Miracle-dz/UniFAR-FTUs huggingface dataset:
Infrared Image Super-resolution by Using Dual-dictionary

This paper addresses the problem of using a single low-resolution infrared image to generate a highresolution infrared image. In this paper, we approach a problem from the point of dictionary-training. The traditional dictionary-training model has some problems. First, the image features, which the traditional fllters extract, are not enough for representing images. Second, traditional super-resolution model hardly reconstructs accurate high-frequency component from the prior information trained by the training images. In order to solve the flrst problem mentioned above, this paper proposes additional fllters to extract image features. The fllters can extract more meaningful image features from training images than traditional fllters. To solve the second problem, a dual-dictionary training model is proposed. The dictionary-training model, which consists of the main-dictionary training and the residual-dictionary training, aims to reconstruct the high-frequency component. In this paper, the image high-frequency component, which is considered as a combination of main high-frequency component and the residual high-frequency component, can be accurately recovered via the dual-dictionary training model. Extensive experimental results demonstrate that the proposed method achieves much better results in terms of both PSNR and visual perception compared with the conventional super-resolution method. In Section 2, the algorithm of sparse representation super-resolution will be introduced. Section 3, the dual-dictionary training modeling is proposed. Section 4 presents experimental results.
On Using ANT for Studying Information Systems: A (Somewhat) Socratic Dialogue

Tai Bruno Latouro straipsnio, kuris parašytas kaip dialogas, „On Using ANT for Studying Information Systems: A (Some­what) Socratic Dialogue“ vertimas. Profesoriaus ir doktoranto dialogo metu atskleidžiamas veikėjo-tinklo-teorijos turinys, išdėstomas B. Latouro požiūris į refeksyvumą, struktūralistinį aiškinimą ir socialinių mokslų paskirtį. Pirmą kartą šį dialogą B. Latouras paskelbė 2004 m. C. Avgerou, C. Ciborra, F. F. Land sudarytoje knygoje The Social Study of Information and Communication Technology. Innovation, Actors, and Contexts(Oxford University Press).
Frozen ground and snow cover monitoring in the South Shetland Islands, Antarctica: Instrumentation, effects on ground thermal behaviour and future research

The study of the thermal behavior of permafrost and active layer on the South Shetland Islands, in the western side of the Antarctic Peninsula (Antarctica), has been our research topic since 1991, especially after 2006 when we established different active layer thickness and ground thermal monitoring sites of the CALM and GTN-P international networks of the International Permafrost Association. Along this period, the snow cover thickness did not change at those sites, but since 2010, we observed an elongation on the snow cover duration, with similar snow onset, but a delay on the snow offset. Due to the important effects of snow cover on the ground thermal behavior, we started in late 2015 a new research project (PERMASNOW) focused on the accurate monitoring of the snow cover (duration, density, snow water equivalent and distribution), from very different approaches, including new instrumentation, pictures analysis and remote sensing on optical and radar bands. Also, this interdisciplinary and international research team intends to compare the snow cover and ground thermal behavior with other monitoring sites in the Eastern Antarctic Peninsula where the snow cover is minimum and remains approximately constant.
A New Digital Signature Algorithm

Digital signature schemes are commonly used as primitives in cryptography protocols. Some applications like multi agent systems transfer messages with low size and capacity. In this paper we introduced a new digital signature which works very well for such applications which have low file size for sending. The new hash function generates dynamic and smaller size of bits which depends on each bytes of message. A simple mechanism for hashing the message and encryption is one of advantages of suggestive algorithms. The main function which is used for hashing is bitwise Or and Multiply functions. Testing new algorithms showed that its hashed file size is 4% of the original file in messages with size lower than 1600 bytes. This algorithm can be used in applications which have low file size for sending and want simple and fast algorithms for generating digital signature.
A simple model of time-dependent ionization in Type IIP supernova envelopes

We propose a model kinetic system of the hydrogen atom (two levels plus continuum) under the conditions typical for atmospheres of Type IIP supernovae in the plateau stage. Despite the simplicity of this system, it describes realistically the basic properties of the complete system. Analysis shows that the ionization ‘freeze-out’ effect is always manifest at large times. We give a simple criterion for checking the statistical equilibrium of a system under the given conditions at any time. It is shown that if the system is in non-equilibrium at early times, the time-dependent effect of ionization necessarily exists. We also generalize this criterion to the case of arbitrary multilevel systems. We discuss various factors that affect the strength of the time-dependent effect in the kinetics during the photospheric phase of a supernova explosion.
An Algorithm for Variable-Length Proper-Name Compression

Viable on-line search systems require reasonable capabilities to automatically detect (and hopefully correct) variations between request format and stored format. An important requirement is the solution of the problem of matching proper names, not only because both input specifications and storage specifications are subject to error, but also because various transliteration schemes exist and can provide variant proper name forms in the same data base. This paper reviews several proper name matching schemes and provides an updated version of these schemes which tests out nicely on the proper name equivalence classes of a suburban telephone book. An appendix lists the corpus of names used for algorithm test.
A comparison of apartment rent price prediction using a large dataset: Kriging versus DNN

The hedonic approach based on a regression model has been widely adopted for the prediction of real estate property price and rent. In particular, a spatial regression technique called Kriging, a method of interpolation that was advanced in the field of spatial statistics, are known to enable high accuracy prediction in light of the spatial dependence of real estate property data. Meanwhile, there has been a rapid increase in machine learning-based prediction using a large (big) dataset and its effectiveness has been demonstrated in previous studies. However, no studies have ever shown the extent to which predictive accuracy differs for Kriging and machine learning techniques using big data. Thus, this study compares the predictive accuracy of apartment rent price in Japan between the nearest neighbor Gaussian processes (NNGP) model, which enables application of Kriging to big data, and the deep neural network (DNN), a representative machine learning technique, with a particular focus on the data sample size (n = 10^4, 10^5, 10^6) and differences in predictive performance. Our analysis showed that, with an increase in sample size, the out-of-sample predictive accuracy of DNN approached that of NNGP and they were nearly equal on the order of n = 10^6. Furthermore, it is suggested that, for both higher and lower end properties whose rent price deviates from the median, DNN may have a higher predictive accuracy than that of NNGP.
Integration of TensorFlow based Acoustic Model with Kaldi WFST Decoder

While the Kaldi framework provides state-of-the-art components for speech recognition like feature extraction, deep neural network (DNN)-based acoustic models, and a weighted finite state transducer (WFST)-based decoder, it is difficult to implement a new flexible DNN model. By contrast, a general-purpose deep learning framework, such as TensorFlow, can easily build various types of neural network architectures using a tensor-based computation method, but it is difficult to apply them to WFST-based speech recognition. In this study, a TensorFlow-based acoustic model is integrated with a WFST-based Kaldi decoder to combine the two frameworks. The features and alignments used in Kaldi are converted so they can be trained by the TensorFlow model, and the DNN-based acoustic model is then trained. In the integrated Kaldi decoder, the posterior probabilities are calculated by querying the trained TensorFlow model, and a beam search is performed to generate the lattice. The advantages of the proposed one-pass decoder include the application of various types of neural networks to WFST-based speech recognition and WFST-based online decoding using a TensorFlow-based acoustic model. The TensorFlow based acoustic models trained using the RM, WSJ, and LibriSpeech datasets show the same level of performance as the model trained using the Kaldi framework.
Computational phase transition signature in Gibbs sampling

Gibbs sampling is fundamental to a wide range of computer algorithms. Such algorithms are set to be replaced by physics based processors—be it quantum or stochastic annealing devices—which embed problem instances and evolve a physical system into a low-energy ensemble to recover a probability distribution. At a critical constraint to variable ratio, satisfiability (SAT) problem instances exhibit a SAT-UNSAT transition (frustrated to frustration free). Algorithms require increasing computational resources from this critical point. This is a so called, algorithmic or computational phase transition and has extensively been studied. In this paper we consider the complexity in sampling and recovering ground states from resultant distributions of a physics based processor. In particular, we first consider the ideal Gibbs distributions at some fixed inverse temperature and observe that the success probability in sampling and recovering ground states decrease for instances starting at the critical density. Furthermore, simulating the Gibbs distribution, we employ Ising spin dynamics, which play a crucial role in understanding of non-equilibrium statistical physics, to find their steady states of 2-SAT Hamiltonians. We observe that beyond the critical density, the probability of sampling ground states decreases. Our results apply to several contemporary devices and provide a means to experimentally probe a signature of the computational phase transition.
The importance of authors ensuring referencing and page proofs are correct (editorial)

Given the importance of correct referencing for tracing ideas and data in the literature and for proper attribution of the contributions of others, I was surprised to read that errors in referencing in published papers are frequent and may be approaching 25% in the general field of environmental science (Lopresti 2010). Most serious are the cases where the reference supporting a claim does not actually make the statement attributed to it, disseminating misinformation (Wright and Armstrong2008;Toddetal.2010). Alsoseriousarecaseswhere an author has copied a citation and reference from another source rather than consulting the original, not realising that the source had made a referencing error or incorrect interpretation ofthethrustofthepaper.Thishasledtohighcitationsforpapers that do not exist (Dubin 2004), an example that delights those who despise the modern obsession with citation rates. Then there are cases that are just plain irritating when readers cannot locate a reference because it is missing or incorrect. Omitting a reference is easy if the entire paper is not checked thoroughly; references in captions of tables and figures are notorious for not appearing in reference lists. Bibliographic packages may help reduce errors, but they are not infallible and authors should manually check that every citation in the text, including captions, appears correctly in the reference list and vice versa. In addition, authors must check to see every citation and reference is given in the format of the particular journal. Although the frequency of errors was news to me, it is common knowledge to the editors of major databases. As recently as 2011, Scopus would not index books or book chapters other than those in a named series because, amongst other difficulties, the diversity of citation styles often made it difficulttodecideexactlywhattheauthorwasciting.Theonline
Topic Matching Based Change Impact Analysis from Feature on User Interface of Mobile Apps.

The complexity of mobile applications often lies in the user interface (UI). To update function provided by UI or just fix bugs related to UI, software maintainers primarily need to obtain the location of source code implementation and detect change set. Since UI related feature is tightly related to the class containing the declaration of the UI component, this paper proposes a topic matching based change impact analysis method from feature on user interface of mobile apps. Our approach combines LDA model with program dependency to realize the change impact analysis. Considering app′s small scale and few comments, a novel preprocessing method combining tf-idf with term weight based on structural information is applied to LDA model. Experiments on 16 update records of 4 open source apps show the effectiveness of our proposed method.
FA-Harris: A Fast and Asynchronous Corner Detector for Event Cameras

Recently, the emerging bio-inspired event cameras have demonstrated potentials for a wide range of robotic applications in dynamic environments. In this paper, we propose a novel fast and asynchronous event-based corner detection method which is called FA-Harris. FA-Harris consists of several components, including an event filter, a Global Surface of Active Events (G-SAE) maintaining unit, a corner candidate selecting unit, and a corner candidate refining unit. The proposed G-SAE maintenance algorithm and corner candidate selection algorithm greatly enhance the real-time performance for corner detection, while the corner candidate refinement algorithm maintains the accuracy of performance by using an improved event-based Harris detector. Additionally, FA-Harris does not require artificially synthesized event-frames and can operate on asynchronous events directly. We implement the proposed method in C++ and evaluate it on public Event Camera Datasets. The results show that our method achieves approximately 8× speed-up when compared with previously reported event-based Harris detector, and with no compromise on the accuracy of performance.
Constrained transport and adaptive mesh refinement in the Black Hole Accretion Code

Context. Worldwide very long baseline radio interferometry (VLBI) arrays are expected to obtain horizon-scale images of supermassive black hole candidates and of relativistic jets in several nearby active galactic nuclei. This, together with the expected detection of electromagnetic counterparts of gravitational-wave signals, motivates the development of models for magnetohydrodynamic flows in strong gravitational fields. Aims. The Black Hole Accretion Code (BHAC) is a publicliy available code intended to aid with the modeling of such sources by performing general relativistic magnetohydrodynamical simulations in arbitrary stationary spacetimes. New additions to the code are required in order to guarantee an accurate evolution of the magnetic field when small and large scales are captured simultaneously. Methods. We discuss the adaptive mesh refinement (AMR) techniques employed in BHAC, which are essential to keep several problems computationally tractable, as well as staggered-mesh-based constrained transport (CT) algorithms to preserve the divergence-free constraint of the magnetic field. We also present a general class of prolongation operators for face-allocated variables compatible with them. Results. After presenting several standard tests for the new implementation, we show that the choice of the divergence-control method can produce qualitative differences in the simulation results for scientifically relevant accretion problems. We demonstrate the ability of AMR to decrease the computational costs of black hole accretion simulations while sufficiently resolving turbulence arising from the magnetorotational instability. In particular, we describe a simulation of an accreting Kerr black hole in Cartesian coordinates using AMR to follow the propagation of a relativistic jet while self-consistently including the jet engine, a problem set up for which the new AMR implementation is particularly advantageous. Conclusions. The CT methods and AMR strategies discussed here are currently being used in the simulations performed with BHAC for the generation of theoretical models for the Event Horizon Telescope collaboration.
Iterative qubit coupled cluster method: A systematic approach to the full-CI limit in quantum chemistry calculations on NISQ devices

Noisy intermediate-scale quantum (NISQ) devices is the current realm of quantum computing. To exploit their limited powers to full extent it is imperative to develop suitable algorithms. We present the iterative qubit coupled cluster (iQCC) method aimed to perform realistic quantum chemistry calculations on such devices. The iQCC method can use as few as a single generator of entanglement (a single "Pauli word") yet is able to reach the full configurational interaction (FCI) limit. The iQCC method is also endowed with a few techniques that allow for relatively large problems to be addressed. We show the capacity of our method on the electronic structure calculations for the LiH (4 qubits), H$_2$O (6 qubits) and N$_2$ (14 qubits) molecules
Entropy-based Dynamic Time Warping Similarity Measures for Financial Time Series Analysis

In this paper, we propose a new method for measuring the similarity between time-varying dynamical financial networks. Specifically, we explore whether the proposed similarity measure can be employed to understand the structural evolution of the financial networks with time. For a family of time-varying financial networks with each vertex representing the individual time series of a stock and each edge between a pair of time series representing their degree of correlation, we commence by computing the commute time matrix for the network structures where each element of the matrix represents the expected correlation value between pairwise stocks. For each network, we show how the commute time matrix allows us to identify a reliable set of dominant correlated time series as well as an associated dominant probability distribution of the stock belonging to this set. Furthermore, we represent each original network as a dominant Shannon entropy time series computed from the dominant probability distribution. With the dominant entropy time series for each pair of financial networks to hand, we define the similarity measure based on the classical dynamic time warping framework, for analyzing the financial time-varying networks. We show that the resulting similarity measure is positive definite and therefore corresponds to a kernel measure on graphs. The proposed kernel bridges the gap between graph kernels and the classical dynamic time warping framework for time series analysis. Experiments on time-varying networks abstracted from New York Stock Exchange (NYSE) database demonstrate that the proposed method can effectively detect abrupt changes in networks as time series structures and can be used to characterize different stages in time-varying financial network evolutions.
An (R, S) Based Heuristic Model for the Stochastic Joint Replenishment Problem

This paper considers the periodic-review stochastic joint replenishment problem (JRP) under Bookbinder and Tan's static-dynamic uncertainty control policy. According to a static-dynamic uncertainty control rule, the decision maker fixes timing of replenishments once and for all at the beginning of the planning horizon, the inventory position is then raised to a predefined order-up-to-position at the beginning of each replenishment period. In this policy, freezing the replenishment times ameliorates the inherent difficulties pertinent to replenishment coordination of multiple products, whereas dynamic order quantities facilitate dealing with uncertain demands. We adapt and extend an earlier mixed integer linear programming (MILP) model for computing static-dynamic uncertainty policy parameters, and demonstrate that the same can be used to approximate the optimal control rule for the JRP, also known as $(\sigma, \vec{S})$ policy. An extensive computational study illustrates the effectiveness of our approach when compared to alternative approaches in the literature.
Electrical Control of the Rashba-Edelstein Effect in a Graphene/2H-TaS2 Van der Waals Heterostructure at Room Temperature

Van der Waals heterostructures are prime candidates to explore interfacial spin-orbit-coupling (SOC) phenomena for both fundamental spintronics research and applications. Proximity-induced SOC and spin dynamics engineering have been recently achieved in graphene/semiconducting dichalcogenide bilayers. However, the emergence of spin-momentum-locked 2D Dirac fermions in a van der Walls material, pivotal for all-electrical control over the electron spin moment, has remained elusive. Here, we report current-induced spin polarization, a direct consequence of spin-momentum locking due to broken mirror symmetry, in a semi-metallic graphene/2H-TaS2 bilayer. Spin-sensitive electrical measurements unveil full spin polarization reversal by gate voltage (i.e., spin switching) at room temperature. The on-demand electrical generation and control of nonequilibrium spin polarization, not previously observed in a nonmagnetic heterointerface, is an elegant manifestation of unconventional 2D Dirac fermions with robust spin-helical structure. Our findings, supported by first-principles calculations, establish a new route to design low-power spin-logic circuits from layered materials.
Deployable probabilistic programming

We propose design guidelines for a probabilistic programming facility suitable for deployment as a part of a production software system. As a reference implementation, we introduce Infergo, a probabilistic programming facility for Go, a modern programming language of choice for server-side software development. We argue that a similar probabilistic programming facility can be added to most modern general-purpose programming languages. Probabilistic programming enables automatic tuning of program parameters and algorithmic decision making through probabilistic inference based on the data. To facilitate addition of probabilistic programming capabilities to other programming languages, we share implementation choices and techniques employed in development of Infergo. We illustrate applicability of Infergo to various use cases on case studies, and evaluate Infergo's performance on several benchmarks, comparing Infergo to dedicated inference-centric probabilistic programming frameworks.