Reviewerly/exHarmony huggingface dataset:
Pixel Difference Convolutional Network for RGB-D Semantic Segmentation

RGB-D semantic segmentation can be advanced with convolutional neural networks due to the availability of Depth data. Although objects cannot be easily discriminated by just the 2D appearance, with the local pixel difference and geometric patterns in Depth, they can be well separated in some cases. Considering the fixed grid kernel structure, CNNs are limited to lack the ability to capture detailed, fine-grained information and thus cannot achieve accurate pixel-level semantic segmentation. To solve this problem in the CNN structure, we propose a Pixel Difference Convolutional Network (PDCNet) to capture detailed intrinsic patterns by aggregating both intensity and gradient information in the local range for Depth data and global range for RGB data, respectively. Precisely, PDCNet consists of a Depth branch and an RGB branch. For the Depth branch, we propose a Pixel Difference Convolution (PDC) to consider local and detailed geometric information in Depth data via aggregating both intensity and gradient information. For the RGB branch, we contribute a lightweight Cascade Large Kernel (CLK) to extend PDC, namely CPDC, to enjoy global contexts for RGB data and further boost performance. Consequently, the local and global pixel differences from both modal data are seamlessly incorporated into PDCNet during the information propagation process. Experiments on three challenging benchmark datasets, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., NYUDv2 (78.4 Pixel Acc., 53.5 mIoU), SUN RGB-D (83.3 Pixel Acc., 49.6 mIoU) and SID Dataset (83.1 Pixel Acc., 61.4 mIoU) reveal that our PDCNet achieves state-of-the-art performance for the semantic segmentation task.
Medical image retrieval via nearest neighbor search on pre-trained image features

Nearest neighbor search, also known as NNS, is a technique used to locate the points in a high-dimensional space closest to a given query point. This technique has multiple applications in medicine, such as searching large medical imaging databases, disease classification, and diagnosis. However, when the number of points is significantly large, the brute-force approach for finding the nearest neighbor becomes computationally infeasible. Therefore, various approaches have been developed to make the search faster and more efficient to support the applications. With a focus on medical imaging, this paper proposes DenseLinkSearch (DLS), an effective and efficient algorithm that searches and retrieves the relevant images from heterogeneous sources of medical images. Towards this, given a medical database, the proposed algorithm builds an index that consists of pre-computed links of each point in the database. The search algorithm utilizes the index to efficiently traverse the database in search of the nearest neighbor. We also explore the role of medical image feature representation in content-based medical image retrieval tasks. We propose a Transformer-based feature representation technique that outperformed the existing pre-trained Transformer-based approaches on benchmark medical image retrieval datasets. We extensively tested the proposed NNS approach and compared the performance with state-of-the-art NNS approaches on benchmark datasets and our created medical image datasets. The proposed approach outperformed the existing approaches in terms of retrieving accurate neighbors and retrieval speed. In comparison to the existing approximate NNS approaches, our proposed DLS approach outperformed them in terms of lower average time per query and ≥99% R@10 on 11 out of 13 benchmark datasets. We also found that the proposed medical feature representation approach is better for representing medical images compared to the existing pre-trained image models. The proposed feature extraction strategy obtained an improvement of 9.37%, 7.0%, and 13.33% in terms of P@5, P@10, and P@20, respectively, in comparison to the best-performing pre-trained image model. The source code and datasets of our experiments are available at https://github.com/deepaknlp/DLS.
Cascaded H-bridge multilevel inverter-based DSTATCOM with an artificial neural fuzzy inference systembased controller

&lt;span&gt;In this paper, &lt;span&gt;a voltage source inverter is utilized as a distributed static compensator (DSTATCOM). The DSTATCOM is used for the elimination of harmonics caused by the nonlinear load. The reference currents of DSTATCOM were estimated with a synchronous reference frame (SRF). Conventionally, the SRF uses voltage, load current and filter current to compute the reference current to track desired source current for DSTATCOM. The sliding mode controller and feedback linearization are used to design the current controller. The feedback linearization technique and sliding mode control are used to cancel nonlinearities and offer invariant stability due to modeling uncertainties due to the DSTATCOM parameter and external load disturbance. The sliding mode controller requires a rigorous mathematical model of the system. To overcome this, an artificial neuro-fuzzy inference system (ANFIS) is devised for the control of DSTATCOM. The training data of the ANFIS controller is generated from the sliding mode controller algorithm. The overall system of DSTATCOM including the ANFIS algorithm is implemented in MATLAB/Simulink SimPower block sets. The simulated response of DSTATCOM was found to be improved and better than the sliding mode controller scheme.&lt;/span&gt;&lt;/span&gt;
Electro-optically programmable photonic circuits enabled by wafer-scale integration on thin-film lithium niobate

Programmable photonic circuits performing universal linear-optical transformations underpin vital functions in photonic quantum information processing, quantum-enhanced sensor networks, machine learning, and many other intriguing applications. Recent advances in photonic integrated circuits facilitate monolithic integration of externally controlled Mach-Zehnder interferometers (MZIs) which can implement arbitrary unitary transformation on a large number of input/output modes. In this work, we demonstrate a $4\ifmmode\times\else\texttimes\fi{}4$ programmable linear photonic circuit on lithium niobate on an insulator platform employing fast, power-efficient, and low-loss electro-optical phase shifters, showing enormous advantages in terms of configuration rate and power consumption. Our device composed of cascaded MZIs possesses a total on-chip power dissipation of only 1.5 mW when operating at 100 MHz modulation rate. Our MZIs exhibit high bandwidth of 22.5 GHz, fast switching with 160-ps rise time and 120-ps fall time, low insertion loss of 0.15 dB, and on-chip extinction ratio of \ensuremath{-}34 dB for both cross and bar routes.
Global challenges of Russia and some problems of the domestic higher education system

Introduction. Russia is currently facing a num- ber of global challenges and threats comparable only to those it faced before the Great Patriotic War. The education system of any country is an indicator of the intellectual capabilities on which its future depends. The article analyzes the state of modern Russian education that has emerged after a number of reforms, its ability to provide adequate responses to modern challenges and threats. The purpose of the article is to analyze the current global challenges and threats to Russia, to consider the features of the existing education system in the country, to show its capabilities to ensure a scien- tific and technological breakthrough, to effectively block existential threats. Methods. Materialistic (the principle of objectivity), dialectical (the principle of development and universal connection), systemic. Scientific novelty. The authors specify the essence of the turning point in the modern history of man- kind, carry out an objective analysis of the state of Russian education, the nature and consequences of the reforms, its possibilities of blocking existing challenges and threats is given. Results. The authors show that at the present stage of Russian history, the role of youth in determining the fate of the country is significantly increasing. The key to the ability of young peo- ple to fulfill this historic mission is the education system. It allows us to preserve historical tradi- tions, national identity, and it is a factor of scientific and technological breakthroughs of society. It is revealed that the reforms in the education system were not focused on preserving historical traditions and the national code of the country. Borrowed educational technologies have led to a significant decrease in the quality of Russian education. As a result of the reforms carried out, the country has lost significant development potential. Conclusions. The reforms in the education sys- tem, as they were carried out, turned out to be counterproductive and did not meet the national interests of the country. Currently, in the condi- tions of existential threats for Russia, there is an urgent need to change the ideology and technology of managing the education system; it is necessary to leave the Bologna system and return to the ele- ments of classical European and national education that have proven their effectiveness. If a country needs a scientific and technological breakthrough, the financing of its education and science cannot be less than those countries that Russia intends to catch up with. To do this, it is necessary to radically change both the volumes and the mechanisms of financing education and science. It is necessary to revise article 13, paragraph 2 of Russia’s Constitu- tion. The state ideology concentrates on expressing the national interests of the country and ultimately determines the goals and objectives of the existing system of education and upbringing. The absence of a state ideology contributes to the desovereigni- zation and desubjectivization of Russian society.
Fast generation of Schrödinger cat states using a Kerr-tunable superconducting resonator

Abstract Schrödinger cat states, quantum superpositions of macroscopically distinct classical states, are an important resource for quantum communication, quantum metrology and quantum computation. Especially, cat states in a phase space protected against phase-flip errors can be used as a logical qubit. However, cat states, normally generated in three-dimensional cavities and/or strong multi-photon drives, are facing the challenges of scalability and controllability. Here, we present a strategy to generate and preserve cat states in a coplanar superconducting circuit by the fast modulation of Kerr nonlinearity. At the Kerr-free work point, our cat states are passively preserved due to the vanishing Kerr effect. We are able to prepare a 2-component cat state in our chip-based device with a fidelity reaching 89.1% under a 96 ns gate time. Our scheme shows an excellent route to constructing a chip-based bosonic quantum processor.
Optimization of Solar Panel Orientation for Maximum Energy Efficiency

This report details the findings of a research that was conducted with the purpose of establishing where exactly solar panels should be positioned in order to make the most of their potential to convert sunlight into usable electrical power. The angle at which solar panels are positioned relative to the sun is one of the most important determinants of the amount of energy they produce; hence, maximizing this characteristic has the potential to considerably boost energy efficiency. For the purpose of the research, statistics were gathered on the amount of energy produced by solar panels positioned in a variety of various angles. An exercise in regression analysis was carried out in order to determine the primary elements that determine the degree to which sun angles and shading factors have an effect on energy efficiency. The results of the regression analysis were put to use in the process of developing a strategy for improving the orientation of solar panels. Following that, an analysis of variance was carried out in order to assess how well the planned approach had been implemented. Alterations in the orientation of solar panels were found to have the greatest effect on the amount of energy produced when both the angle of sunlight's incidence and the degree of shadowing were taken into consideration. The findings of our research might have important implications for the development of environmentally friendly energy alternatives and for the reduction of the negative effects of climate change. In addition to this, they are important considerations in the design and installation of systems that use solar panels. Because it may assist in the optimization of panel orientation to achieve maximum energy output, the suggested methodology can serve as a useful tool for stakeholders who are involved in the development and implementation of solar panel systems. This is because the proposed methodology can help achieve maximum energy output. In next research initiatives, it may be worthwhile to investigate the possibility of using more sophisticated modelling approaches, like as machine learning algorithms, in order to improve the accuracy of solar panel orientation forecasts for various geographical areas and meteorological situations.
Experimental Analysis of Energy Transfers between a Quantum Emitter and Light Fields

Energy can be transferred between two quantum systems in two forms: unitary energy---that can be used to drive another system---and correlation energy---that reflects past correlations. We propose and implement experimental protocols to access these energy transfers in interactions between a quantum emitter and light fields. Upon spontaneous emission, we measure the unitary energy transfer from the emitter to the light field and show that it never exceeds half the total energy transfer and is reduced when introducing decoherence. We then study the interference of the emitted field and a coherent laser field at a beam splitter and show that the nature of the energy transfer quantitatively depends on the quantum purity of the emitted field.
Label Distribution Learning by Partitioning Label Distribution Manifold

Researchers have suggested leveraging label correlation to deal with the exponentially sized output space of label distribution learning (LDL). Among them, some have proposed to exploit local label correlation. They first partition the training set into different groups and then exploit local label correlation on each one. However, these works usually apply clustering algorithms, such as K -means, to split the training set and obtain the clustering results independent of label correlation. The structures (e.g., low rank and manifold) learned on such clusters may not efficiently capture label correlation. To solve this problem, we put forward a novel LDL method called LDL by partitioning label distribution manifold (LDL-PLDM). First, it jointly bipartitions the training set and learns the label distribution manifold to model label correlation. Second, it recurses until the reconstruction error of learning the label distribution manifold cannot be reduced. LDL-PLDM achieves label-correlation-related partition results, on which the learned label distribution manifold can better capture label correlation. We conduct extensive experiments to justify that LDL-PLDM statistically outperforms state-of-the-art LDL methods.
Problem representations and illusions in reasoning

The mental model theory of reasoning postulates that reasoners build models of the situations described in premises, and that these models normally make explicit only what is true. The theory has an unexpected consequence: it predicts the occurrence of inferences that are systematically invalid. These inferences should arise from reasoners failing to take into account what is false. We report an experiment that corroborated the occurrence of these illusory inferences, and that eliminated a number of alternative explanations for them. Results illuminate the controversy among various current theories of reasoning.
Telepathology and telecytology in developing countries

The field of surgical pathology and cytology involves a cascade of operator-dependent technical, scientific and medical processes. The preparation and interpretation of surgical pathology and cytology material requires trained technical personnel, excellent technical support, skill, experience and some basic infrastructure. The quality of the images is largely dependent on the quality of the basic starting material, which is the stained slide. The essential components of any telepathology system, the microscope, a camera with software and a computer, may not available in rural areas of developing countries. The limiting step in telepathology is availability of a well-processed histology or cytology glass slide. In the developing world, the lack of infrastructure, equipment and uniform standards makes for poor pathology material or suboptimal technical material. A well-processed stained slide is the most important factor for success in telepathology. Lack of trained manpower for histopathology processing has a very negative effect on the subsequent quality of a telepathology consultation.
Phonotactic Constraint Ranking for Speech Recognition

AbstractEmpirical studies in inductive language learning point at pure memory-based learning as a successful approach to many language processing tasks, often performing better than methods that abstract from the learning material. The possibility is left open, however, that limited, careful abstraction in memory-based learning may be harmless to generalization. We test this hypothesis by investigating a careful abstraction method that generalizes instances into instance families. The method is applied to a range of language learning tasks. Results show that the method reduces memory requirements to a reasonable to considerable degree, while being able to maintain the performance accuracy of pure memory-based learning on three of the six tasks studied. We discuss the inclusion of the concept of instance families as a working unit in memory-based language learning.
Statistical mechanical evaluation of a spread-spectrum watermarking model with image restoration

In cases in which an original image is blind, a decoding method where both the image and the messages can be estimated simultaneously is desirable. We propose a spread spectrum watermarking model with image restoration based on Bayes estimation. We therefore need to assume some prior probabilities. The probability for estimating the messages is given by the uniform distribution, and the ones for the image are given by the infinite range model and 2D Ising model. Any attacks from unauthorized users can be represented by channel models. We can obtain the estimated messages and image by maximizing the posterior probability. We analyzed the performance of the proposed method by the replica method in the case of the infinite range model. We first calculated the theoretical values of the bit error rate from obtained saddle point equations and then verified them by computer simulations. For this purpose, we assumed that the image is binary and is generated from a given prior probability. We also assume that attacks can be represented by the Gaussian channel. The computer simulation retults agreed with the theoretical values. In the case of prior probability given by the 2D Ising model, in which each pixel is statically connected with four-neighbors, we evaluated the decoding performance by computer simulations, since the replica theory could not be applied. Results using the 2D Ising model showed that the proposed method with image restoration is as effective as the infinite range model for decoding messages. We compared the performances in a case in which the image was blind and one in which it was informed. The difference between these cases was small as long as the embedding and attack rates were small. This demonstrates that the proposed method with simultaneous estimation is effective as a watermarking decoder.
On the average-case complexity of pattern matching with wildcards

Pattern matching with wildcards is a string matching problem with the goal of finding all factors of a text t of length n that match a pattern x of length m, where wildcards (characters that match everything) may be present. In this paper we present a number of complexity results and fast average-case algorithms for pattern matching where wildcards are allowed in the pattern, however, the results are easily adapted to the case where wildcards are allowed in the text as well. We analyse the average-case complexity of these algorithms and derive non-trivial time bounds. These are the first results on the average-case complexity of pattern matching with wildcards which provide a provable separation in time complexity between exact pattern matching and pattern matching with wildcards. We introduce the wc-period of a string which is the period of the binary mask xb where xb[i]=a iff x[i]≠ϕ and b otherwise. We denote the length of the wc-period of a string x by . We show the following results for constant 0<ϵ<1 and a pattern x of length m and g wildcards with the prefix of length p contains gp wildcards: If limm→∞⁡gpp=0 there is an optimal algorithm running in O(nlogσ⁡mm)-time on average. If limm→∞⁡gpp=1−ϵ there is an algorithm running in O(nlogσ⁡mlog2⁡pm)-time on average. If limm→∞⁡gm=limm→∞⁡1−f(m)=1 any algorithm takes at least Ω(nlogσ⁡mf(m))-time on average.
Strong Systematicity within Connectionism: The Tensor-Recurrent Network

Systematicity, the ability to represent and process structurally related objects, is a significant and pervasive property of cognitive behaviour, and clearly evident in language. In the case of Connectionist models that learn from examples, systematicity is generalization over examples sharing a common structure. Although Connectionist models (e.g., the recurrent network and its variants) have demonstrated generalization over structured domains, there has not been a clear demonstration of strong systematicity (i.e., generalization across syntactic position). The tensor has been proposed as a way of representing structured objects, however, there has not been an effective learning mechanism (in the strongly systematic sense) to explain how these representations may be acquired. I address this issue through an analysis of tensor learning dynamics. These ideas are then implemented as the tensor-recurrent network which is shown to exhibit strong systematicity on a simple language task. Finally, it is suggested that the properties of the tensor-recurrent network that give rise to strong systematicity are analogous to the concepts of variables and types in the Classical paradigm.