Reviewerly/exHarmony huggingface dataset:
Explain and Conquer: Personalised Text-based Reviews to Achieve Transparency

There are many contexts in which dyadic data are present. Social networks are a well-known example. In these contexts, pairs of elements are linked building a network that reflects interactions. Explaining why these relationships are established is essential to obtain transparency, an increasingly important notion. These explanations are often presented using text, thanks to the spread of the natural language understanding tasks. Our aim is to represent and explain pairs established by any agent (e.g., a recommender system or a paid promotion mechanism), so that text-based personalisation is taken into account. We have focused on the TripAdvisor platform, considering the applicability to other dyadic data contexts. The items are a subset of users and restaurants and the interactions the reviews posted by these users. We propose the PTER (Personalised TExt-based Reviews) model. We predict, from the available reviews for a given restaurant, those that fit to the specific user interactions. PTER leverages the BERT (Bidirectional Encoders Representations from Transformers) transformer-encoder model. We customised a deep neural network following the feature-based approach, presenting a LTR (Learning To Rank) downstream task. We carried out several comparisons of our proposal with a random baseline and other models of the state of the art, following the EXTRA (EXplanaTion RAnking) benchmark. Our method outperforms other collaborative filtering proposals.
A NOVEL COMPUTER-AIDED DETECTION (CADE) MODEL FOR NEOPLASIA DETECTION IN INFLAMMATORY BOWEL DISEASE. THE BEGINNING OF THE END OF CHROMOENDOSCOPY?

Aims IBD neoplasia is often subtle and easily missed, hence chromoendoscopy is recommended. CADe showed promising results in neoplasia detection in non-IBD patients. However, data on its use for neoplasia detection in IBD is lacking. We aimed to develop and validate a dedicated CADe system for neoplasia detection in IBD and compare it with an existing generic CADe system used on IBD colons.
Efficient implementation of incremental proximal-point methods

Model training algorithms which observe a small portion of the training set in each computational step are ubiquitous in practical machine learning, and include both stochastic and online optimization methods. In the vast majority of cases, such algorithms typically observe the training samples via the gradients of the cost functions the samples incur. Thus, these methods exploit are the \emph{slope} of the cost functions via their first-order approximations. To address limitations of gradient-based methods, such as sensitivity to step-size choice in the stochastic setting, or inability to exploit small function variability in the online setting, several streams of research attempt to exploit more information about the cost functions than just their gradients via the well-known proximal framework of optimization. However, implementing such methods in practice poses a challenge, since each iteration step boils down to computing a proximal operator, which may not be easy. In this work we provide efficient algorithms and corresponding implementations of proximal operators in order to make experimentation with incremental proximal optimization algorithms accessible to a larger audience of researchers and practitioners, and in particular to promote additional theoretical research into these methods by closing the gap between their theoretical description in research papers and their use in practice. The corresponding code is published at https://github.com/alexshtf/inc_prox_pt.
Context binning, model clustering and adaptivity for data compression of genetic data

Rapid growth of genetic databases means huge savings from improvements in their data compression, what requires better inexpensive statistical models. This article proposes automatized optimizations e.g. of Markov-like models, especially context binning and model clustering. While it is popular to just remove low bits of the context, proposed context binning automatically optimizes such reduction as tabled: state=bin[context] determining probability distribution, this way extracting nearly all useful information also from very large contexts, into a relatively small number of states. The second proposed approach: model clustering uses k-means clustering in space of general statistical models, allowing to optimize a few models (as cluster centroids) to be chosen e.g. separately for each read. There are also briefly discussed some adaptivity techniques to include data non-stationarity.
P3IV: Probabilistic Procedure Planning from Instructional Videos with Weak Supervision

In this paper, we study the problem of procedure planning in instructional videos. Here, an agent must produce a plausible sequence of actions that can transform the environment from a given start to a desired goal state. When learning procedure planning from instructional videos, most recent work leverages intermediate visual observations as supervision, which requires expensive annotation efforts to localize precisely all the instructional steps in training videos. In contrast, we remove the need for expensive temporal video annotations and propose a weakly supervised approach by learning from natural language instructions. Our model is based on a transformer equipped with a memory module, which maps the start and goal observations to a sequence of plausible actions. Furthermore, we augment our model with a probabilistic generative module to capture the uncertainty inherent to procedure planning, an aspect largely overlooked by previous work. We evaluate our model on three datasets and show our weaklysupervised approach outperforms previous fully supervised state-of-the-art models on multiple metrics.
Coded Transaction Broadcasting for High-throughput Blockchains

High-throughput blockchains require efficient transaction broadcast mechanisms that can deliver transactions to most network nodes with low bandwidth overhead and latency. Existing schemes coordinate transmissions across peers to avoid sending redundant data, but they either incur a high latency or are not robust against adversarial network nodes. We present Strokkur, a new transaction broadcasting mechanism that provides both low bandwidth overhead and low latency. The core idea behind Strokkur is to avoid explicit coordination through randomized transaction coding. Rather than forward individual transactions. Strokkur nodes send out codewords -- XOR sums of multiple transactions selected at random. Since almost every codeword is useful for the receiver to decode new transactions, Strokkur nodes do not require coordination, for example, to determine which transactions the receiver is missing. Strokkur's coding strategy builds on LT codes, a popular class of rateless erasure codes, and extends them to support multiple uncoordinated senders with partially-overlapping continual streams of transaction data. Strokkur introduces mechanisms to cope with adversarial senders that may send corrupt codewords, and a simple rate control algorithm that enables each node to independently determine an appropriate sending rate of codewords for each peer. Our implementation of Strokkur in Golang supports 647k transactions per second using only one CPU core. Our evaluation across a 19-node Internet deployment and large-scale simulation show that Strokkur consumes 2--7.6x less bandwidth than the existing scheme in Bitcoin, and 9x lower latency that Shrec when only 4% of nodes are adversarial.
Fair Feature Subset Selection using Multiobjective Genetic Algorithm

The feature subset selection problem aims at selecting the relevant subset of features to improve the performance of a Machine Learning (ML) algorithm on training data. Some features in data can be inherently noisy, costly to compute, improperly scaled, or correlated to other features, and they can adversely affect the accuracy, cost, and complexity of the induced algorithm. The goal of traditional feature selection approaches has been to remove such irrelevant features. In recent years ML is making a noticeable impact on the decision-making processes of our everyday lives. We want to ensure that these decisions do not reflect biased behavior towards certain groups or individuals based on protected attributes such as age, sex, or race. In this paper, we present a feature subset selection approach that improves both fairness and accuracy objectives and computes Pareto-optimal solutions using the NSGA-II algorithm. We use statistical disparity as a fairness metric and F1-Score as a metric for model performance. Our experiments on the most commonly used fairness benchmark datasets with three different machine learning algorithms show that using the evolutionary algorithm we can effectively explore the trade-off between fairness and accuracy.
Power Bundle Adjustment for Large-Scale 3D Reconstruction

We introduce Power Bundle Adjustment as an expansion type algorithm for solving large-scale bundle adjustment problems. It is based on the power series expansion of the inverse Schur complement and constitutes a new family of solvers that we call inverse expansion methods. We theoretically justify the use of power series and we prove the convergence of our approach. Using the real-world BAL dataset we show that the proposed solver challenges the state-of-the-art iterative methods and significantly accelerates the solution of the normal equation, even for reaching a very high accuracy. This easy-to-implement solver can also complement a recently presented distributed bundle adjustment framework. We demonstrate that employing the proposed Power Bundle Adjustment as a sub-problem solver significantly improves speed and accuracy of the distributed optimization.
Branch &amp; Learn for Recursively and Iteratively Solvable Problems in Predict+Optimize

This paper proposes Branch & Learn, a framework for Predict+Optimize to tackle optimization problems containing parameters that are unknown at the time of solving. Given an optimization problem solvable by a recursive algorithm satisfying simple conditions, we show how a corresponding learning algorithm can be constructed directly and methodically from the recursive algorithm. Our framework applies also to iterative algorithms by viewing them as a degenerate form of recursion. Extensive experimentation shows better performance for our proposal over classical and state-of-the-art approaches.
Long-Range Bell States from Local Measurements and Many-Body Teleportation without Time-Reversal

In this work, we study quantum many-body teleportation, where a single qubit is teleported through a strongly-interacting quantum system, as a result of a scrambling unitary and local measurements on a few qubits. Usual many-body teleportation protocols require a double copy of the system, and backward time evolution, we demonstrate that teleportation is possible in the 2D spin-$1/2$ XY model, without these constraints. The necessary long-range entanglement for teleportation is generated from the model hosting special eigenstates known as rainbow scars. We outline a specific protocol for preparing this highly entangled state by evolving a product state and performing iterative measurements on only two qubits with feedback control.
Neurocryptographic Information Protection

Along with the rapid development of digital communication technologies, which allowed to transmit messages in different forms over the network, the need to protect transmitted data from access by third parties has increased. One of the main ways to protect data is encryption. The main reason for encryption is that users must be aware of encryption methods and keys, without which information is meaningless in the form of symbols. As the efficiency of cryptanalysis methods has increased with computational performance, there has been a need for more sophisticated approaches to encryption. In particular, the use of such a promising approach as neural networks for data encryption – neurocryptography. Due to the fact that the increased power of technological tools continues to grow, today’s neural networks have been used in practice. Any encryption algorithm is based on generating different variants of a distorted code that can be recognized or reconstructed by a neural network with specified characteristics, and includes the following stages: preliminary, performing preliminary data processing and formation of a training sample; formation of a neural network, including training; and the main one, performing encryption or decryption. The article deals with the issue of increasing the efficiency of data protection by means of neurocryptography. Improving efficiency is achieved by selecting a group of cryptographic primitives, the implementation of which in the form of a neural network is the most effective. Efficiency in this case means the ratio of data encryption speed to the time of formation of the neural network.
From legal proof to scientific proof

Legal proof and scientific proof have similarities and differences. Legal proof is a customary affair, while the nature of scientific proof separates it from general custom, and it is a burdensome and methodological affair. Philosophical issues of scientific proof, including induction and transcendence inherent in any description, can penetrate legal cases and turn into seemingly reasonable grounds for suspicions and abuses. Disregarding the nature of proving scientific laws and the difference between a legal claim and a scientific hypothesis can lead to failure in scientific proof. Disregarding the distinction between natural laws and specific observable events in international cases such as beef hormones and EC- biotech was one of the factors of failure in scientific proof and prolongation of proceedings. In order to have a better approach to scientific evidence, it is necessary not to confuse these concepts in court.&#x0D;
High-dimensional Asymptotics of Feature Learning: How One Gradient Step Improves the Representation

We study the first gradient descent step on the first-layer parameters $\boldsymbol{W}$ in a two-layer neural network: $f(\boldsymbol{x}) = \frac{1}{\sqrt{N}}\boldsymbol{a}^\top\sigma(\boldsymbol{W}^\top\boldsymbol{x})$, where $\boldsymbol{W}\in\mathbb{R}^{d\times N}, \boldsymbol{a}\in\mathbb{R}^{N}$ are randomly initialized, and the training objective is the empirical MSE loss: $\frac{1}{n}\sum_{i=1}^n (f(\boldsymbol{x}_i)-y_i)^2$. In the proportional asymptotic limit where $n,d,N\to\infty$ at the same rate, and an idealized student-teacher setting, we show that the first gradient update contains a rank-1 "spike", which results in an alignment between the first-layer weights and the linear component of the teacher model $f^*$. To characterize the impact of this alignment, we compute the prediction risk of ridge regression on the conjugate kernel after one gradient step on $\boldsymbol{W}$ with learning rate $\eta$, when $f^*$ is a single-index model. We consider two scalings of the first step learning rate $\eta$. For small $\eta$, we establish a Gaussian equivalence property for the trained feature map, and prove that the learned kernel improves upon the initial random features model, but cannot defeat the best linear model on the input. Whereas for sufficiently large $\eta$, we prove that for certain $f^*$, the same ridge estimator on trained features can go beyond this "linear regime" and outperform a wide range of random features and rotationally invariant kernels. Our results demonstrate that even one gradient step can lead to a considerable advantage over random features, and highlight the role of learning rate scaling in the initial phase of training.
Mixed-effects transformers for hierarchical adaptation

Language use differs dramatically from context to context. To some degree, modern language models like GPT-3 are able to account for such variance by conditioning on a string of previous input text, or prompt. Yet prompting is ineffective when contexts are sparse, out-of-sample, or extra-textual; for instance, accounting for when and where the text was produced or who produced it. In this paper, we introduce the mixed-effects transformer (MET), a novel approach for learning hierarchically-structured prefixes -- lightweight modules prepended to the input -- to account for structured variation. Specifically, we show how the popular class of mixed-effects models may be extended to transformer-based architectures using a regularized prefix-tuning procedure with dropout. We evaluate this approach on several domain-adaptation benchmarks, finding that it efficiently adapts to novel contexts with minimal data while still effectively generalizing to unseen contexts.
Exact Paired-Permutation Testing for Structured Test Statistics

Significance testing-especially the pairedpermutation test-has played a vital role in developing NLP systems to provide confidence that the difference in performance between two systems (i.e., the test statistic) is not due to luck.However, practitioners rely on Monte Carlo approximation to perform this test due to a lack of a suitable exact algorithm.In this paper, we provide an efficient exact algorithm for the paired-permutation test for a family of structured test statistics.Our algorithm runs in O(GN (log GN )(log N )) time where N is the dataset size and G is the range of the test statistic.We found that our exact algorithm was 10x faster than the Monte Carlo approximation with 20000 samples on a common dataset.
Assistance devices and autonomous vehicles, or the fourth industrial revolution in the automotive industry. Reflections on criminal liability

The fourth technical revolution has undoubtedly become a fact.It has affected, and that to a large extent, the automotive industry.Motor vehicles provided with driver-assistance systems have appeared, such as those warning of obstacles, maintaining a safe distance from preceding vehicles, informing of a blind spot, or autonomously automatically adjusting speed, slowing down, following navigation, etc., the role of the driver only being to indicate the destination.However, should the driver have full confidence in those systems, and allow for the mistakes they make, and what impact will this have on their possible criminal liability?These are the issues raised in the publication, which at the same time points out that the legal systems are absolutely not adapted to modern technologies, artificial intelligence, or the assessment of so-called robot drivers.
State Estimation Methods for Continuous-Discrete Nonlinear Systems involving Stochastic Differential Equations

In this work, we present methods for state estimation in continuous-discrete nonlinear systems involving stochastic differential equations. We present the extended Kalman filter, the unscented Kalman filter, the ensemble Kalman filter, and a particle filter. We implement the state estimation methods in Matlab. We evaluate the performance of the methods on a simulation of the modified four-tank system. We implement the state estimation methods for non-stiff systems, i.e., using an explicit numerical integration scheme. The implementation of the extended Kalman filter utilises the Joseph stabilising form for numerical stability. We evaluate the accuracy of the state estimation methods in terms of the mean absolute percentage error over the simulation horizon. We show that each method successfully estimates the states and unmeasured disturbances of the simulated modified four-tank system. Finally, we present conclusions.
Local Stochastic Bilevel Optimization with Momentum-Based Variance Reduction

Bilevel Optimization has witnessed notable progress recently with new emerging efficient algorithms and has been applied to many machine learning tasks such as data cleaning, few-shot learning, and neural architecture search. However, little attention has been paid to solve the bilevel problems under distributed setting. Federated learning (FL) is an emerging paradigm which solves machine learning tasks over distributed-located data. FL problems are challenging to solve due to the heterogeneity and communication bottleneck. However, it is unclear how these challenges will affect the convergence of Bilevel Optimization algorithms. In this paper, we study Federated Bilevel Optimization problems. Specifically, we first propose the FedBiO, a deterministic gradient-based algorithm and we show it requires $O(\epsilon^{-2})$ number of iterations to reach an $\epsilon$-stationary point. Then we propose FedBiOAcc to accelerate FedBiO with the momentum-based variance-reduction technique under the stochastic scenario. We show FedBiOAcc has complexity of $O(\epsilon^{-1.5})$. Finally, we validate our proposed algorithms via the important Fair Federated Learning task. More specifically, we define a bilevel-based group fair FL objective. Our algorithms show superior performances compared to other baselines in numerical experiments.
FederatedScope: A Flexible Federated Learning Platform for Heterogeneity

Although remarkable progress has been made by existing federated learning (FL) platforms to provide infrastructures for development, these platforms may not well tackle the challenges brought by various types of heterogeneity, including the heterogeneity in participants' local data, resources, behaviors and learning goals. To fill this gap, in this paper, we propose a novel FL platform, named FederatedScope, which employs an event-driven architecture to provide users with great flexibility to independently describe the behaviors of different participants. Such a design makes it easy for users to describe participants with various local training processes, learning goals and backends, and coordinate them into an FL course with synchronous or asynchronous training strategies. Towards an easy-to-use and flexible platform, FederatedScope enables rich types of plug-in operations and components for efficient further development, and we have implemented several important components to better help users with privacy protection, attack simulation and auto-tuning. We have released FederatedScope at https://github.com/alibaba/FederatedScope to promote academic research and industrial deployment of federated learning in a wide range of scenarios.
Open-set Recognition via Augmentation-based Similarity Learning

The primary assumption of conventional supervised learning or classification is that the test samples are drawn from the same distribution as the training samples, which is called closed set learning or classification. In many practical scenarios, this is not the case because there are unknowns or unseen class samples in the test data, which is called the open set scenario, and the unknowns need to be detected. This problem is referred to as the open set recognition problem and is important in safety-critical applications. We propose to detect unknowns (or unseen class samples) through learning pairwise similarities. The proposed method works in two steps. It first learns a closed set classifier using the seen classes that have appeared in training and then learns how to compare seen classes with pseudo-unseen (automatically generated unseen class samples). The pseudo-unseen generation is carried out by performing distribution shifting augmentations on the seen or training samples. We call our method OPG (Open set recognition based on Pseudo unseen data Generation). The experimental evaluation shows that the learned similarity-based features can successfully distinguish seen from unseen in benchmark datasets for open set recognition.