Reviewerly/exHarmony huggingface dataset:
CCIL: Context-conditioned imitation learning for urban driving

Imitation learning holds great promise for addressing the complex task of autonomous urban driving, as experienced human drivers can navigate highly challenging scenarios with ease.While behavior cloning is a widely used imitation learning approach in autonomous driving due to its exemption from risky online interactions, it suffers from the covariate shift issue.To address this limitation, we propose a context-conditioned imitation learning approach that employs a policy to map the context state into the ego vehicle's future trajectory, rather than relying on the traditional formulation of both ego and context states to predict the ego action.Additionally, to reduce the implicit ego information in the coordinate system, we design an ego-perturbed goal-oriented coordinate system.The origin of this coordinate system is the ego vehicle's position plus a zero mean Gaussian perturbation, and the x-axis direction points towards its goal position.Our experiments on the real-world large-scale Lyft and nuPlan datasets show that our method significantly outperforms state-of-the-art approaches.
Axion field influence on Josephson junction quasipotential

The direct effect of an axion field on Josephson junctions is analyzed through the consequences on the effective potential barrier that prevents the junction from switching from the superconducting to the finite-voltage state. We describe a method to reliably compute the quasipotential with stochastic simulations, which allows to span the coupling parameter from weakly interacting axion to tight interactions. As a result, we obtain that the axion field induces a change in the potential barrier, therefore determining a significant detectable effect for such a kind of elusive particle.
Concerning Measures in a First-order Logic with Actions and Meta-beliefs

The unification of logic and probability has been seen as a long-standing concern in philosophy and mathematical logic. In this paper, we propose a new general probabilistic modal logic of belief and only-believing in the situation calculus. Our logic can express both continuous and discrete degrees of belief. More importantly, expressing degrees of belief for arbitrary first-order formulas in a dynamic setting is possible for the first time, going well beyond previous proposals where fluents are assumed to be nullary or discrete. We show that our notion of belief retains many of the properties known from the previous related work.
A Singly Exponential Transformation of LTL[X, F] into Pure Past LTL

Confronting the past can be hard. This is true even in Linear Temporal Logic (LTL), interpreted on either infinite or finite traces, when faced with the problem of transforming a temporally future formula into an equivalent one that contains past temporal modalities only. To our knowledge, the best among the available pastification procedures for full LTL, as well as for expressive enough fragments of it (that is, containing at least one temporal modality other than tomorrow), are triply exponential in the size of the input. In this paper, we focus on the fragment of LTL that features the tomorrow and eventually modalities, and provide a singly exponential pastification algorithm for it. The transformation is based on a normalisation procedure that requires a non-trivial complexity analysis, and on the subsequent generation of a pure past formula from suitably-defined dependency tree structures. Moreover, leveraging its purely syntactic nature, we present an implementation of our procedure in a temporal satisfiability checking tool that deals with both future and past modalities.
Succinctness and Complexity of ALC with Counting Perceptrons

Perceptron operators have been introduced to knowledge representation languages such as description logics in order to define concepts by listing features with associated weights and by giving a threshold. Semantically, an individual then belongs to such a concept if the weighted sum of the listed features it belongs to reaches that threshold. Such operators have been subsequently applied to cognitively-motivated modelling scenarios and to building bridges between learning and reasoning. However, they suffer from the basic limitation that they cannot consider the weight or number of role fillers. This paper introduces an extension of the basic perceptron operator language to address this shortcoming, defining the language ALCP and answering some basic questions regarding the succinctness and complexity of the new language. Namely, we show firstly that in ALCP+, when weights are positive, the language is expressively equivalent to ALCQ, whilst it is strictly more expressive in the general case allowing also negative weights. Secondly, ALCP+ is shown to be strictly more succinct than ALCQ. Thirdly, capitalising on results concerning the logic ALCSCC, we show that despite the added expressivity, reasoning in ALCP remains EXPTIME-complete.
On Conflict-free Labellings – Realizability, Construction and Patterns of Redundancy

The paper deals with the topic of realizability in abstract argumentation. More precisely, we consider the most basic kind of labellings, so-called conflict-free labellings. The understanding of these labellings is essential as any mature labelling-based semantics selects its output among these labellings. We show how to decide whether a given set of labellings is the conflict-free outcome of a certain Dungean framework. To this end we introduce several new criteria like L-tightness, reject-witness and reject-compositionality. These properties play a decisive role in the central characterization theorem. Moreover, we present a construction method, showing how to realize a conflict-free realizable labelling-set. Finally, we study the representational freedom in case of such labellings. This leads to an uniqueness result for a certain sub-class and a surprising connection to strong equivalence in case of extension-based semantics.
Forgetting Aspects in Assumption-Based Argumentation

We address the issue of forgetting in assumption-based argumentation (ABA). Forgetting is driven by the goal to remove certain elements from a knowledge base, while preserving the structure of its models as well as possible. We introduce several forgetting operators tailored to accomplish the removal of different pieces of the ABA knowledge base—assumptions, contraries, and atoms—formalizing a diverse selection of perspectives on this issue. We examine the quality of our operators by studying their compliance with suitable desiderata we propose. Thereby, we investigate the impact of the operators on the syntax of the given ABA knowledge base, its semantics, but also the instantiated argumentation framework; thus bridging recent forgetting studies on non-monotonic formalisms including argumentation theory.
Morphological anomalies of blood cells in COVID-19 patients

Since March 2020, the laboratorys workflow has been significantly modified and implemented to address the pandemic. Molecular
A Sampled-Data Control Method Related to Time for Takagi-Sugeno Fuzzy Systems via Novel Sampling-Dependent Functional Approach

This paper proposes a new sampled-data control method related to time for the stability and stabilization of Takagi-Sugeno (T-S) fuzzy systems, and provides a novel sampling-dependent functional approach to research the problem. The new sampled-data control signal involves sampling-time variable and changes with time, which is beneficial to improving control effect. As for the sampling-dependent functional approach, it improves the previously common functionals from the following two innovations. First, the distinctive basic term is able to combine aperiodic sampling in various degrees and thus become discontinuous. Second, the proposed sampling-dependent looped-functional terms are with different matrix variables for aperiodic sampling intervals. Therefore, the approach reduces the conservatism of the stability and stabilization results for the systems with aperiodic sampling. Finally, simulation examples are applied to numerically demonstrate the control effect and improvements of the new approaches and results.
A Logical Encoding for k-m-Realization of Extensions in Abstract Argumentation

We study the notion of realization of extensions in abstract argumentation. It consists in reversing the usual reasoning process: instead of computing the extensions of an argumentation framework, we want to determine whether a given set of extensions corresponds to some (set of) argumentation framework(s) (AFs); and more importantly we want to identify such an AF (or set of AFs) that realizes the set of extensions. While deep theoretical studies have been concerned with realizability of extensions sets, there are few computational approaches for solving this problem. In this paper, we generalize the concept of realizability by introducing two parameters: the number k of auxiliary arguments (i.e. those that do not appear in any extension), and the number m of AFs in the result. We define a translation of k-m-realizability into Quantified Boolean Formulas (QBFs) solving. We also show that our method allows to guarantee that the result of the realization is as close as possible to some input AF. Our method can be applied in the context of AF revision operators, where revised extensions must be mapped to a set of AFs while ensuring some notion of proximity with the initial AF.
Structured Knowledge Distillation for Accurate and Efficient Object Detection

Knowledge distillation, which aims to transfer the knowledge learned by a cumbersome teacher model to a lightweight student model, has become one of the most popular and effective techniques in computer vision. However, many previous knowledge distillation methods are designed for image classification and fail in more challenging tasks such as object detection. In this paper, we first suggest that the failure of knowledge distillation on object detection is mainly caused by two reasons: (1) the imbalance between pixels of foreground and background and (2) lack of knowledge distillation on the relation among different pixels. Then, we propose a structured knowledge distillation scheme, including <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">attention-guided distillation</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">non-local distillation</i> to address the two issues, respectively. Attention-guided distillation is proposed to find the crucial pixels of foreground objects with an attention mechanism and then make the students take more effort to learn their features. Non-local distillation is proposed to enable students to learn not only the feature of an individual pixel but also the relation between different pixels captured by non-local modules. Experimental results have demonstrated the effectiveness of our method on thirteen kinds of object detection models with twelve comparison methods for both object detection and instance segmentation. For instance, Faster RCNN with our distillation achieves 43.9 mAP on MS COCO2017, which is 4.1 higher than the baseline. Additionally, we show that our method is also beneficial to the robustness and domain generalization ability of detectors. Codes and model weights have been released on GitHub <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">†</sup>
YOUTUBE: A TEACHING MEDIA TO OPTIMIZE STUDENTS’ ENGLISH LISTENING COMPREHENSION SKILL

Listening is one of important skills in English language learning since it provides input for the students. However, many students still encounter a lot of listening difficulties caused by various factors, one of which that should be urgently solved is the use of poor and restricted materials in the language teaching and learning. As the Information and Communication Technology (ICT) develops rapidly, educators should take the advantages of it. Educators can use teaching media that provide various kinds of listening materials and can be accessed online. One of the most popular media that is accessible anytime and anywhere is YouTube. This literature review article aims to describe the use of YouTube as a media to optimize students’ English listening comprehension skill. YouTube provides a wide variety of materials for teaching language. Educators and students can use some of many recommended YouTube channels, namely BBC News Review, TED Talks, and Easy English. They provide listening materials; news review, talks, and daily conversation which are very useful for the students to improve their listening comprehension as well as their communication skills. However, several things need considering when using YouTube in language teaching. Educators need to plan the activities and find materials that suit the students’ needs, interest, and level in order to optimize the teaching of listening so that at the end, the listening comprehension skill of the students can be optimized as well. In addition, it is highly suggested that educators invite parents to work together in guiding the students when doing individual learning.
Tackling Sampling Noise in Physical Systems for Machine Learning Applications: Fundamental Limits and Eigentasks

The expressive capacity of physical systems employed for learning is limited by the unavoidable presence of noise in their extracted outputs. Though present in physical systems across both the classical and quantum regimes, the precise impact of noise on learning remains poorly understood. Focusing on supervised learning, we present a mathematical framework for evaluating the resolvable expressive capacity (REC) of general physical systems under finite sampling noise and provide a methodology for extracting its extrema, the eigentasks. Eigentasks are a native set of functions that a given physical system can approximate with minimal error. We show that the REC of a quantum system is limited by the fundamental theory of quantum measurement and obtain a tight upper bound for the REC of any finitely sampled physical system. We then provide empirical evidence that extracting low-noise eigentasks can lead to improved performance for machine learning tasks such as classification, displaying robustness to overfitting. We present analyses suggesting that correlations in the measured quantum system enhance learning capacity by reducing noise in eigentasks. The applicability of these results in practice is demonstrated with experiments on superconducting quantum processors. Our findings have broad implications for quantum machine learning and sensing applications.3 MoreReceived 4 January 2023Revised 30 August 2023Accepted 5 September 2023DOI:https://doi.org/10.1103/PhysRevX.13.041020Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasMachine learningQuantum information processingQuantum Information, Science & Technology
Variational Skill Embeddings for Meta Reinforcement Learning

Meta reinforcement learning (meta-RL) aims to learn useful prior knowledge across tasks which can be generalized to unseen but similar tasks with only a small number of adaptation steps. Traditionally, the gradient-based metal RL was proposed to use the gradients to learn the parameters of an adaptive policy from different tasks which likely lacked sample efficiency. Recently, the context-based meta-RL improved the efficiency by learning the embeddings of the trajectories based on context representation. The learned policy can be adapted to new tasks, but the performance is bounded due to a simple context encoder. To deal with this insufficiency, this paper presents a novel regularized meta-RL where the generalization of policy is enhanced through a context-based meta-RL where the conditional variational autoencoder consisting of a context-skill encoder and a soft-actor-critic decoder is implemented. The proposed method pursues the model regularization by discovering the shared skill patterns across tasks in implementation of context-based meta-RL. The experiments on a number of benchmark tasks show the merit of variational skill embeddings for regularized meta-RL.
The Effectiveness of Using the Zoom Application as an Online Learning Media for Post-Pandemic Karawang Campus Students

The learning process carried out online after the pandemic is something that needs to be studied and also evaluated, considering that during the pandemic there are obstacles during the learning process. The purpose of this study is to see an overview of the effectiveness of using the Zoom application after the pandemic. This research is qualitative research using the Grounded Theory method. The resource persons in this study amounted to 26 people consisting of students around the campus in Karawang. The data validation technique in this study uses source triangulation which is done by checking data from several sources but with different techniques. Based on the results of the study, it was found that 85% liked face-to-face learning, because face-to-face learning was more communicative, the learning process was more effective. And 58% prefer the Zoom application as the most effective Online learning medium compared to other applications
Study of Characteristics and Management of Drainage Problems in Ubud District

Ubud District is one of the tourism areas in Bali which is growing very rapidly. Ubud District has problems related to inundation and flooding which often occur during the rainy season. The drainage canals cannot accommodate the peak flood discharge. Because the capacity of the channel is insufficient, besides the influence of sediment and garbage, water overflows through the freeboard of the channel. This study aims to determine the characteristics of inundation and drainage in Ubud District. Primary and secondary data are used in this study which will then be used in data analysis. The analytical method used are field survey, identification of inundation and flood locations, inventory of existing drainage system, hydrology and hydraulics analysis, and drainage network planning. The result is 27 inundation locations were found in Ubud District. The inundation locations included 5 points in Lodtunduh Village, 3 points in Mas Village, 2 points in Peliatan Village, 5 points in Petulu Village, 2 points in Sayan Village, and 10 points in Ubud Village. The duration of the inundation that occurs is a minimum of 15 minutes and a maximum of 360 minutes. The height of the inundation that occurs is 12 – 50 cm with an inundation area of 0.032 – 4.67 ha and an inundation length of 12 – 2.309 m. The frequency of inundation is 3 to 10 times per year. Parameters for determining priority for drainage management based on priority scale are determined based on the characteristic parameters of inundation, economic losses, social disturbances and government facilities, transportation disturbances, and losses in residential areas. The recommendations given include normalizing the existing canal with river stone pairs of the required size that are more adequate to accommodate water discharge, especially during rains; making diversion channel with precast box culvert with the required size; and construction of new channels equipped with inlet drains per segment as entry points for inundation on the roads into the drainage channels.
Retracted: Qualitative Analysis of Text Summarization Techniques and Its Applications in Health Domain

[This retracts the article DOI: 10.1155/2022/3411881.].
Faster Convergence for Transformer Fine-tuning with Line Search Methods

Recent works have shown that line search methods greatly increase performance of traditional stochastic gradient descent methods on a variety of datasets and architectures [1], [2]. In this work we succeed in extending line search methods to the novel and highly popular Transformer architecture and dataset domains in natural language processing. More specifically, we combine the Armijo line search with the Adam optimizer and extend it by subdividing the networks architecture into sensible units and perform the line search separately on these local units. Our optimization method outperforms the traditional Adam optimizer and achieves significant performance improvements for small data sets or small training budgets, while performing equal or better for other tested cases. Our work is publicly available as a python package, which provides a hyperparameter-free pytorch optimizer that is compatible with arbitrary network architectures.
Evolutionary Reinforcement Learning of Neural Network Controller for Acrobot Task – Part1: Evolution Strategy

Evolutionary algorithms find applicability in the reinforcement learning of neural networks due to their independence from gradient-based methods. To achieve successful training of neural networks using evolutionary algorithms, careful considerations must be made to select appropriate algorithms due to the availability of various algorithmic variations. The author previously reported experimental evaluations on Evolution Strategy for reinforcement learning of neural networks, utilizing the pendulum control task. In this study, the Acrobot control task is adopted as another task. Experimental results demonstrate that ES successfully trained a Multi-Layer Perceptron to achieve a remarkable height of 99.85% concerning the maximum height. However, the trained MLP failed to maintain the chain end in an upright position throughout an episode. In this study, it was observed that employing 8 hidden units in the neural network yielded better results with statistical significance compared to using 4, 16, or 32 hidden units. Furthermore, the findings indicate that a larger population size in ES led to a more extensive exploration of potential solutions over a greater number of generations, which aligns with the previous study.
GPT-4 Can't Reason

GPT-4 was released in March 2023 to wide acclaim, marking a very substantial improvement across the board over GPT-3.5 (OpenAI's previously best model, which had powered the initial release of ChatGPT). Despite the genuinely impressive improvement, however, there are good reasons to be highly skeptical of GPT-4's ability to reason. This position paper discusses the nature of reasoning; criticizes the current formulation of reasoning problems in the NLP community and the way in which the reasoning performance of LLMs is currently evaluated; introduces a collection of 21 diverse reasoning problems; and performs a detailed qualitative analysis of GPT-4's performance on these problems. Based on the results of this analysis, the paper argues that, despite the occasional flashes of analytical brilliance, GPT-4 at present is utterly incapable of reasoning.