Miracle-dz/UniFAR-FTUs huggingface dataset:
Saguaro: Efficient Processing of Transactions in Wide Area Networks using a Hierarchical Permissioned Blockchain

The next frontier for the Internet leading by innovations in mobile computing, in particular, 5G, together with blockchains’ transparency, immutability, provenance, and authenticity, indicates the potentials of running a new generation of applications on the mobile internet. A 5G-enabled blockchain system is structured as a hierarchy and needs to deal with diﬀerent challenges such as maintaining blockchain ledger at diﬀerent spatial domains and various levels of the networks, eﬃcient processing of cross-domain transactions, establishing consensus among heterogeneous nodes, and supporting delay-tolerant mobile transactions. In this paper, we present Saguaro , a hierarchical permissioned blockchain designed speciﬁc-ally for Internet-scale mobile networks. Saguaro beneﬁts from the hierarchical structure of mobile network infrastructure to address the aforementioned challenges. Our extensive experimental results demonstrate the high potential of Saguaro being the ﬁrst 5G-enabled permissioned blockchain system in the community.
Recovery of the Order of Derivation for Fractional Diffusion Equations in an Unknown Medium

In this work, we investigate the recovery of a parameter in a diffusion process given by the order of derivation in time for a class of diffusion type equations, including both classical and time-fractional diffusion equations, from the flux measurement observed at one point on the boundary. The mathematical model for time-fractional diffusion equations involves a Djrbashian-Caputo fractional derivative in time. We prove a uniqueness result in an unknown medium (e.g., diffusion coefficients, obstacle, initial condition and source), i.e., the recovery of the order of derivation in a diffusion process having several pieces of unknown information. The proof relies on the analyticity of the solution at large time, asymptotic decay behavior, strong maximum principle of the elliptic problem and suitable application of the Hopf lemma. Further we provide an easy-to-implement reconstruction algorithm based on a nonlinear least-squares formulation, and several numerical experiments are presented to complement the theoretical analysis.
On the Frank–Wolfe algorithm for non-compact constrained optimization problems

ABSTRACT This paper deals with the Frank–Wolfe algorithm to solve a special class of non-compact constrained optimization problems. The notion of asymptotic cone is one the main concept used to introduce the class of problems considered as well as to establish the well definition of the algorithm. This class of optimization problems, with closed and convex constraint set, are characterized by two conditions on the gradient of the objective function. The first one establishes that the gradient of the objective function is Lipschitz continuous, which is quite usual in the analysis of this algorithm. The second one, which is new in this subject, establishes that the gradient belongs to the interior of dual asymptotic cone of the constraint set. Classical results on asymptotic behaviour and iteration complexity bounds for the sequence generated by Frank–Wolfe algorithm are extended to this new class of problems. Some examples of problems with non-compact constraints and objective functions satisfying the aforementioned conditions are provided.
A Universal Deep Learning Framework for Real-Time Denoising of Ultrasound Images

Ultrasound images are widespread in medical diagnosis for muscle-skeletal, cardiac, and obstetrical diseases, due to the efficiency and non-invasiveness of the acquisition methodology. However, ultrasound acquisition introduces noise in the signal, which corrupts the resulting image and affects further processing steps, e.g., segmentation and quantitative analysis. We define a novel deep learning framework for the real-time denoising of ultrasound images. Firstly, we compare state-of-the-art methods for denoising (e.g., spectral, low-rank methods) and select WNNM (Weighted Nuclear Norm Minimisation) as the best denoising in terms of accuracy, preservation of anatomical features, and edge enhancement. Then, we propose a tuned version of WNNM (tuned-WNNM) that improves the quality of the denoised images and extends its applicability to ultrasound images. Through a deep learning framework, the tuned-WNNM qualitatively and quantitatively replicates WNNM results in real-time. Finally, our approach is general in terms of its building blocks and parameters of the deep learning and high-performance computing framework; in fact, we can select different denoising algorithms and deep learning architectures.
Laminar flow past a permeable sphere

Flow past an isolated permeable sphere has been studied. The complete Navier-Stokes equation governs the fluid motion outside the sphere, while Brinkman's extension of Darcy's Law is assumed to hold within the porous sphere. The Navier-Stokes equation is solved using a finite difference scheme. The flow within the porous sphere is solved in two different ways, each being efficient over a particular range of Reynolds number. Drag Coefficients are presented for dimensionless permeability, β, of 5, 10, 15, and 30 and for Reynolds numbers up to 50. The computed drag coefficients are within 10% of the experimental values observed by Masliyah and Polikar for 15 33, the range covered in their work. Separation was observed only for β > 10. The onset of separation is delayed considerably in porous spheres. On a etudie l'ecoulement au voisinage d'une sphere permeable et isolee. L'equation complete de Navier-Stokes regit le mouvement du fluide a l'exterieur de la sphere, tandis qu'on suppose que l'extension de Brinkman de la Loi de Darcy s'applique a l'interieur de la sphere poreuse. On resout l'equation de Navier-Stokes en employant un systeme de differences finies. On resout le probleme d'ecoulement a l'interieur de la sphere poreuse en procedant de deux manieres qui sont egalement efficaces sur une gamme particuliere de nombres de Reynolds. On presente les coefficients de traǐnee dans le cas d'une permeabilite adimensionnelle (β) de 5, 10, 15 et pour des nombres de Reynolds allant jusqu'a 50. Les valeurs des coefficients de traǐnee calcules se tiennent a 10% des valeurs experimentales observees par Masliyat et Polikar dans le cas de 15 10. Il se produit un retard considerable pour le debut de la separation dans le cas ou les spheres sont poreuses.
Entanglement detection in quantum many-body systems using entropic uncertainty relations

We study experimentally accessible lower bounds on entanglement measures based on entropic uncertainty relations. Experimentally quantifying entanglement is highly desired for applications of quantum simulation experiments to fundamental questions, e.g., in quantum statistical mechanics and condensed-matter physics. At the same time it poses a signiﬁcant challenge because the evaluation of entanglement measures typically requires the full reconstruction of the quantum state, which is extremely costly in terms of measurement statistics. We derive an improved entanglement bound for bipartite systems, which requires measuring joint probability distributions in only two diﬀerent measurement settings per subsystem, and demonstrate its power by applying it to currently operational experimental setups for quantum simulation with cold atoms. Examining the tightness of the derived entanglement bound, we ﬁnd that the set of pure states for which our relation is tight is strongly restricted. We show that for measurements in mutually unbiased bases the only pure states that saturate the bound are maximally entangled states on a subspace of the bipartite Hilbert space (this includes product states). We further show that our relation can also be employed for entanglement detection using generalized measurements, i.e., when not all measurement outcomes can be resolved individually by the detector. In addition, the impact of local conserved quantities on the detectable entanglement is discussed.
Repeated randomized algorithm for the Multicovering Problem

Let H = (V, E) be a hypergraph with maximum edge size l and maximum degree ∆. For given numbers bv ∈ N≥2, v ∈ V , a set multicover in H is a set of edges C ⊆ E such that every vertex v in V belongs to at least bv edges in C. set multicover is the problem of finding a minimum-cardinality set multicover. Peleg, Schechtman and Wool conjectured that unless P = NP , for any fixed ∆ and b := minv∈V bv, no polynomial-time approximation algorithm for the set multicover problem has an approximation ratio less than δ := ∆− b+1. Hence, it’s a challenge to know whether the problem of set multicover is not approximable within a ratio of βδ with a constant β < 1. This paper proposes a repeated randomized algorithm for the set multicover problem combined with an initial deterministic threshold step. Boosting success by repeated trials, our algorithm yields an approximation ratio of max {
Direct Spatial Implementation of Sparse Matrix Multipliers for Reservoir Computing

Reservoir computing is a nascent sub-field of machine learning which relies on the recurrent multiplication of a very large, sparse, fixed matrix. We argue that direct spatial implementation of these fixed matrices minimizes the work performed in the computation, and allows for significant reduction in latency and power through constant propagation and logic minimization. Bit-serial arithmetic enables massive static matrices to be implemented. We present the structure of our bit-serial matrix multiplier, and evaluate using canonical signed digit representation to further reduce logic utilization. We have implemented these matrices on a large FPGA and provide a cost model that is simple and extensible. These FPGA implementations, on average, reduce latency by 50x up to 86x versus GPU libraries. Comparing against a recent sparse DNN accelerator, we measure a 4.1x to 47x reduction in latency depending on matrix dimension and sparsity.
My Mouse, My Rules: Privacy Issues of Behavioral User Profiling via Mouse Tracking

This paper aims to stir debate about a disconcerting privacy issue on web browsing that could easily emerge because of unethical practices and uncontrolled use of technology. We demonstrate how straightforward is to capture behavioral data about the users at scale, by unobtrusively tracking their mouse cursor movements, and predict user's demographics information with reasonable accuracy using five lines of code. Based on our results, we propose an adversarial method to mitigate user profiling techniques that make use of mouse cursor tracking, such as the recurrent neural net we analyze in this paper. We also release our data and a web browser extension that implements our adversarial method, so that others can benefit from this work in practice.
Snapshot Hyperspectral Imaging Based on Weighted High-order Singular Value Regularization

Snapshot hyperspectral imaging can capture the 3D hyperspectral image (HSI) with a single 2D measurement and has attracted increasing attention recently. Recovering the underlying HSI from the compressive measurement is an ill-posed problem and exploiting the image prior is essential for solving this ill-posed problem. However, existing reconstruction methods always start from modeling image prior with the 1D vector or 2D matrix and cannot fully exploit the structurally spectral-spatial nature in 3D HSI, thus leading to a poor fidelity. In this paper, we propose an effective high-order tensor optimization based method to boost the reconstruction fidelity for snapshot hyperspectral imaging. We first build high-order tensors by exploiting the spatial-spectral correlation in HSI. Then, we propose a weight high-order singular value regularization (WHOSVR) based low-rank tensor recovery model to characterize the structure prior of HSI. By integrating the structure prior in WHOSVR with the system imaging process, we develop an optimization framework for HSI reconstruction, which is finally solved via the alternating minimization algorithm. Extensive experiments implemented on two representative systems demonstrate that our method outperforms state-of-the-art methods.
Performance Analysis for Cache-enabled Cellular Networks with Cooperative Transmission

The large amount of deployed smart devices put tremendous traffic pressure on networks. Caching at the edge has been widely studied as a promising technique to solve this problem. To further improve the successful transmission probability (STP) of cache-enabled cellular networks (CEN), we combine the cooperative transmission technique with CEN and propose a novel transmission scheme. Local channel state information (CSI) is introduced at each cooperative base station (BS) to enhance the strength of the signal received by the user. A tight approximation for the STP of this scheme is derived using tools from stochastic geometry. The locally optimal content placement strategy of this scheme is obtained using a numerical method to maximize the STP. Simulation results demonstrate the optimal strategy achieves significant gains in STP over several comparative baselines with the proposed scheme.
Nonstationary Stochastic Multiarmed Bandits: UCB Policies and Minimax Regret

We study the nonstationary stochastic Multi-Armed Bandit (MAB) problem in which the distribution of rewards associated with each arm are assumed to be time-varying and the total variation in the expected rewards is subject to a variation budget. The regret of a policy is defined by the difference in the expected cumulative rewards obtained using the policy and using an oracle that selects the arm with the maximum mean reward at each time. We characterize the performance of the proposed policies in terms of the worst-case regret, which is the supremum of the regret over the set of reward distribution sequences satisfying the variation budget. We extend Upper-Confidence Bound (UCB)-based policies with three different approaches, namely, periodic resetting, sliding observation window and discount factor and show that they are order-optimal with respect to the minimax regret, i.e., the minimum worst-case regret achieved by any policy. We also relax the sub-Gaussian assumption on reward distributions and develop robust versions the proposed polices that can handle heavy-tailed reward distributions and maintain their performance guarantees.
Lexical semantic change for Ancient Greek and Latin

Change and its precondition, variation, are inherent in languages. Over time, new words enter the lexicon, others become obsolete, and existing words acquire new senses. Associating a word's correct meaning in its historical context is a central challenge in diachronic research. Historical corpora of classical languages, such as Ancient Greek and Latin, typically come with rich metadata, and existing models are limited by their inability to exploit contextual information beyond the document timestamp. While embedding-based methods feature among the current state of the art systems, they are lacking in the interpretative power. In contrast, Bayesian models provide explicit and interpretable representations of semantic change phenomena. In this chapter we build on GASC, a recent computational approach to semantic change based on a dynamic Bayesian mixture model. In this model, the evolution of word senses over time is based not only on distributional information of lexical nature, but also on text genres. We provide a systematic comparison of dynamic Bayesian mixture models for semantic change with state-of-the-art embedding-based models. On top of providing a full description of meaning change over time, we show that Bayesian mixture models are highly competitive approaches to detect binary semantic change in both Ancient Greek and Latin.
Everything You Wanted to Know About Noninvasive Glucose Measurement and Control

Diabetes is a chronicle disease where the body of a human is irregular to dissolve the blood glucose properly. The diabetes is due to lack of insulin in human body. The continuous monitoring of blood glucose is main important aspect for health care. Most of the successful glucose monitoring devices is based on methodology of pricking of blood. However, such kind of approach may not be advisable for frequent measurement. The paper presents the extensive review of glucose measurement techniques. The paper covers various non-invasive glucose methods and its control with smart healthcare technology. To fulﬁll the imperatives for non-invasive blood glucose monitoring system, there is a need to conﬁgure an accurate measurement device. Noninvasive glucose-level monitoring device for clinical test overcomes the problem of frequent pricking for blood samples. There is requirement to develop the Internet-Medical-Things (IoMT) integrated Healthcare Cyber-Physical System (H-CPS) based Smart Healthcare framework for glucose measurement with purpose of continuous health monitoring. The paper also covers selective consumer products along with selected state of art glucose measurement approaches. The paper has also listed several challenges and open problems for glucose measurement.
Randomization Inference beyond the Sharp Null: Bounded Null Hypotheses and Quantiles of Individual Treatment Effects

Randomization inference (RI) is typically interpreted as testing Fisher's"sharp"null hypothesis that all unit-level effects are exactly zero. This hypothesis is often criticized as restrictive and implausible, making its rejection scientifically uninteresting. We show, however, that many randomization tests are also valid for a"bounded"null hypothesis under which the unit-level effects are all non-positive (or all non-negative) but are otherwise heterogeneous. In addition to being more plausible a priori, bounded nulls are closely related to substantively important concepts such as monotonicity and Pareto efficiency. Reinterpreting RI in this way also dramatically expands the range of inferences possible in this framework. We show that exact confidence intervals for the maximum (or minimum) unit-level effect can be obtained by inverting tests for a sequence of bounded nulls. We also generalize RI to cover inference for quantiles of the individual effect distribution as well as for the proportion of individual effects larger (or smaller) than a given threshold. The proposed confidence intervals for all effect quantiles are simultaneously valid, in the sense that no correction for multiple analyses is required, and are thus a"free lunch"added to conventional RI. In sum, our reinterpretation and generalization provide a broader justification for randomization tests and a basis for exact nonparametric inference for effect quantiles. We illustrate our methods with simulations and applications, finding that Stephenson rank statistics can provide more informative results than the more common Wilcoxon rank or difference-in-means statistics. We also provide an R package RIQITE implementing the proposed approach.
Implications of High Polarization Degree for the Surface State of Ryugu

The asteroid exploration project “Hayabusa2” has successfully returned samples from the asteroid (162173) Ryugu. In this study, we measured the linear polarization degrees of Ryugu using four ground-based telescopes from 2020 September 27 to December 25, covering a wide-phase angle (Sun-target-observer’s angle) range from 28° to 104°. We found that the polarization degree of Ryugu reached 53% around a phase angle of 100°, the highest value among all asteroids and comets thus far reported. The high polarization degree of Ryugu can be attributed to the scattering properties of its surface layers, in particular the relatively small contribution of multiply scattered light. Our polarimetric results indicate that Ryugu’s surface is covered with large grains. On the basis of a comparison with polarimetric measurements of pulverized meteorites, we can infer the presence of submillimeter-sized grains on the surface layer of Ryugu. We also conjecture that this size boundary represents the grains that compose the aggregate. It is likely that a very brittle structure has been lost in the recovered samples, although they may hold a record of its evolution. Our data will be invaluable for future experiments aimed at reproducing the surface structure of Ryugu.
Knowledge Graph Completion with Text-aided Regularization

Knowledge Graph Completion is a task of expanding the knowledge graph/base through estimating possible entities, or proper nouns, that can be connected using a set of predefined relations, or verb/predicates describing interconnections of two things. Generally, we describe this problem as adding new edges to a current network of vertices and edges. Traditional approaches mainly focus on using the existing graphical information that is intrinsic of the graph and train the corresponding embeddings to describe the information; however, we think that the corpus that are related to the entities should also contain information that can positively influence the embeddings to better make predictions. In our project, we try numerous ways of using extracted or raw textual information to help existing KG embedding frameworks reach better prediction results, in the means of adding a similarity function to the regularization part in the loss function. Results have shown that we have made decent improvements over baseline KG embedding methods.
Capillary Electrophoresis with Indirect Electrochemiluminescence Detection

Abstract An indirect electrochemiluminescence (IECL) detection for capillary electrophoresis (CE) is presented in this article. IECL is based on the inhibition of Ru(bpy)3 2+/TPA system. The developed technique is successfully applied to the analysis of phenolic compounds: phenol, o‐chlorophenol, p‐nitrophenol, and 2,4,6‐trinitrophenol. The factors that influenced the separation and detection were investigated in detail. On the optimized condition, the tested phenols (phenol, o‐chlorophenol, p‐nitrophenol, and 2,4,6‐trinitropheno) could inhibit the ECL signal from the luminescence reagent, and the components of the mixture could be separated and recognized. In addition, when 2 mM phenol was the analyte, the relative standard deviations for migration time and negative peak height were 0.27% and 2.29%, respectively (n=6). And the linear correlation coefficient was 0.996 for phenol (0.5–5 mM). The preliminary results showed that the CE‐IECL might be applied to the detection of phenolic compounds or other samples. Supported by the National Natural Science Foundation of China (No. 20299030 and 20335040), and National Key Basic Research Program 2001CB5102.
Bridging the gap between Human Action Recognition and Online Action Detection

Action recognition, early prediction, and online action detection are complementary disciplines that are often studied independently. Most online action detection networks use a pre-trained feature extractor, which might not be optimal for its new task. We address the task-specific feature extraction with a teacher-student framework between the aforementioned disciplines, and a novel training strategy. Our network, Online Knowledge Distillation Action Detection network (OKDAD), embeds online early prediction and online temporal segment proposal subnetworks in parallel. Low interclass and high intraclass similarity are encouraged during teacher training. Knowledge distillation to the OKDAD network is ensured via layer reuse and cosine similarity between teacher-student feature vectors. Layer reuse and similarity learning significantly improve our baseline which uses a generic feature extractor. We evaluate our framework on infrared videos from two popular datasets, NTU RGB+D (action recognition, early prediction) and PKU MMD (action detection). Unlike previous attempts on those datasets, our student networks perform without any knowledge of the future. Even with this added difficulty, we achieve state-of-the-art results on both datasets. Moreover, our networks use infrared from RGB-D cameras, which we are the first to use for online action detection, to our knowledge.