Miracle-dz/UniFAR-FTUs huggingface dataset:
Leggett collective excitations in a two-band Fermi superfluid at finite temperatures

The Leggett collective excitations for a two-band Fermi gas with s-wave pairing and Josephson interband coupling in the BCS-BEC crossover at finite temperatures are investigated within the Gaussian pair fluctuation approach. Eigenfrequencies and damping factors for Leggett modes are determined in a nonperturbative way, using the analytic continuation of the fluctuation propagator through a branch cut in the complex frequency plane, as in Kurkjian et al (2019 Phys. Rev. Lett. 122 093403). The treatment is performed beyond the low-energy expansion, which is necessary when the collective excitation energy reaches the pair-breaking continuum edge. The results are applied in particular to cold atomic gases at the orbital Feshbach resonance and in a regime far from BEC, which can be relevant for future experiments.
Translating Mathematical Formula Images to LaTeX Sequences Using Deep Neural Networks with Sequence-level Training

In this paper we propose a deep neural network model with an encoder-decoder architecture that translates images of math formulas into their LaTeX markup sequences. The encoder is a convolutional neural network (CNN) that transforms images into a group of feature maps. To better capture the spatial relationships of math symbols, the feature maps are augmented with 2D positional encoding before being unfolded into a vector. The decoder is a stacked bidirectional long short-term memory (LSTM) model integrated with soft attention mechanism, which works as a language model to translate the encoder output into a sequence of LaTeX tokens. The neural network is trained in two steps. The first step is token-level training using the Maximum-Likelihood Estimation (MLE) as the objective function. Next, at completion of the token-level training, we advance to a sequence-level training based on a new objective function inspired by the policy gradient algorithm from reinforcement learning. Our design also overcomes the exposure bias problem by closing the feedback loop in the decoder during sequence-level training, i.e., feeding in the predicted token instead of the ground truth token at every time step. The model is trained and evaluated on the IM2LATEX-100K dataset and shows state-of-the-art performance on both sequence-based and image-based evaluation metrics.
StairDag: Cross-DAG Validation For Scalable BFT Consensus

This paper introduces a new consensus protocol, so-called \emph{\stair}, for fast consensus in DAG-based trustless system. In \stair, we propose a new approach to creating local block DAG, namely \emph{x-DAG} (cross-DAG), on each node. \emph{\stair} protocol is based on our Proof-of-Stake StakeDag framework \cite{stakedag} that distinguishes participants into users and validators by their stake. Both users and validators can create and validate event blocks. Unlike StakeDag's DAG, x-DAG ensures that each new block has to have parent blocks from both Users and Validators to achieve more safety and liveness. Our protocol leverages a pool of validators to expose more validating power to new blocks for faster consensus in a leaderless asynchronous system. Further, our framework allows participants to join as observers / monitors, who can retrieve DAG for post-validation, but do not participate in onchain validation.
Earlier Isn’t Always Better: Sub-aspect Analysis on Corpus and System Biases in Summarization

Despite the recent developments on neural summarization systems, the underlying logic behind the improvements from the systems and its corpus-dependency remains largely unexplored. Position of sentences in the original text, for example, is a well known bias for news summarization. Following in the spirit of the claim that summarization is a combination of sub-functions, we define three sub-aspects of summarization: position, importance, and diversity and conduct an extensive analysis of the biases of each sub-aspect with respect to the domain of nine different summarization corpora (e.g., news, academic papers, meeting minutes, movie script, books, posts). We find that while position exhibits substantial bias in news articles, this is not the case, for example, with academic papers and meeting minutes. Furthermore, our empirical study shows that different types of summarization systems (e.g., neural-based) are composed of different degrees of the sub-aspects. Our study provides useful lessons regarding consideration of underlying sub-aspects when collecting a new summarization dataset or developing a new system.
A Methodology for Analyzing Uptake of Software Technologies Among Developers

Motivation: The question of what combination of attributes drives the adoption of a particular software technology is critical to developers. It determines both those technologies that receive wide support from the community and those which may be abandoned, thus rendering developers’ investments worthless. Aim and Context: We model software technology adoption by developers and provide insights on specific technology attributes that are associated with better visibility among alternative technologies. Thus, our findings have practical value for developers seeking to increase the adoption rate of their products. Approach: We leverage social contagion theory and statistical modeling to identify, define, and test empirically measures that are likely to affect software adoption. More specifically, we leverage a large collection of open source version control repositories (containing over 4 billion unique versions) to construct a software dependency chain for a specific set of R language source-code files. We formulate logistic regression models, where developers’ software library choices are modeled, to investigate the combination of technological attributes that drive adoption among competing data frame (a core concept for a data science languages) implementations in the R language: tidy and data.table. To describe each technology, we quantify key project attributes that might affect adoption (e.g., response times to raised issues, overall deployments, number of open defects, knowledge base) and also characteristics of developers making the selection (performance needs, scale, and their social network). Results: We find that a quick response to raised issues, a larger number of overall deployments, and a larger number of high-score StackExchange questions are associated with higher adoption. Decision makers tend to adopt the technology that is closer to them in the technical dependency network and in author collaborations networks while meeting their performance needs. To gauge the generalizability of the proposed methodology, we investigate the spread of two popular web JavaScript frameworks Angular and React, and discuss the results. Future work: We hope that our methodology encompassing social contagion that captures both rational and irrational preferences and the elucidation of key measures from large collections of version control data provides a general path toward increasing visibility, driving better informed decisions, and producing more sustainable and widely adopted software.
Classifying single-qubit noise using machine learning

Quantum characterization, validation, and verification (QCVV) techniques are used to probe, characterize, diagnose, and detect errors in quantum information processors (QIPs). An important component of any QCVV protocol is a mapping from experimental data to an estimate of a property of a QIP. Machine learning (ML) algorithms can help automate the development of QCVV protocols, creating such maps by learning them from training data. We identify the critical components of "machine-learned" QCVV techniques, and present a rubric for developing them. To demonstrate this approach, we focus on the problem of determining whether noise affecting a single qubit is coherent or stochastic (incoherent) using the data sets originally proposed for gate set tomography. We leverage known ML algorithms to train a classifier distinguishing these two kinds of noise. The accuracy of the classifier depends on how well it can approximate the "natural" geometry of the training data. We find GST data sets generated by a noisy qubit can reliably be separated by linear surfaces, although feature engineering can be necessary. We also show the classifier learned by a support vector machine (SVM) is robust under finite-sample noise.
On the Ethereum blockchain structure: A complex networks theory perspective

In this paper, we analyze the Ethereum blockchain using the complex networks modeling framework. Accounts acting on the blockchain are represented as nodes, while the interactions among these accounts, recorded on the blockchain, are treated as links in the network. Using this representation, it is possible to derive interesting mathematical characteristics that improve the understanding of the actual interactions happening in the blockchain. Not only, by looking at the history of the blockchain, it is possible to verify if radical changes in the blockchain evolution happened.
Classifying topics in speech when all you have is crummy translations

Given a large amount of unannotated speech in a language with few resources, can we classify the speech utterances by topic? We show that this is possible if text translations are available for just a small amount of speech (less than 20 hours), using a recent model for direct speech-to-text translation. While the translations are poor, they are still good enough to correctly classify 1-minute speech segments over 70% of the time - a 20% improvement over a majority-class baseline. Such a system might be useful for humanitarian applications like crisis response, where incoming speech must be quickly assessed for further action.
Want to Gather? No Need to Chatter!

A team of mobile agents, starting from different nodes of an unknown network, possibly at different times, have to meet at the same node and declare that they have all met. Agents have different labels which are positive integers, and move in synchronous rounds along links of the network. The above task is known as gathering and was traditionally considered under the assumption that when some agents are at the same node then they can talk, i.e., exchange currently available information. In this paper we ask the question of whether this ability of talking is needed for gathering. The answer turns out to be no. Our main contribution are two deterministic algorithms that always accomplish gathering in a much weaker model. We only assume that at any time an agent knows how many agents are at the node that it currently occupies but agents do not see the labels of other co-located agents and cannot exchange any information with them. They also do not see other nodes than the current one. Our first algorithm works under the assumption that agents know a priori some upper bound N on the size of the network, and it works in time polynomial in N and in the length ℓ of the smallest label. Our second algorithm does not assume any a priori knowledge about the network but its complexity is exponential in the size of the network and in the labels of agents. Its purpose is to show feasibility of gathering under this harsher scenario. As a by-product of our techniques we obtain, in the same weak model, the solution of the fundamental problem of leader election among agents: One agent is elected a leader and all agents learn its identity. As an application of our result we also solve, in the same model, the well-known gossiping problem: if each agent has a message at the beginning, we show how to make all messages known to all agents, even without any a priori knowledge about the network. If agents know an upper bound N on the size of the network then our gossiping algorithm works in time polynomial in N, in the length of the smallest label and in the length of the largest message. This result about gossiping is perhaps our most surprising finding: agents devoid of any transmitting devices can solve the most general information exchange problem, as long as they can count the number of agents present at visited nodes.
Detect Camouflaged Spam Content via StoneSkipping: Graph and Text Joint Embedding for Chinese Character Variation Representation

The task of Chinese text spam detection is very challenging due to both glyph and phonetic variations of Chinese characters. This paper proposes a novel framework to jointly model Chinese variational, semantic, and contextualized representations for Chinese text spam detection task. In particular, a Variation Family-enhanced Graph Embedding (VFGE) algorithm is designed based on a Chinese character variation graph. The VFGE can learn both the graph embeddings of the Chinese characters (local) and the latent variation families (global). Furthermore, an enhanced bidirectional language model, with a combination gate function and an aggregation learning function, is proposed to integrate the graph and text information while capturing the sequential information. Extensive experiments have been conducted on both SMS and review datasets, to show the proposed method outperforms a series of state-of-the-art models for Chinese spam detection.
The habitat, abundance and burrowing habits of the gerbil, Tatera robusta, in the Serengeti National Park, Tanzania

L'observation de terriers et une campagne de pie*geages montrent que le Gerbillido Tatera robusta est le rongeur dominant et le plus abondant dans les plaines ä herbes basses du Pare National du Serengeti. Ces rongeurs vivent sur les pentes vallonnoes et sont absents du fond des vallees qui sont inonde*es une partie de Tannde. L'horaire des captures et les observations faites ä la lampe montrent qu'ils sont actifs la nuit. Les terriers sont creuses sur des emplacements de*pourvus de ve*ge*tation.
Messen und Verarbeiten von Ladungssignalen piezoelektrischer Aufnehmer/Measuring and processing charge signals from piezoelectric transducers

Eingangs werden die beiden Meßprinzipien Elektrometerverstärker und Ladungsverstärker einander gegenübergestellt und auf die Vorteile des heute verwendeten Ladungsverstärkers hingewiesen. Dann folgt eine Analyse des realen Ladungsverstärkers unter Berücksichtigung der Störeinflüsse durch dessen nicht ideale Bauelemente. Nach der Behandlung der Kalibrierung von Ladungsverstärkern werden einige der verschiedenen Ausführungsformen beschrieben.
Mossi salutations

In 1887 the French explorer, Louis Binger, left the colony of Senegal with the idea of annexing the land of the Mossi for France. After a journey lasting several months he arrived in the village of Banema, not far from the Mossi capital of Ouagadougou. There he was entertained by Boukary Koutou, a nobleman who had been exiled from Ouagadougou by his brother, the emperor of the Mossi. During his stay in Banema, Binger observed and made notes on the local customs and trading practices. However, what struck him most was the curious manner in which people saluted his host.
Mining Energy Consumption Behavior Patterns for Households in Smart Grid

Inarguably, buying-in consumer confidence through respecting their energy consumption behavior and preferences in various energy programs is imperative but also demanding. Household energy consumption patterns, which provide great insight into consumers energy consumption behavioral traits, can be learned by understanding user activities along with appliances used and their time of use. Such information can be retrieved from the context-rich smart meters big data. However, the main challenge is how to extract complex interdependencies among multiple appliances operating concurrently, and identify appliances responsible for major energy consumption. Furthermore, due to the continuous generation of energy consumption data, over a period of time, appliance associations can change. Therefore, they need to be captured regularly and continuously. In this paper, we propose an unsupervised progressive incremental data mining mechanism applied to smart meters energy consumption data through frequent pattern mining to overcome these challenges. This can establish a foundation for efficient energy demand management while ameliorating end-user participation. The details and the results of evaluation of the proposed mechanism using real smart meters dataset are also presented in this paper.
Paper Deacidification: A Bibliographie Survey. Part II

Suggestions are made for less destructive methods of binding (in addition, criteria are listed for the selection of binders for rare books). Several methods of deacidification of bound books are noted (British Museum Research Laboratory, W. H. Langwell, R. D. Smith, and Washington State University). The paper concludes with the note that book conservation should be elevated from the level of housekeeping to a specialized profession.
Tangible Resources and Performance of County Health Services in Kenya

This paper investigated the influence of tangible resources on the performance of county health services in Kenya using resource based view approach. The research collected secondary data of all counties from ministry of health records and reports regarding current tangible resources owned by county health services departments as well as data on performance indicator achievements (over the last three years) then analyzed the relationship between the two variables to identify tangible resources associated and most useful to performance. The findings of this study confirm that there is tangible resource heterogeneity across Kenya’s county health departments that explain performance indicator achievement differentials. Not all resources contribute to superior performance. It’s just some specific resources that are responsible for superior performance. These are the critical strategic resources that the study suggests may be currently needed for improved performance in given health indicators. The study makes a recommendation for an improved approach that uses a composite performance index, a single measure of overall health performance, upon which resources are evaluated. This study that relates health resources with performance has the potential of advancing resource based theory from being a mere theoretical framework to being a practical framework for practicing managers, policy makers and planners in the health sector.
LAMINAR-TURBULENT TRANSITION CORRELATIONS IN SUPERSONIC / HYPERSONIC FLAT PLATE FLOW

Flat plate transition data in the supersonic and hypersonic flow regimes, is manipulated to provide simple transition prediction formulations. The extensive data set, which comes from a significant number of experiments with finite leading edge thickness models in various wind tunnels, has been previously assembled in a database, and elaborated to reveal the primary correlation parameters. The data is organized in a convenient way for regression analysis and the establishment of simple mathematical correlation forms. Noting the significant size of the database, and the successful correlation of the majority of the data, practicable flat plate transition prediction tools are proposed and their ranges of applicability identified. The proposed formulations capture, among other things, transition reversal at strong bluntness conditions, while at weak bluntness conditions there are indications of important initial disturbance and receptivity effects, which are not included in the present analysis. List of symbols A constant of proportionality b leading edge thickness C Chapman-Rubesin linear viscosity law constant,
Effect of using smartphones as clickers and tablets as digital whiteboards on students’ engagement and learning

This work addresses the use of tablets and smartphones to enhance both student learning and engagement. Tablets were tested as potential substitutes for digital whiteboards, while smartphones were tested as potential survey media in the classroom using a question and answer method. Two teaching strategies were evaluated and compared: (1) traditional, which includes the use of the blackboard together with PowerPoint presentations and (2) interactive, where lessons are taught with the aid of a tablet, using interactive activities and digital materials. Measures of performance outcomes achieved with these strategies were made using the question and answer method at two points, during the classes and during the end-of-term examination. Three different question and answer methodologies were studied and compared, ranging from the very traditional method where students raise their hand to answer a question to the most modern where students use their smartphones to answer. The findings demonstrated higher outcomes with interactive lessons than with traditional learning (PowerPoint included). Tablets proved to be an effective and cheaper substitute for interactive whiteboards during lessons. The results suggested that the use of interactive procedures increased student participation. Furthermore, the students were very receptive to the use of smartphones as survey media. The use of smartphones as clickers is an interesting and instant way for both teachers and students to check students’ learning and perceived engagement. Considering that students are advanced users of smartphones and/or tablets, the use of these may be considered as more convenient than clickers. In addition, it is economically more acceptable than some other audience response systems.
MMGCN: Multi-modal Graph Convolution Network for Personalized Recommendation of Micro-video

Personalized recommendation plays a central role in many online content sharing platforms. To provide quality micro-video recommendation service, it is of crucial importance to consider the interactions between users and items (i.e. micro-videos) as well as the item contents from various modalities (e.g. visual, acoustic, and textual). Existing works on multimedia recommendation largely exploit multi-modal contents to enrich item representations, while less effort is made to leverage information interchange between users and items to enhance user representations and further capture user's fine-grained preferences on different modalities. In this paper, we propose to exploit user-item interactions to guide the representation learning in each modality, and further personalized micro-video recommendation. We design a Multi-modal Graph Convolution Network (MMGCN) framework built upon the message-passing idea of graph neural networks, which can yield modal-specific representations of users and micro-videos to better capture user preferences. Specifically, we construct a user-item bipartite graph in each modality, and enrich the representation of each node with the topological structure and features of its neighbors. Through extensive experiments on three publicly available datasets, Tiktok, Kwai, and MovieLens, we demonstrate that our proposed model is able to significantly outperform state-of-the-art multi-modal recommendation methods.