Miracle-dz/UniFAR-FTUs huggingface dataset:
Computer Vision Aided Codebook Design for MIMO Communications Systems

mmWave communications systems usually rely on analog or hybrid analog/digital architectures and thus need a predefined codebook to perform beamforming. Traditional codebooks are designed for universal environments, although in practice a particular BS will only serve a particular environment. In this paper, we propose novel site-specific codebook design methods by utilizing the visual information captured through cameras. Different from other site-specific codebook design methods that require a large amount of measured channel state information (CSI), the proposed ones need only a simple snapshot of the environment followed by efficient computer vision (CV) techniques. Thus the proposed CV-aided codebook design reduces the overhead of communications system, such as the cost of time, human resources, as well as the hardware installation and calibration. Specifically, we propose a CV-based approach that detects the LOS area around the BS and reconstructs the LOS channel vectors set (CVS). With this knowledge, we build a vision-based beam codebook using Lloyd algorithm. Further, we design a FusionNet to generate the codebook that can serve the non-line-of-sight (NLOS) users. The simulation results demonstrate the effectiveness of the proposed CV-aided codebook design methods and their superiority compared to the conventional methods.
A Positive-Unlabeled Learning Algorithm for Urban Flood Susceptibility Modeling

Flood susceptibility modeling helps understand the relationship between influencing factors and occurrence of urban flooding and further provides spatial distribution of flood risk, which is critical for flood-risk reduction. Machine learning methods have been widely applied in flood susceptibility modeling, but traditional supervised learning requires both positive (flood) and negative (non-flood) samples in model training. Historical flood inventory data usually contain positive-only data, whereas negative data selected from areas without flood records are prone to be contaminated by positive data, which is referred to as case-control sampling with contaminated controls. In order to address this problem, we propose to apply a novel positive-unlabeled learning algorithm, namely positive and background learning with constraints (PBLC), in flood susceptibility modeling. PBLC trains a binary classifier from case-control positive and unlabeled samples without requiring truly labeled negative data. With historical records of flood locations and environmental covariates, including elevation, slope, aspect, plan curvature, profile curvature, slope length factor, stream power index, topographic position index, topographic wetness index, distance to rivers, distance to roads, land use, normalized difference vegetation index, and precipitation, we compared the performances of the traditional artificial neural network (ANN) and the novel PBLC in flood susceptibility modeling in the city of Guangzhou, China. Experimental results show that PBLC can produce more calibrated probabilistic prediction, more accurate binary prediction, and more reliable susceptibility mapping of urban flooding than traditional ANN, indicating that PBLC is effective in addressing the problem of case-control sampling with contaminated controls and it can be successfully applied in urban flood susceptibility mapping.
Role of neuroinflammation mediated potential alterations in adult neurogenesis as a factor for neuropsychiatric symptoms in Post-Acute COVID-19 syndrome—A narrative review

Persistence of symptoms beyond the initial 3 to 4 weeks after infection is defined as post-acute COVID-19 syndrome (PACS). A wide range of neuropsychiatric symptoms like anxiety, depression, post-traumatic stress disorder, sleep disorders and cognitive disturbances have been observed in PACS. The review was conducted based on PRISMA-S guidelines for literature search strategy for systematic reviews. A cytokine storm in COVID-19 may cause a breach in the blood brain barrier leading to cytokine and SARS-CoV-2 entry into the brain. This triggers an immune response in the brain by activating microglia, astrocytes, and other immune cells leading to neuroinflammation. Various inflammatory biomarkers like inflammatory cytokines, chemokines, acute phase proteins and adhesion molecules have been implicated in psychiatric disorders and play a major role in the precipitation of neuropsychiatric symptoms. Impaired adult neurogenesis has been linked with a variety of disorders like depression, anxiety, cognitive decline, and dementia. Persistence of neuroinflammation was observed in COVID-19 survivors 3 months after recovery. Chronic neuroinflammation alters adult neurogenesis with pro-inflammatory cytokines supressing anti-inflammatory cytokines and chemokines favouring adult neurogenesis. Based on the prevalence of neuropsychiatric symptoms/disorders in PACS, there is more possibility for a potential impairment in adult neurogenesis in COVID-19 survivors. This narrative review aims to discuss the various neuroinflammatory processes during PACS and its effect on adult neurogenesis.
Toric degenerations: a bridge between representation theory, tropical geometry and cluster algebras

In this thesis we study toric degenerations of projective varieties. We compare different constructions to understand how and why they are related. In focus are toric degenerations obtained from representation theory, tropical geometry or cluster algebras. Often those rely on valuations and the theory of Newton-Okounkov bodies. Toric degenerations can be seen as a combinatorial shadow of the original objects. The goal is therefore to understand why the different theories are so closely related, by understanding the toric degenerations they yield first. We choose Grassmannians, flag varieties and Schubert varieties as starting point as here many different constructions are applicable. One of our main results shows how toric degenerations obtained using full-rank valuations, independent of how these are constructed, can (under certain conditions) be realized using tropical geometry.
Effects of biochar addition on greenhouse gas emissions during freeze–thaw cycles in a black soil region, Northeast China

As global warming intensifies, the soil environment in middle to high latitudes will undergo more extensive and frequent freeze–thaw cycles (FTCs), which will significantly affect the carbon and nitrogen cycles of soil ecosystems and aggravate greenhouse gas (GHG) emissions. Biochar can increase soil organic carbon storage and mitigate climate change. To effectively control GHG emissions, soil supplemented with biochar at different application rates (0%, 2%, 4% and 6% [w/w]) under different numbers of FTCs (0, 3, 6, 9, and 12) was selected as the research object. The soil GHG emission characteristics in different experimental treatments and their relationships with soil physical and chemical properties were determined. Our results showed that N2O and CO2 emissions were promoted during FTCs, with values of 3.13–50.37 and 16.22–135.50 μg m−2 h−1, respectively. The order of N2O and CO2 emissions with respect to biochar application rate was as follows: 2% > 0% > 4% > 6%. CH4 emissions were negative during FTCs, varying from −1.62 to −10.59 μg m−2 h−1, and negative CH4 emissions were promoted by biochar. Correlation analysis showed that N2O, CO2 and CH4 emissions were significantly correlated with pH, soil moisture and soil organic matter (SOM), total nitrogen (TN) and NH4+ –N contents (p < .01). The conceptual path model demonstrated that GHG emissions were significantly influenced by FTCs, moisture, SOM and biochar application rate. Our results indicate that the effects of FTCs on GHG emissions were greater than those of biochar application. Biochar application rates of 4% or 6% should be considered in the future to reduce soil GHG emissions in the black soil region of Northeast China. Our results can help provide a theoretical basis and effective strategy to reduce soil GHG emissions during FTCs in seasonally frozen regions.
Variational integrators in plasma physics

In this work, special numerical methods, so called variational integrators, are developed for several models of plasma physics. Special attention is given to conservation of physical quantities like energy and momentum, which appears to be crucial in long-time simulations or the modeling of complicated phenomena like turbulence. With the variational integrators, the errors in the conserved quantities are reduced down to machine accuracy.
The Construction and Evaluation of a Multi-Task Convolutional Neural Network for a Cone-Beam Computed-Tomography-Based Assessment of Implant Stability

Objectives: Assessing implant stability is integral to dental implant therapy. This study aimed to construct a multi-task cascade convolution neural network to evaluate implant stability using cone-beam computed tomography (CBCT). Methods: A dataset of 779 implant coronal section images was obtained from CBCT scans, and matching clinical information was used for the training and test datasets. We developed a multi-task cascade network based on CBCT to assess implant stability. We used the MobilenetV2-DeeplabV3+ semantic segmentation network, combined with an image processing algorithm in conjunction with prior knowledge, to generate the volume of interest (VOI) that was eventually used for the ResNet-50 classification of implant stability. The performance of the multitask cascade network was evaluated in a test set by comparing the implant stability quotient (ISQ), measured using an Osstell device. Results: The cascade network established in this study showed good prediction performance for implant stability classification. The binary, ternary, and quaternary ISQ classification test set accuracies were 96.13%, 95.33%, and 92.90%, with mean precisions of 96.20%, 95.33%, and 93.71%, respectively. In addition, this cascade network evaluated each implant’s stability in only 3.76 s, indicating high efficiency. Conclusions: To our knowledge, this is the first study to present a CBCT-based deep learning approach CBCT to assess implant stability. The multi-task cascade network accomplishes a series of tasks related to implant denture segmentation, VOI extraction, and implant stability classification, and has good concordance with the ISQ.
Nonlocal Schr\"odinger-Kirchhoff equations with external magnetic field

The paper deals with existence and multiplicity of solutions of the fractional Schr\"{o}dinger--Kirchhoff equation involving an external magnetic potential. As a consequence, the results can be applied to the special case \begin{equation*} (a+b[u]_{s,A}^{2\theta-2})(-\Delta)_A^su+V(x)u=f(x,|u|)u\,\, \quad \text{in $\mathbb{R}^N$}, \end{equation*} where $s\in (0,1)$, $N>2s$, $a\in \mathbb{R}^+_0$, $b\in \mathbb{R}^+_0$, $\theta\in[1,N/(N-2s))$, $A:\mathbb{R}^N\rightarrow\mathbb{R}^N$ is a magnetic potential, $V:\mathbb{R}^N\rightarrow \mathbb{R}^+$ is an electric potential, $(-\Delta )_A^s$ is the fractional magnetic operator. In the super- and sub-linear cases, the existence of least energy solutions for the above problem is obtained by the mountain pass theorem, combined with the Nehari method, and by the direct methods respectively. In the superlinear-sublinear case, the existence of infinitely many solutions is investigated by the symmetric mountain pass theorem.
Equivariant control data and neighborhood deformation retractions

In this article we study Whitney (B) regular stratified spaces with the action of a compact Lie group $G$ which preserves the strata. We prove an equivariant submersion theorem and use it to show that such a $G$-stratified space carries a system of $G$-equivariant control data. As an application, we show that if $A \subset X$ is a closed $G$-stratified subspace which is a union of strata of $X$, then the inclusion $i : A \hookrightarrow X$ is a $G$-equivariant cofibration. In particular, this theorem applies whenever $X$ is a $G$-invariant analytic subspace of an analytic $G$-manifold $M$ and $A \hookrightarrow X$ is a closed $G$-invariant analytic subspace of $X$.
Inducing $\pi$-partial characters with a given vertex

Let $G$ be a solvable group. Let $p$ be a prime and let $Q$ be a $p$-subgroup of a subgroup $V$. Suppose $\phi \in \ibr G$. If either $|G|$ is odd or $p = 2$, we prove that the number of Brauer characters of $H$ inducing $\phi$ with vertex $Q$ is at most $|\norm GQ: \norm VQ|$.
The boundary of the complex of free factors

We give a description of the boundary of a complex of free factors that is analogous to E. Klarreich's description of the boundary of a curve complex. The argument uses the geometry of folding paths developed by Bestvina and Feighn as well as structural results about very small trees developed by Coulbois, Hilion, Lustig, and Reynolds.
DIANA: An End-to-End Hybrid DIgital and ANAlog Neural Network SoC for the Edge

DIgital-ANAlog (DIANA), a heterogeneous multi-core accelerator, combines a reduced instruction set computer - five (RISC-V) host processor with an analog in-memory computing (AIMC) artificial intelligence (AI) accelerator and a digital reconfigurable deep neural network (DNN) accelerator in a single system-on-chip (SoC) to support a wide variety of neural network (NN) workloads. AIMC cores can bring extreme computational parallelism and efficiency at the expense of accuracy and dataflow flexibility. Digital AI co-processors, on the other hand, guarantee accuracy through deterministic compute, but cannot achieve the same computational density and efficiency. DIANA exploits this fundamental tradeoff by integrating both types of cores in a shared and optimized memory system, to enable seamless execution of the workloads on the parallel cores. The system’s performance benefits further from pipelined parallel execution across both accelerator cores and enhanced AIMC spatial unrolling techniques, leading to drastically reduced execution latency and reduced memory footprints. The design has been implemented in a 22-nm technology and achieves peak efficiencies of 600 TOP/s/W for the AIMC core (I/W/O: 7/1.5/6 bit) and 14 TOP/s/W (I/W/O: 8/8/8 bit) for the digital accelerator, respectively. End-to-end performance evaluation of CIFAR-10 and ImageNet classification workloads is carried out on the chip, reporting 7.02 and 5.56 TOP/s/W, respectively, at the system level.
Topological Hochschild homology and the Hasse-Weil zeta function

We consider the Tate cohomology of the circle group acting on the topological Hochschild homology of schemes. We show that in the case of a scheme smooth and proper over a finite field, this cohomology theory naturally gives rise to the cohomological interpretation of the Hasse-Weil zeta function by regularized determinants envisioned by Deninger. In this case, the periodicity of the zeta function is reflected by the periodicity of said cohomology theory, whereas neither is periodic in general.
On a fractional stochastic Hodgkin–Huxley model

The model studied in this paper is a stochastic extension of the so-called neuron model introduced by Hodgkin and Huxley. In the sense of rough paths, the model is perturbed by a multiplicative noise driven by a fractional Brownian motion, with a vector field satisfying the viability condition of Coutin and Marie for [Formula: see text]. An application to the modeling of the membrane potential of nerve fibers damaged by a neuropathy is provided.
A Local Limit Theorem for Robbins-Monro Procedure

The Robbins-Monro algorithm is a recursive, simulation-based stochastic procedure to approximate the zeros of a function that can be written as an expectation. It is known that under some technical assumptions, a Gaussian convergence can be established for the procedure. Here, we are interested in the local limit theorem, that is, quantifying this convergence on the density of the involved objects. The analysis relies on a parametrix technique for Markov chains converging to diffusions, where the drift is unbounded.
On the well-posedness of Reynolds-Rayleigh-Plesset coupling

In the lubrication area, which is concerned with thin film flow, cavitation has been considered as a fundamental element to correctly describe the characteristics of lubricated mechanisms. Here, the well-posedness of a cavitation model that can explain the interaction between viscous effects and micro-bubbles of gas is studied. This cavitation model consists in a coupled problem between the compressible Reynolds PDE (that describes the flow) and the Rayleight-Plesset ODE (that describes micro-bubbles evolution). Local times existence results are proved and stability theorems are obtained based on the continuity of the spectrum for bounded linear operators. Numerical results are presented to illustrate these theoretical results. Particularly, a lost of well-posedness depending upon a geometrical parameter is shown numerically.
A Survey on EEG-Based Solutions for Emotion Recognition With a Low Number of Channels

The market uptake of Brain-Computer Interface technologies for clinical and non-clinical applications is attracting the scientific world towards the development of daily-life wearable systems. Beyond the use of dry electrodes and wireless technology, reducing the number of channels is crucial to enhance the ergonomics of devices. This paper presents a review of the studies exploiting a number of channels less than 16 for electroencephalographic (EEG) based-emotion recognition. The main findings of this review concern: (i) the criteria to select the most promising scalp areas for EEG acquisitions; (ii) the attention to prior neurophysiological knowledge; and (iii) the convergences among different studies with respect to preferable areas of the scalp for signal acquisition. Three main approaches emerge for channel selection: data-driven, prior knowledge-based, and based on commercially-available wearable solutions. The most spread is the data-driven, but the neurophysiology of emotions is rarely taken into account. Furthermore, commercial EEG devices usually do not provide electrodes purposefully chosen to assess emotions. Considerable convergences emerge for some electrodes: Fp1, Fp2, F3 and F4 resulted the most informative channels for the valence dimension, according to both data-driven and neurophysiological prior knowledge approaches. The P3 and P4 resulted in being significant for the arousal dimension.
On the existence and approximation of a dissipating feedback

Given a matrix $A\in \R^{n\times n}$ and a tall rectangular matrix $B \in \R^{n\times q}$, $q < n$, we consider the problem of making the pair $(A,B)$ dissipative, that is the determination of a {\it feedback} matrix $K \in \R^{q\times n}$ such that the field of values of $A-B K$ lies in the left half open complex plane. We review and expand classical results available in the literature on the existence and parameterization of the class of dissipating matrices, and we explore new matrix properties associated with the problem. In addition, we discuss various computational strategies for approximating the minimal Frobenius norm dissipating $K$.
Sheaves of Cherednik Algebras from the Point of View of Formal Geometry

In this note we realize the sheaf of Cherednik algebras $H_{1, c, X, G}$ on a general good complex orbifold $X/G$, originally introduced by Etingof for smooth complex varieties with an action by a finite group, by gluing sheaves of flat sections of flat holomorphic vector bundles on orbit type strata in $X$ which result from a localization procedure. In the case, when $c$ is formal, this construction can be interpreted as a formal deformation of $D_X\rtimes\mathbb CG$ via Gel'fand-Kazhdan formal geometry. Contrary to the original definition of $H_{1, c, X, G}$ the presented construction permits the computation of trace densities, Hochschild homologies and an algebraic index theorem for formal deformations of $D_X\rtimes\mathbb CG$. We also hope that the methods developed here will contribute towards a full proof of Dolgushev-Etingof's conjecture.
Higher moments of Banach space valued random variables

We define the $k$:th moment of a Banach space valued random variable as the expectation of its $k$:th tensor power; thus the moment (if it exists) is an element of a tensor power of the original Banach space. We study both the projective and injective tensor products, and their relation. Moreover, in order to be general and flexible, we study three different types of expectations: Bochner integrals, Pettis integrals and Dunford integrals. One of the problems studied is whether two random variables with the same injective moments (of a given order) necessarily have the same projective moments; this is of interest in applications. We show that this holds if the Banach space has the approximation property, but not in general. Several sections are devoted to results in special Banach spaces, including Hilbert spaces, $C(K)$ and $D[0,1]$. The latter space is non-separable, which complicates the arguments, and we prove various preliminary results on e.g. measurability in $D[0,1]$ that we need. One of the main motivations of this paper is the application to Zolotarev metrics and their use in the contraction method. This is sketched in an appendix.
ON THE DISTRIBUTION OF GALOIS GROUPS

Let G be a subgroup of the symmetric group S n , and let δ G =∣ S n / G ∣ −1 where ∣ S n / G ∣ is the index of G in S n . Then there are at most O n ,ϵ ( H n −1+ δ G +ϵ ) monic integer polynomials of degree n that have Galois group G and height not exceeding H , so there are only a “few” polynomials having a “small” Galois group.
Concurrency Control and Consistency Over Erasure Coded Data

For over a decade, erasure codes have become an integral part of large-scale data storage solutions and data-centers. However, in commercial systems, they are, so far, used predominantly for static data. In the meanwhile, there has also been almost a decade and a half of research on mutable erasure coded data, looking at various associated issues, including update computation, concurrency control and consistency, which has led to a variety of reasonably mature techniques. In this work we aim at curating and systematizing this knowledge on managing mutable erasure coded data. We believe the time is right, both because of the richness and maturity of the literature itself, and also, given the pervasiveness of erasure codes in data-centers, because it is natural to expect a transition to accommodate mutable content using erasure coded redundancy in order to support more diverse and versatile overlying applications, while benefiting from the advantages (particularly, that of significantly lower storage overhead) of erasure codes.
Algebraic Curves A ∘ l ( x ) − U ( y ) = 0 and Arithmetic of Orbits of Rational Functions

We give a description of pairs of complex rational functions $A$ and $U$ of degree at least two such that for every $d\geq 1$ the algebraic curve $A^{\circ d}(x)-U(y)=0$ has a factor of genus zero or one. In particular, we show that if $A$ is not a "generalized Latt\`es map", then this condition is satisfied if and only if there exists a rational function $V$ such that $U\circ V=A^{\circ l}$ for some $l\geq 1. $ We also prove a version of the dynamical Mordell-Lang conjecture, concerning intersections of orbits of points from $\mathbb P^1(K)$ under iterates of $A$ with the value set $U(\mathbb P^1(K))$, if $A$ and $U$ are defined over a number field $K. $