Miracle-dz/UniFAR-FTUs huggingface dataset:
Are SNNs Really More Energy-Efficient Than ANNs? an In-Depth Hardware-Aware Study

Spiking Neural Networks (SNNs) hold the promise of lower energy consumption in embedded hardware due to their spike-based computations compared to traditional Artificial Neural Networks (ANNs). The relative energy efficiency of this emerging technology compared to traditional digital hardware has not been fully explored. Many studies do not consider memory accesses, which account for an important fraction of the energy consumption, use naive ANN hardware implementations, or lack generality. In this paper, we compare the relative energy efficiency of classical digital implementations of ANNs with novel event-based SNN implementations based on variants of the Integrate and Fire (IF) model. We provide a theoretical upper bound on the relative energy efficiency of ANNs, by computing the maximum possible benefit from ANN data reuse and sparsity. We also use the Eyeriss ANN accelerator as a case study. We show that the simpler IF model is more energy-efficient than the Leaky IF and temporal continuous synapse models. Moreover, SNNs with the IF model can compete with efficient ANN implementations when there is a very high spike sparsity, i.e. between 0.15 and 1.38 spikes per synapse per inference, depending on the ANN implementation. Our analysis shows that hybrid ANN-SNN architectures, leveraging a SNN event-based approach in layers with high sparsity and ANN parallel processing for the others, are a promising new path for further energy savings.
Trees within trees: simple nested coalescents

We consider the compact space of pairs of nested partitions of $\mathbb N$, where by analogy with models used in molecular evolution, we call "gene partition" the finer partition and "species partition" the coarser one. We introduce the class of nondecreasing processes valued in nested partitions, assumed Markovian and with exchangeable semigroup. These processes are said simple when each partition only undergoes one coalescence event at a time (but possibly the same time). Simple nested exchangeable coalescent (SNEC) processes can be seen as the extension of $\Lambda$-coalescents to nested partitions. We characterize the law of SNEC processes as follows. In the absence of gene coalescences, species blocks undergo $\Lambda$-coalescent type events and in the absence of species coalescences, gene blocks lying in the same species block undergo i.i. d. $\Lambda$-coalescents. Simultaneous coalescence of the gene and species partitions are governed by an intensity measure $\nu_s$ on $(0,1]\times {\mathcal M}_1 ([0,1])$ providing the frequency of species merging and the law in which are drawn (independently) the frequencies of genes merging in each coalescing species block. As an application, we also study the conditions under which a SNEC process comes down from infinity.
Badly approximable vectors in affine subspaces: jarnik — type result

Consider irrational affine subspace $ A\subset \mathbb{R}^d$ of dimension $a$. We prove that the set $$ \{\xi =(\xi_1,.,\xi_d) \in {A}:\,\,\, \ q^{1/a}\cdot \max_{1\le i \le d} ||q\xi_i|| \to \infty,\,\,\,\, q\to \infty\} $$ is an $\alpha$-winning set for every $\alpha \in (0,1/2]$
SECOND QUANTIZATION AND THE Lp-SPECTRUM OF NONSYMMETRIC ORNSTEIN–UHLENBECK OPERATORS

The spectra of the second quantization and the symmetric second quantization of a strict Hilbert space contraction are computed explicitly and shown to coincide. As an application, we compute the spectrum of the nonsymmetric Ornstein–Uhlenbeck operator L associated with the infinite-dimensional Langevin equation where A is the generator of a strongly continuous semigroup on a Banach space E and W is a cylindrical Wiener process in E. Assuming the existence of an invariant measure μ for L, under suitable assumptions on A we show that the spectrum of L in the space Lp (E, μ) (1< p< ∞) is given by where Aμ is the generator of a Hilbert space contraction semigroup canonically associated with A and μ. We prove that the assumptions on A are always satisfied in the strong Feller case and in the finite-dimensional case. In the latter case we recover the recent Metafune–Pallara–Priola formula for σ(L).
Spectral Classification and Cloning of Photonic Integrated Filters for Volume Testing

Volume testing is rapidly becoming a key step in the production chain of photonic integrated circuits (PICs), which are ever increasing their integration density and complexity, and are penetrating many market sectors. What makes PIC testing peculiar with respect to testing of electronic integrated circuits (EICs) is the fact that it generally requires also control and calibration procedures. Here, we present a method to perform time and cost-efficient volume testing of frequency-selective PICs. The described techniques enable to evaluate the deviation between the spectral responses of a device under test (DUT) and a reference (REF) device, without a direct measurement of the transfer function, which is a time and resource consuming procedure. Information on the DUT status is inferred from the integral power of a top-flat broadband optical source, which is shaped by the REF device and is transmitted through the DUT. In this way it is possible to classify the DUT according to specifically defined metrics, automatically tune it to replicate the REF spectral response and build LookUp Tables (LUTs) to be used in operative conditions. The proposed technique is validated experimentally on a reconfigurable silicon-photonics microring resonator filter implementing a Tuneable Optical Add/Drop Multiplexer, but we also provide conditions for its use for testing of frequency-selective devices.
Arbitrarily Tight Bounds on a Singularly Perturbed Linear-Quadratic Optimal Control Problem

We calculate arbitrarily tight upper and lower bounds on an unconstrained control, linear-quadratic, singularly perturbed optimal control problem whose exact solution is computationally intractable. It is well known that for the aforementioned problem, an approximate solution $\bar{V}^N(\epsilon)$ can be constructed such that it is asymptotically equivalent in $\epsilon$ to the solution $V(\epsilon)$ of the singularly perturbed problem in the sense that $|V(\epsilon)-\bar{V}^N(\epsilon)| =O(\epsilon^{N+1})$ for any integer $N\geq0$ as $\epsilon \rightarrow 0$. For this approximation to be considered useful, the parameter $\epsilon$ is typically restricted to be in some sufficiently small set; however, for values of $\epsilon$ outside this set, a poor approximation can result. We improve on this approximation by incorporating a duality theory into the singularly perturbed optimal control problem and derive an upper bound $\chi^N_u(\epsilon)$ and a lower bound $\chi^N_l(\epsilon)$ of $V(\epsilon)$ that hold for arbitrary $\epsilon$ and, furthermore, satisfy the inequality $|\chi^N_u(\epsilon)-\chi^N_l(\epsilon)|=O(\epsilon^{N+1})$ for any integer $N \geq 0$ as $\epsilon \rightarrow 0$.
Uniformly Lipschitzian group actions on hyperconvex spaces

Suppose that $\{T_{a}:a\in G\}$ is a group of uniformly $L$-Lipschitzian mappings with bounded orbits $\left\{T_{a}x:a\in G\right\}$ acting on a hyperconvex metric space $M$. We show that if $L<\sqrt{2}$, then the set of common fixed points $Fix \, G$ is a nonempty H\"older continuous retract of $M$. As a consequence, it follows that all surjective isometries acting on a bounded hyperconvex space have a common fixed point. A fixed point theorem for $L$-Lipschitzian involutions and some generalizations to the case of $\lambda$-hyperconvex spaces are also given.
Commutators of Cauchy type integrals for domains in $\C^n$ with minimal smoothness

In this paper we study the commutator of Cauchy type integrals $\EuScript C$ on a bounded strongly pseudoconvex domain $D$ in $\C^n$ with boundary $bD$ satisfying the minimum regularity condition $C^{2}$ as in the recent result of Lanzani--Stein. We point out that in this setting the Cauchy type integrals $\EuScript C$ is the sum of the essential part $\EuScript C^\sharp$ which is a Calderon--Zygmund operator and a remainder $\EuScript R$ which is no longer a Calderon--Zygmund operator. We show that the commutator $[b, \EuScript C]$ is bounded on $L^p(bD)$ ($1<p<\infty$) if {\color{black}and only if}\ $b$ is in the BMO space on $bD$. Moreover, the commutator $[b, \EuScript C]$ is compact on $L^p(bD)$ ($1<p<\infty$) if {\color{black}and only if}\ $b$ is in the VMO space on $bD$. Our method can also be applied to the commutator of Cauchy--Leray integral in a bounded, strongly $\C$-linearly convex domain $D$ in $\C^n$ with the boundary $bD$ satisfying the minimum regularity $C^{1,1}$. Such a Cauchy--Leray integral is a Calderon--Zygmund operator as proved in the recent result of Lanzani--Stein. We also point out that our method provides another proof of the boundedness and compactness of commutator of Cauchy--Szego operator on a bounded strongly pseudoconvex domain $D$ in $\C^n$ with smooth boundary (first established by Krantz--Li).
A Note on the Daubechies Approach in the Construction of Spline Type Orthogonal Scaling Functions

We use Lorentz polynomials to present the solutions explicitly of equations (6.1.7) of [I. Daubechies, Ten lectures on wavelets, CBMS-NSF Regional Conference Series in Applied Mathematics, 61. Society for Industrial and Applied Mathematics (SIAM), Philadelphia, PA, 1992] and (4.9) of [I. Daubechies, Orthonormal bases of compactly supported wavelets. Comm. Pure Appl. Math. 41 (1988), no. 7, 909--996] sot that we give an efficient way to prove Daubechies' results on the existence of spline type orthogonal scaling functions and to evaluate Daubechies scaling functions.
JBNN: A Hardware Design for Binarized Neural Networks Using Single-Flux-Quantum Circuits

As a high-performance application of low-temperature superconductivity, superconducting single-flux-quantum (SFQ) circuits have high speed and low-power consumption characteristics, which have recently received extensive attention, especially in the field of neural network inference accelerations. Despite these promising advantages, they are still limited by storage capacity and manufacture reliability, making them unfriendly for feedback loops and very large-scale circuits. The Binarized Neural Network (BNN), with minimal memory requirements and no reliance on multiplication, is undoubtedly an attractive candidate for implementing inference hardware using SFQ circuits. This work presents the first SFQ-based Binarized Neural Network inference accelerator, namely JBNN, with a new representation to binarize weights and activation variables. Every SFQ gate is essentially a pipeline stage, making conventional design methods of the accumulator unsuitable for SFQ circuits. So an SFQ-based accumulative parallel counter using SFQ logic cells including T1, OR, and AND is designed to realize the accumulation, where the data size is reduced to a quarter after passing the XNOR column and the AU layer, largely declining the hardware cost. Our evaluation shows that the proposed design outperforms a cryogenic CMOS-based BNN accelerator design running at 77K by 70.92 times while maintaining 97.89% accuracy on the MNIST benchmark dataset. Without the cooling cost, the power efficiency increases up to 929.18 times.
Algebraic group actions on normal varieties

Let $G$ be a connected algebraic $k$-group acting on a normal $k$-variety, where $k$ is a field. We show that $X$ is covered by open $G$-stable quasi-projective subvarieties; moreover, any such subvariety admits an equivariant embedding into the projectivization of a $G$-linearized vector bundle on an abelian variety, quotient of $G$. This generalizes a classical result of Sumihiro for actions of smooth connected affine algebraic groups.
Numerical Semigroups Of small embedding dimension

This paper will represent in a simple way some known facts about semigroups especially when the number of minimal generators (that is the embedding dimension) is at most three and at least two minimal generators are coprime. For some numerical semigroups an explicit algorithm is given to find a possible Pseudo-Frobenius number. The originality of this exposition is that it is a straight application of a remark written by Sylvester himself.
Document Embedding Generation for Cyber-Aggressive Comment Detection using Supervised Machine Learning Approach

Cyber-bullying may be deﬁned as the employment of technological means for the purpose of harassing, threatening, embar-rassing, or targeting a particular person. It is also possible for Cyber-bullying to have occurred accidentally. One of the major challenges in identifying cyber-bullying or cyber-aggressive comments is to detect a sender’s tone in a particular text message, email or comments on social media, since what a person may consider to be a joke, may act as a hurting insult to another. Nevertheless, cyber-bullying may prove to be non-accidental in spe-ciﬁc cases where a repetition in the pattern of text in emails, messages, and on-line posts is existent. In order to curb such a social threat, this Paper proposes the usage of a combination of document embeddings along with different supervised machine learning algorithms to get optimized results in ﬂagging cyber-aggressive comments. Extensive experimentation indicates that the SVM model with rbf kernel combined with document embeddings is capable of efﬁciently classifying unseen test comments with an accuracy score of 88.465 % and has surpassed other models in various evaluation metrics.
Visual Sentiment Prediction Using Cross-Way Few-Shot Learning Based on Knowledge Distillation

This paper presents a visual sentiment prediction method using cross-way few-shot learning based on knowledge distillation. Previous studies on visual sentiment prediction methods have focused only on one sentiment dataset although there are several sentiment datasets following different sentiment theories. Originally, sentiments are abstract notions common to humans regardless of the difference between sentiment theories. Thus, the use of knowledge obtained from several sentiment datasets can be the effective way to realize robust visual sentiment prediction. To collaboratively use sentiment datasets, there are the following two concerns: training of the model introducing different sentiment theories and prediction of a newly given sample whose sentiment theory is unknown. Thus, we focus on knowledge distillation, which can improve the generalization ability of several tasks, and effective training becomes feasible. In addition, to deal with the different numbers of sentiment labels in a test phase, we newly introduce the cross-way few-shot learning scheme into knowledge distillation. The main contribution in this paper is to integrate knowledge distillation and the cross-way approach for the visual sentiment prediction, and this is the first work for dealing with the difference of sentiment datasets used in the training and test phases. In the end of this paper, the effectiveness of the proposed method is confirmed through experiments using several open datasets.
Existence, uniqueness and approximation for L p solutions of reflected BSDEs under weaker assumptions ✩

We put forward and prove several existence and uniqueness results for L p (p > 1) solutions of reflected BSDEs with continuous barriers and generators satisfying a one-sided Osgood condition together with a general growth condition in y and a uniform continuity condition or a linear growth condition in z. A necessary and sufficient condition with respect to the growth of barrier is also explored to ensure the existence of a solution. And, we show that the solutions may be approximated by the penalization method and by some sequences of solutions of reflected BSDEs. Our results improve considerably some known works.
Moduli Spaces of Abelian Vortices on Kahler Manifolds

We consider the self-dual vortex equations on a positive line bundle L --> M over a compact Kaehler manifold of arbitrary dimension. When M is simply connected, the moduli space of vortex solutions is a projective space. When M is an abelian variety, the moduli space is the projectivization of the Fourier-Mukai transform of L. We extend this description of the moduli space to the abelian GLSM, i.e. to vortex equations with a torus gauge group acting linearly on a complex vector space. After establishing the Hitchin-Kobayashi correspondence appropriate for the general abelian GLSM, we give explicit descriptions of the vortex moduli space in the case where the manifold M is simply connected or is an abelian variety. In these examples we compute the Kaehler class of the natural L^2-metric on the moduli space. In the simplest cases we compute the volume and total scalar curvature of the muduli space. Finally, we note that for abelian GLSM the vortex moduli space is a compactification of the space of holomorphic maps from M to toric targets, just as in the usual case of M being a Riemann surface. This leads to various natural conjectures, for instance explicit formulae for the volume of the space of maps CP^m --> CP^n.
Decomposition Algorithm for Distributionally Robust Optimization using Wasserstein Metric

We study distributionally robust optimization (DRO) problems where the ambiguity set is defined using the Wasserstein metric. We show that this class of DRO problems can be reformulated as semi-infinite programs. We give an exchange method to solve the reformulated problem for the general nonlinear model, and a central cutting-surface method for the convex case, assuming that we have a separation oracle. We used a distributionally robust generalization of the logistic regression model to test our algorithm. Numerical experiments on the distributionally robust logistic regression models show that the number of oracle calls are typically 20 ? 50 to achieve 5-digit precision. The solution found by the model is generally better in its ability to predict with a smaller standard error.
Kesulitan dalam Belajar Biologi saat Pembelajaran Daring

Kesulitan belajar adalah suatu kondisi di mana peserta didik tidak dapat belajar secara wajar di sebabkan adanya ancaman, hambatan ataupun gangguan dalam belajar. Semenjak diterapkannya belajar dari rumah atau pembelajaran daring (dalam jaringan) banyak kesulitan-kesulitan yang dialami oleh peserta didik. Peserta didik yang terindikasi sejumlah kesulitan belajar akan mendapat hasil belajar yang rendah. Kesulitan belajar dapat terjadi karena ada faktor-faktor yang mempengaruhi proses pembelajaran. Penelitian ini merupakan penelitian deskriptif. Pengambilan sampel dilakukan dengan teknik saturation sampling yaitu seluruh populasi dijadikan sebagai sampel. Sampel penelitian berjumlah 43 orang peserta didik. Pengambilan data pada penelitian ini menggunakan angket dengan 35 butir pernyataan. Skor hasil kesulitan dalam belajar biologi saat pembelajaran daring ini tersebar ke dalam empat kategori yaitu sangat setuju, setuju, tidak setuju, sangat tidak setuju. Hasil penelitian menujukkan bahwa kesulitan dalam belajar biologi saat pembelajaran daring di MAN 3 Kampar berada pada kategori Tinggi. Di mana rata-rata persentase kesulitan peserta didik dari faktor internal sebesar 67,4%, kesulitan oleh faktor eksternal sebesar 59%, dan kesulitan peserta didik berhubungan dengan karakteristik materi biologi sebesar 67%. Dengan demikian dapat disimpulkan bahwa kesulitan peserta didik dalam belajar biologi saat pembelajaran daring di berada pada kategori kesulitan yang tinggi.
The Inﬂuence of Diﬀerent Types of Social Support in Caregivers of People with Dementia

Abstract Being a caregiver for a person with dementia has negative health consequences, like depression, stress, anxiety, and burden. Consequently, it is important that caregivers receive social support and social resources during the trajectory of the disease. This study analyzes how diﬀerent types of social resources impact caregivers’ well-being. A cross-sectional sample of 260 caregivers was divided into groups, depending on the social resources they had access to. Namely, social media (N = 157), day-care centers (N = 40) or residential centers (N = 63). Social support, isolation, burden, coping strategies, and satisfaction measures were administered to participants, and groups were compared using ANOVA and SEM models. Help-seeking and social support received through social resources predicted satisfaction and caregivers’ burden. Formal resources positively inﬂuence care providers and their absence diminishes the impact of social support on perceived burden. This research reﬂects the importance of formal resources in the lives of caregivers and the beneﬁts they generate throughout the course of the pathology. Supporting caregivers and bringing the social resources closer to them indirectly improves the informal response oﬀered to people with dementia.
Maharah al-Qira'ah Learning Planning and Strategies in Improving Islamic Understanding

The learning process is a complex activity, so careful planning is needed so that it can run systematically and more organized. In Arabic learning, the term learning planning is better known as "I'dad" which means a form of planning and is prepared before the learning process is carried out and is more specific. There are at least four elements that must be in the planning, including objectives, supporting resources, strategy and implementation. The steps of the preparation of the learning plan are: first to formulate specific objectives along with the subject matter, secondly to choose learning experiences, third to choose teaching and learning activities, fourth to determine the people involved, fifth to determine the materials, tools and media to be used, sixth to determine physical facilities or pre-facilities, and seventh to determine evaluation and development materials. In order for this maharah al-qira’ah learning process to be more interesting, educators must be skilled in choosing the strategies to be used. In this case, there are several strategies that can be used in the learning process of maharah al-qira’ah, including empety outline, analysis, snow bolling, broken square / text, and card match indexes.
L2-boundedness of gradients of single-layer
potentials and uniform rectifiability

Let $A(\cdot)$ be an $(n+1)\times (n+1)$ uniformly elliptic matrix with H\"older continuous real coefficients and let $\mathcal E_A(x,y)$ be the fundamental solution of the PDE $\mathrm{div} A(\cdot) \nabla u =0$ in $\mathbb R^{n+1}$. Let $\mu$ be a compactly supported $n$-AD-regular measure in $\mathbb R^{n+1}$ and consider the associated operator $$T_\mu f(x) = \int \nabla_x\mathcal E_A(x,y)\,f(y)\,d\mu(y). $$ We show that if $T_\mu$ is bounded in $L^2(\mu)$, then $\mu$ is uniformly $n$-rectifiable. This extends the solution of the codimension $1$ David-Semmes problem for the Riesz transform to the gradient of the single layer potential. Together with a previous result of Conde-Alonso, Mourgoglou and Tolsa, this shows that, given $E\subset\mathbb R^{n+1}$ with finite Hausdorff measure $\mathcal H^n$, if $T_{\mathcal H^n|_E}$ is bounded in $L^2(\mathcal H^n|_E)$, then $E$ is $n$-rectifiable. Further, as an application we show that if the elliptic measure associated to the above PDE is absolute continuous with respect to surface measure, then it must be rectifiable, analogously to what happens with harmonic measure.
The Complex of Non-Chromatic Scales

We consider the space of all musical scales with the ambition to systematize it. To do this, we pursue the idea to view certain scales as basic constituents and to "mix" all remaining scales from these. The German version of this article appeared in Mitteilungen der DMV, volume 25, issue 1.
Local-global convergence, an analytic and structural approach

Based on methods of structural convergence we provide a unifying view of local-global convergence, fitting to model theory and analysis. The general approach outlined here provides a possibility to extend the theory of local-global convergence to graphs with unbounded degrees. As an application, we extend previous results on continuous clustering of local convergent sequences and prove the existence of modeling quasi-limits for local-global convergent sequences of nowhere dense graphs.