Miracle-dz/UniFAR-FTUs huggingface dataset:
Improved S-AF and S-DF Relaying Schemes Using Machine Learning Based Power Allocation Over Cascaded Rayleigh Fading Channels

We investigate the performance of a dual-hop inter-vehicular communications (IVC) system with relay selection strategy. We assume a generalized fading channel model, known as cascaded Rayleigh (also called <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>*Rayleigh), which involves the product of <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> independent Rayleigh random variables. This channel model provides a realistic description of IVC, in contrast to the conventional Rayleigh fading assumption, which is more suitable for cellular networks. Unlike existing works, which mainly consider double-Rayleigh fading channels (i.e, <inline-formula> <tex-math notation="LaTeX">$n=2$ </tex-math></inline-formula>); our system model considers the general cascading order <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>, for which we derive an approximate analytic solution for the outage probability under the considered scenario. Also, in this study we propose a machine learning-based power allocation scheme to improve the link reliability in IVC. The analytical and simulation results show that both selective decode-and-forward (S-DF) and amplify-and-forward (S-AF) relaying schemes have the same diversity order in the high signal-to-noise ratio regime. In addition, our results indicate that machine learning algorithms can play a central role in selecting the best relay and allocation of transmission power.
MAXI/SSC all-sky maps from 0.7 keV to 4 keV

By accumulating data from the Solid-state Slit Camera (SSC) on board the MAXI mission from 2009 to 2011, diffuse X-ray background maps were obtained in energies of 0.7–1.0, 1.0–2.0, and 2.0–4.0 keV. They are the first to be derived with a solid-state instrument, and to be compared with the previous ROSAT all-sky survey result. While the SSC map in the highest energy band is dominated by point sources and the Galactic diffuse X-ray emission, that in 0.7–1.0 keV reveals an extended X-ray structure, of which the brightness distribution is very similar to that observed with ROSAT about 20 years before. As in the ROSAT result, the emission is dominated by a bright arc-like structure, which appears to be part of a circle of ∼50° radius centered at about (l,b) ∼ (340°, 15°). In addition, the SSC map suggests a fainter and larger ellipse, which is elongated in the north–south direction and roughly centered at the Galactic center. The spectrum of these structures is explained as thin thermal emission from a plasma, with a temperature of ∼0.31 keV and an abundance of ∼0.3 solar. Based on SSC observation conditions including low solar activity, the solar wind charge exchange signals are estimated to be negligible in the present SSC maps, as well as in the >0.56 keV ROSAT map. A brief discussion is given on the results obtained.
Statistical inference for fractional diffusion process with random effects at discrete observations

This paper deals with the problem of inference associated with linear fractional diffusion process with random effects in the drift. In particular we are concerned with the maximum likelihood estimators (MLE) of the random effect parameters. First of all, we estimate the Hurst parameter H from one single subject. Second, assuming the Hurst index H is known, we derive the MLE and examine their asymptotic behavior as the number of subjects under study becomes large, with random effects normally distributed.
Chronic Kidney Disease Caused by Hypothyroidism

Primary hypothyroidism refers to a systemic hypometabolic status due to hypothyroxinemia or thyroid hormone resistance caused by pathologic changes to the thyroid gland. The main role of thyroid hormone is to promote energy metabolism and facilitate growth and development processes within the body. In a hypothyroidism state caused by thyroid hormone deficiency, the body′s thermogenic effect is down-regulated and metabolisms of protein, sugar, and fat were slowed down, which might further lead to cardiovascular disease, hyperlipidemia, myocardial damage and myxedema, as well as other changes. Hypothyroidism tends to be neglected or misdiagnosed due to its trivial and nonspecific clinical manifestations, and a combined renal dysfunction is rarely reported. Herein a case of kidney dysfunction caused by hypothyroidism is reported and relevant literature was reviewed.
Multi-resolution Graph Neural Network for Identifying Disease-specific Variations in Brain Connectivity

Convolution Neural Network (CNN) recently have been adopted in several neuroimaging studies for diagnosis capturing disease-specific changes in the brain. While many of these methods are designed to work with images in $\mathbb R^n$ exploiting regular structure of the domain, they are not well-suited to analyze data with irregular structure such as brain connectivity. As there is significant interest in understanding the altered interactions between different brain regions that lead to neuro-disorders, it is important to develop data-driven methods that work with a population of graph data for traditional prediction tasks. In this regime, we propose a novel CNN-based framework with adaptive graph transforms to learn the most disease-relevant connectome feature maps which have the highest discrimination power across diagnostic categories. The backbone of our framework is a multi-resolution representation of the graph matrix which is steered by a set of wavelet-like graph transforms. In this context, our supervised graph learning framework outperforms conventional graph methods that predict diagnostic label only based on the underlying individual graph. Our extensive experiments on two real datasets of functional and structural brain networks show that our multi-resolution framework achieves significantly higher accuracy, precision and recall in predicting diagnostic labels and identifying disease-specific brain connectivities that are associated with brain disorders such as Attention-Deficit/Hyperactivity Disorder (ADHD) and Alzheimer's Disease (AD).
Multi-Criterion Evolutionary Design of Deep Convolutional Neural Networks

Convolutional neural networks (CNNs) are the backbones of deep learning paradigms for numerous vision tasks. Early advancements in CNN architectures are primarily driven by human expertise and elaborate design. Recently, neural architecture search was proposed with the aim of automating the network design process and generating task-dependent architectures. While existing approaches have achieved competitive performance in image classification, they are not well suited under limited computational budget for two reasons: (1) the obtained architectures are either solely optimized for classification performance or only for one targeted resource requirement; (2) the search process requires vast computational resources in most approaches. To overcome this limitation, we propose an evolutionary algorithm for searching neural architectures under multiple objectives, such as classification performance and FLOPs. The proposed method addresses the first shortcoming by populating a set of architectures to approximate the entire Pareto frontier through genetic operations that recombine and modify architectural components progressively. Our approach improves the computation efficiency by carefully down-scaling the architectures during the search as well as reinforcing the patterns commonly shared among the past successful architectures through Bayesian Learning. The integration of these two main contributions allows an efficient design of architectures that are competitive and in many cases outperform both manually and automatically designed architectures on benchmark image classification datasets, CIFAR, ImageNet and human chest X-ray. The flexibility provided from simultaneously obtaining multiple architecture choices for different compute requirements further differentiates our approach from other methods in the literature.
Band hybridization at the semimetal-semiconductor transition of 
Ta2NiSe5
 enabled by mirror-symmetry breaking

We present a combined study from angle-resolved photoemission and density-functional-theory calculations of the temperature-dependent electronic structure in the excitonic insulator candidate Ta2NiSe5. Our experimental measurements unambiguously establish the normal state as a semimetal with a significant band overlap of >100 meV. Our temperature-dependent measurements indicate how these low-energy states hybridize when cooling through the well-known 327 K phase transition in this system. From our calculations and polarizationdependent photoemission measurements, we demonstrate the importance of a loss of mirror symmetry in enabling the band hybridization, driven by a shearlike structural distortion which reduces the crystal symmetry from orthorhombic to monoclinic. Our results thus point to the key role of the lattice distortion in enabling the phase transition of Ta2NiSe5.
BADGER: Learning to (Learn [Learning Algorithms] through Multi-Agent Communication)

In this work, we propose a novel memory-based multi-agent meta-learning architecture and learning procedure that allows for learning of a shared communication policy that enables the emergence of rapid adaptation to new and unseen environments by learning to learn learning algorithms through communication. Behavior, adaptation and learning to adapt emerges from the interactions of homogeneous experts inside a single agent. The proposed architecture should allow for generalization beyond the level seen in existing methods, in part due to the use of a single policy shared by all experts within the agent as well as the inherent modularity of 'Badger'.
Estimates of sub and super solutions of Schr\"odinger equations with very singular potentials

Consider operators $L^{V}:=\Delta + V$ in a bounded smooth domain $\Omega\subset \mathbb{R}^N$. Assume that $V\in C^1(\Omega)$ satisfies $V(x) \leq \bar a\,\mathrm{dist}(x,\partial\Omega)^{-2}$ in $\Omega$ and that $1<c_H(V)$ ($=$ the Hardy constant relative to $V$). Under these assumptions and some conditions on the ground state, we derive sharp, two-sided estimates of weighted integrals of positive $L_V$ superharmonic and $L_V$ subharmonic functions. We show that these conditions hold for large classes of uperators.
First-order primal-dual algorithm with correction

This paper is devoted to the design of efficient primal-dual algorithm (PDA) for solving convex optimization problems with known saddle-point structure. We present a new PDA with larger acceptable range of parameters and correction, which result in larger step sizes. The step sizes are predicted by using a local information of the linear operator and corrected by linesearch to satisfy a very weak condition, even weaker than the boundedness of sequence generated. The convergence and ergodic convergence rate are established for general cases, and in case when one of the prox-functions is strongly convex. The numerical experiments illustrate the improvements in efficiency from the larger step sizes and acceptable range of parameters.
Variable Selection with Rigorous Uncertainty Quantification using Deep Bayesian Neural Networks: Posterior Concentration and Bernstein-von Mises Phenomenon

This work develops rigorous theoretical basis for the fact that deep Bayesian neural network (BNN) is an effective tool for high-dimensional variable selection with rigorous uncertainty quantification. We develop new Bayesian non-parametric theorems to show that a properly configured deep BNN (1) learns the variable importance effectively in high dimensions, and its learning rate can sometimes "break" the curse of dimensionality. (2) BNN's uncertainty quantification for variable importance is rigorous, in the sense that its 95% credible intervals for variable importance indeed covers the truth 95% of the time (i.e., the Bernstein-von Mises (BvM) phenomenon). The theoretical results suggest a simple variable selection algorithm based on the BNN's credible intervals. Extensive simulation confirms the theoretical findings and shows that the proposed algorithm outperforms existing classic and neural-network-based variable selection methods, particularly in high dimensions.
A Proof of the Gan-Loh-Sudakov Conjecture

We prove that any max-degree $d$ graph on $n$ vertices has at most $q{d+1 \choose 3}+{r \choose 3}$ triangles, where $n = q(d+1)+r$, $0 \le r \le d$. This resolves a conjecture of Gan, Loh, and Sudakov.
Absence of temporal order in states with fast decay of spatial correlations

In quantum lattice systems, we prove that any stationary state with sufficiently fast powerlaw (or even exponential) decay of spatial correlations has vanishing macroscopic temporal order in the thermodynamic limit. Assuming translational invariance, we obtain a similar bound on the temporal order between local operators at late times. Our proofs do not require any locality of the Hamiltonian. Applications in quantum time crystals are briefly discussed.
Generalization Error Bounds via Rényi-, f-Divergences and Maximal Leakage

In this work, the probability of an event under some joint distribution is bounded by measuring it with the product of the marginals instead (which is typically easier to analyze) together with a measure of the dependence between the two random variables. These results find applications in adaptive data analysis, where multiple dependencies are introduced and in learning theory, where they can be employed to bound the generalization error of a learning algorithm. Bounds are given in terms of Sibson’s Mutual Information, <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>-Divergences, Hellinger Divergences, and <inline-formula> <tex-math notation="LaTeX">$f$ </tex-math></inline-formula>-Divergences. A case of particular interest is the Maximal Leakage (or Sibson’s Mutual Information of order infinity), since this measure is robust to post-processing and composes adaptively. The corresponding bound can be seen as a generalization of classical bounds, such as Hoeffding’s and McDiarmid’s inequalities, to the case of dependent random variables.
Noise2Blur: Online Noise Extraction and Denoising

We propose a new framework called Noise2Blur (N2B) for training robust image denoising models without pre-collected paired noisy/clean images. The training of the model requires only some (or even one) noisy images, some random unpaired clean images, and noise-free but blurred labels obtained by predefined filtering of the noisy images. The N2B model consists of two parts: a denoising network and a noise extraction network. First, the noise extraction network learns to output a noise map using the noise information from the denoising network under the guidence of the blurred labels. Then, the noise map is added to a clean image to generate a new "noisy/clean" image pair. Using the new image pair, the denoising network learns to generate clean and high-quality images from noisy observations. These two networks are trained simultaneously and mutually aid each other to learn the mappings of noise to clean/blur. Experiments on several denoising tasks show that the denoising performance of N2B is close to that of other denoising CNNs trained with pre-collected paired data.
Las enfermedades crónicas no transmisibles y el uso de tecnologías de información y comunicación: revisión sistemática

Introduccion: El cuidado ofrecido a las personas con enfermedades cronicas no transmisibles requiere educacion y entrenamiento no solo al equipo de salud; sino tambien a las familias con personas en condicion de cronicidad. En este punto se encuentra las tecnologias de informacion y comunicacion como herramientas novedosas que por su facil acceso permiten la difusion de la informacion y por ende sirven como apoyo social. Este estudio tuvo como objetivo identificar los medios de comunicacion mas utilizados que ofrecen soporte social en el cuidado de personas con enfermedad cronica no transmisible. Materiales y Metodos: Estudio descriptivo de revision sistematica de la literatura, se realizo una busqueda de articulos en las bases de datos: MEDLINE, IME, LILACS, Centro Cochrane Iberoamericano, EMBASE, CUIDEN, CINAHL, BDIE, en el primer semestre de 2013. Resultados y Discusion: Se identificaron 35 estudios potenciales, de los cuales se incluyeron 23, (articulos de soporte social, uso de tecnologias de informacion y comunicacion, enfermedades cronicas no trasmisibles). A continuacion se presenta un analisis de los resultados generales obtenidos en la revision. Conclusiones: La intervencion en apoyo social con uso de tecnologias de informacion y comunicacion es de alta aplicabilidad en el contexto colombiano, es un metodo rentable y practico de proveer cuidado teniendo en cuenta que la tecnologia hace las intervenciones mas costo-efectivas porque disminuye la necesidad de desplazamiento de profesionales de la salud y cuidadores, mejora la capacidad de la diada paciente- cuidador para acceder a los programas y recursos tecnologicos existentes al tiempo que afianza el empoderamiento y la autoconfianza en el cuidado. Palabras clave: Tecnologia de la Informacion, Apoyo Social, Cuidadores, Enfermedad Cronica. (Fuente: DeCS BIREME). Como citar este articulo: Campos de Aldana MS, Moya Plata D, Mendoza Matajira JD, Duran Nino EY. Las enfermedades cronicas no transmisibles y el uso de tecnologias de informacion y comunicacion: revision sistematica. Rev Cuid. 2014; 5(1): 661-9.
CHILES VI: H i and H α observations for z < 0.1 galaxies; probing H i spin alignment with filaments in the cosmic web

We present neutral hydrogen (H i) and ionized hydrogen (H α) observations of 10 galaxies out to a redshift of 0.1. The H i observations are from the first epoch (178 h) of the COSMOS H i Large Extragalactic Survey (CHILES). Our sample is H i biased and consists of 10 late-type galaxies with H i masses that range from 1.8 × 107 M⊙ to 1.1 × 1010 M⊙. We find that although the majority of galaxies show irregularities in the morphology and kinematics, they generally follow the scaling relations found in larger samples. We find that the H i and H α velocities reach the flat part of the rotation curve. We identify the large-scale structure in the nearby CHILES volume using DisPerSE with the spectroscopic catalogue from SDSS. We explore the gaseous properties of the galaxies as a function of location in the cosmic web. We also compare the angular momentum vector (spin) of the galaxies to the orientation of the nearest cosmic web filament. Our results show that galaxy spins tend to be aligned with cosmic web filaments and show a hint of a transition mass associated with the spin angle alignment.
Radial-Layer Jamming Mechanism for String Configuration

In the soft robotics field, soft structures are required for safe human interactions, adaptive shape grippers, and haptic interfaces. Conversely, rigid structures are necessary for high force transmission devices and precise position control applications. The tunable stiffness mechanism is a crucial technology in such areas, and various methods were proposed. Extant studies examine fixing the segments of the string by the tension of a tendon to instantly stiffen the structure and they can be constituted with rigid materials. However, the conventional wire tensioning mechanism exhibits a limitation in the holding torque, and the optimum segment morphology and stiffening performance are not clear. In the study, we demonstrate a new tendon-driven jamming mechanism referred to as a “radial-layer jamming” with multiple layered joints that can realize high holding torque. Three basic segment structures are presented, and the design method is demonstrated. We measured the holding torque of the joints and compared the torque between three different designs. The results indicate that all three structures can control the holding torque by the pulling force of the wire. Furthermore, the findings suggest that the radial layer jamming can maintain the joints as fixed even if it has an extended structure. The holding force of the radial layer jamming did not significantly decrease when compared to other structures even if the number of joints increased. These layered approaches can contribute to the performance improvement of various variable stiffness mechanisms that utilize friction.
Asymmetric Co-Teaching for Unsupervised Cross Domain Person Re-Identification

Person re-identification (re-ID), is a challenging task due to the high variance within identity samples and imaging conditions. Although recent advances in deep learning have achieved remarkable accuracy in settled scenes, i.e., source domain, few works can generalize well on the unseen target domain. One popular solution is assigning unlabeled target images with pseudo labels by clustering, and then retraining the model. However, clustering methods tend to introduce noisy labels and discard low confidence samples as outliers, which may hinder the retraining process and thus limit the generalization ability. In this study, we argue that by explicitly adding a sample filtering procedure after the clustering, the mined examples can be much more efficiently used. To this end, we design an asymmetric co-teaching framework, which resists noisy labels by cooperating two models to select data with possibly clean labels for each other. Meanwhile, one of the models receives samples as pure as possible, while the other takes in samples as diverse as possible. This procedure encourages that the selected training samples can be both clean and miscellaneous, and that the two models can promote each other iteratively. Extensive experiments show that the proposed framework can consistently benefit most clustering based methods, and boost the state-of-the-art adaptation accuracy. Our code is available at https://github.com/FlyingRoastDuck/ACT_AAAI20.
Abelian Bosonization, OPEs, and the "String Scale" of Fermion Fields

This paper establishes a precise mapping between lattice and continuum operators in theories of (1 + 1)D fermions. To reach the continuum regime of a lattice theory, renormalization group techniques are here supplemented by a new kind of decimation called smoothing out. Smoothing out amounts to imposing constraints that make fermion fields vary slowly in position space; in momentum space, this corresponds to introducing boundary conditions below a certain depth of the Dirac sea. This procedure necessitates the introduction of a second small parameter to describe the continuum limit. This length scale, much larger than the lattice spacing but much smaller than the macroscopic system size, controls the derivative expansions of fields, and hence plays the role of the "string scale" in quantum field theory. Smoothing out a theory of Dirac fermions on the lattice provides a transparent, fully lattice-based derivation of the operator product expansion, current algebra, and Abelian bosonization rules in the continuum. Nontrivial high-momentum effects are demonstrated in all these derivations. In particular, the radius of the compact scalar dual to the Dirac fermion is found to vary with momentum, smoothly shrinking to zero at a cutoff set by the "string scale. "
EVALUATION OF METHANOLIC EXTRACT OF CASSIA FISTULA BARK FOR CARDIOPROTECTIVE ACTIVITY

Cassia fistula of the family Leguminaceae is traditionally used in cardiopathy and heart disease. The cardioprotective effect of the methanolic extract of C. fistula (MECB) bark against dox orubicin (DXR) induced cardiotoxicity in wistar rat was studied. DXR 10 mg/kg i.p. single dose to male Wistar rats causes cardiac damage and increases the levels of cardiac biomarker enzymes Viz. AST, ALT, LDH and CKMB. The histopathological changes in DXR treated animals showed sever degeneration of the myofibrils with focal necrosis and vacuolated cytoplasm. Pretreatment with MECB at a dose of 400 mg/kg significantly decreased the elevated levels of serum enzymes histological disturbances and electro card iogram changes to normal myocardium functioning. The result suggests that MECB has cardio protective effect in DXR induced myocardial damage rats.