Reviewerly/exHarmony huggingface dataset:
Partitioned Variational Inference: A Framework for Probabilistic Federated Learning

The proliferation of computing devices has brought about an opportunity to deploy machine learning models on new problem domains using previously inaccessible data. Traditional algorithms for training such models often require data to be stored on a single machine with compute performed by a single node, making them unsuitable for decentralised training on multiple devices. This deficiency has motivated the development of federated learning algorithms, which allow multiple data owners to train collaboratively and use a shared model whilst keeping local data private. However, many of these algorithms focus on obtaining point estimates of model parameters, rather than probabilistic estimates capable of capturing model uncertainty, which is essential in many applications. Variational inference (VI) has become the method of choice for fitting many modern probabilistic models. In this paper we introduce partitioned variational inference (PVI), a general framework for performing VI in the federated setting. We develop new supporting theory for PVI, demonstrating a number of properties that make it an attractive choice for practitioners; use PVI to unify a wealth of fragmented, yet related literature; and provide empirical results that showcase the effectiveness of PVI in a variety of federated settings.
Pointers for Quantum Measurement Theory

In the iconic measurements of atomic spin-1/2 or photon polarization, one employs two spatially separated and noninteracting detectors. Each detector is binary, registering the presence or absence of the atom or the photon. For measurements on a $d$-state particle we recast the standard von Neumann measurement formalism by replacing the familiar pointer variable with an array of such detectors, one for each of the $d$ possible outcomes. We show that the unitary dynamics of the premeasurement process restricts the detector outputs to the subspace of single outcomes, so that the pointer emerges from the apparatus. We propose a physical extension of this apparatus which replaces each detector with an ancilla qubit coupled to a readout device. This explicitly separates the pointer into distinct quantum and (effectively) classical parts, and delays the quantum to classical transition. As a result, one not only recovers the collapse scenario of an ordinary apparatus, but one can also observe a superposition of the quantum pointer states.
Corpus Design and Representativeness in Practice – With Daniel Keller

We propose that the representativeness of a corpus directly depends on its suitability for a specific research goal (including the domain and the linguistic feature(s) of interest). Creating a new corpus involves establishing linguistic research question(s), addressing domain considerations, including describing the domain, operationalizing the domain, evaluating the operational domain (relative to the full domain), designing the corpus, and evaluating the corpus (relative to the operational domain), addressing distribution considerations, including defining a linguistic variable and evaluating the required sample size, collecting the corpus, and documenting and reporting corpus design and representativeness. The steps for evaluating an existing corpus are similar: establishing linguistic research question(s), identifying and acquire the corpus and its documentation, addressing domain considerations, including describing the domain and evaluating the operational domain relative to the full domain, and the corpus relative to the operational domain, addressing distribution considerations, including defining a linguistic variable and evaluating the required sample size, and documenting and reporting corpus design and representativeness. We conclude the book by arguing that corpus representativeness is important for both corpus designers/builders, and corpus researchers who need to evaluate whether a corpus is appropriate for their research goals.
Distinctive Marker ‘-an’ on the Lingual Unit in Online Mass Media Diction

The Indonesian language continues to experience extraordinary developments as seen from its vocabularies. However, the large number of vocabularies turns often caused errors and mistakes in choosing diction pairs in an article, especially in the mass media. Therefore, this research needed to be done to classify vocabulary ending in -an based on its form, category, marker, and meaning. It was aslo intended that the vocabulary contained in the Indonesian language could be classified according to their classes. The study made use of primary data sourcing from the written language. The data came from online daily newspapers, namely pikiran-rakyat.com, republica.co.id, and mediaindonesia.com. In analyzing these data, the research applied descriptive and prescriptive qualitative methods. The result of the study indicated that the final form of -an in Indonesian vocabulary had certain categories, markers, and meanings. This happened because of the similarity or identical form of a word. The research concluded that there were word forms ending in -an functioning suffixes and some others were part of the word itself.
A New Nonlinear speaker parameterization algorithm for speaker identification

In this paper we propose a new parameterization algorithm based on nonlinear prediction, which is an extension of the classical LPC parameters. The parameters performances are estimated by two different methods: the Arithmetic-Harmonic Sphericity (AHS) and the Auto-Regressive Vector Model (ARVM). Two different methods are proposed for the parameterization based on the Neural Predictive Coding (NPC): classical neural networks initialization and linear initialization. We applied these two parameters to speaker identification. The fist parameters obtained smaller rates. We show for the first parameters how they can be combined with the classical parameters (LPCC, MFCC, etc.) in order to improve the results of only one classical parameterization (MFCC provides 97.55% and MFCC+NPC 98.78%). For the linear initialization, we obtain 100% which is great improvement. This study opens a new way towards different parameterization schemes that offer better accuracy on speaker recognition tasks.
An upper bound for the number of chess diagrams without promotion

In 2015, Steinerberger showed that the number of legal chess diagrams without promotion is bounded from above by 2 × 10 40 . This number was obtained by restricting both bishops and pawns position and by a precise bound when no chessman has been captured. We improve this estimate and show that the number of legal diagrams is less than 4 × 10 37 . To achieve this, we define a graph on the set of diagrams and a notion of class of pawn arrangements, leading to a method for bounding pawn positions with any number of men on the board.
On Development of Web Application for Corpus of Archival Documents

This work is a part of the project on creation the linguistic corpus of the fund “Mikhailovsky stanitsa ataman” documents. This fund contains historically valuable administrative documents of the Don Cossacks Army of the 18th-19th centuries, stored in the State Archives of the Volgograd Region. To introduce it to scientific society, a lot of preliminary work to digitize them was done by group of scientists from Volgograd State University headed by Professor O.A. Gorban. In their current form, these documents are suitable for computer processing. The only significant problem is outdated vocabulary and graphics, but it was generally solved in our previous works. At the current stage, the main task is to develop the technical and software parts of the corpus. In fact, this means the creation of an “engine” for a document corpus, that is, software for storing a database of marked-up texts, executing queries to this database, and also providing user-friendly interface that does not require special IT-skills. At the same time, in the process of working on the previous tasks, we decided to integrate the document markup tool into the general corpus software. Thus, the present work is devoted to the development of a REST service that allows you to perform automated morphological analysis of texts, save a special form of processed documents in a database, search in database by a query with morphological features of elements in the texts. The software also provides a function for manual correction of errors that occur in automated analysis of Old Slavonic texts with obsolete characters.
ARM: Any-Time Super-Resolution Method

This paper proposes an Any-time super-Resolution Method (ARM) to tackle the over-parameterized single image super-resolution (SISR) models. Our ARM is motivated by three observations: (1) The performance of different image patches varies with SISR networks of different sizes. (2) There is a tradeoff between computation overhead and performance of the reconstructed image. (3) Given an input image, its edge information can be an effective option to estimate its PSNR. Subsequently, we train an ARM supernet containing SISR subnets of different sizes to deal with image patches of various complexity. To that effect, we construct an Edge-to-PSNR lookup table that maps the edge score of an image patch to the PSNR performance for each subnet, together with a set of computation costs for the subnets. In the inference, the image patches are individually distributed to different subnets for a better computation-performance tradeoff. Moreover, each SISR subnet shares weights of the ARM supernet, thus no extra parameters are introduced. The setting of multiple subnets can well adapt the computational cost of SISR model to the dynamically available hardware resources, allowing the SISR task to be in service at any time. Extensive experiments on resolution datasets of different sizes with popular SISR networks as backbones verify the effectiveness and the versatility of our ARM. The source code is available at https://github.com/chenbong/ARM-Net.
Recursive Monte Carlo and Variational Inference with Auxiliary Variables

A key design constraint when implementing Monte Carlo and variational inference algorithms is that it must be possible to cheaply and exactly evaluate the marginal densities of proposal distributions and variational families. This takes many interesting proposals off the table, such as those based on involved simulations or stochastic optimization. This paper broadens the design space, by presenting a framework for applying Monte Carlo and variational inference algorithms when proposal densities cannot be exactly evaluated. Our framework, recursive auxiliary-variable inference (RAVI), instead approximates the necessary densities using meta-inference: an additional layer of Monte Carlo or variational inference, that targets the proposal, rather than the model. RAVI generalizes and unifies several existing methods for inference with expressive approximating families, which we show correspond to specific choices of meta-inference algorithm, and provides new theory for analyzing their bias and variance. We illustrate RAVI's design framework and theorems by using them to analyze and improve upon Salimans et al.'s Markov Chain Variational Inference, and to design a novel sampler for Dirichlet process mixtures, achieving state-of-the-art results on a standard benchmark dataset from astronomy and on a challenging datacleaning task with Medicare hospital data.
Towards an Understanding of Default Policies in Multitask Policy Optimization

Much of the recent success of deep reinforcement learning has been driven by regularized policy optimization (RPO) algorithms with strong performance across multiple domains. In this family of methods, agents are trained to maximize cumulative reward while penalizing deviation in behavior from some reference, or default policy. In addition to empirical success, there is a strong theoretical foundation for understanding RPO methods applied to single tasks, with connections to natural gradient, trust region, and variational approaches. However, there is limited formal understanding of desirable properties for default policies in the multitask setting, an increasingly important domain as the field shifts towards training more generally capable agents. Here, we take a first step towards filling this gap by formally linking the quality of the default policy to its effect on optimization. Using these results, we then derive a principled RPO algorithm for multitask learning with strong performance guarantees.
Tight Bound for Estimating Expectation Values from a System of Linear  Equations

The System of Linear Equations Problem (SLEP) is specified by a complex invertible matrix $A$, the condition number $\kappa$ of $A$, a vector $b$, a Hermitian matrix $M$ and an accuracy $\epsilon$, and the task is to estimate $x^\dagger Mx$, where $x$ is the solution vector to the equation $Ax = b$. We aim to establish a lower bound on the complexity of the end-to-end quantum algorithms for SLEP with respect to $\epsilon$, and devise a quantum algorithm that saturates this bound. To make lower bounds attainable, we consider query complexity in the setting in which a block encoding of $M$ is given, i.e., a unitary black box $U_M$ that contains $M/\alpha$ as a block for some $\alpha \in \mathbb R^+$. We show that the quantum query complexity for SLEP in this setting is $\Theta(\alpha/\epsilon)$. Our lower bound is established by reducing the problem of estimating the mean of a black box function to SLEP. Our $\Theta(\alpha/\epsilon)$ result tightens and proves the common assertion of polynomial accuracy dependence (poly$(1/\epsilon)$) for SLEP, and shows that improvement beyond linear dependence on accuracy is not possible if $M$ is provided via block encoding.
FROB: Few-shot ROBust Model for Classification and Out-of-Distribution Detection

Nowadays, classification and Out-of-Distribution (OoD) detection in the few-shot setting remain challenging aims due to rarity and the limited samples in the few-shot setting, and because of adversarial attacks. Accomplishing these aims is important for critical systems in safety, security, and defence. In parallel, OoD detection is challenging since deep neural network classifiers set high confidence to OoD samples away from the training data. To address such limitations, we propose the Few-shot ROBust (FROB) model for classification and few-shot OoD detection. We devise FROB for improved robustness and reliable confidence prediction for few-shot OoD detection. We generate the support boundary of the normal class distribution and combine it with few-shot Outlier Exposure (OE). We propose a self-supervised learning few-shot confidence boundary methodology based on generative and discriminative models. The contribution of FROB is the combination of the generated boundary in a self-supervised learning manner and the imposition of low confidence at this learned boundary. FROB implicitly generates strong adversarial samples on the boundary and forces samples from OoD, including our boundary, to be less confident by the classifier. FROB achieves generalization to unseen OoD with applicability to unknown, in the wild, test sets that do not correlate to the training datasets. To improve robustness, FROB redesigns OE to work even for zero-shots. By including our boundary, FROB reduces the threshold linked to the model's few-shot robustness; it maintains the OoD performance approximately independent of the number of few-shots. The few-shot robustness analysis evaluation of FROB on different sets and on One-Class Classification (OCC) data shows that FROB achieves competitive performance and outperforms benchmarks in terms of robustness to the outlier few-shot sample population and variability.
Models and Datasets for Cross-Lingual Summarisation

We present a cross-lingual summarisation corpus with long documents in a source language associated with multi-sentence summaries in a target language. The corpus covers twelve language pairs and directions for four European languages, namely Czech, English, French and German, and the methodology for its creation can be applied to several other languages. We derive cross-lingual document-summary instances from Wikipedia by combining lead paragraphs and articles' bodies from language aligned Wikipedia titles. We analyse the proposed cross-lingual summarisation task with automatic metrics and validate it with a human study. To illustrate the utility of our dataset we report experiments with multi-lingual pre-trained models in supervised, zero- and few-shot, and out-of-domain scenarios.
Classifying Human Activities with Inertial Sensors: A Machine Learning Approach

Human Activity Recognition (HAR) is an ongoing research topic. It has applications in medical support, sports, fitness, social networking, human-computer interfaces, senior care, entertainment, surveillance, and the list goes on. Traditionally, computer vision methods were employed for HAR, which has numerous problems such as secrecy or privacy, the influence of environmental factors, less mobility, higher running costs, occlusion, and so on. A new trend in the use of sensors, especially inertial sensors, has lately emerged. There are several advantages of employing sensor data as an alternative to traditional computer vision algorithms. Many of the limitations of computer vision algorithms have been documented in the literature, including research on Deep Neural Network (DNN) and Machine Learning (ML) approaches for activity categorization utilizing sensor data. We examined and analyzed different Machine Learning and Deep Learning approaches for Human Activity Recognition using inertial sensor data of smartphones. In order to identify which approach is best suited for this application.
Contrastive Visual Representation Learning Enhanced with Knowledge Embedding for Reinforcement Learning

We present CURLKG: CURL [1] enhanced with Knowledge Graph method. Inspired by knowledge graph embedding, environment states are represented as entities and actions are relations between knowledge entities in CURLKG. This knowledge embedding auxiliary task gives “similarity” a more precise measure compared with previous contrastive image representation learning models. Experiments are conducted in Deepmind Controal Suit environment and shows that CURLKG outperforms original CURL with a 1.29x performance gain at 100K environment steps. The main advantage of our model is simplicity and generality. CURLKG can be combined with any reinforcement learning model and no specialized network structure is used. CURLKG is the first pixel representation method inspired with the idea of knowledge embedding which is used as a supplementary constraint to contrastive learning.
Bribes to Miners: Evidence from Ethereum

In blockchain, bribery is an inevitable problem since users with various goals can bribe miners by transferring cryptoassets. To alleviate the negative effects of such collusion, Ethereum blockchain implemented new transaction fee mechanism in the London Fork, which was deployed on August 5th, 2021. In this paper, we first filter potential bribery by scanning Ethereum transactions, and the potential bribers and bribees are centralized in a small group. Then we construct bribing proxies to measure the active level of bribery and then investigate the effects of bribery. Consequently, bribery can influence both Ethereum and other mainstream blockchains, in aspects of underlying cryptocurrency, transaction statistics, and network adoption. Moreover, the London Fork shows complicated effects on relationship between bribery and blockchain factors. Besides, bribery in Ethereum relates to stock markets, e.g., S&P 500 and Nasdaq, implying implicit interlinks between blockchain and traditional finance.
RETRACTED ARTICLE: A Method of Fuzzy Enhancement for Degraded Image Based on Deep Learning

The proposed research work is aiming at the problem of enhancement in the sharpness of degraded images by using a deep learning method. A deep neural network is introduced to study the enhancement process of degraded images. The construction of the training sample set is done to perform the weight parameter training on the self-encoding neural network which obtains a self-encoding neural network and completes the initial detection of the fuzzy region. Segmentation of the images is performed based on the threshold methods to detect the local blurred areas in degraded images. The results are input to a multi-scale deblurring network to estimate the residuals of clear and blurred images at multiple scales. The obtained network processes the blurred image residuals under different scales to achieve enhancement in resolution and quality of degraded images. The experimental results show that the proposed method can enhance the quality of the various complex and degraded images. It can accurately detect the motion blur area in the image. The detection result is quite closer to the true value image. The standard performance metrics is used to compare the performance of the deep learning-based method for enhancement of degraded images. The efficacy of the proposed work is proved in the result section of the paper. The method is effective on degraded images to infer hidden and important information.
Quantum computing with differentiable quantum transforms

We present a framework for differentiable quantum transforms. Such transforms are metaprograms capable of manipulating quantum programs in a way that preserves their differentiability. We highlight their potential with a set of relevant examples across quantum computing (gradient computation, circuit compilation, and error mitigation), and implement them using the transform framework of PennyLane, a software library for differentiable quantum programming. In this framework, the transforms themselves are differentiable and can be parametrized and optimized, which opens up the possibility of improved quantum resource requirements across a spectrum of tasks.
Global Convergence of MAML and Theory-Inspired Neural Architecture Search for Few-Shot Learning

Model-agnostic meta-learning (MAML) and its variants have become popular approaches for few-shot learning. However, due to the non-convexity of deep neural nets (DNNs) and the bi-level formulation of MAML, the theoretical properties of MAML with DNNs remain largely unknown. In this paper, we first prove that MAML with over-parameterized DNNs is guaranteed to converge to global optima at a linear rate. Our convergence analysis indicates that MAML with over-parameterized DNNs is equivalent to kernel regression with a novel class of kernels, which we name as Meta Neural Tangent Kernels (MetaNTK). Then, we propose MetaNTK-NAS, a new training-free neural architecture search (NAS) method for few-shot learning that uses MetaNTK to rank and select architectures. Empirically, we compare our MetaNTK-NAS with previous NAS methods on two popular few-shot learning benchmarks, miniImageNet, and tieredImageNet. We show that the performance of MetaNTK-NAS is comparable or better than the state-of-the-art NAS method designed for few-shot learning while enjoying more than 100x speedup. We believe the efficiency of MetaNTK-NAS makes itself more practical for many real-world tasks.