Reviewerly/exHarmony huggingface dataset:
Complex spatio-temporal polarization dynamics of a broad-area VCSEL in continuous-wave operation

The dynamics of vertical-cavity surface emitting lasers (VCSELs) is interesting since they exhibit modes with different polarization states and can exhibit ultrafast dynamics. Chaotic dynamics and polarization switching have been investigated in VCSELs with a single spatial mode, as well as their use for dual-task reservoir computing. Broad-area VCSELs feature additionally multiple transverse modes. Both the formation of static spatial structures in free-running broad-area VCSELs and cavity solitons in an external cavity have been studied. There are, however, very few experimental works on the spatio-temporal dynamics of free-running broad-area VCSELs. We present an experimental study of the complex ultrafast dynamics of a broad-area VCSEL under continuous-wave electrical pumping in the spatial, spectral and polarization domains. The VCSEL has a 15 µm wide circular aperture and exhibits multi-mode lasing in different transverse modes. The lasing modes feature two orthogonal linear polarization states, and the dominant polarization as well as the active transverse modes change multiple times with increasing pump current. The switching of the polarization and the transverse modes are thermally induced and correlated with one another, hinting at coupling of the spatial and polarization degrees of freedom via the active medium. The polarization switching points are accompanied by changes in the temporal dynamics, with series of bifurcations yielding increasingly complex temporal dynamics. The dominant frequency of the unstable temporal dynamics is similar to the frequency splitting between modes of different polarization, suggesting an important role of the birefringence in the laser dynamics. The combined analysis of the spatial, spectral and polarization data shows a complex interplay of the different spatial and polarization modes and opens the way for possible applications for example in neuromorphic computing.
Learning by Doing

This scholarly essay is about research-creation in the field of interactive, immersive and digital non-fiction storytelling. It seeks to shed light and update this approach to research, and to identify ways in which it can be rendered more accessible to both practitioners and researchers. The essay revisits two recent factual narratives—web-documentary Field Trip (2019) and VR experience Myriad (2021), projects in which the authors were directly involved as practitioners. It positions these two digital practices in the body of literature on media innovations, before qualifying them as interactive and immersive documentaries (i-docs). Field Trip is a browser-based 92-minutes documentary taking a deep dive into the history and social struggles around the Tempelhof Field, Berlin’s former airport turned public park. Myriad is a 32-minutes immersive VR experience narrated from the perspective of globally migrating animals, sensitizing the audience to the interplay and interrelationship of all life forms. The authors explain how these projects have, in their respective and singular ways, opened spaces for iterative loops between creative practice and theory. Field Trip serves to exemplify how triangulation can be applied to creative media practice. This method, albeit a relatively classic one in research, continues to be a challenge for practitioners. The case of Myriad, in turn, is used to discuss two additional methods: core to audience and interdisciplinary iteration in experimental aesthetics. While these two methods in format development and artistic enquiry might sound familiar to practitioners, they are promising for closing some gaps in the design of academic research. The two case studies speak to the diversity of research-creation approaches, methods and formats, while at the same illustrating how research-creation, as a research mindset, can facilitate the output of two things at once: a more ‘educated’ artistic expression and more grounded academic knowledge. The essay further identifies the need to systematize and offer continuous support to researchers-creators. It argues that this fits the mandate of higher education art and design schools, which should be understood as research-creation competence centers. The paper ends on five learnings meant to encourage digital media practitioners to develop an enquiry reflex and media scholars to “get their hands dirty” in partaking in innovative creative media projects.
Construction of Virtual Process Tracking System for Agricultural Product Marketing Based on Blockchain Model

In the era of high concentration of Internet information technology, blockchain technology has been effectively applied in the marketing management of agricultural products, and actively promoted the transformation and upgrading of the agricultural economic structure. Therefore, the deep integration of blockchain technology and agricultural product marketing management is a necessary requirement for high-quality economic and social development. Taking the concept definition as the starting point, combined with the actual situation, from the construction of the e-commerce system of agricultural products. And adopt the practical Byzantine Fault Tolerance (PBFT) consensus algorithm to ensure the consistency of the blockchain data information, reduce the consensus time of the whole network, and ensure the information related to agricultural products. Immutability and traceability.
A Monolithic Three-Dimensional Linear Ion Trap

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BBTv2: Pure Black-Box Optimization Can Be Comparable to Gradient Descent  for Few-Shot Learning

Black-Box Tuning (BBT) is a derivative-free approach to optimize continuous prompt tokens prepended to the input of language models. Although BBT has achieved comparable performance to full model tuning on simple classification tasks under few-shot settings, it requires pre-trained prompt embedding to match model tuning on hard tasks (e.g., entailment tasks), and therefore does not completely get rid of the dependence on gradients. In this paper we present BBTv2, a pure black-box optimization approach that can drive language models to achieve comparable results to gradient-based optimization. In particular, we prepend continuous prompt tokens to every layer of the language model and propose a divide-and-conquer algorithm to alternately optimize the prompt tokens at different layers. For the optimization at each layer, we perform derivative-free optimization in a low-dimensional subspace, which is then randomly projected to the original prompt parameter space. Experimental results show that BBTv2 not only outperforms BBT by a large margin, but also achieves comparable or even better performance than full model tuning and state-of-the-art parameter-efficient methods (e.g., Adapter, LoRA, BitFit, etc.) under few-shot learning settings, while maintaining much fewer tunable parameters.
Down and Across: Introducing Crossword-Solving as a New NLP Benchmark

Solving crossword puzzles requires diverse reasoning capabilities, access to a vast amount of knowledge about language and the world, and the ability to satisfy the constraints imposed by the structure of the puzzle. In this work, we introduce solving crossword puzzles as a new natural language understanding task. We release the specification of a corpus of crossword puzzles collected from the New York Times daily crossword spanning 25 years and comprised of a total of around nine thousand puzzles. These puzzles include a diverse set of clues: historic, factual, word meaning, synonyms/antonyms, fill-in-the-blank, abbreviations, prefixes/suffixes, wordplay, and cross-lingual, as well as clues that depend on the answers to other clues. We separately release the clue-answer pairs from these puzzles as an open-domain question answering dataset containing over half a million unique clue-answer pairs. For the question answering task, our baselines include several sequence-to-sequence and retrieval-based generative models. We also introduce a non-parametric constraint satisfaction baseline for solving the entire crossword puzzle. Finally, we propose an evaluation framework which consists of several complementary performance metrics.
A Novel Approach Towards Degree and Walsh-Transform of Boolean Functions

Boolean functions are fundamental bricks in the development of various applications in Cryptography and Coding theory by making benefit from the weights of related Boolean functions (Walsh spectrum). Towards this, the discrete Fourier transform (Walsh–Hadamard) plays a pivotal tool. The work in this paper is dedicated towards the algebraic and numerical degrees, together with the relationship between weights of Boolean function and their Walsh transforms. We introduce Walsh matrices and generalize them to any arbitrary Boolean function. This improves the complexity in computation of Walsh–Hadamard and Fourier transform in certain cases. We also discuss some useful results related to the degree of the algebraic normal form using Walsh–Hadamard transform.
Fusion of PHOG and Gabor for facial expression recognition

Facial expression is the real expression of people's inner emotions. Research on facial expression recognition can make machines better understand human emotional information, among which feature extraction is particularly important. This paper proposes a facial expression feature extraction method based on PHOG (Pyramid Histogram of Oriented Gradients) and Gabor. PHOG can better represent the gradient information and edge direction gradient information of facial expression images. In the frequency domain, Gabor uses the multi-scale filter to extract texture features of images and filter out irrelevant face information. In this paper, we first perform principal component analysis (PCA) on the two feature operators, then fuse the two feature vectors, and finally realize facial expression recognition in static images by a support vector machine (SVM).
Any-to-any connected cavity-mediated architecture for quantum computing with trapped ions or Rydberg arrays

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Data Narratives

The context of the COVID-19 pandemic provides an opportunity to revisit the relations between database and narrative, to look at how data narratives represent the larger conventional distinctions between science and storytelling. Broadly, data accrues meaning through the processes of interpretation and framing that are involved in creating narratives, while narrative extends the reach and impact of data. This chapter traces a continuum across a range of data narratives published during the pandemic, with interactivity and visualization playing mediating roles in order to represent both the scope of the problem and the ground-level human perspective.
DNN Diagnosis and Cure Based on Aggregated Concentration Ratio and Residual Connection

Designing deep neural network (DNN) models, empirically or automatically, has become a popular research area in recent years. Successful examples include ResNet, ConvNeXt, MnasNet, and NetAdaptV2. A standard pipeline of model users is to pretrain a designed DNN network, and concatenate it with additional layers which are specified for a downstream application. However, we find such paradigm, i.e., pretraining and concatenation, cannot always guarantee a satisfactory prediction ability, even if the pre-trained network has been extensively applied before. In this work, we propose a self diagnosis and cure method for empirical DNN design. Our diagnosis method can automatically identify the layer that incurs the largest accuracy degradation. The diagnosis criterion, aggregated concentration ratio, adopts the difficulty of linearly representing a layer as the indicator, instead of traditional means such as subjective evaluation and extra datasets. Moreover, the problematic layer will be cured by adding a residual connection with a 3x3 convolutional kernel. Experiments on object detection show that accuracy of our cured detector with 2.8M parameters is close to ResNet-101 with 44.5M parameters. Experiments on unsupervised domain adaptation further show that accuracy of our cured domain adaptor with 2.8M parameters is close to AlexNet with 61M parameters. Sensitivity analysis validates that curing our identified layer achieves better accuracy compared to curing other 2, 3, 4, and 5 layers in a DNN.
From Easy to Hard: Two-stage Selector and Reader for Multi-hop Question Answering

Multi-hop question answering (QA) is a challenging task requiring QA systems to perform complex reasoning over multiple documents and provide supporting facts together with the exact answer. Existing works tend to utilize graph-based reasoning and question decomposition to obtain the reasoning chain, which inevitably introduces additional complexity and cumulative error to the system. To address the above issue, we propose a simple yet effective novel framework, From Easy to Hard (FE2H), to remove distracting information and obtain better contextual representations for the multi-hop QA task. Inspired by the iterative document selection process and the progressive learning custom of humans, FE2H divides both the document selector and reader into two stages following an easy-to-hard manner. Specifically, we first select the document most relevant to the question and then utilize the question together with this document to select other pertinent documents. As for the QA phase, our reader is first trained on a single-hop QA dataset and then transferred into the multi-hop QA task. We comprehensively evaluate our model on the popular multi-hop QA benchmark HotpotQA. Experimental results demonstrate that our method ourperforms all other methods in the leaderboard of HotpotQA (distractor setting).
Learning Program Synthesis for Integer Sequences from Scratch

We present a self-learning approach for synthesizing programs from integer sequences. Our method relies on a tree search guided by a learned policy. Our system is tested on the On-Line Encyclopedia of Integer Sequences. There, it discovers, on its own, solutions for 27987 sequences starting from basic operators and without human-written training examples.
Byzantine-Robust Federated Learning with Optimal Statistical Rates and Privacy Guarantees

We propose Byzantine-robust federated learning protocols with nearly optimal statistical rates. In contrast to prior work, our proposed protocols improve the dimension dependence and achieve a tight statistical rate in terms of all the parameters for strongly convex losses. We benchmark against competing protocols and show the empirical superiority of the proposed protocols. Finally, we remark that our protocols with bucketing can be naturally combined with privacy-guaranteeing procedures to introduce security against a semi-honest server. The code for evaluation is provided in https://github.com/wanglun1996/secure-robust-federated-learning.
Real Time Detection Free Tracking of Multiple Objects Via Equilibrium Optimizer

Multiple objects tracking (MOT) is a difficult task, as it usually requires special hardware and higher computation complexity. In this work, we present a new framework of MOT by using of equilibrium optimizer (EO) algorithm and reducing the resolution of the bounding boxes of the objects to solve such problems in the detection free framework. First, in the first frame the target objects are initialized and its size is computed, then its resolution is reduced if it is higher than a threshold, and then modeled by their kernel color histogram to establish a feature model. The Bhattacharya distances between the histogram of object models and other candidates are used as the fitness function to be optimized. Multiple agents are generated by EO, according to the number of the target objects to be tracked. EO algorithm is used because of its efficiency and lower computation cost compared to other algorithms in global optimization. Experimental results confirm that EO multi-object tracker achieves satisfying tracking results then other trackers.
Impact of Digitalisation on the Cognitive Functions of University Students in the Framework of Foreign Language Learning

The aim of the study is to identify the impact of education digitalisation on the cognitive functions of students at the State University of Aerospace Instrumentation (SUAI) in the process of foreign language learning. The paper discusses the concepts of “education digitalisation”, “cognition of foreign language learning” and its impairments in the process of digitalisation for their further consideration in the development of comprehensive measures to exclude or reduce cognitive impairments in students when teaching a foreign language. Scientific novelty lies in describing students’ cognitive functions during digitalisation in the learning environment, as well as in developing comprehensive measures aimed at improving the efficiency of classroom work and extracurricular independent work during digitalisation. As a result, the author characterises the main issues of education digitalisation in the process of foreign language learning that affect students’ cognitive functions and suggests ways to eliminate these impairments.
Non-volatile Memory Application to Quantum Error Correction with Non-uniformly Quantized CiM

Non-volatile memory (NVM)-based Computation-in-Memory (CiM) decoder is proposed for Quantum Error Correction (QEC). When the QEC circuitry is placed at cryogenic temperature, the power and area budgets are limited in order to save the cooling power. Compared with the SRAM-based CiM, NVMs have the advantage of saving memory size, power consumption, and data transfer to load the weights. Through the investigation on bit error rate (BER) tolerance, it is revealed that the accuracy of QEC can be maintained even with the high weight BER (up to 0.3%), opening the door to NVM application to QEC. In addition, two types of non-uniform weight quantization schemes (gate focused and layer focused) are proposed through the comprehensive investigation on bit-precision sensitivity. In the proposed schemes, the memory size is reduced by over 40% compared with the uniformly quantized CiM.
Efficient feature matching algorithms applied to robust image processing

Image feature matching is an essential problem in computer vision. Also, feature matching is an important task in many computer vision applications, such as in VR technology, drones, and autonomous driving. In this paper, four algorithms for matching are presented first. We perform outdoor scene feature matching experiments on four algorithms: Surf, Sift, Orb, Brisk. The original image is rotated, the sharpness is continuously blurred, the scene is transformed, specific regions are scaled, and the resolution is changed. The number of feature points that correctly matched the transformed image to the original image was compared. In this paper, the stability is determined by comparing the operation time of the four algorithms in the case of unified processing. Then, the RANSAC algorithm detects the proportion of internal and external points and compares the robustness. It is concluded that the ORB algorithm is the fastest, and the SURF algorithm is the most robust.
GRETEL: A unified framework for Graph Counterfactual Explanation  Evaluation

Machine Learning (ML) systems are a building part of the modern tools which impact our daily life in several application domains. Due to their black-box nature, those systems are hardly adopted in application domains (e.g. health, finance) where understanding the decision process is of paramount importance. Explanation methods were developed to explain how the ML model has taken a specific decision for a given case/instance. Graph Counterfactual Explanations (GCE) is one of the explanation techniques adopted in the Graph Learning domain. The existing works of Graph Counterfactual Explanations diverge mostly in the problem definition, application domain, test data, and evaluation metrics, and most existing works do not compare exhaustively against other counterfactual explanation techniques present in the literature. We present GRETEL, a unified framework to develop and test GCE methods in several settings. GRETEL is a highly extensible evaluation framework which promotes the Open Science and the evaluations reproducibility by providing a set of well-defined mechanisms to integrate and manage easily: both real and synthetic datasets, ML models, state-of-the-art explanation techniques, and evaluation measures. To present GRETEL, we show the experiments conducted to integrate and test several synthetic and real datasets with several existing explanation techniques and base ML models.
Streamlining online student library access

The article addresses how a traditional Public commuter college library in the Bronx began making strides to automate student ID barcodes so online library access was granted without in person registrations. How does a barcode number get generated for an individual student and what offices need to be included in automating a library account? The author discusses the College's expansion of its online education program, strategies to serve this community and the eventual development of a digital ID card allowing students instant access to online resources. The article also reports data from a trouble ticket system developed for patron log in issues using Libwizard from Springshare.
THE ROLE OF GROUP WORK IN TEACHING WRITTEN SPEECH IN ENGLISH FOR SENIOR STUDENTS

The relevance of the article is to study the effectiveness of group work in teaching written speech in English to high school students. The authors consider how group work can help improve English writing skills, including essays. The main purpose of the study is to determine the effectiveness of group work in teaching written language in English. The objectives of the study are to describe the results of group work to improve written skills, compare the results of written skills compared with students who use traditional (based on the curriculum and textbooks) methods of teaching English written. The influence of the method of group work during the teaching of written speech on the attitude and perception of students of this type of foreign language speech was also established. The basis of group work in teaching written speech is based on the idea of well-known scholars about the need to collaborate with others, bringing ideas for quality learning and growth. Social interaction, as the basis of cognitive development in sociocultural theory, is the basis of this article. Our main belief is that the cognitive growth of each child is the result of social interaction. This theory plays an important role in influencing the practice of teaching writing fatigue in the classroom. Some scholars have stated that their research has shown that by activating written learning through group work, students can benefit from collaboration, which is an important factor in improving the various components of writing, including grammatical and structural accuracy. The results of the study showed that group work contributes to the generation of ideas and the development of changes in attitudes to written speech in English. The results showed that group work is a strategic tool that helps students effectively master writing skills in English lessons and is consistent with the findings of other scholars mentioned in the literature review.