pwc-archive/papers-with-abstracts huggingface dataset:
Exploring the Limits of Historical Information for Temporal Knowledge Graph Extrapolation
Authors: Yi Xu, Junjie Ou, Hui Xu, Luoyi Fu, Lei Zhou, Xinbing Wang, Chenghu Zhou
Date: 2023-08-29 00:00:00
Tasks: Contrastive Learning, Knowledge Graphs

Temporal knowledge graphs, representing the dynamic relationships and interactions between entities over time, have been identified as a promising approach for event forecasting. However, a limitation of most temporal knowledge graph reasoning methods is their heavy reliance on the recurrence or periodicity of events, which brings challenges to inferring future events related to entities that lack historical interaction. In fact, the current state of affairs is often the result of a combination of historical information and underlying factors that are not directly observable. To this end, we investigate the limits of historical information for temporal knowledge graph extrapolation and propose a new event forecasting model called Contrastive Event Network (CENET) based on a novel training framework of historical contrastive learning. CENET learns both the historical and non-historical dependency to distinguish the most potential entities that best match the given query. Simultaneously, by launching contrastive learning, it trains representations of queries to probe whether the current moment is more dependent on historical or non-historical events. These representations further help train a binary classifier, whose output is a boolean mask, indicating the related entities in the search space. During the inference process, CENET employs a mask-based strategy to generate the final results. We evaluate our proposed model on five benchmark graphs. The results demonstrate that CENET significantly outperforms all existing methods in most metrics, achieving at least 8.3% relative improvement of Hits@1 over previous state-of-the-art baselines on event-based datasets.
LLM-Based Human-Robot Collaboration Framework for Manipulation Tasks
Authors: Haokun Liu, Yaonan Zhu, Kenji Kato, Izumi Kondo, Tadayoshi Aoyama, Yasuhisa Hasegawa
Date: 2023-08-29 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, Task Planning

This paper presents a novel approach to enhance autonomous robotic manipulation using the Large Language Model (LLM) for logical inference, converting high-level language commands into sequences of executable motion functions. The proposed system combines the advantage of LLM with YOLO-based environmental perception to enable robots to autonomously make reasonable decisions and task planning based on the given commands. Additionally, to address the potential inaccuracies or illogical actions arising from LLM, a combination of teleoperation and Dynamic Movement Primitives (DMP) is employed for action correction. This integration aims to improve the practicality and generalizability of the LLM-based human-robot collaboration system.
Transfusor: Transformer Diffusor for Controllable Human-like Generation of Vehicle Lane Changing Trajectories
Authors: Jiqian Dong, Sikai Chen, Samuel Labi
Date: 2023-08-28 00:00:00
Tasks: Autonomous Driving

With ongoing development of autonomous driving systems and increasing desire for deployment, researchers continue to seek reliable approaches for ADS systems. The virtual simulation test (VST) has become a prominent approach for testing autonomous driving systems (ADS) and advanced driver assistance systems (ADAS) due to its advantages of fast execution, low cost, and high repeatability. However, the success of these simulation-based experiments heavily relies on the realism of the testing scenarios. It is needed to create more flexible and high-fidelity testing scenarios in VST in order to increase the safety and reliabilityof ADS and ADAS.To address this challenge, this paper introduces the "Transfusor" model, which leverages the transformer and diffusor models (two cutting-edge deep learning generative technologies). The primary objective of the Transfusor model is to generate highly realistic and controllable human-like lane-changing trajectories in highway scenarios. Extensive experiments were carried out, and the results demonstrate that the proposed model effectively learns the spatiotemporal characteristics of humans' lane-changing behaviors and successfully generates trajectories that closely mimic real-world human driving. As such, the proposed model can play a critical role of creating more flexible and high-fidelity testing scenarios in the VST, ultimately leading to safer and more reliable ADS and ADAS.
Patient-specific, mechanistic models of tumor growth incorporating artificial intelligence and big data
Authors: Guillermo Lorenzo, Syed Rakin Ahmed, David A. Hormuth II, Brenna Vaughn, Jayashree Kalpathy-Cramer, Luis Solorio, Thomas E. Yankeelov, Hector Gomez
Date: 2023-08-28 00:00:00

Despite the remarkable advances in cancer diagnosis, treatment, and management that have occurred over the past decade, malignant tumors remain a major public health problem. Further progress in combating cancer may be enabled by personalizing the delivery of therapies according to the predicted response for each individual patient. The design of personalized therapies requires patient-specific information integrated into an appropriate mathematical model of tumor response. A fundamental barrier to realizing this paradigm is the current lack of a rigorous, yet practical, mathematical theory of tumor initiation, development, invasion, and response to therapy. In this review, we begin by providing an overview of different approaches to modeling tumor growth and treatment, including mechanistic as well as data-driven models based on ``big data" and artificial intelligence. Next, we present illustrative examples of mathematical models manifesting their utility and discussing the limitations of stand-alone mechanistic and data-driven models. We further discuss the potential of mechanistic models for not only predicting, but also optimizing response to therapy on a patient-specific basis. We then discuss current efforts and future possibilities to integrate mechanistic and data-driven models. We conclude by proposing five fundamental challenges that must be addressed to fully realize personalized care for cancer patients driven by computational models.
Proceedings 39th International Conference on Logic Programming
Authors: Enrico Pontelli, Stefania Costantini, Carmine Dodaro, Sarah Gaggl, Roberta Calegari, Artur d'Avila Garcez, Francesco Fabiano, Alessandra Mileo, Alessandra Russo, Francesca Toni
Date: 2023-08-28 00:00:00
Tasks: Ethics

This volume contains the Technical Communications presented at the 39th International Conference on Logic Programming (ICLP 2023), held at Imperial College London, UK from July 9 to July 15, 2023. Technical Communications included here concern the Main Track, the Doctoral Consortium, the Application and Systems/Demo track, the Recently Published Research Track, the Birds-of-a-Feather track, the Thematic Tracks on Logic Programming and Machine Learning, and Logic Programming and Explainability, Ethics, and Trustworthiness.
Construct validation of a DCM for resting state fMRI
Authors: Adeel Razi, Joshua Kahan, Geraint Rees, Karl J. Friston
Date: 2014-09-21 00:00:00
Proceeding: NeuroImage 2014 9
Tasks: Functional Connectivity

Recently, there has been a lot of interest in characterising the connectivity of resting state brain networks. Most of
the literature uses functional connectivity to examine these intrinsic brain networks. Functional connectivity has
well documented limitations because of its inherent inability to identify causal interactions. Dynamic causal
modelling (DCM) is a framework that allows for the identification of the causal (directed) connections among
neuronal systems — known as effective connectivity. This technical note addresses the validity of a recently
proposed DCM for resting state fMRI – as measured in terms of their complex cross spectral density – referred
to as spectral DCM. Spectral DCM differs from (the alternative) stochastic DCM by parameterising neuronal fluctuations using scale free (i.e., power law) forms, rendering the stochastic model of neuronal activity deterministic.
Spectral DCM not only furnishes an efficient estimation of model parameters but also enables the detection of
group differences in effective connectivity, the form and amplitude of the neuronal fluctuations or both. We compare and contrast spectral and stochastic DCM models with endogenous fluctuations or state noise on hidden
states. We used simulated data to first establish the face validity of both schemes and show that they can recover
the model (and its parameters) that generated the data. We then used Monte Carlo simulations to assess the
accuracy of both schemes in terms of their root mean square error. We also simulated group differences and compared the ability of spectral and stochastic DCMs to identify these differences. We show that spectral DCM was not
only more accurate but also more sensitive to group differences. Finally, we performed a comparative evaluation
using real resting state fMRI data (from an open access resource) to study the functional integration within default
mode network using spectral and stochastic DCMs.
Bearing-based Formation with Disturbance Rejection
Authors: Haoshu Cheng, Jie Huang
Date: 2023-08-29 00:00:00
Tasks: continuous-control, Continuous Control

This paper considers the problem of the bearing-based formation control with disturbance rejection for a group of agents under the leader-follower structure. The disturbances are in the form of a trigonometric polynomial with arbitrary unknown amplitudes, unknown initial phases, and known or unknown frequencies. For the case of the known frequencies, we employ the canonical internal model to solve the problem, and, for the case of the unknown frequencies, we combine the canonical internal model and {some} distributed adaptive control technique to deal with the problem. It is noted that the existing results can only handle constant input disturbances by continuous control laws or disturbances with known {bounds} by discontinuous control laws. The first case is a special case of our result. The second case cannot cover our results because the bound of our disturbance is unknown. Moreover, our control law is smooth.
Unsupervised Active Learning: Optimizing Labeling Cost-Effectiveness for Automatic Speech Recognition
Authors: Zhisheng Zheng, Ziyang Ma, Yu Wang, Xie Chen
Date: 2023-08-28 00:00:00
Tasks: Active Learning, Automatic Speech Recognition, Automatic Speech Recognition (ASR), Self-Supervised Learning, speech-recognition, Speech Recognition

In recent years, speech-based self-supervised learning (SSL) has made significant progress in various tasks, including automatic speech recognition (ASR). An ASR model with decent performance can be realized by fine-tuning an SSL model with a small fraction of labeled data. Reducing the demand for labeled data is always of great practical value. In this paper, we further extend the use of SSL to cut down labeling costs with active learning. Three types of units on different granularities are derived from speech signals in an unsupervised way, and their effects are compared by applying a contrastive data selection method. The experimental results show that our proposed data selection framework can effectively improve the word error rate (WER) by more than 11% with the same amount of labeled data, or halve the labeling cost while maintaining the same WER, compared to random selection.
EEG-Derived Voice Signature for Attended Speaker Detection
Authors: Hongxu Zhu, Siqi Cai, Yidi Jiang, Qiquan Zhang, Haizhou Li
Date: 2023-08-28 00:00:00
Tasks: EEG

\textit{Objective:} Conventional EEG-based auditory attention detection (AAD) is achieved by comparing the time-varying speech stimuli and the elicited EEG signals. However, in order to obtain reliable correlation values, these methods necessitate a long decision window, resulting in a long detection latency. Humans have a remarkable ability to recognize and follow a known speaker, regardless of the spoken content. In this paper, we seek to detect the attended speaker among the pre-enrolled speakers from the elicited EEG signals. In this manner, we avoid relying on the speech stimuli for AAD at run-time. In doing so, we propose a novel EEG-based attended speaker detection (E-ASD) task. \textit{Methods:} We encode a speaker's voice with a fixed dimensional vector, known as speaker embedding, and project it to an audio-derived voice signature, which characterizes the speaker's unique voice regardless of the spoken content. We hypothesize that such a voice signature also exists in the listener's brain that can be decoded from the elicited EEG signals, referred to as EEG-derived voice signature. By comparing the audio-derived voice signature and the EEG-derived voice signature, we are able to effectively detect the attended speaker in the listening brain. \textit{Results:} Experiments show that E-ASD can effectively detect the attended speaker from the 0.5s EEG decision windows, achieving 99.78\% AAD accuracy, 99.94\% AUC, and 0.27\% EER. \textit{Conclusion:} We conclude that it is possible to derive the attended speaker's voice signature from the EEG signals so as to detect the attended speaker in a listening brain. \textit{Significance:} We present the first proof of concept for detecting the attended speaker from the elicited EEG signals in a cocktail party environment. The successful implementation of E-ASD marks a non-trivial, but crucial step towards smart hearing aids.
Meta-learning for model-reference data-driven control
Authors: Riccardo Busetto, Valentina Breschi, Simone Formentin
Date: 2023-08-29 00:00:00
Tasks: Meta-Learning, model, Philosophy

One-shot direct model-reference control design techniques, like the Virtual Reference Feedback Tuning (VRFT) approach, offer time-saving solutions for the calibration of fixed-structure controllers for dynamic systems. Nonetheless, such methods are known to be highly sensitive to the quality of the available data, often requiring long and costly experiments to attain acceptable closed-loop performance. These features might prevent the widespread adoption of such techniques, especially in low-data regimes. In this paper, we argue that the inherent similarity of many industrially relevant systems may come at hand, offering additional information from plants that are similar (yet not equal) to the system one aims to control. Assuming that this supplementary information is available, we propose a novel, direct design approach that leverages the data from similar plants, the knowledge of controllers calibrated on them, and the corresponding closed-loop performance to enhance model-reference control design. More specifically, by constructing the new controller as a combination of the available ones, our approach exploits all the available priors following a meta-learning philosophy, while ensuring non-decreasing performance. An extensive numerical analysis supports our claims, highlighting the effectiveness of the proposed method in achieving performance comparable to iterative approaches, while at the same time retaining the efficiency of one-shot direct data-driven methods like VRFT.
Defining and Constraining the Electrical Cardinality of Multiport Converter Mission Profiles
Authors: Matthew Deakin
Date: 2023-08-29 00:00:00
Tasks: Management

Mission profiles describe a representative set of conditions that a power converter is designed to operate under, and are known to be more complicated for multiport converter applications due to a wider range of combinations of powers that can be transferred between ports. This paper studies the properties of mission profiles derived from operational optimization of multiport converters in distribution system applications (e.g., soft open points). The electrical cardinality of the mission profile is introduced as a useful, naturally varying property of multiport mission profiles derived from optimal operation within distribution system, with the cardinality equal to the number of non-zero power transfers at a given time. Furthermore, it is shown that the cardinality can be conveniently constrained within the framework of conventional mixed-integer conic optimization problems, yielding a family of mission profiles that can enable converter designers to simplify multiport designs via converter reconfiguration. Results demonstrate the potential to reduce the cardinality in a four-terminal multiport converter from four to two whilst still effectively supporting congestion management and achieving 91.7% of the loss reduction capabilities of a conventional design.
A Scalable Approach for Analysing Multi-Agent Systems with Heterogeneous Stochastic Packet Loss
Authors: Christian Hespe, Herbert Werner
Date: 2023-08-29 00:00:00

An important aspect in jointly analysing networked control systems and their communication is to model the networking in a sufficiently rich but at the same time mathematically tractable way. As such, this paper improves on a recently proposed scalable approach for analysing multi-agent systems with stochastic packet loss by allowing for heterogeneous transmission probabilities and temporal correlation in the communication model. The key idea is to consider the transmission probabilities as uncertain, which facilitates the use of tools from robust control. Due to being formulated in terms of linear matrix inequalities that grow linearly with the number of agents, the result is applicable to very large multi-agent systems, which is demonstrated by numerical simulations with up to 10000 agents.
Enhancements on a saturated control for stabilizing a quadcopter: adaptive and robustness analysis in the flat output space
Authors: Huu-Thinh Do, Franco Blanchini, Ionela Prodan
Date: 2023-08-29 00:00:00

This paper extends our previous study on an explicit saturated control for a quadcopter, which ensures both constraint satisfaction and stability thanks to the linear representation of the system in the flat output space. The novelty here resides in the adaptivity of the controller's gain to enhance the system's performance without exciting its parasitic dynamics and avoid lavishing the input actuation with excessively high gain parameters. Moreover, we provide a thorough robustness analysis of the proposed controller when additive disturbances are affecting the system behavior. Finally, simulation and experimental tests validate the proposed controller.
Interplay of degeneracy and non-degeneracy in fluctuations propagation in coherent feed-forward loop motif
Authors: Tuhin Subhra Roy, Mintu Nandi, Pinaki Chaudhury, Sudip Chattopadhyay, Suman K Banik
Date: 2023-08-29 00:00:00

We present a stochastic framework to decipher fluctuations propagation in classes of coherent feed-forward loops. The systematic contribution of the direct (one-step) and indirect (two-step) pathways is considered to quantify fluctuations of the output node. We also consider both additive and multiplicative integration mechanisms of the two parallel pathways (one-step and two-step). Analytical expression of the output node's coefficient of variation shows contributions of intrinsic, one-step, two-step, and cross-interaction in closed form. We observe a diverse range of degeneracy and non-degeneracy in each of the decomposed fluctuations term and their contribution to the overall output fluctuations of each coherent feed-forward loop motif. Analysis of output fluctuations reveals a maximal level of fluctuations of the coherent feed-forward loop motif of type 1.
May the Force be with You: Unified Force-Centric Pre-Training for 3D Molecular Conformations
Date: 2023-08-24 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Atomic Forces, Denoising, molecular representation, Property Prediction

Recent works have shown the promise of learning pre-trained models for 3D molecular representation. However, existing pre-training models focus predominantly on equilibrium data and largely overlook off-equilibrium conformations. It is challenging to extend these methods to off-equilibrium data because their training objective relies on assumptions of conformations being the local energy minima. We address this gap by proposing a force-centric pretraining model for 3D molecular conformations covering both equilibrium and off-equilibrium data. For off-equilibrium data, our model learns directly from their atomic forces. For equilibrium data, we introduce zero-force regularization and forced-based denoising techniques to approximate near-equilibrium forces. We obtain a unified pre-trained model for 3D molecular representation with over 15 million diverse conformations. Experiments show that, with our pre-training objective, we increase forces accuracy by around 3 times compared to the un-pre-trained Equivariant Transformer model. By incorporating regularizations on equilibrium data, we solved the problem of unstable MD simulations in vanilla Equivariant Transformers, achieving state-of-the-art simulation performance with 2.45 times faster inference time than NequIP. As a powerful molecular encoder, our pre-trained model achieves on-par performance with state-of-the-art property prediction tasks.
Mixed-Effects Methods for Search and Matching Research
Authors: John M. Abowd, Kevin L. McKinney
Date: 2023-08-29 00:00:00

We study mixed-effects methods for estimating equations containing person and firm effects. In economics such models are usually estimated using fixed-effects methods. Recent enhancements to those fixed-effects methods include corrections to the bias in estimating the covariance matrix of the person and firm effects, which we also consider.
Another Look at the Linear Probability Model and Nonlinear Index Models
Authors: Kaicheng Chen, Robert S. Martin, Jeffrey M. Wooldridge
Date: 2023-08-29 00:00:00

We reassess the use of linear models to approximate response probabilities of binary outcomes, focusing on average partial effects (APE). We confirm that linear projection parameters coincide with APEs in certain scenarios. Through simulations, we identify other cases where OLS does or does not approximate APEs and find that having large fraction of fitted values in [0, 1] is neither necessary nor sufficient. We also show nonlinear least squares estimation of the ramp model is consistent and asymptotically normal and is equivalent to using OLS on an iteratively trimmed sample to reduce bias. Our findings offer practical guidance for empirical research.
Forecasting with Feedback
Authors: Robert P. Lieli, Augusto Nieto-Barthaburu
Date: 2023-08-29 00:00:00

Systematically biased forecasts are typically interpreted as evidence of forecasters' irrationality and/or asymmetric loss. In this paper we propose an alternative explanation: when forecasts inform economic policy decisions, and the resulting actions affect the realization of the forecast target itself, forecasts may be optimally biased even under quadratic loss. The result arises in environments in which the forecaster is uncertain about the decision maker's reaction to the forecast, which is presumably the case in most applications. We illustrate the empirical relevance of our theory by reviewing some stylized properties of Green Book inflation forecasts and relating them to the predictions from our model. Our results point out that the presence of policy feedback poses a challenge to traditional tests of forecast rationality.