, Chao Yang, Ying Wen, Yu Liu, Yu Qiao
Date: 2023-12-21 00:00:00
Tasks: D4RL, Offline RL, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Trajectory Modeling

Recent advancements in offline reinforcement learning (RL) have underscored the capabilities of Return-Conditioned Supervised Learning (RCSL), a paradigm that learns the action distribution based on target returns for each state in a supervised manner. However, prevailing RCSL methods largely focus on deterministic trajectory modeling, disregarding stochastic state transitions and the diversity of future trajectory distributions. A fundamental challenge arises from the inconsistency between the sampled returns within individual trajectories and the expected returns across multiple trajectories. Fortunately, value-based methods offer a solution by leveraging a value function to approximate the expected returns, thereby addressing the inconsistency effectively. Building upon these insights, we propose a novel approach, termed the Critic-Guided Decision Transformer (CGDT), which combines the predictability of long-term returns from value-based methods with the trajectory modeling capability of the Decision Transformer. By incorporating a learned value function, known as the critic, CGDT ensures a direct alignment between the specified target returns and the expected returns of actions. This integration bridges the gap between the deterministic nature of RCSL and the probabilistic characteristics of value-based methods. Empirical evaluations on stochastic environments and D4RL benchmark datasets demonstrate the superiority of CGDT over traditional RCSL methods. These results highlight the potential of CGDT to advance the state of the art in offline RL and extend the applicability of RCSL to a wide range of RL tasks.
A Constraint Programming Model for Scheduling the Unloading of Trains in Ports: Extended
Authors: Guillaume Perez, Gael Glorian, Wijnand Suijlen, Arnaud Lallouet
Date: 2023-12-21 00:00:00
Tasks: Scheduling

In this paper, we propose a model to schedule the next 24 hours of operations in a bulk cargo port to unload bulk cargo trains onto stockpiles. It is a problem that includes multiple parts such as splitting long trains into shorter ones and the routing of bulk material through a configurable network of conveyors to the stockpiles. Managing such trains (up to three kilometers long) also requires specialized equipment. The real world nature of the problem specification implies the necessity to manage heterogeneous data. Indeed, when new equipment is added (e.g. dumpers) or a new type of wagon comes in use, older or different equipment will still be in use as well. All these details need to be accounted for. In fact, avoiding a full deadlock of the facility after a new but ineffective schedule is produced. In this paper, we provide a detailed presentation of this real world problem and its associated data. This allows us to propose an effective constraint programming model to solve this problem. We also discuss the model design and the different implementations of the propagators that we used in practice. Finally, we show how this model, coupled with a large neighborhood search, was able to find 24 hour schedules efficiently.
HGE: Embedding Temporal Knowledge Graphs in a Product Space of Heterogeneous Geometric Subspaces
Authors: Jiaxin Pan, Mojtaba Nayyeri, Yinan Li, Steffen Staab
Date: 2023-12-21 00:00:00
Tasks: Knowledge Graphs

Temporal knowledge graphs represent temporal facts $(s,p,o,\tau)$ relating a subject $s$ and an object $o$ via a relation label $p$ at time $\tau$, where $\tau$ could be a time point or time interval. Temporal knowledge graphs may exhibit static temporal patterns at distinct points in time and dynamic temporal patterns between different timestamps. In order to learn a rich set of static and dynamic temporal patterns and apply them for inference, several embedding approaches have been suggested in the literature. However, as most of them resort to single underlying embedding spaces, their capability to model all kinds of temporal patterns was severely limited by having to adhere to the geometric property of their one embedding space. We lift this limitation by an embedding approach that maps temporal facts into a product space of several heterogeneous geometric subspaces with distinct geometric properties, i.e.\ Complex, Dual, and Split-complex spaces. In addition, we propose a temporal-geometric attention mechanism to integrate information from different geometric subspaces conveniently according to the captured relational and temporal information. Experimental results on standard temporal benchmark datasets favorably evaluate our approach against state-of-the-art models.