Miracle-dz/UniFAR-FTUs huggingface dataset:
Training Deep Neural Networks via Direct Loss Minimization: Supplementary Materials 1 Proof of the General Loss Gradient Theorem

In order to lay the foundation for the proof of the general loss gradient theorem, we first show the following lemma. In short, it provides the bases for exchanging integral bounds when ϵ approaches 0 from above. Lemma 1. lim ϵ→0 + 1 ϵ ∫ aϵ+o(ϵ) 0 ∫ ∞ bϵ+o(ϵ) f (x, y, ϵ)dxdy = lim ϵ→0 + 1 ϵ ∫ aϵ+o(ϵ) 0 ∫ ∞ 0 f (x, y, ϵ)dxdy Proof. We have lim ϵ→0 + 1 ϵ ∫ aϵ+o(ϵ) 0 ∫ ∞ bϵ+o(ϵ) f (x, y, ϵ)dxdy = lim ϵ→0 + 1 ϵ ∫ aϵ+o(ϵ) 0 ∫ ∞ 0 f (x, y, ϵ)dxdy − lim ϵ→0 + 1 ϵ ∫ aϵ+o(ϵ) 0 ∫ bϵ+o(ϵ) 0 f (x, y, ϵ)dxdy. Suppose f is continuous w.r. t x, y, ϵ, then as ϵ → 0 + , it can be bounded by some constant M. As a result, we have 1 ϵ ∫ aϵ+o(ϵ) 0 ∫ bϵ+o(ϵ) 0 |f (x, y, ϵ)|dxdy ≤ M (abϵ + ao(ϵ) + bo(ϵ) + o(ϵ)o(ϵ)/ϵ), (1) which means lim ϵ→0 + 1 ϵ ∫ aϵ+o(ϵ) 0 ∫ bϵ+o(ϵ) 0 f (x, y, ϵ)dxdy = 0. (2) Repeated application of Lemma 1 as demonstrated in the following is directly helpful for the proof of the general loss gradient theorem, which is why we state it explicitly: Lemma 2. Let a > 0, then we assert lim ϵ→0 + 1 ϵ ∫ aϵ+o(ϵ) 0 ∫ ∞ b1ϵ+o(ϵ) · · · ∫ ∞ bnϵ+o(ϵ) f (x, y 1 , · · · , y n)dxdy 1 · · · dy n = lim ϵ→0 + 1 ϵ a ∫ ϵ 0 ∫ ∞ 0 · · · ∫ ∞ 0 f (x, y 1 , · · · , y n)dxdy 1 · · · dy n Proof. Let f (x, y 1 , ϵ) = ∫ ∞ b2ϵ+o(ϵ) · · · ∫ ∞ bnϵ+o(ϵ) f (x, y 1 , · · · , y n)dy 2 · · · dy n. Due to Lemma 1 we obtain the following: lim ϵ→0 + 1 ϵ ∫ aϵ+o(ϵ) 0 ∫ ∞ b1ϵ+o(ϵ) f (x, y 1 , ϵ)dxdy 1 = lim ϵ→0 + 1 ϵ ∫ aϵ+o(ϵ) 0 ∫ ∞ 0 f (x, y 1 , ϵ)dxdy 1 = lim ϵ→0 + 1 ϵ ∫ aϵ+o(ϵ) 0 ∫ ∞ 0 ∫ ∞ b2ϵ+o(ϵ) · · · ∫ ∞ bnϵ+o(ϵ) f …
Practical Investigation of a MIMO radar system capabilities forÂ€small drones detection

National Major Research Instruments (NSFC), Grant/Award Number: 31727901; National Natural Science Foundation of China, Grant/Award Number: 61771050; 111 Project of China, Grant/ Award Number: B14010; China Scholarship Council, Grant/Award Number: 201906030191 Abstract The latest progress of the multiple‐input multiple‐output (MIMO) radar system developed for small drones detection at Beijing Institute of Technology is presented herein. A low‐cost S‐band MIMO scanning radar system is designed for the detection of small drones. A practical design optimisation and implementation of a sparse array covering the whole airspace, and a target‐basedMIMO radar array calibrationmethod to improve the signal‐to‐ noise ratio of the target are proposed. An experiment in the microwave chamber was conducted to verify the designed sparse‐array beam‐pattern, and measurement campaigns for small drones were performed to test the system performance where the radar achieved detection and tracking of a small DJI‐Phantom 4 drone at a range of up to 5 km. Target features, includingmicro‐Doppler and radar cross‐section signatureswere extracted to assist target classification. The experimental results indicate that the system has promising capability in small drones detection and tracking, despite the challenges of lowdwell time on targets due to the scanning nature of the system.
Random subgraphs of finite graphs: I. The scaling window under the triangle condition

We study random subgraphs of an arbitrary finite connected transitive graph $\mathbb G$ obtained by independently deleting edges with probability $1-p$. Let $V$ be the number of vertices in $\mathbb G$, and let $\Omega$ be their degree. We define the critical threshold $p_c=p_c(\mathbb G,\lambda)$ to be the value of $p$ for which the expected cluster size of a fixed vertex attains the value $\lambda V^{1/3}$, where $\lambda$ is fixed and positive. We show that for any such model, there is a phase transition at $p_c$ analogous to the phase transition for the random graph, provided that a quantity called the triangle diagram is sufficiently small at the threshold $p_c$. In particular, we show that the largest cluster inside a scaling window of size $|p-p_c|=\Theta(\cn^{-1}V^{-1/3})$ is of size $\Theta(V^{2/3})$, while below this scaling window, it is much smaller, of order $O(\epsilon^{-2}\log(V\epsilon^3))$, with $\epsilon=\cn(p_c-p)$. We also obtain an upper bound $O(\cn(p-p_c)V)$ for the expected size of the largest cluster above the window. In addition, we define and analyze the percolation probability above the window and show that it is of order $\Theta(\cn(p-p_c))$. Among the models for which the triangle diagram is small enough to allow us to draw these conclusions are the random graph, the $n$-cube and certain Hamming cubes, as well as the spread-out $n$-dimensional torus for $n>6$.
Distributed Random Access Algorithm: Scheduling and Congesion Control

This paper provides proofs of the rate stability, Harris recurrence, and epsilon-optimality of CSMA algorithms where the backoff parameter of each node is based on its backlog. These algorithms require only local information and are easy to implement. The setup is a network of wireless nodes with a fixed conflict graph that identifies pairs of nodes whose simultaneous transmissions conflict. The paper studies two algorithms. The first algorithm schedules transmissions to keep up with given arrival rates of packets. The second algorithm controls the arrivals in addition to the scheduling and attempts to maximize the sum of the utilities of the flows of packets at the different nodes. For the first algorithm, the paper proves rate stability for strictly feasible arrival rates and also Harris recurrence of the queues. For the second algorithm, the paper proves the epsilon-optimality. Both algorithms operate with strictly local information in the case of decreasing step sizes, and operate with the additional information of the number of nodes in the network in the case of constant step size.
Global Journal of Computer Sciences: Theory and Research

Due to fast technological progress in the power engineering field, the need for new information/communication technologies is more and more underlined. e-Learning has become a viable alternative to traditional teaching/learning techniques, adopted especially because of the advantages offered by the possibility of continuous training. This paper presents a Microsoft Internet of Things Platform for a very large scale smart power metre reading, used for training operative staff of the Distribution Network Operator, but also to help end-users to control their electrical energy consumption. The strength of this platform for the Distribution Network Operator is that the read data can be used for energy forecast, which is very useful for the future energy consumption optimisation. The platform can be reached via the Internet using a user name and password. A comparison between the results provided by classical teaching/learning methods and the ones achieved using this platform is presented.
High Data-Rate Single-Symbol ML Decodable Distributed STBCs for Cooperative Networks

High data-rate Distributed Orthogonal Space-Time Block Codes (DOSTBCs) which achieve the single-symbol decodability and full diversity order are proposed in this paper. An upper bound of the data-rate of the DOSTBC is derived and it is approximately twice larger than that of the conventional repetition-based cooperative strategy. In order to facilitate the systematic constructions of the DOSTBCs achieving the upper bound of the data-rate, some special DOSTBCs, which have diagonal noise covariance matrices at the destination terminal, are investigated. These codes are referred to as the row-monomial DOSTBCs. An upper bound of the data-rate of the row-monomial DOSTBC is derived and it is equal to or slightly smaller than that of the DOSTBC. Lastly, the systematic construction methods of the row-monomial DOSTBCs achieving the upper bound of the data-rate are presented.
Information Propagation Speed in Mobile and Delay Tolerant Networks

The goal of this paper is to increase our understanding of the fundamental performance limits of mobile and Delay Tolerant Networks (DTNs), where end-to-end multihop paths may not exist and communication routes may only be available through time and mobility. We use analytical tools to derive generic theoretical upper bounds for the information propagation speed in large scale mobile and intermittently connected networks. In other words, we upper-bound the optimal performance, in terms of delay, that can be achieved using any routing algorithm. We then show how our analysis can be applied to specific mobility models to obtain specific analytical estimates. In particular, in 2-D networks, when nodes move at a maximum speed v and their density is small (the network is sparse and asymptotically almost surely disconnected), we prove that the information propagation speed is upper bounded by (1 + O(v2))v in random waypoint-like models, while it is upper bounded by O(√vvv) for other mobility models (random walk, Brownian motion). We also present simulations that confirm the validity of the bounds in these scenarios. Finally, we generalize our results to 1-D and 3-D networks.
ec 2 00 3 Toward a Quantum Process Algebra

Quantum computations operate in the quantum world. For their results to be useful in any way, there is an intrinsic necessity of cooperation and communication controlled by the classical world. As a consequence, full formal descriptions of algorithms making use of quantum principles must take into account both quantum and classical computing components and assemble them so that they communicate and cooperate. This paper aims at defining a high level language allowing the description of classical and quantum programming, and their cooperation. Since process algebras provide a framework to model cooperating computations and have well defined semantics, they have been chosen as a basis for this language. Starting with a classical process algebra, this paper explains how to transform it for including quantum computation. The result is a quantum process algebra with its operational semantics, which can be used to fully describe quantum algorithms in their classical context.
Statistical models for cores decomposition of an undirected random graph

We develop a rigorous and interpretable statistical model for networks using their shell structure to construct minimal sufficient statistics. The model provides the formalism necessary for using k-cores in statistical considerations of random graphs, and in particular social networks. It cannot be specialized to any known degree-centric model and does not generalize Erd\"os-R\'enyi. We propose a sampling algorithm for constructing all graphs with a given shell structure, and an MCMC algorithm to simulate from the model. These algorithms will serve as a basis for parameter estimation and model fit tests.
Automatically Securing Permission-Based Software by Reducing the Attack Surface : An Application to Android

Android based devices are becoming widespread. As a result and since those devices contain personal and confidential data, the security model of the android software stack has been analyzed extensively. One key feature of the security model is that applications must declare a list of permissions they are using to access resources. Using static analysis, we first extracted a table from the Android API which maps methods to permissions. Then, we use this mapping within a tool we developed to check that applications effectively need all the permissions they declare. Using our tool on a set of android applications, we found out that a non negligible part of the applications do not use all the permissions they declare. Consequently, the attack surface of such applications can be reduced by removing the non-needed permissions. Keywords-permissions, call-graph, android, security, soot, java, static analysis
C R ] 2 0 M ar 2 01 3 Automatically Securing Permission-Based Software by Reducing the Attack Surface : An Application to Android

A common security architecture, called the permission-based security model (used e.g. in Android and Blackberry), entails intrinsic risks. For instance, applications can be granted more permissions than they actually need, what we call a “permission gap”. Malware can leverage the unused permissions for achieving their malicious goals, for instance using code injection. In this paper, we present an approach to detecting permission gaps using static analysis. Our prototype implementation in the context of Android shows that the static analysis must take into account a significant amount of platform-specific knowledge. Using our tool on two datasets of Android applications, we found out that a non negligible part of applications suffers from permission gaps, i.e. does not use all the permissions they declare.
Near Optimal Routing for Small-World Networks with Augmented Local Awareness

In order to investigate the routing aspects of small-world networks, Kleinberg proposes a network model based on a $d$-dimensional lattice with long-range links chosen at random according to the $d$-harmonic distribution. Kleinberg shows that the greedy routing algorithm by using only local information performs in $O(\log^2 n)$ expected number of hops, where $n$ denotes the number of nodes in the network. Martel and Nguyen have found that the expected diameter of Kleinberg's small-world networks is $\Theta(\log n)$. Thus a question arises naturally: Can we improve the routing algorithms to match the diameter of the networks while keeping the amount of information stored on each node as small as possible? We extend Kleinberg's model and add three augmented local links for each node: two of which are connected to nodes chosen randomly and uniformly within $\log^2 n$ Mahattan distance, and the third one is connected to a node chosen randomly and uniformly within $\log n$ Mahattan distance. We show that if each node is aware of $O(\log n)$ number of neighbors via the augmented local links, there exist both non-oblivious and oblivious algorithms that can route messages between any pair of nodes in $O(\log n \log \log n)$ expected number of hops, which is a near optimal routing complexity and outperforms the other related results for routing in Kleinberg's small-world networks. Our schemes keep only $O(\log^2 n)$ bits of routing information on each node, thus they are scalable with the network size. Besides adding new light to the studies of social networks, our results may also find applications in the design of large-scale distributed networks, such as peer-to-peer systems, in the same spirit of Symphony.
Partially Observable Mean Field Multi-Agent Reinforcement Learning Based on Graph-Attention

Multiple unmanned aerial vehicles (Multi-UAV) systems have recently demonstrated significant advantages in some real-world scenarios, but the limited communication range of UAVs poses great challenges to multi-UAV collaborative decision-making. By constructing the multi-UAV cooperation problem as a multi-agent system (MAS), the cooperative decision-making among UAVs can be realized by using multi-agent reinforcement learning (MARL). Following this paradigm, this work focuses on developing partially observable MARL models that capture important information from local observations in order to select effective actions. Previous related studies employ either probability distributions or weighted mean field to update the average actions of neighborhood agents. However, they do not fully consider the feature information of surrounding neighbors, resulting in a local optimum often. In this paper, we propose a novel partially multi-agent reinforcement learning algorithm to remedy this flaw, which is based on graph attention network and partially observable mean field and is named as the GPMF algorithm for short. GPMF uses a graph attention module and a mean field module to describe how an agent is influenced by the actions of other agents at each time step. The graph attention module consists of a graph attention encoder and a differentiable attention mechanism, outputting a dynamic graph to represent the effectiveness of neighborhood agents against central agents. The mean field module approximates the effect of a neighborhood agent on a central agent as the average effect of effective neighborhood agents. Aiming at the typical task scenario of large-scale multi-UAV cooperative roundup, the proposed algorithm is evaluated based on the MAgent framework. Experimental results show that GPMF outperforms baselines including state-of-the-art partially observable mean field reinforcement learning algorithms, providing technical support for large-scale multi-UAV coordination and confrontation tasks in communication-constrained environments.
Approximate computation of DFT without performing any multiplications: Application to radar signal processing

In many radar problems it is not necessary to compute the ambiguity function in a perfect manner. In this article a new multiplication free algorithm for approximate computation of the ambiguity function is introduced. All multiplications (a × b) in the ambiguity function are replaced by an operator which computes sign(a × b)(|a| + |b|). The new transform is especially useful when the signal processing algorithm requires correlations. Ambiguity function in radar signal processing requires high number of correlations and DFT computations. This new additive operator enables an approximate computation of the ambiguity function without requiring any multiplications. Simulation examples involving passive radars are presented.
Performance Analysis of Conventional Machine Learning Algorithms for Diabetic Sensorimotor Polyneuropathy Severity Classification

Background: Diabetic peripheral neuropathy (DSPN), a major form of diabetic neuropathy, is a complication that arises in long-term diabetic patients. Even though the application of machine learning (ML) in disease diagnosis is a very common and well-established field of research, its application in diabetic peripheral neuropathy (DSPN) diagnosis using composite scoring techniques like Michigan Neuropathy Screening Instrumentation (MNSI), is very limited in the existing literature. Method: In this study, the MNSI data were collected from the Epidemiology of Diabetes Interventions and Complications (EDIC) clinical trials. Two different datasets with different MNSI variable combinations based on the results from the eXtreme Gradient Boosting feature ranking technique were used to analyze the performance of eight different conventional ML algorithms. Results: The random forest (RF) classifier outperformed other ML models for both datasets. However, all ML models showed almost perfect reliability based on Kappa statistics and a high correlation between the predicted output and actual class of the EDIC patients when all six MNSI variables were considered as inputs. Conclusions: This study suggests that the RF algorithm-based classifier using all MNSI variables can help to predict the DSPN severity which will help to enhance the medical facilities for diabetic patients.
How to Decide Consensus? A Combinatorial Necessary and Sufficient Condition and a Proof that Consensus is Decidable but NP-Hard

A set of stochastic matrices ${\cal P}$ is a consensus set if for every sequence of matrices $P(1), P(2), \ldots$ whose elements belong to ${\cal P}$ and every initial state $x(0)$, the sequence of states defined by $x(t) = P(t) P(t-1) \cdots P(1) x(0)$ converges to a vector whose entries are all identical. In this paper, we introduce an “avoiding set condition” for compact sets of matrices and prove in our main theorem that this explicit combinatorial condition is both necessary and sufficient for consensus. We show that several of the conditions for consensus proposed in the literature can be directly derived from the avoiding set condition. The avoiding set condition is easy to check with an elementary algorithm, and so our result also establishes that consensus is algorithmically decidable. Direct verification of the avoiding set condition may require more than a polynomial time number of operations. This is, however, likely to be the case for any consensus checking algorithm since we also prove in th.
A Linear-Time Approximation Algorithm forMinimum Rectangular

We consider the problem of covering polygons, without any acute interior angles, with rectangles. The rectangles must lie entirely within the polygon and it is preferable to cover the polygon with as few rectangles as possible. This problem has an application in fabricating masks for integrated circuits. Let P be an arbitrary hole-free input polygon, with n vertices, cover-able by rectangles. Let (P) denote the minimum number of rectangles required to cover P. In this paper we show, by using new techniques, that it is possible to construct a covering within an O((n)) approximation factor in O(n+(P)) time, where (n) is the extremely slowly growing inverse of Ackermann's function. This improves the O(n 0:5? ) approximation factor in time O(n log n+(P)) known before.
2 A Mixture-based Defense Against Poisoning of Spam Filters 2 . 1 Notation

Naive Bayes spam filters are highly susceptible to data poisoning attacks. Here, known spam sources/blacklisted IPs exploit the fact that their received emails will be treated as (ground truth) labeled spam examples, and used for classifier training (or re-training). The attacking source thus generates emails that will skew the spam model, potentially resulting in great degradation in classifier accuracy. Such attacks are successful mainly because of the poor representation power of the naive Bayes (NB) model, with only a single (component) density to represent spam (plus a possible attack). We propose a defense based on the use of a mixture of NB models. We demonstrate that the learned mixture almost completely isolates the attack in a second NB component, with the original spam component essentially unchanged by the attack. Our approach addresses both the scenario where the classifier is being re-trained in light of new data and, significantly, the more challenging scenario where the attack is embedded in the original spam training set. Even for weak attack strengths, BIC-based model order selection chooses a two-component solution, which invokes the mixture-based defense. Promising results are presented on the TREC 2005 spam corpus.
ON THE RATES OF CONVERGENCE OF SIMULATION-BASED OPTIMIZATION ALGORITHMS FOR OPTIMAL STOPPING PROBLEMS BY DENIS BELOMESTNY1

In this paper, we study simulation-based optimization algorithms for solving discrete time optimal stopping problems. Using large deviation theory for the increments of empirical processes, we derive optimal convergence rates for the value function estimate and show that they cannot be improved in general. The rates derived provide a guide to the choice of the number of simulated paths needed in optimization step, which is crucial for the good performance of any simulation-based optimization algorithm. Finally, we present a numerical example of solving optimal stopping problem arising in finance that illustrates our theoretical findings.