Reviewerly/exHarmony huggingface dataset:
Typical and atypical solutions in non-convex neural networks with discrete and continuous weights

We study the binary and continuous negative-margin perceptrons as simple non-convex neural network models learning random rules and associations. We analyze the geometry of the landscape of solutions in both models and find important similarities and differences. Both models exhibit subdominant minimizers which are extremely flat and wide. These minimizers coexist with a background of dominant solutions which are composed by an exponential number of algorithmically inaccessible small clusters for the binary case (the frozen 1-RSB phase) or a hierarchical structure of clusters of different sizes for the spherical case (the full RSB phase). In both cases, when a certain threshold in constraint density is crossed, the local entropy of the wide flat minima becomes non-monotonic, indicating a break-up of the space of robust solutions into disconnected components. This has a strong impact on the behavior of algorithms in binary models, which cannot access the remaining isolated clusters. For the spherical case the behaviour is different, since even beyond the disappearance of the wide flat minima the remaining solutions are shown to always be surrounded by a large number of other solutions at any distance, up to capacity. Indeed, we exhibit numerical evidence that algorithms seem to find solutions up to the SAT/UNSAT transition, that we compute here using an 1RSB approximation. For both models, the generalization performance as a learning device is shown to be greatly improved by the existence of wide flat minimizers even when trained in the highly underconstrained regime of very negative margins.
Neural Keyphrase Generation: Analysis and Evaluation

Keyphrase generation aims at generating topical phrases from a given text either by copying from the original text (present keyphrases) or by producing new keyphrases (absent keyphrases) that capture the semantic meaning of the text. Encoder-decoder models are most widely used for this task because of their capabilities for absent keyphrase generation. However, there has been little to no analysis on the performance and behavior of such models for keyphrase generation. In this paper, we study various tendencies exhibited by three strong models: T5 (based on a pre-trained transformer), CatSeq-Transformer (a non-pretrained Transformer), and ExHiRD (based on a recurrent neural network). We analyze prediction confidence scores, model calibration, and the effect of token position on keyphrases generation. Moreover, we motivate and propose a novel metric framework, SoftKeyScore, to evaluate the similarity between two sets of keyphrases by using softscores to account for partial matching and semantic similarity. We find that SoftKeyScore is more suitable than the standard F1 metric for evaluating two sets of given keyphrases.
Improved path planning algorithms for non-holonomic autonomous vehicles in industrial environments with narrow corridors: Roadmap Hybrid A* and Waypoints Hybrid B*. Roadmap hybrid A* and Waypoints hybrid A* Pseudocodes

This paper proposes two novel path planning algorithms, Roadmap Hybrid A* and Waypoints Hybrid A*, for car-like autonomous vehicles in logistics and industrial contexts with obstacles (e.g., pallets or containers) and narrow corridors. Roadmap Hybrid A* combines Hybrid A* with a graph search algorithm applied to a static roadmap. The former enables obstacle avoidance and flexibility, whereas the latter provides greater robustness, repeatability, and computational speed. Waypoint Hybrid A*, on the other hand, generates waypoints using a topological map of the environment to guide Hybrid A* to the target pose, reducing complexity and search time. Both algorithms enable predetermined control over the shape of desired parts of the path, for example, to obtain precise docking maneuvers to service machines and to eliminate unnecessary steering changes produced by Hybrid A* in corridors, thanks to the roadmap and/or the waypoints. To evaluate the performance of these algorithms, we conducted a simulation study in an industrial plant where a robot must navigate narrow corridors to serve machines in different areas. In terms of computational time, total length, reverse length path, and other metrics, both algorithms outperformed the standard Hybrid A*.
Scalable quantum circuits for $n$-qubit unitary matrices

This work presents an optimization-based scalable quantum neural network framework for approximating $n$-qubit unitaries through generic parametric representation of unitaries, which are obtained as product of exponential of basis elements of a new basis that we propose as an alternative to Pauli string basis. We call this basis as the Standard Recursive Block Basis, which is constructed using a recursive method, and its elements are permutation-similar to block Hermitian unitary matrices.
Spherical Inducing Features for Orthogonally-Decoupled Gaussian Processes

Despite their many desirable properties, Gaussian processes (GPs) are often compared unfavorably to deep neural networks (NNs) for lacking the ability to learn representations. Recent efforts to bridge the gap between GPs and deep NNs have yielded a new class of inter-domain variational GPs in which the inducing variables correspond to hidden units of a feedforward NN. In this work, we examine some practical issues associated with this approach and propose an extension that leverages the orthogonal decomposition of GPs to mitigate these limitations. In particular, we introduce spherical inter-domain features to construct more flexible data-dependent basis functions for both the principal and orthogonal components of the GP approximation and show that incorporating NN activation features under this framework not only alleviates these shortcomings but is more scalable than alternative strategies. Experiments on multiple benchmark datasets demonstrate the effectiveness of our approach.
Efficacy of RBC histogram in the diagnosis of morphological types of anaemia compared with peripheral smear

Background: Anaemia constitutes an important diagnostic and clinical category of haematological disorders. Along with peripheral smear histogram is used to interpret the abnormal RBC morphology. The present study is to find out the efficacy of RBC histogram in diagnosis of morphological types of anaemia. Methods: A total of 354 blood samples of adults &gt;18 years, Hb &lt;10 mg/dl included in the study conducted at Medical College, Kottayam. Peripheral smear evaluated for RBC morphology without referring to the histogram. RBC histogram from same sample analyzed separately. The results obtained, classified into 4 morphological types of anemia -Normocytic normochromic, microcytic hypochromic, macrocytic and hemolytic anemias. Sensitivity, specificity, positive predictive value, negative predictive value and accuracy were calculated. Results: The major proportion of study is in the age group of 51-70. Major morphologic type in both, is normocytic. Concordant samples for normocytic - 161, microcytic- 97, macrocytic- 7, Hemolytic- 1. Histogram show more sensitivity and less specificity for normocytic. Microcytic has less sensitivity, more specificity. Macrocytic showed &gt;90% sensitivity and specificity. Hemolytic has less sensitivity, more specificity. Overall sensitivity is 75%. Conclusions: Histogram is efficient in detecting normocytic and microcytic anaemia. In macrocytic anaemia among 40 cases detected by histogram only 7 showed concordance because histogram may detect slight variation in morphology not appreciable on light microscopy. In haemolytic anaemia only one case detected by histogram. The discordance is due to low sample size.
Compositional 3D Human-Object Neural Animation

Human-object interactions (HOIs) are crucial for human-centric scene understanding applications such as human-centric visual generation, AR/VR, and robotics. Since existing methods mainly explore capturing HOIs, rendering HOI remains less investigated. In this paper, we address this challenge in HOI animation from a compositional perspective, i.e., animating novel HOIs including novel interaction, novel human and/or novel object driven by a novel pose sequence. Specifically, we adopt neural human-object deformation to model and render HOI dynamics based on implicit neural representations. To enable the interaction pose transferring among different persons and objects, we then devise a new compositional conditional neural radiance field (or CC-NeRF), which decomposes the interdependence between human and object using latent codes to enable compositionally animation control of novel HOIs. Experiments show that the proposed method can generalize well to various novel HOI animation settings. Our project page is https://zhihou7.github.io/CHONA/
Applications of linear algebra in encryption

Information security is always a crucial issue in the continuously developing society. There are many solutions that have been proposed to secure information. In this article, the author discusses the introduction of encryption using knowledge from the Linear Algebra course to first-year students so that they can perceive the application of Linear Algebra. The information encryption process uses the knowledge of matrices, matrix operations, inverse matrices, transposition, Gauss elimination, linear transformation...
Risk Management Practices Among Accommodation Facilities in Ormoc City

This study aims to ascertain the degree of impact and likelihood of the various risks, as well as the risk management method implementation among lodging facilities. In addition, the study took into account the link between the likelihood and impact of the various risks. To achieve this goal, a quantitative method engaging in a descriptive approach was with 128 respondents from 30 establishments who got involved in the study from the top management to rank and file through a self-completed survey. The level of the risk management process among the establishments showed a "slightly high level" of risk management implementation that signifies vulnerability to risks and could be detrimental to the establishment. To ensure impacts are regulated and the likelihood of happening will be controlled numerous risks based on the risk spectrum need to be addressed, treated, watched for, and reviewed. The results also revealed no significant relationships between the various types of risks, except people risks, which had a slight but favorable association between its impact and likelihood, which caused the null hypothesis to be rejected. According to the study's findings, firms should not take any risks lightly, regardless of how serious or likely they are as long as they present a threat. If risks are not properly handled, they can exacerbate or escalate their impact or likelihood. Moreover, a risk management plan based on ISO 31000-2009 needed to be proposed to have a more structured and systematic process.
Quarterly Pasidungog to Parents: An Intervention to Improve Parental Involvement in Printed Modular Instruction

Parental involvement in children’s education is crucial for their academic success. Active participation and collaboration between parents and schools can enhance children’s learning experiences and improve their academic performance. Thus, this action research focused on the effectiveness of quarterly Pasidungog as an intervention for parents to improve the academic performance of Grade 2 learners in Getafe 2 Central Elementary School, Getafe 2 District, Bohol Division. The researcher aimed to improve the academic performance of twelve (12) Grade II–2 learners. The twelve (12) learners, or 55% of the class, showed deficient academic performance. The monitoring checklist form was used as a data-gathering tool. Descriptive statistics such as frequency, percentage, and weighted mean were used for data analysis. Results showed that the learners’ academic performance improved after the Quarterly Pasidungog to parents, as shown in the mean difference before and after applying the intervention. It implies that the conduct of Pasidungog toward parents motivated them to perform their roles as para-teachers to their learners. Recommendations were proposed to encourage parental involvement and strengthen home-school collaboration.
Traced Types for Safe Strategic Rewriting

Strategy languages enable programmers to compose rewrite rules into strategies and control their application. This is useful in programming languages, e.g., for describing program transformations compositionally, but also in automated theorem proving, where related ideas have been studies with tactics languages. Clearly, not all compositions of rewrites are correct, but how can we assist programmers in writing correct strategies? In this paper, we present a static type system for strategy languages. We combine a structural type system capturing how rewrite strategies transform the shape of the rewritten syntax with a novel tracing system that keeps track of all possible legal strategy execution paths. Our type system raises warnings when parts of a composition are guaranteed to fail at runtime, and errors when no legal execution for a strategy is possible. We present a formalization of our strategy language and novel tracing type system, and formally prove its type soundness. We present formal results, showing that ill-traced strategies are guaranteed to fail at runtime and that well-traced strategy executions "can't go wrong", meaning that they are guaranteed to have a possible successful execution path.
A bridge between trace anomaly and deconfinement phase transition

Inspired by the fact that both the dilaton potential encoding the trace anomaly of QCD and the Polyakov loop potential measuring the deconfinement phase transition can be expressed in the logarithmic forms and the scale symmetry is expected to restore and colors are deconfined at extreme conditions such as the high temperature and/or density, we conjecture a relation between the dilaton potential and the Polyakov loop potential. Explicitly, we start from the Coleman-Weinberg type potential of a real scalar field -- conformal compensator -- and make an ansatz of the relation between this scalar field and the Polyakov loop to obtain the Polyakov loop potential which can be parameterized in Lattice QCD (LQCD) in the pure gauge sector. We find that the coefficients of the Polyakov potential fitted from Lattice data are automatically satisfied in this ansatz and the locations of the deconfinement and scale restoration are locked to each other and the first-order phase transition can be realized based on this ansatz. Extensions to the low-energy effective quark models are also discussed. The conjectured relation may deepen our understanding on the evolution of the universe, the mechanism of electroweak symmetry breaking, the phase diagram of QCD matter, and the properties of neutron stars.
ART TECHNOLOGIES OF CREATIVE PERSONAL DEVELOPMENT

The article substantiates the relevance of studying the problem of using modern art technologies in the digital educational space. The purpose of the article is to consider art technologies as a tool for the creative development of the individual in the educational process. The content of the article reveals the creative potential of art technologies and the features of their use for the creative development of the individual based on the gaming paradigm. The art technologies of virtual and augmented reality are characterized: interactive longread, hologram, media testing, virtual performance, insta-series, insta-marathon. The significance of art technologies as a means of the psychological impact of digital art on the activation of artistic representations and imagination, unconscious processes and creative self-expression of the individual is highlighted. It is noted that the performance of creative tasks during the game in conjunction with technological solutions of digital art (creation of video projection, hologram, computer graphics, sound palette, installations, editing, special effects) contributes to the formation of original ideas, the ability to model images in three-dimensional art space. The conclusions emphasize that the use of art technologies in the process of creative development of the individual ensures the mastery of competencies in creating artistic images and their transformation in virtual and augmented reality, in establishing communicative interaction in a multi-channel game format, understanding the value meaning of works of modern creativity and artistic values ​​of high aesthetics.
Revolutionizing Computing: A Comprehensive Introduction to Quantum Computing

&lt;p&gt; Quantum computing is a revolutionary technology that has the potential to solve complex problems that classical computers cannot. This article provides an overview of the current state of quantum computing, including a brief history of its development, an explanation of the principles of quantum mechanics that underlie quantum computing, and a description of the major types of quantum computers and their current capabilities. The article also covers recent advances in quantum computing research, including efforts to develop practical quantum algorithms and to overcome the challenges of quantum error correction. Finally, the article concludes with a discussion of the potential applications of quantum computing in areas such as cryptography, drug discovery, and machine learning, as well as the challenges that must be overcome to realize these applications. Overall, this article aims to provide a comprehensive introduction to the exciting and rapidly-evolving field of quantum computing. &lt;/p&gt;
A Fully Convolutional Network of Self-supervised Monocular Depth Estimation with Global Receptive Field and Unreasonable Matching Penalty

&lt;p&gt;This paper proposes one Fully Convolutional Network of Self-supervised Monocular Depth Estimation with Global Receptive Field and Unreasonable Matching Penalty&lt;/p&gt;
Physical Interpretation of Backpropagated Error in Neural Networks

&lt;p&gt; &lt;/p&gt; &lt;p&gt;In this note, we shed light on the physical meaning for the backpropagated error used by the backpropagation training algorithm. Essentially, for a given scalar output of the neural network, its backpropagated error is a linear apportionment of the error at it, in proportion to the linear gain between the outputs of neurons and the output according to a linearised-systems-view of the network. For multiple outputs, superposition provides the total/nett backpropagated error at the outputs of neurons. &lt;/p&gt; &lt;p&gt;&lt;br&gt;&lt;/p&gt; &lt;p&gt;Subsequently, we present some elementary statistical analysis for backpropagated errors in the network.&lt;/p&gt;
Delving Deep into Adversarial Attack for Multi-label Learning

&lt;p&gt;This research mainly focuses on adversarial attacks in multi-label learning. We analyze the shortcomings of existing research in optimization objectives and solution methods. Then, we constructed a new measure for the optimization objects based on the Jaccrd index. Furthermore, we propose a novel approach to solving the optimization problem.&lt;/p&gt;
Breaking Barriers: Unleashing the Power of Quantum Algorithms

&lt;p&gt; Quantum computing is a rapidly developing field with the potential to revolutionize our ability to solve certain computational problems . While classical computers operate using classical bits, quantum computers use quantum bits (qubits), which can exist in a superposition of states, allowing for parallel computations. The existence of quantum algorithms, which can solve certain problems exponentially faster than classical algorithms, is one of the most exciting aspects of quantum computing. These exotic algorithms are designed to take advantage of quantum mechanical properties such as entanglement and interference, and have the potential to transform a range of fields, including cryptography, optimization, and machine learning. &lt;/p&gt;
Research Paper on Career Dendogram

Abstract: Inside the modern-day profession steering system, there are distinct counsellors available who provide individual counselling to help college students to pick their profession in a scientific way. There are numerous web sites to be had to conduct on line character check, aptitude test, personal interest takes a look at, domain take a look at too are expecting which professional area a specific pupil should choose. Knowledge the contemporary schooling trend and suggesting the most suitable area correctly is very tough venture in career steering system. So, college students are not appeared to be glad by way of the current machine due to inconsistent end result and consequently an application is needed that could generate a good deal correct end result. For that reason, Survey was taken if you want to recognize what troubles they face so that we attempt to conquer those problems for the imminent era. We will be enforcing 2-3 gadget studying algorithms accompanied by means of trying out and the one so as to give more accurate outcomes may be taken into consideration.
Two Way Sign Language for Deaf and Dumb using Deep Convolution Neural Network

Abstract: The goal is to build a bridge between the hearing and hearing-impaired communities and to start two-way communication. Here we propose a two-way sign language translator that translates speech in real time into sign language and vice versa. Each video frame is processed using Python's Open CV Library. Additionally, we use the Gaussian Mixture-based Background/Foreground Segmentation Algorithm to remove the background from each frame. The hearing impaired community will then be more effectively integrated into regular schools thanks to this technology, making education more affordable and available to them.
A Modular Approach for Multilingual Timex Detection and Normalization using Deep Learning and Grammar-based methods

Detecting and normalizing temporal expressions is an essential step for many NLP tasks. While a variety of methods have been proposed for detection, best normalization approaches rely on hand-crafted rules. Furthermore, most of them have been designed only for English. In this paper we present a modular multilingual temporal processing system combining a fine-tuned Masked Language Model for detection, and a grammar-based normalizer. We experiment in Spanish and English and compare with HeidelTime, the state-of-the-art in multilingual temporal processing. We obtain best results in gold timex normalization, timex detection and type recognition, and competitive performance in the combined TempEval-3 relaxed value metric. A detailed error analysis shows that detecting only those timexes for which it is feasible to provide a normalization is highly beneficial in this last metric. This raises the question of which is the best strategy for timex processing, namely, leaving undetected those timexes for which is not easy to provide normalization rules or aiming for high coverage.
Logarithmic-Regret Quantum Learning Algorithms for Zero-Sum Games

We propose the first online quantum algorithm for zero-sum games with $\tilde O(1)$ regret under the game setting. Moreover, our quantum algorithm computes an $\varepsilon$-approximate Nash equilibrium of an $m \times n$ matrix zero-sum game in quantum time $\tilde O(\sqrt{m+n}/\varepsilon^{2.5})$, yielding a quadratic improvement over classical algorithms in terms of $m, n$. Our algorithm uses standard quantum inputs and generates classical outputs with succinct descriptions, facilitating end-to-end applications. As an application, we obtain a fast quantum linear programming solver. Technically, our online quantum algorithm "quantizes" classical algorithms based on the optimistic multiplicative weight update method. At the heart of our algorithm is a fast quantum multi-sampling procedure for the Gibbs sampling problem, which may be of independent interest.