Miracle-dz/UniFAR-FTUs huggingface dataset:
One-Step Abductive Multi-Target Learning with Diverse Noisy Samples

Recent studies have demonstrated the effectiveness of the combination of machine learning and logical reasoning in inventing advanced artificial intelligence technologies. One-step abductive multi-target learning (OSAMTL), an approach that only combines machine learning and logical reasoning in a one-step balanced way, has as well shown its effectiveness in handling complex noisy labels of a single noisy sample in medical histopathology whole slide image analysis (MHWSIA). However, OSAMTL is not suitable for the situation where diverse noisy samples (DiNS) are provided for a learning task. In this paper, giving definition of DiNS, we propose one-step abductive multi-target learning with DiNS (OSAMTL-DiNS) to expand the original OSAMTL to handle complex noisy labels of DiNS. Applying OSAMTL-DiNS to tumour segmentation for breast cancer in MHWSIA, we show that OSAMTL-DiNS is able to enable various state-of-the-art approaches for learning from noisy labels to achieve more rational predictions.
On optimal wavelet bases for the realization of microcanonical cascade processes

Multiplicative cascades are often used to represent the structure of multiscaling variables in many physical systems, specially turbulent flows. In processes of this kind, these variables can be understood as the result of a successive tranfer in cascade from large to small scales. For a given signal, only its optimal wavelet basis can represent it in such a way that the cascade relation between scales becomes explicit, i.e., it is geometrically achieved at each point of the system. Finding such a basis is a data-demanding, highly-complex task. In this paper we propose a formalism that allows to find the optimal wavelet in an efficient, less data-demanding way. We confirm the appropriateness of this approach by analyzing the results on synthetic signals constructed with prescribed optimal bases. Although we show the validity of our approach constrainted to given families of wavelets, it can be generalized for a continuous unconstrainted search scheme. PACS numbers: 89.75. Da,47.53. +n, 05.45. Df Submitted to: New J. Phys. ar X iv :0 80 5. 48 10 v1 [ co nd -m at . s ta tm ec h] 3 0 M ay 2 00 8 Optimal wavelet for microcanonical cascades 2
The Gamma Function : basic properties and some applications

Resumen El objetivo del presente trabajo es estudiar algunas propiedades y aplicaciones de la función Gamma, denotada por Γ. Inicialmente, se utiliza la teorı́a de la integral de Lebesgue para demostrar que la integral impropia, dada por Γ es convergente. Después de esto, no solo se describe la extensión del dominio de Γ sino también se deducen algunas propiedades elementales. Se presentan dos maneras de probar que B(x, y) = Γ(x)Γ(y) Γ(x+y) , donde B es la función Beta. Finalmente se incluyen algunas aplicaciones de la función Gamma como herramienta útil en la ingenierı́a de confiabilidad. Palabras clave. Integral de Lebesgue, función Gamma, función Beta, convolución, distribución continua. Abstract The goal of the present work is to study some properties and applications of the Gamma Function, denoted by Γ. Initially, we use the Lebesgue Integral Theory in order to prove that the improper integral given by Γ is convergent. We describe the extended domain property of Γ, and we also deduce some elementary properties. We present two different ways of proving that B(x, y) = Γ(x)Γ(y) Γ(x+y) , where B is the Beta Function. Finally, we include some applications of the Gamma Function, between them some serve up as tools on Reliability Engineering.
A O ] 4 J an 2 00 6 Reinforcement learning of recurrent neural network for temporal coding

We propose a reinforcement learning method for the neural network that consists of FitzHugh-Nagumo elements. Here information is coded by the timing of each neuronal firing like the relative phase differences of firing or the order of firing. We derive the learning rule for this network and we show that the network can learn the appropriate timing of each neuronal firing. We mention that we can also apply this learning rule to learning of the network that consists of different neuron model, such as integrate-and-fire model.
Renyi-entropic bounds on quantum communication

In this article we establish new bounds on the quantum communication complexity of distributed problems. Specifically, we consider the amount of communication that is required to transform a bipartite state into another, typically more entangled, state. We obtain lower bounds in this setting by studying the Renyi entropy of the marginal density matrices of the distributed system. The communication bounds on quantum state transformations also imply lower bounds for the model of communication complexity where the task consists of the the distributed evaluation of a function f(x,y). Our approach encapsulates several known lower bound methods that use the log-rank or the von Neumann entropy of the density matrices involved. The technique is also effective for proving lower bounds on problems involving a promise or for which the "hard" distributions of inputs are correlated. As examples, we show how to prove a nearly tight bound on the bounded-error quantum communication complexity of the inner product function in the presence of unlimited amounts of EPR-type entanglement and a similarly strong bound on the complexity of the shifted quadratic character problem.
Automatically Identifying Parameter Constraints for Complex Web APIs: A Case Study at Adyen

Web APIs can have constraints on parameters, such that not all parameters are either always required or always optional. Sometimes the presence or value of one parameter could cause another parameter to be required. Additionally, parameters could have restrictions on what kinds of values are valid. We refer to these as inter-parameter and single-parameter constraints respectively. Having a clear overview of the constraints can help API consumers to integrate without the need for additional support and with fewer integration faults. We developed two approaches for identifying parameter constraints in complex web APIs. One approach uses online documentation to infer interparameter constraints, the other depends on static code analysis to extract interand single-parameter constraints from the control flow of the API’s source code. In our case study at several APIs at Adyen, the documentationand code-based approach can identify 21% and 53% percent of the constraints respectively. When the constraints identified by both approaches are combined, 66% of the inter-parameter constraints can be identified. Code analysis is able to identify 78% of the single-parameter constraints.
Approximate Graph Coloring by Semideenite Programming

We consider the problem of coloring k-colorable graphs with the fewest possible colors. We present a randomized polynomial time algorithm which colors a 3-colorable graph on n vertices with minfO((1=3 log 4=3); O(n 1=4 logn)g colors where is the maximum degree of any vertex. Besides giving the best known approximation ratio in terms of n, this marks the rst non-trivial approximation result as a function of the maximum degree. This result can be generalized to k-colorable graphs to obtain a coloring using minf ~ O((1? 2=k); ~ O(n 1? 3=(k+1))g colors. Our results are inspired by the recent work of Goemans and Williamson GW93] who used an algorithm for semideenite optimization problems (cf. GLS81, Ali92]), which generalize linear programs, to obtain improved approximations for the MAX CUT and MAX 2-SAT problems. An intriguing outcome of our work is a duality relationship established between the value of the optimum solution to our semideenite program and the Lovv asz #-function. We show lower bounds on the gap between the optimum solution of our semideenite program and the actual chromatic number; by duality this also demonstrates interesting new facts about the #-function.
Demographic pressure impacts on forests in Rwanda

1 Hubei Key Laboratory of Wetland Evolution and Ecological Restoration, Institute of Ecology and Environmental Sciences, China University of Geosciences, Wuhan, China. 2 Independent Institute of Lay Adventists of Kigali (INILAK), Department of rural development, Rwanda. 3 School of Environmental Studies, China University of Geosciences, Wuhan, China. 4 Xinjiang Institute of Ecology and Geography, Chinese Academy of Science, 818 Beijing Road South, Urumqi, Xinjiang, 830011, China.
Alternating minimal energy methods for linear systems in higher dimensions. Part II: Faster algorithm and application to nonsymmetric systems*

In this paper we accomplish the development of the fast rank–adaptive solver for tensor–structured symmetric positive definite linear systems in higher dimensions. In [9] this problem is approached by alternating minimization of the energy function, which we combine with steps of the basis expansion in accordance with the steepest descent algorithm. In this paper we combine the same steps in such a way that the resulted algorithm works with one or two neighboring cores at a time. The recurrent interpretation of the algorithm allows to prove the global convergence and to estimate the convergence rate. We also propose several strategies, both rigorous and heuristic, to compute new subspaces for the basis enrichment in a more efficient way. We test the algorithm on a number of high–dimensional problems, including the non-symmetrical Fokker–Planck and chemical master equations, for which the efficiency of the method is not fully supported by the theory. In all examples we observe a convincing fast convergence and high efficiency of the proposed method.
Doubly Robust Thompson Sampling for linear payoffs

A challenging aspect of the bandit problem is that a stochastic reward is observed only for the chosen arm and the rewards of other arms remain missing. Since the arm choice depends on the past context and reward pairs, the contexts of chosen arms suffer from correlation and render the analysis difﬁcult. We propose a novel multi-armed contextual bandit algorithm called Doubly Robust (DR) Thompson Sampling (TS) that applies the DR technique used in missing data literature to TS. The proposed algorithm improves the bound of TS by a factor of √ d , where d is the dimension of the context. A beneﬁt of the proposed method is that it uses all the context data, chosen or not chosen, thus allowing to circumvent the technical deﬁnition of unsaturated arms used in theoretical analysis of TS. Empirical studies show the advantage of the proposed algorithm over TS.
Wireless Network Simplification: The Gaussian  \(N\) -Relay Diamond Network

We consider the Gaussian N-relay diamond network, where a source wants to communicate to destination node through a layer of N-relay nodes. We investigate the following question: what fraction of the capacity can we maintain using only k out of the N available relays? We show that independent of the channel configurations and operating SNR, we can always find a subset of k relays, which alone provide a rate k/(k + 1) C̅ - G, where C̅ is the information theoretic cutset upper bound on the capacity of the whole network and G is independent of the channel coefficients and the SNR and depends only on N and k (logarithmic in N and linear in k). In particular, for k = 1, this means that half of the capacity of any N-relay diamond network can be approximately achieved by routing information over a single relay. We also show that this fraction is tight: there are configurations of the N-relay diamond network, where every subset of k relays alone can at most provide approximately a fraction k/(k + 1) of the total capacity. These high-capacity k-relay subnetworks can be also discovered efficiently. We propose an algorithm that computes a constant gap approximation to the capacity of the Gaussian N-relay diamond network in O(N log N) running time and discovers a high-capacity k-relay subnetwork in O(kN) running time. This result also provides a new approximation to the capacity of the Gaussian N-relay diamond network, which is hybrid in nature: it has both multiplicative and additive gaps. In the intermediate SNR regime, this hybrid approximation is tighter than existing purely additive or purely multiplicative approximations to the capacity of this network.
Composing Di erential Privacy and Secure Computation : A case study on scaling private record linkage

Private record linkage (PRL) is the problem of identifying pairs of records that are similar as per an input matching rule from databases held by two parties that do not trust one another. We identify three key desiderata that a PRL solution must ensure: (1) perfect precision and high recall of matching pairs, (2) a proof of end-to-end privacy, and (3) communication and computational costs that scale subquadratically in the number of input records. We show that all of the existing solutions for PRL– including secure 2-party computation (S2PC), and their variants that use non-private or dierentially private (DP) blocking to ensure subquadratic cost – violate at least one of the three desiderata. In particular, S2PC techniques guarantee end-to-end privacy but have either low recall or quadratic cost. In contrast, no end-to-end privacy guarantee has been formalized for the variants of S2PC techniques, even for the solutions that compose DP and S2PC, because of a fundamental disconnect between these two privacy guarantees: DP does not permit the release of any exact information about the databases, while S2PC algorithms for PRL allow the release of matching records. In light of this deciency, we propose a novel privacy model, called output constrained dierential privacy, that shares the strong privacy protection of DP, but allows for the truthful release of the output of a certain function applied to the data. We apply this to PRL, and show that protocols satisfying this privacy model permit the disclosure of the true matching records, but their execution is insensitive to the presence or absence of a single non-matching record. We nd that prior work that combine DP and S2PC techniques even fail to satisfy this end-to-end privacy model. Hence, we develop novel protocols that provably achieve this end-to-end privacy guarantee, together with the other two desiderata of PRL. Our empirical evaluation also shows that our protocols obtain high recall, scale near linearly in the size of the input databases and the output set of matching pairs, and have communication and computational costs that are at least 2 orders of magnitude smaller than S2PC baselines. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for prot or commercial advantage and that copies bear this notice and the full citation on the rst page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permied. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specic permission and/or a fee. Request permissions from permissions@acm.org. CCS ’17, Dallas, TX, USA © 2017 ACM. 978-1-4503-4946-8/17/10. $15.00 DOI: 10.1145/3133956.3134030
IT / 0 41 00 19 v 1 1 0 O ct 2 00 4 Finite-Length Scaling and Finite-Length Shift for Low-Density Parity-Check Codes

Consider communication over the binary erasure channel BEC(ǫ) using random low-density parity-check codes with finite-blocklength n from ‘standard’ ensembles. We show that large error events is conveniently described within a scaling theory, and explain how to estimate heuristically their effect. Among other quantities, we consider the finite length threshold ǫ∗(n), defined by requiring a block error probability PB = 1/2. For ensembles with minimum variable degree larger than two, the following expression is argued to hold ǫ(n) = ǫ − ǫ1 n + Θ(n) , with a calculable shift parameter ǫ1 > 0.
Finding the most biased coin with fewest flips

We study the problem of learning a most biased coin among a set of coins by tossing the coins adaptively. The goal is to minimize the number of tosses until we identify a coin i* whose posterior probability of being most biased is at least 1-delta for a given delta. Under a particular probabilistic model, we give an optimal algorithm, i.e., an algorithm that minimizes the expected number of future tosses. The problem is closely related to finding the best arm in the multi-armed bandit problem using adaptive strategies. Our algorithm employs an optimal adaptive strategy -- a strategy that performs the best possible action at each step after observing the outcomes of all previous coin tosses. Consequently, our algorithm is also optimal for any starting history of outcomes. To our knowledge, this is the first algorithm that employs an optimal adaptive strategy under a Bayesian setting for this problem. Our proof of optimality employs tools from the field of Markov games.
Performance analysis of spatial smoothing schemes in the context of large arrays

This paper addresses the statistical behaviour of spatial smoothing subspace DoA estimation schemes using a sensor array in the case where the number of observations N is significantly smaller than the number of sensors M, and that the number of virtual arrays L is such that M and NL are of the same order of magnitude. This context is modelled by an asymptotic regime in which NL and M both converge towards 1 at the same rate. As in recent works devoted to the study of (unsmoothed) subspace methods in the case where M and N are of the same order of magnitude, it is shown that it is still possible to derive improved DoA estimators termed as Generalized-MUSIC (G-MUSIC). The key ingredient of this work is a technical result showing that the largest singular values and corresponding singular vectors of low rank deterministic perturbation of certain Gaussian block-Hankel large random matrices behave as if the entries of the latter random matrices were independent identically distributed.
Improving the Effectiveness of CSIRTs

This paper reports on research designed to measure the effectiveness of national Computer Security Incident Response Teams (CSIRTs). Specifically, our aim is to identify: 1) the ways in which a CSIRT might be considered to be effective; 2) the issues which may limit the performance of a CSIRT; and 3) approaches towards developing CSIRT effectiveness metrics. A primary motive for doing so is to enable more effective CSIRTs to be implemented, focusing on activities with the maximum impact on threat mitigation. The research was conducted using both online survey and interviews, in two phases. The study participants were experts within the existing CSIRT community. In total, 46 participants responded to the survey, from 27 countries in Europe, Africa, South and North America, and Asia. Three experts working for CSIRTs in the UK and USA were also interviewed. Questions asked during the interviews and the online survey queried the personal knowledge and experience of participants regarding CSIRTs. In our analysis, issues such as cooperation, data-sharing and trust are discussed as crucial components of an effective CSIRT. Existing measurement approaches for computer security incident response are presented, before a set of suggested direct and indirect measures of the effectiveness of
Quantum-enhanced neural networks in the neural tangent kernel framework

Recently quantum neural networks or quantum-classical neural networks (QCNN) have been actively studied, as a possible alternative to the conventional classical neural network (CNN), but their practical and theoretically-guaranteed performance is still to be investigated. On the other hand, CNNs and especially the deep CNNs, have acquired several solid theoretical basis; one of those signiﬁcant basis is the neural tangent kernel (NTK) theory, which indeed can successfully explain the mechanism of various desirable properties of CNN, e.g., global convergence and good generalization properties. In this paper, we study a class of QCNN where NTK theory can be directly applied. The output of the proposed QCNN is a function of the projected quantum kernel, in the limit of large number of nodes of the CNN part; hence this scheme may have a potential quantum advantage. Also, because the parameters can be tuned only around the initial random variables chosen from unitary 2-design and Gaussian distributions, the proposed QCNN casts as a scheme that realizes the quantum kernel method with less computational complexity. Moreover, NTK is identical to the covariance matrix of a Gaussian process, which allows us to analytically study the learning process and as a consequence to have a condition of the dataset such that QCNN may perform better than the classical correspondence. These properties are all observed in a thorough numerical experiment.
Studies in Informatics and Control

Immersive videoconferences have added a new dimension to remote collaboration by bringing participants together in a common virtual space. To achieve this, the conferencing system must extract in real-time the foreground from each incoming video stream and translate it into the shared virtual space. The method presented in this paper differentiates itself in the sense that no prior training or assumptions on the video content are used during foreground extraction. A temporally coherent mask is created based on motion cues obtained from the video stream and is used to provide a set of hard constraints. Based on these constraints, a graph cut algorithm is employed to produce the pixel-accurate foreground segmentation. The obtained results are evaluated using a state-of-the-art perceptual metric to provide an objective assessment of the method accuracy and reliability. Furthermore, the presented approach makes use of parallel execution in order to achieve real-time processing capabilities.
Adoption of e-government in the Republic of Kazakhstan

This paper identifies factors that influence Kazakhstan's e-Government portal usage. It determines challenges encountered by citizens while using the portal. Targeted respondents for the web-based questionnaire survey were the citizens of Kazakhstan. The technology acceptance model was used as a methodology to measure attitude towards portal usage. In addition, this paper discusses the barriers which were experienced by the respondents and can prevent the successful adoption of e-Government initiative. The results of the analysis demonstrate that awareness among citizens is high, i.e. the majority of people have visited it at least once and they perceive the portal to be useful, but only a limited percentage of citizens' use it on the regular basis. Further, paper can be used to help IT managers of the portal to improve management of informational content and maintain a more effective adoption among citizens.
Exploiting the Path Propagation Time Differences in Multipath Transmission with FEC

We consider a transmission of a delay-sensitive data stream (e.g., a video call) from a single source to a single destination. The reliability of this transmission may suffer from bursty packet losses - the predominant type of failures in today's Internet. An effective and well studied solution to this problem is to protect the data by a Forward Error Correction (FEC) code and send the FEC packets over multiple paths. In this paper, we show that the loss rate of such a classic multipath FEC scheme can often be significantly reduced, while keeping the total transmission rate and delay unchanged. Our key observation is that the propagation times on the available paths often significantly differ, usually by 10-100ms. We propose to exploit these differences by appropriate packet scheduling that we call `Spread'. We evaluate our solution with a precise, analytical formulation and trace-driven simulations. Our studies show that Spread substantially outperforms the state-of-the-art solutions. It typically achieves two- to five-fold improvement (reduction) in the effective loss rate. Or conversely, keeping the same level of effective loss rate, Spread significantly decreases the observed delays and helps fighting the delay jitter.