Reviewerly/exHarmony huggingface dataset:
On Card guessing games: limit law for one-time riffle shuffle

We consider a card guessing game with complete feedback. A ordered deck of n cards labeled 1 up to n is riffle-shuffled exactly one time. Then, the goal of the game is to maximize the number of correct guesses of the cards, where one after another a single card is drawn from the top, and shown to the guesser until no cards remain. Improving earlier results, we provide a limit law for the number of correct guesses. As a byproduct, we relate the number of correct guesses in this card guessing game to the number of correct guesses under a two-color card guessing game with complete feedback. Using this connection to two-color card guessing, we can also show a limiting distribution result for the first occurrence of a pure luck guess.
Refining the Adaptivity Notion in the Huge Object Model

The Huge Object model for distribution testing, first defined by Goldreich and Ron in 2022, combines the features of classical string testing and distribution testing. In this model we are given access to independent samples from an unknown distribution $P$ over the set of strings $\{0,1\}^n$, but are only allowed to query a few bits from the samples. The distinction between adaptive and non-adaptive algorithms, which is natural in the realm of string testing (but is not relevant for classical distribution testing), plays a substantial role in the Huge Object model as well. In this work we show that in fact, the full picture in the Huge Object model is much richer than just that of the ``adaptive vs. non-adaptive'' dichotomy. We define and investigate several models of adaptivity that lie between the fully-adaptive and the completely non-adaptive extremes. These models are naturally grounded by viewing the querying process from each sample independently, and considering the ``algorithmic flow'' between them. For example, if we allow no information at all to cross over between samples (up to the final decision), then we obtain the locally bounded adaptive model, arguably the ``least adaptive'' one apart from being completely non-adaptive. A slightly stronger model allows only a ``one-way'' information flow. Even stronger (but still far from being fully adaptive) models follow by taking inspiration from the setting of streaming algorithms. To show that we indeed have a hierarchy, we prove a chain of exponential separations encompassing most of the models that we define.
Avoidance of Concave Obstacles through Rotation of Nonlinear Dynamics

Controlling complex tasks in robotic systems, such as circular motion for cleaning or following curvy lines, can be dealt with using nonlinear vector fields. In this paper, we introduce a novel approach called rotational obstacle avoidance method (ROAM) for adapting the initial dynamics when the workspace is partially occluded by obstacles. ROAM presents a closed-form solution that effectively avoids star-shaped obstacles in spaces of arbitrary dimensions by rotating the initial dynamics towards the tangent space. The algorithm enables navigation within obstacle hulls and can be customized to actively move away from surfaces, while guaranteeing the presence of only a single saddle point on the boundary of each obstacle. We introduce a sequence of mappings to extend the approach for general nonlinear dynamics. Moreover, ROAM extends its capabilities to handle multi-obstacle environments and provides the ability to constrain dynamics within a safe tube. By utilizing weighted vector-tree summation, we successfully navigate around general concave obstacles represented as a tree-of-stars. Through experimental evaluation, ROAM demonstrates superior performance in terms of minimizing occurrences of local minima and maintaining similarity to the initial dynamics, outperforming existing approaches in multi-obstacle simulations. The proposed method is highly reactive, owing to its simplicity, and can be applied effectively in dynamic environments. This was demonstrated during the collision-free navigation of a 7 degree-of-freedom robot arm around dynamic obstacles
Enhancing Dialogue Generation via Dynamic Graph Knowledge Aggregation

Incorporating external graph knowledge into neural chatbot models has been proven effective for enhancing dialogue generation. However, in conventional graph neural networks (GNNs), message passing on a graph is independent from text, resulting in the graph representation hidden space differing from that of the text. This training regime of existing models therefore leads to a semantic gap between graph knowledge and text. In this study, we propose a novel framework for knowledge graph enhanced dialogue generation. We dynamically construct a multi-hop knowledge graph with pseudo nodes to involve the language model in feature aggregation within the graph at all steps. To avoid the semantic biases caused by learning on vanilla subgraphs, the proposed framework applies hierarchical graph attention to aggregate graph features on pseudo nodes and then attains a global feature. Therefore, the framework can better utilise the heterogeneous features from both the post and external graph knowledge. Extensive experiments demonstrate that our framework outperforms state-of-the-art (SOTA) baselines on dialogue generation. Further analysis also shows that our representation learning framework can fill the semantic gap by coagulating representations of both text and graph knowledge. Moreover, the language model also learns how to better select knowledge triples for a more informative response via exploiting subgraph patterns within our feature aggregation process. Our code and resources are available at https://github.com/tangg555/SaBART.
Successive Cancellation Automorphism List Decoding of Polar Codes

The discovery of suitable automorphisms of polar codes gained a lot of attention by applying them in Automorphism Ensemble Decoding (AED) to improve the error-correction performance, especially for short block lengths. This paper introduces Successive Cancellation Automorphism List (SCAL) decoding of polar codes as a novel application of automorphisms in advanced Successive Cancellation List (SCL) decoding. Initialized with L permutations sampled from the automorphism group, a superposition of different noise realizations and path splitting takes place inside the decoder. In this way, the SCAL decoder automatically adapts to the channel conditions and outperforms the error-correction performance of conventional SCL decoding and AED. For a polar code of length 128, SCAL performs near Maximum Likelihood (ML) decoding with L=8, in contrast to M=16 needed decoder cores in AED. Application-Specific Integrated Circuit (ASIC) implementations in a 12 nm technology show that high-throughput, pipelined SCAL decoders outperform AED in terms of energy efficiency and power density, and SCL decoders additionally in area efficiency.
Assessment of alternative covariance functions for joint input-state estimation via Gaussian Process latent force models in structural dynamics

Digital technologies can be used to gather accurate information about the behavior of structural components for improving systems design, as well as for enabling advanced Structural Health Monitoring strategies. New avenues for achieving automated and continuous structural assessment are opened up via development of virtualization approaches delivering so-called Digital Twins, i.e., digital mirrored representations of physical. In this framework, the main motivation of this work stems from the existing challenges in the implementation and deployment of a real-time predictive framework for virtualization of dynamic systems. Kalman-based filters are usually employed in this context to address the task of joint input-state prediction in structural dynamics. A Gaussian Process Latent Force Model (GPLFM) approach is exploited in this work to construct flexible data-driven a priori models for the unknown inputs, which are then coupled with a mechanistic model of the structural component under study for input-state estimation. The use of GP regression for this task overcomes the limitations of the conventional random-walk model, thus limiting the necessity of offline user-dependent calibration of this type of data assimilation methods. This paper proposes the use of alternative covariance functions for GP regression in structural dynamics. A theoretical analysis of the GPLFMs linked to the investigated covariance functions is offered. The outcome of this study provides insights into the applicability of each covariance type for GP-based input-state estimation. The proposed framework is validated via an illustrative simulated example, namely a 3 Degrees of Freedom system subjected to an array of different loading scenarios. Additionally, the performance of the method is experimentally assessed on the task of joint input-state estimation during testing of a 3D-printed scaled wind turbine blade.
An Adversarial Multi-Task Learning Method for Chinese Text Correction with Semantic Detection

Text correction, especially the semantic correction of more widely used scenes, is strongly required to improve, for the fluency and writing efficiency of the text. An adversarial multi-task learning method is proposed to enhance the modeling and detection ability of character polysemy in Chinese sentence context. Wherein, two models, the masked language model and scoring language model, are introduced as a pair of not only coupled but also adversarial learning tasks. Moreover, the Monte Carlo tree search strategy and a policy network are introduced to accomplish the efficient Chinese text correction task with semantic detection. The experiments are executed on three datasets and five comparable methods, and the experimental results show that our method can obtain good performance in Chinese text correction task for better semantic rationality.
Representation Learning via Variational Bayesian Networks

We present Variational Bayesian Network (VBN) - a novel Bayesian entity representation learning model that utilizes hierarchical and relational side information and is particularly useful for modeling entities in the ``long-tail'', where the data is scarce. VBN provides better modeling for long-tail entities via two complementary mechanisms: First, VBN employs informative hierarchical priors that enable information propagation between entities sharing common ancestors. Additionally, VBN models explicit relations between entities that enforce complementary structure and consistency, guiding the learned representations towards a more meaningful arrangement in space. Second, VBN represents entities by densities (rather than vectors), hence modeling uncertainty that plays a complementary role in coping with data scarcity. Finally, we propose a scalable Variational Bayes optimization algorithm that enables fast approximate Bayesian inference. We evaluate the effectiveness of VBN on linguistic, recommendations, and medical inference tasks. Our findings show that VBN outperforms other existing methods across multiple datasets, and especially in the long-tail.
Centrality-dependent Lévy HBT analysis in $\sqrt{s_{_{\text{NN}}}}=5.02$ TeV PbPb collisions at CMS

The measurement of two-particle Bose-Einstein momentum correlation functions are presented using $\sqrt{s_{_{\text{NN}}}}=5.02$ TeV PbPb collision data, recorded by the CMS experiment in 2018. The measured correlation functions are discussed in terms of Lévy type source distributions. The Lévy source parameters are extracted as functions of transverse mass and collision centrality. These source parameters include the correlation strength $λ$, the Lévy stability index $α$, and the Lévy scale parameter $R$. The source shape, characterized by $α$, is found to be neither Gaussian nor Cauchy. A hydrodynamic-like scaling of $R$ is also observed.
Multi-Site Clinical Federated Learning using Recursive and Attentive Models and NVFlare

The prodigious growth of digital health data has precipitated a mounting interest in harnessing machine learning methodologies, such as natural language processing (NLP), to scrutinize medical records, clinical notes, and other text-based health information. Although NLP techniques have exhibited substantial potential in augmenting patient care and informing clinical decision-making, data privacy and adherence to regulations persist as critical concerns. Federated learning (FL) emerges as a viable solution, empowering multiple organizations to train machine learning models collaboratively without disseminating raw data. This paper proffers a pragmatic approach to medical NLP by amalgamating FL, NLP models, and the NVFlare framework, developed by NVIDIA. We introduce two exemplary NLP models, the Long-Short Term Memory (LSTM)-based model and Bidirectional Encoder Representations from Transformers (BERT), which have demonstrated exceptional performance in comprehending context and semantics within medical data. This paper encompasses the development of an integrated framework that addresses data privacy and regulatory compliance challenges while maintaining elevated accuracy and performance, incorporating BERT pretraining, and comprehensively substantiating the efficacy of the proposed approach.
cuSLINK: Single-linkage Agglomerative Clustering on the GPU

In this paper, we propose cuSLINK, a novel and state-of-the-art reformulation of the SLINK algorithm on the GPU which requires only $O(Nk)$ space and uses a parameter $k$ to trade off space and time. We also propose a set of novel and reusable building blocks that compose cuSLINK. These building blocks include highly optimized computational patterns for $k$-NN graph construction, spanning trees, and dendrogram cluster extraction. We show how we used our primitives to implement cuSLINK end-to-end on the GPU, further enabling a wide range of real-world data mining and machine learning applications that were once intractable. In addition to being a primary computational bottleneck in the popular HDBSCAN algorithm, the impact of our end-to-end cuSLINK algorithm spans a large range of important applications, including cluster analysis in social and computer networks, natural language processing, and computer vision. Users can obtain cuSLINK at https://docs.rapids.ai/api/cuml/latest/api/#agglomerative-clustering
On Practical Aspects of Aggregation Defenses against Data Poisoning Attacks

The increasing access to data poses both opportunities and risks in deep learning, as one can manipulate the behaviors of deep learning models with malicious training samples. Such attacks are known as data poisoning. Recent advances in defense strategies against data poisoning have highlighted the effectiveness of aggregation schemes in achieving state-of-the-art results in certified poisoning robustness. However, the practical implications of these approaches remain unclear. Here we focus on Deep Partition Aggregation, a representative aggregation defense, and assess its practical aspects, including efficiency, performance, and robustness. For evaluations, we use ImageNet resized to a resolution of 64 by 64 to enable evaluations at a larger scale than previous ones. Firstly, we demonstrate a simple yet practical approach to scaling base models, which improves the efficiency of training and inference for aggregation defenses. Secondly, we provide empirical evidence supporting the data-to-complexity ratio, i.e. the ratio between the data set size and sample complexity, as a practical estimation of the maximum number of base models that can be deployed while preserving accuracy. Last but not least, we point out how aggregation defenses boost poisoning robustness empirically through the poisoning overfitting phenomenon, which is the key underlying mechanism for the empirical poisoning robustness of aggregations. Overall, our findings provide valuable insights for practical implementations of aggregation defenses to mitigate the threat of data poisoning.
Development of Android-Based Learning Media on Operations of Integers

The ongoing digitalizing education makes the use of technology is necessary to develop the learning media. Therefore, this research describes the development process of android-based learning media with the ADDIE model and find out the validity and practicality of it. The material is operations of integers because it’s a prerequisite for many other mathematics material. The research subjects were nine 7th-grade students in SMPN 5 Jombang since they had never been given the kind of math learning media based on Android in that school. They were consist of three categories of low, medium, and high mathematical ability to determine whether all types of students can understand the presence of media to achieve their learning mastery. The data analyzed using qualitative descriptive and quantitative data analysis techniques. The result of this research and development are : 1) The Android-based learning media called Math Moist; 2) The learning media is very valid based on the results of media and material validation with a validity percentage of 83.541%; 3) The media is very practical based on the student's response questionnaire scores, with a percentage of practicality is 88.7301%; 4) Based on the result of learning outcomes test, 8 of the 9 research subjects have completed learning after using media.&#x0D; Keywords: development, media, android-based, ADDIE, integer.
Bringing Intercultural Perspectives into General EFL Learning during Covid-19: Some Insights

Studies on intercultural language learning have revolved around the descriptive report of cultural facts, adjustment to the target language's culture, and cross-cultural do's and don'ts. They have not yet exposed the issue in much detail, especially in an emergency situation. This study aims to investigate practical ideas on how to engage Indonesian Islamic higher education students with intercultural perspectives during the Covid-19 pandemic. It addresses what the teachers think about interculturality in the general EFL learning and how they accommodate intercultural perspectives during the virtual class. Using a narrative inquiry, the data were collected through semi-structured interviews and observations. The researcher employed thematic analysis and theoretical lenses on interculturality-based language learning. The study showed that interculturality in EFL learning was not only unavoidable but also indispensable. This learning suggested an equal position between teachers and students in terms of authority of knowledge and vocal participation. The teachers also accommodated the core principles of intercultural perspectives in general EFL learning through proactive construction, connection-making, social interaction, reflection, and commitment, even though they did not take place optimally during virtual learning. Here, the students engaged with one another to exchange information, link ideas, and make sense of meanings. The overall findings of this study imply the vital role of teachers in helping students recognize, appreciate, and respond to cultural variety during the virtual learning process.
Component Training of Personnel for the Documentary Information Communications System in Ukraine: Methodology and Research Methods

The aim of the article is to substantiate the main methodological approaches and research methods of the personnel training for the document and information communication (DIC) system in Ukraine as a direction within the integrated speciality 029 “Information, library and archival affairs” (ILAA), and a scientific specialty 27.00.03 – Book studies, library studies, bibliographic studies (scientific field 27 – social communications).&#x0D; Research methods. Methodological approaches to the study of the chosen object are determined by the social orientation of document and information activity, the historical nature of the processes of its development as a pedagogical system, professionalization and differentiation of the professional space, and are determined by the social order for the training of personnel with appropriate qualifications.&#x0D; Scientific novelty consists in the substantiation of methodological approaches and in determination the research methods of the offered components, which are distinguished by explication of the research object, among which are these ones: theoretical and methodological foundations of the study of the specialists’ training; social requirements for the training of specialists; organizational and educational conditions for the training of specialists, and the conceptual model of training specialists for the system of document and information communications in conditions of digitalization.&#x0D; Main conclusions. The development of the personnel training system for the DIC system is an innovative process that is invested in the task of social design as a type of scientific and practical activity, the construction of desired future states and the diagnosis of current and promising problems of training new generation specialists.&#x0D; The broad context of the study of the chosen object – historical, sociocultural and pedagogical – requires the application of appropriate methods of these sciences at each of the stages of study and to each of the components of the object.&#x0D; It is emphasized that the study of the formation and development of personnel training for the system of document and information communications in Ukraine in the historical context and the processes of development of innovative content and organizational and educational conditions of functioning are not exhausted by the offered methodological approaches to the research of the chosen direction, which are variable and involve the use of other research methods as well.
Local Knowledge Electronic Resources Development as a Priority Field of Ukrainian Public Libraries Activity

The aim of the article is to analyze the formation of local knowledge electronic resources as a priority field of public libraries activity in Ukraine.&#x0D; Research methods of the article grounded on general scientific principles of objectivity and systematicity, complexity and comprehensiveness of knowledge. The use of formal-logical and social-communication approaches contributed to the achievement of the set goal.&#x0D; Scientific novelty consists in substantiating the importance of intensifying the work on the formation of local knowledge electronic resources by Ukrainian public book collections, which will become an important component of the National Electronic Library of Ukraine in future.&#x0D; Main conclusions. Management of local knowledge electronic resources is an urgent issue for all public libraries of the country. That is why it is extremely important to focus attention on the questions of forming consolidated electronic catalogs, elaborating a mechanism for electronic ibraries integration, and preparing a unified plan for digitization of local history publications. Nowadays, it is necessary to ensure an effective coordination and communication between the leading libraries of the state for the preparation of a single plan of current and planned projects, definition of the development strategy, and provision of practical recommendations for the further formation of local knowledge electronic resources. The unification of all electronic libraries of the country into an integrated corporate system “The National Electronic Library of Ukraine” will contribute to the optimization of costs for its formation in future, as well as increase the effectiveness of communication processes organization. Thanks to the creation of this interactive digital space, there will occur more opportunities for implementing various models of the country’s information space functioning, including libraries, archives, and museums. This new innovative paradigm involves electronic, service and partnership models of work. It will also cause to strengthening the sense of unity with the entire information community, increase motivation to contribute to the development of the library sphere.