Reviewerly/exHarmony huggingface dataset:
Image Captioning using Neural Networks

Technology is changing the way that we live our lives. With the advances in technology images and videos attain a lot of attention from the people around us. Captioning images that facilitate the selection of related and the filtration of irrelevant data from congested images is an important goal. Rapid growth of artificial intelligence(AI) in recent years, picture caption has increasingly grabbed the attention of many AI researchers and has become an intriguing and difficult task. The use of image captions is widespread and important, as evidenced by the realisation of human-computer interaction. The attention mechanism, which plays a significant role in computer vision and has lately become widely employed in picture caption generation tasks, is the subject of this study, which reviews the associated methods and concentrates on it.The paper focuses on techniques such as Inception V3, Convolutional Neural Network and training deep learning models for captioning images. Provided a training dataset of captioned images, we want to find correlations between visual features and keywords so that we can find decent keywords for new images instantaneously.Additionally, the benefits and drawbacks of these methodologies are explored, as well as the most widely utilised datasets and model evaluation criteria in this sector and discusses some of the remaining issues in the image captioning assignment..
Revisiting Algebraic Attacks on MinRank and on the Rank Decoding Problem

The Rank Decoding problem (RD) is at the core of rank-based cryptography. This problem can also be seen as a structured version of MinRank, which is ubiquitous in multivariate cryptography. Recently, \cite{BBBGNRT20,BBCGPSTV20} proposed attacks based on two new algebraic modelings, namely the MaxMinors modeling which is specific to RD and the Support-Minors modeling which applies to MinRank in general. Both improved significantly the complexity of algebraic attacks on these two problems. In the case of RD and contrarily to what was believed up to now, these new attacks were shown to be able to outperform combinatorial attacks and this even for very small field sizes. However, we prove here that the analysis performed in \cite{BBCGPSTV20} for one of these attacks which consists in mixing the MaxMinors modeling with the Support-Minors modeling to solve RD is too optimistic and leads to underestimate the overall complexity. This is done by exhibiting linear dependencies between these equations and by considering an $\fqm$ version of these modelings which turns out to be instrumental for getting a better understanding of both systems. Moreover, by working over $\Fqm$ rather than over $\ff{q}$, we are able to drastically reduce the number of variables in the system and we (i) still keep enough algebraic equations to be able to solve the system, (ii) are able to analyze rigorously the complexity of our approach. This new approach may improve the older MaxMinors approach on RD from \cite{BBBGNRT20,BBCGPSTV20} for certain parameters. We also introduce a new hybrid approach on the Support-Minors system whose impact is much more general since it applies to any MinRank problem. This technique improves significantly the complexity of the Support-Minors approach for small to moderate field sizes.
EvolveHypergraph: Group-Aware Dynamic Relational Reasoning for Trajectory Prediction

While the modeling of pair-wise relations has been widely studied in multi-agent interacting systems, its ability to capture higher-level and larger-scale group-wise activities is limited. In this paper, we propose a group-aware relational reasoning approach (named EvolveHypergraph) with explicit inference of the underlying dynamically evolving relational structures, and we demonstrate its effectiveness for multi-agent trajectory prediction. In addition to the edges between a pair of nodes (i.e., agents), we propose to infer hyperedges that adaptively connect multiple nodes to enable group-aware relational reasoning in an unsupervised manner without fixing the number of hyperedges. The proposed approach infers the dynamically evolving relation graphs and hypergraphs over time to capture the evolution of relations, which are used by the trajectory predictor to obtain future states. Moreover, we propose to regularize the smoothness of the relation evolution and the sparsity of the inferred graphs or hypergraphs, which effectively improves training stability and enhances the explainability of inferred relations. The proposed approach is validated on both synthetic crowd simulations and multiple real-world benchmark datasets. Our approach infers explainable, reasonable group-aware relations and achieves state-of-the-art performance in long-term prediction.
Active Sampling of Multiple Sources for Sequential Estimation

Consider $K$ processes, each generating a sequence of identical and independent random variables. The probability measures of these processes have random parameters that must be estimated. Specifically, they share a parameter $θ$ common to all probability measures. Additionally, each process $i\in\{1, \dots, K\}$ has a private parameter $α_i$. The objective is to design an active sampling algorithm for sequentially estimating these parameters in order to form reliable estimates for all shared and private parameters with the fewest number of samples. This sampling algorithm has three key components: (i)~data-driven sampling decisions, which dynamically over time specifies which of the $K$ processes should be selected for sampling; (ii)~stopping time for the process, which specifies when the accumulated data is sufficient to form reliable estimates and terminate the sampling process; and (iii)~estimators for all shared and private parameters. Owing to the sequential estimation being known to be analytically intractable, this paper adopts \emph {conditional} estimation cost functions, leading to a sequential estimation approach that was recently shown to render tractable analysis. Asymptotically optimal decision rules (sampling, stopping, and estimation) are delineated, and numerical experiments are provided to compare the efficacy and quality of the proposed procedure with those of the relevant approaches.
Evaluating the Resilience of Variational Quantum Algorithms to Leakage Noise

As we are entering the era of constructing practical quantum computers, suppressing the inevitable noise to accomplish reliable computational tasks will be the primary goal. Leakage noise, as the amplitude population leaking outside the qubit subspace, is a particularly damaging source of error that error correction approaches cannot handle. However, the impact of this noise on the performance of variational quantum algorithms (VQAs), a type of near-term quantum algorithms that is naturally resistant to a variety of noises, is yet unknown. Here, {we consider a typical scenario with the widely used hardware-efficient ansatz and the emergence of leakage in two-qubit gates}, observing that leakage noise generally reduces the expressive power of VQAs. Furthermore, we benchmark the influence of leakage noise on VQAs in real-world learning tasks. Results show that, both for data fitting and data classification, leakage noise generally has a negative impact on the training process and final outcomes. Our findings give strong evidence that VQAs are vulnerable to leakage noise in most cases, implying that leakage noise must be effectively suppressed in order to achieve practical quantum computing applications, whether for near-term quantum algorithms and long-term error-correcting quantum computing.
How Effective is Byte Pair Encoding for Out-Of-Vocabulary Words in Neural Machine Translation?

Neural Machine Translation (NMT) is an open vocabulary problem. As a result, dealing with the words not occurring during training (a.k.a. out-of-vocabulary (OOV) words) have long been a fundamental challenge for NMT systems. The predominant method to tackle this problem is Byte Pair Encoding (BPE) which splits words, including OOV words, into sub-word segments. BPE has achieved impressive results for a wide range of translation tasks in terms of automatic evaluation metrics. While it is often assumed that by using BPE, NMT systems are capable of handling OOV words, the effectiveness of BPE in translating OOV words has not been explicitly measured. In this paper, we study to what extent BPE is successful in translating OOV words at the word-level. We analyze the translation quality of OOV words based on word type, number of segments, cross-attention weights, and the frequency of segment n-grams in the training data. Our experiments show that while careful BPE settings seem to be fairly useful in translating OOV words across datasets, a considerable percentage of OOV words are translated incorrectly. Furthermore, we highlight the slightly higher effectiveness of BPE in translating OOV words for special cases, such as named-entities and when the languages involved are linguistically close to each other.
Robust Reinforcement Learning using Offline Data

The goal of robust reinforcement learning (RL) is to learn a policy that is robust against the uncertainty in model parameters. Parameter uncertainty commonly occurs in many real-world RL applications due to simulator modeling errors, changes in the real-world system dynamics over time, and adversarial disturbances. Robust RL is typically formulated as a max-min problem, where the objective is to learn the policy that maximizes the value against the worst possible models that lie in an uncertainty set. In this work, we propose a robust RL algorithm called Robust Fitted Q-Iteration (RFQI), which uses only an offline dataset to learn the optimal robust policy. Robust RL with offline data is significantly more challenging than its non-robust counterpart because of the minimization over all models present in the robust Bellman operator. This poses challenges in offline data collection, optimization over the models, and unbiased estimation. In this work, we propose a systematic approach to overcome these challenges, resulting in our RFQI algorithm. We prove that RFQI learns a near-optimal robust policy under standard assumptions and demonstrate its superior performance on standard benchmark problems.
Fractional revival on abelian Cayley graphs

Fractional revival, known as a quantum transport phenomenon, is essential for entanglement generation in quantum spin networks. The concept of fractional revival is a generalization of perfect state transfer and periodicity on graphs. In this paper, we propose a sufficient and necessary condition for abelian Cayley graphs having fractional revival between any two distinct vertices. With this characterization, two general constructions of abelian Cayley graphs having fractional revival is presented. Meanwhile, we establish several new families of abelian Cayley graphs admitting fractional revival.
Comparison of BIRADS lexicon to breast biopsy findings in low resource countries

Breast cancer is the most common malignancy in women worldwide and early detection is of utmost importance. In developed countries, mandatory mammographic screening programs help in early detection, whereas, in developing countries cancer is often detected at an advanced stage. The BIRADS guidelines permit a standard approach and follow up for breast lesions. Many newer imaging modalities are being available for better diagnosis. Breast lesions have a varied spectrum and the gold standard for diagnosis of breast cancer is based on histopathological examination of tissue. At times, even on trucut biopsy, it is difficult to categorize the lesion as the tissue studied is limited and some evolving lesions may have overlapping features. As there are limitations to both radiologic and pathologic approaches, the general and accepted way is to combine both modalities to arrive at a diagnosis. The aim: The aim of the study was to find out how well the BIRADS radiological findings correlate with histopathological findings on breast biopsies. Materials and methods: A MEDLINE search for articles published in English language, with key words as breast biopsy histopathology and BIRADS was done for the years between 1985 and 2021. In addition, other cross-referenced articles were also searched for relevant data. Results: There is good correlation between BIRADS category 1, 2 and 5 with the findings on core needle biopsy in breast lumps i.e., good correlation is seen at the end of spectrum of breast lesions in totally benign and unequivocally malignant lesions. But this correlation is lacking in the middle of the spectrum i.e., in borderline/intermediate category of BIRADS. Conclusion: The non-suspicious (BIRADS 1/ 2) and highly suspicious (BIRADS 4C/5) compare very well with the histopathologic findings. It is the grey zone i.e., BIRADS 3/4A which has a wide and variable predictive value for breast cancer when compared with histopathology and imaging study alone is insufficient and mandates histopathology in all such cases
G-PCC Post-Processing Using Fractional Super-Resolution

We present a method for post-processing point clouds' geometric information by applying a previously proposed fractional super-resolution technique to clouds compressed and decoded with MPEG's G-PCC codec. In some sense, this is a continuation of that previous work, which requires only a down-scaled point cloud and a scaling factor, both of which are provided by the G-PCC codec. For non-solid point clouds, an a priori down-scaling is required for improved efficiency. The method is compared to the GPCC itself, as well as machine-learning-based techniques. Results show a great improvement in quality over GPCC and comparable performance to the latter techniques, with the
Continual Prune-and-Select: Class-incremental learning with specialized subnetworks

The human brain is capable of learning tasks sequentially mostly without forgetting. However, deep neural networks (DNNs) suffer from catastrophic forgetting when learning one task after another. We address this challenge considering a class-incremental learning scenario where the DNN sees test data without knowing the task from which this data originates. During training, Continual-Prune-and-Select (CP&amp;S) finds a subnetwork within the DNN that is responsible for solving a given task. Then, during inference, CP&amp;S selects the correct subnetwork to make predictions for that task. A new task is learned by training available neuronal connections of the DNN (previously untrained) to create a new subnetwork by pruning, which can include previously trained connections belonging to other subnetwork(s) because it does not update shared connections. This enables to eliminate catastrophic forgetting by creating specialized regions in the DNN that do not conflict with each other while still allowing knowledge transfer across them. The CP&amp;S strategy is implemented with different subnetwork selection strategies, revealing superior performance to state-of-the-art continual learning methods tested on various datasets (CIFAR-100, CUB-200-2011, ImageNet-100 and ImageNet-1000). In particular, CP&amp;S is capable of sequentially learning 10 tasks from ImageNet-1000 keeping an accuracy around 94% with negligible forgetting, a first-of-its-kind result in class-incremental learning. To the best of the authors' knowledge, this represents an improvement in accuracy above 10% when compared to the best alternative method.
RADARS: Memory Efficient Reinforcement Learning Aided Differentiable Neural Architecture Search

Differentiable neural architecture search (DNAS) is known for its capacity in the automatic generation of superior neural networks. However, DNAS based methods suffer from memory usage explosion when the search space expands, which may prevent them from running successfully on even advanced GPU platforms. On the other hand, reinforcement learning (RL) based methods, while being memory efficient, are extremely time-consuming. Combining the advantages of both types of methods, this paper presents RADARS, a scalable RL-aided DNAS framework that can explore large search spaces in a fast and memory-efficient manner. RADARS iteratively applies RL to prune undesired architecture candidates and identifies a promising subspace to carry out DNAS. Experiments using a workstation with 12 GB GPU memory show that on CIFAR-10 and ImageNet datasets, RADARS can achieve up to 3.41% higher accuracy with 2.5X search time reduction compared with a state-of-the-art RL-based method, while the two DNAS baselines cannot complete due to excessive memory usage or search time. To the best of the authors' knowledge, this is the first DNAS framework that can handle large search spaces with bounded memory usage.
UNIVERSAL INTELLECTUAL POTENTIAL AND SIGNIFICANT CREATIVE ACHIEVEMENTS OF THE FAMOUS ENCYCLOPEDIST SCIENTIST, DOCTOR OF HISTORICAL SCIENCES, PROFESSOR A.I. PAVKO: BIOGRAPHICAL STORIES FOR THE ANNIVERSARY

UNIVERSAL INTELLECTUAL POTENTIAL AND SIGNIFICANT CREATIVE ACHIEVEMENTS OF THE FAMOUS ENCYCLOPEDIST SCIENTIST, DOCTOR OF HISTORICAL SCIENCES, PROFESSOR A.I. PAVKO: BIOGRAPHICAL STORIES FOR THE ANNIVERSARY
Fuzzy c-Means Clusterization and ANN- MLP Prediction of Malign Breast Cancer in a Cohort of Patients

A model based on to cluster and predict the presence of malign breast cancer. The algorithms are applied to an open dataset of 569 patients that have either benign or malign breast cancer. First the article presents a detailed data description that is followed by a correlation matrix analysis and a regression analysis to verify the select the best variables for the clustering analysis and the predictive model. The clusterization is realized with the fuzzy c-Means algorithms. The prediction is performed by the Artificial Neural Network (ANN) Multilayer Perceptron (MLP) algorithm showing the best performance in comparison with the results of other machine learning algorithms. The use of all the tools in a definite sequence is the model proposed in this work providing an auto consistent approach to automatize the risk calculus with a good efficiency. The proposed approach can be applied for other typologies of cancers.
Sampling-based Estimation of the Number of Distinct Values in Distributed Environment

In data mining, estimating the number of distinct values (NDV) is a fundamental problem with various applications. Existing methods for estimating NDV can be broadly classified into two categories: i) scanning-based methods, which scan the entire data and maintain a sketch to approximate NDV; and ii) sampling-based methods, which estimate NDV using sampling data rather than accessing the entire data warehouse. Scanning-based methods achieve a lower approximation error at the cost of higher I/O and more time. Sampling-based estimation is preferable in applications with a large data volume and a permissible error restriction due to its higher scalability. However, while the sampling-based method is more effective on a single machine, it is less practical in a distributed environment with massive data volumes. For obtaining the final NDV estimators, the entire sample must be transferred throughout the distributed system, incurring a prohibitive communication cost when the sample rate is significant. This paper proposes a novel sketch-based distributed method that achieves sub-linear communication costs for distributed sampling-based NDV estimation under mild assumptions. Our method leverages a sketch-based algorithm to estimate the sample's {\em frequency of frequency} in the {\em distributed streaming model}, which is compatible with most classical sampling-based NDV estimators. Additionally, we provide theoretical evidence for our method's ability to minimize communication costs in the worst-case scenario. Extensive experiments show that our method saves orders of magnitude in communication costs compared to existing sampling- and sketch-based methods.
A Construction of Involutory Recursive-Like MDS Matrices via Self-Reciprocal Generator Polynomials of Reed-Solomon Codes

Abstract Using a recursive MDS matrix over a finite field in the diffusion layer of block ciphers is a standard method to maximize the diffusion while minimizing the implementation cost. On the other hand, an involutory MDS matrix (the matrix is the inverse of itself) simplifies the implementation of the cipher as the same module can be used for both encryption and decryption. However, it has been shown (Gupta et al.’21) that there doesn’t exist a recursive MDS matrix M that is also involutory over a field of charateristic 2 except for a trivial case. Another option is to use a recursive-like involutory MDS matrix of the form RM, where M is a recursive matrix and R is a permutation matrix. In fact, Gupta et al. proved that over a field of characteristic 2, if f is a monic, self-reciprocal polynomial of degree m, Cf is the companion matrix of f and M = Cfm is MDS, then RM is an involuntary MDS matrix, where R is the matrix reversing the order of rows in M. With RM in the diffusion layer, one can use the same LFSR for both encryption and decryption while still taking advantage of the fact that M is recursive. In this paper, we extend the work of Gupta et al. and establish a necessary and sufficient condition for the generator polynomials of Reed-Solomon codes over a finite field of arbitrary characteristic to be self-reciprocal, from which the desired RM matrices can be efficiently constructed for all feasible parameters.
Towards Efficient Simulation-Based Constrained Temporal Graph Pattern Matching

In the context of searching a single data graph $G$, graph pattern matching is to find all the occurrences of a pattern graph Q in G , specified by a matching rule. It is of paramount importance in many real applications such as bioinformatics and cyber security, among others. A wide spectrum of studies target general graph pattern matching. However, to analyse time-relevant services such as studying the spread of diseases and detecting attack pattern, it is attractive to study inexact temporal graph pattern matching. Hence, in this paper, we propose a relaxed matching rule called constrained temporal dual simulation, and study simulation-based constrained temporal graph pattern matching which guarantees that the matching result (i) preserves the ancestor and descendant temporal connectivities; and (ii) implements edge-to-temporal path mapping. We devise a decomposition-based matching method, which first decompose the data graph into Source Temporal Connected Components, and then perform matching on decomposed subgraphs. To speed up the matching, we define child/parent dependency relation tables and propose an efficient double hierarchical traverse strategy. Considering that the temporal graphs are natural dynamic, we further propose update algorithms. An extensive empirical study over real-world and synthetic temporal graphs has demonstrated the effectiveness and efficiency of our approach.
Minimizing Congestion for Balanced Dominators

A primary challenge in metagenomics is reconstructing individual microbial genomes from the mixture of short fragments created by sequencing. Recent work leverages the sparsity of the assembly graph to find r-dominating sets which enable rapid approximate queries through a dominator-centric graph partition. In this paper, we consider two problems related to reducing uncertainty and improving scalability in this setting.