Miracle-dz/UniFAR-FTUs huggingface dataset:
When computing meets heterogeneous cluster: Workload assignment in graph computation

In order to process very large graphs, existing graph processing systems, such as Pregel and Giraph, usually partition and distribute the graph computation on large number of nodes (i.e., workers). However, due to the heterogeneity of computing clusters (e.g., nodes with various bandwidth or CPU resource), blindly increasing the number of workers for a job may even degrade the overall performance. In this paper, we address the question of how to distribute the graph computation over the heterogeneous cluster to maximize performance. Based on the practical constraints of current systems, we address this problem in two scenarios. For systems using hash-based partition method (for avoiding the overhead of indexing and searching vertex), we propose a coarse-grained mechanism to greedily select suitable worker set to execute the job. For systems allowing arbitrary graph partition, we further propose a heterogeneity-aware streaming graph partitioning model that can assign workload in fine-grained level. We implement the scheduling mechanisms as a general middleware which can be easily adopted in existing graph computing systems. Our experiments on both university lab cluster (46 machines) and EC2 cluster (100 instances) show that, the proposed framework can significantly improve the execution performance. Compared with the default configurations (i.e., using the whole set of workers and hash-based graph partition), our framework can reduce the overall execution time by 55.9% for lab cluster and 44.7% for EC2 cluster respectively.
Probabilistic km-anonymity efficient anonymization of large set-valued datasets

Set-valued dataset contains different types of items/values per individual, for example, visited locations, purchased goods, watched movies, or search queries. As it is relatively easy to re-identify individuals in such datasets, their release poses significant privacy threats. Hence, organizations aiming to share such datasets must adhere to personal data regulations. In order to get rid of these regulations and also to beneit from sharing, these datasets should be anonymized before their release. In this paper, we revisit the problem of anonymizing set-valued data. We argue that anonymization techniques targeting traditional km-anonymity model, which limits the adversarial background knowledge to at most m items per individual, are impractical for large real-world datasets. Hence, we propose a probabilistic relaxation of km-anonymity and present an anonymization technique to achieve it. This relaxation also improves the utility of the anonymized data. We also demonstrate the effectiveness of our scalable anonymization technique on a real-world location dataset consisting of more than 4 million subscribers of a large European telecom operator. We believe that our technique can be very appealing for practitioners willing to share such large datasets.
Maximum Margin Learning of t-SPNs for Cell Classiﬁcation with Filtering

—An algorithm based on a deep probabilistic architecture referred to as a tree-structured sum-product network (t-SPN) is considered for cell classiﬁcation. The t-SPN is constructed such that the unnormalized probability is represented as conditional probabilities of a subset of most similar cell classes. The constructed t-SPN architecture is learned by maximizing the margin, which is the difference in the conditional probability between the true and the most competitive false label. To enhance the generalization ability of the architecture, L2-regularization (REG) is considered along with the maximum margin (MM) criterion in the learning process. To highlight cell features, this paper investigates the effectiveness of two generic high-pass ﬁlters: ideal high-pass ﬁltering and the Laplacian of Gaussian (LOG) ﬁltering. On both HEp-2 and Feulgen benchmark datasets, the t-SPN architecture learned based on the max-margin criterion with regularization produced the highest accuracy rate compared to other state-of-the-art algorithms that include convolutional neural network (CNN) based algorithms. The ideal high-pass ﬁlter was more effective on the HEp-2 dataset, which is based on immunoﬂuorescence staining, while the LOG was more effective on the Feulgen dataset, which is based on Feulgen staining.
The Impact of a Principles-Based Approach to Director Gender Diversity

We study the impact of a principles-based regulatory approach to female representation on boards, requiring firms to disclose gender diversity policies or lack thereof. Although adding females to boards is not mandatory, firms subject to the regulation increase their fraction of female directors by 38% relative to control groups. Firms most likely to be impacted by the regulation exhibit positive abnormal announcement returns. Post-regulation, firms are increasingly likely to adopt female director target ratios and disclose that they consider gender diversity in board nominations. Firms with less access to female directors are most likely to explain their lack of diversity. JEL Classification: G34; G38; G14; K22; J21
Modelling the time‐dependent behaviour of granular material with hypoplasticity

This paper presents a constitutive model for time‐dependent behaviour of granular material. The model consists of 2 parts representing the inviscid and viscous behaviour of granular materials. The inviscid part is a rate‐independent hypoplastic constitutive model. The viscous part is represented by a rheological model, which contains a high‐order term denoting the strain acceleration. The proposed model is validated by simulating some element tests on granular soils. Our model is able to model not only the non‐isotach behaviour but also the 3 creep stages, namely, primary, secondary, and tertiary creep, in a unified way.
Comments on “Unobservable Selection and Coefficient Stability: Theory and Evidence” and “Poorly Measured Confounders are More Useful on the Left Than on the Right”

Abstract– We establish a link between the approaches proposed by Oster (2019) and Pei, Pischke, and Schwandt (2019) which contribute to the development of inferential procedures for causal effects in the challenging and empirically relevant situation where the unknown data-generation process is not included in the set of models considered by the investigator. We use the general misspecification framework recently proposed by De Luca, Magnus, and Peracchi (2018) to analyze and understand the implications of the restrictions imposed by the two approaches.
A path-independent approach to integrated variance under the CEV model

In this paper, a closed form path-independent approximation of the fair variance strike for a variance swap under the constant elasticity of variance (CEV) model is obtained by applying the small disturbance asymptotic expansion. The realized variance is sampled continuously in a risk-neutral market environment. With the application of a Brownian bridge, we derive a theorem for the conditionally expected product of a Brownian motion at two different times for arbitrary powers. This theorem enables us to provide a conditional Monte-Carlo scheme for simulating the fair variance strike. Compared with results in the recent literature, the method outlined in our paper leads to a simplified approach for pricing variance swaps. The method may also be applied to other more sophisticated volatility derivatives. JEL classification: C02, C63, G13
Constrained dynamical systems, robust model reference adaptive control, and unreliable reference signals

ABSTRACT In this paper, we address the robust control design problem for nonlinear dynamical systems tracking unreliable reference signals. Specifically, we present robust model reference adaptive control laws that guarantee uniform ultimate boundedness of the trajectory tracking error for nonlinear plants that are affected by matched, unmatched, and parametric uncertainties, and are subject to constraints on the state space and the measured output. These control laws guarantee satisfactory results even in case the reference trajectory or the reference output signal do not verify the given constraints and hence, may draw the plant's trajectory or measured output outside their constraint sets. A numerical example involving the attitude control of a spacecraft illustrates the feasibility of the theoretical results presented.
Some functional equations in Schwartz space and their applications

In the paper we consider functional equations of form (B + rE)u(z) = 0, where B is a constant complex n × n matrix, E is the unit n × n matrix, z is a complex variable, r = |z|, u(z) is the sought generalized vector function. For this equation, we study the existence of non-trivial solutions and the manifold of all solutions in the functional space D′ = D′(C,Cn) of generalized vector function and in the space S′ = S′(C,Cn) of tempered distributions. We also study the existence of solutions growing at most polynomially at infinity. Such study is motivated by the problem on finding the solutions in S′ for elliptic systems of first order elliptic equations. Here an important role is played by the statement on the structure of distributions supported in a circumference. This statement provides an explicit representation of distributions supported in a circumference and this representation consists of a linear combinations of Cartesian product of periodic distributions and δfunction and its derivatives. The process of finding all solutions to this equation in the space D′ consists of three stages. At the first stage, by reducing the matrix to the normal Jordan form, we split this equation into one-dimensional equations. At the second stage we prove that if the matrix B has non negative and zero eigenvalues, that is, σ(B) ∩ (−∞, 0] = ∅, where σ(B) is the spectrum of the matrix B, then in the space D′, this equation has only the trivial solution. At the third stage, in the case σ(B) ∩ (−∞, 0] ̸= ∅, we find all solutions to this equation in the space D′. Subject to the eigenvalues of the matrix B, the set of all solutions to this equation in the space D′ is either zero or depends on finitely many arbitrary 2π-periodic distributions of one variable and finitely many arbitrary constants. The number of these functions and constants depend on the order of the solution; the order is prescribed. As an application, we find solutions in the space S′, in particular, polynomially growing solutions to elliptic systems of partial differential equations and to overdetermined systems. The results obtained in the work can be employed in studying the problems on solutions defined on the entire complex plane or a half-plane and in studying more general linear multi-dimensional elliptic systems and overdetermined systems of partial differential equations.
J an 2 00 0 An Introduction to Quantum Computing for Non-Physicists

Richard Feynman’s observation that certain quantum mechanical effects cannot be simulated efficiently on a computer led to speculation that computation in general could be done more efficiently if it used these quantum effects. This speculation proved justified when Peter Shor described a polynomial time quantum algorithm for factoring integers. In quantumsystems, the computational space increases exponentially with the size of the system which enables exponential parallelism. This parallelism could lead to exponentially faster quantum algorithms than possible classically. The catch is that accessing the results, which requires measurement, proves tricky and requires new non-traditional programming techniques. The aim of this paper is to guide computer scientists through the barriers that separate quantum computing from conventional computing. We introduce basic principles of quantum mechanics to explain where the power of quantum computers comes from and why it is difficult to harness. We describe quantum cryptography, teleportation, and dense coding. Various approaches to exploiting the power of quantum parallelism are explained. We conclude with a discussion of quantum error correction.
Wave-scattering method for waveguide-microcavity coupling

The coupling between a waveguide and a dielectric cavity is an important issue in many studies. Time differential coupling-mode theory is commonly used but invalid when the coupling is too strong due to the restriction of perturbation method. Although the finite-element method (FEM) is an accurate numerical method, in any case, it often requires extensive computing resources and costs too much time. In our work, a universal method is introduced to solve the coupling question in the two-dimensional waveguide–microcavity system. Through considering the field as a waveform which scatters between cavity and waveguide back and forth, the practical field can be obtained by summing these scattering waves. As a result, transmission spectrum, resonance frequency shift of a high-Q cavity mode, and mode weight inside the cavity are acquired. Furthermore, we have also analyzed the case under the strong coupling condition, in which the spectrum manifests a non-Lorentzian shape. Meanwhile, the field distribution and mode weight analysis show that multiple cavity modes are excited simultaneously. In terms of computational efficiency, our method is two orders of magnitude faster than FEM.
Comptes Rendus Mécanique

We present a (partial) historical summary of the mathematical analysis of ﬁnite di ﬀ erence and ﬁnite volume methods, paying special attention to the Lax–Richtmyer and Lax–Wendro ﬀ theorems. We then state a Lax–Wendro ﬀ consistency result for convection operators on staggered grids (often used in ﬂuid ﬂow simulations), which illustrates a recent generalization of the ﬂux consistency notion designed to cope with general discrete functions
Dynamic Sparse Training for Deep Reinforcement Learning

Deep reinforcement learning has achieved signiﬁcant success in many decision-making tasks in various ﬁelds. However, it requires a large training time of dense neural networks to obtain a good performance. This hinders its applicability on low-resource devices where memory and computation are strictly constrained. In a step towards enabling deep reinforcement learning agents to be applied to low-resource devices, in this work, we propose for the ﬁrst time to dynamically train deep reinforcement learning agents with sparse neural networks from scratch. We adopt the evolution principles of dynamic sparse training in the reinforcement learning paradigm and introduce a training algorithm that optimizes the sparse topology and the weight values jointly to dynamically ﬁt the incoming data. Our approach is easy to be integrated into existing deep reinforcement learning algorithms and has many favorable advantages. First, it allows for signiﬁcant compression of the network size which reduces the memory and computation costs substantially. This would accelerate not only the agent inference but also its training process. Second, it speeds up the agent learning process and allows for reducing the number of required training steps. Third, it can achieve higher performance than training the dense counterpart network. We evaluate our approach on OpenAI gym continuous control tasks 1 . The experimental results show the effectiveness of our approach in achieving higher performance than one of the state-of-art baselines with a 50% reduction in the network size and ﬂoating-point operations (FLOPs). Moreover, our proposed approach can reach the same performance achieved by the dense network with a 40-50% reduction in the number of training steps.
OBSERVABLE MARKOV DECISION PROCESSES WITH CONTINUOUS OBSERVATIONS FOR DIALOGUE MANAGEMENT

This work shows how a spoken dialogue system can be represent ed as a Partially Observable Markov Decision Process (POMDP) with com p site observations consisting of discrete elements representing dial ogue acts and continuous components representing confidence scores. Using a test bed simulated dialogue management problem and recently developed optimisat ion techniques, we demonstrate that this continuous POMDP can outperform trad itional approaches in which confidence score is tracked discretely. Further, we present a method for automatically improving handcrafted dialogue manager s by incorporating POMDP belief state monitoring, including confidence score i nformation. Experiments on the testbed system show significant improvements f or several example handcrafted dialogue managers across a range of operating c onditions. 192 RECENT TRENDS IN DISCOURSE AND DIALOGUE
Reinforcement Learning with Limited Reinforcement : Using Bayes Risk for Active Learning in POMDPs Finale

Acting in domains where an agent must plan several steps ahea d to achieve a goal can be a challenging task, especially if the agent’s se n ors provide only noisy or partial information. In this setting, Partially Ob servable Markov Decision Processes (POMDPs) provide a planning framework that o ptimally trades between actions that contribute to the agent’s knowledge an d actions that increase the agent’s immediate reward. However, the task of specifyi ng the POMDP’s parameters is often onerous. In particular, setting the immed iat rewards to achieve a desired balance between information-gathering and actin g is often not intuitive. In this work, we propose an approximation based on minimizin g the immediate Bayes risk for choosing actions when transition, observ ation, and reward models are uncertain. The Bayes risk criterion avoids the compu tational intractability of solving a POMDP a with multi-dimensional continuous stat e space; we show it performs well in a variety of problems. We use policy queries —in which we ask an expert for the correct action—to infer the consequences o f a potential pitfall without experiencing its effects. More important for human -robot interaction settings, policy queries allow the agent to learn the reward mod el without the reward values ever being specified.
Partially Observable Markov Decision Process Modelling for Assessing Hierarchies: Supplemental Document

〈c0, 1〉 , 〈c0, 0〉 , 〈c1, 1〉 , 〈c1, 0〉 , 〈c2, 1〉 , 〈c2, 0〉 , 〈∅, 1〉 , 〈∅, 0〉 . The POMDP for this simple tree yields belief states bc0 , bc1 , bc2 when the bot is at the corresponding node, and the trivial belief states at the fully observed terminal states 〈∅, 1〉 and 〈∅, 0〉. The bot moves using the guidance function values η̃ , η(c0, c1) and η(c0, c2) = 1− η̃ to determine the next node when the action ad is selectedguid The set of reachable belief states is represented in an AND-OR tree in Fig. 1 (see a similar figure in (Ross et al., 2008)). In this figure, an action must be chosen at an OR node, the choice of which leads to the set belief states, over all possible observations, that must be considered at the AND nodes. Expected rewards, R(b, a) are represented on the arcs from ORto AND-nodes, while the transition probabilities p(o | b, a) are represented on the arcs from ANDto OR-nodes. Working from the leaf nodes back to the root, we can read from the tree that action as at node c0, would lead to an expected reward of
Application of unsupervised nearest-neighbor density-based approaches to sequential dimensionality reduction and clustering of hyperspectral images

In this communication, we address the problem of unsupervised dimensionality reduction (DR) for hyperspectral images (HSIs), using nearest-neighbor density-based (NN-DB) approaches. Dimensionality reduction is an important tool in the HSI processing chain, aimed at reducing the high redundancy among the HSI spectral bands, while preserving the maximum amount of relevant information for further processing. Basically, the idea is to formalize DR as the process of partitioning the spectral bands into coherent band sets. Two DR schemes can be set up directly, one based on band selection, and the other one based on band averaging. Another scheme is proposed here, based on compact band averaging. Experiments are conducted with hyperspectral images composed of an AISA Eagle HSI issued from our acquisition platform, and the AVIRIS Salinas HSI. We evaluate the efficiency of the reduced HSIs for final classification results under the three schemes, and compare them to the classification results without reduction. We show that despite a high dimensionality reduction (< 8% of the bands left), the clustering results provided by NN-DB methods remain comparable to the ones obtained without DR, especially for GWENN in the band averaging case. We also compare the classification results obtained after applying other unsupervised or semi-supervised DR schemes, based either on band selection or band averaging, and show the superiority of the proposed DR scheme.
Environmental changes, climate and anthropogenic impact in south-east Tunisia during the last 8 kyr

Abstract. Pollen and clay mineralogical analyses of a Holocene sequence from Sebkha Boujmel (southern Tunisia) trace the climatic and environmental dynamics in the lower arid bioclimatic zone over the last 8000 years. During the mid- to late Holocene transition, between ca. 8 and 3 ka BP, a succession of five wet–dry oscillations is recorded. An intense arid event occurs between ca. 5.7 and 4.6 ka BP. This episode marks the onset of a long-term aridification trend with a progressive retreat of Mediterranean woody xerophytic vegetation and of grass steppes. It ends with the establishment of pre-desert ecosystems around 3 ka BP. The millennial-scale climate change recorded in the data from Sebkha Boujmel is consistent with records from the south and east Mediterranean, as well as with climatic records from the desert region for the end of the African Humid Period (AHP). Eight centennial climatic events are recorded at Sebkha Boujmel and these are contemporary with those recorded in the Mediterranean and in the Sahara. They indicate a clear coupling between the southern Mediterranean and the Sahara before 3 ka BP. The event at 4.2 ka BP is not evidenced and the link between events recorded in Sebkha Boujmel and the North Atlantic cooling events is clearer from ca. 3 ka BP onwards. These variations indicate the importance of climatic determinism in the structuring of landscapes, with the establishment of the arid climatic conditions of the late Holocene. It is only from ca. 3 ka BP onwards that the dynamic of plant associations is modified by both human activity and climatic variability. The climatic episodes identified during the historic period indicate strong regionalisation related to the differential impact of the North Atlantic Oscillation (NAO) and the Mediterranean Oscillation (MO) on the Mediterranean Basin. The local human impact on regional ecosystems is recorded in the form of episodes of intensification of pastoral and/or agricultural activities. The development of olive production and of several taxa associated with agriculture attest to increasing sedentism among human populations during classical antiquity. The significant increase in Artemisia (wormwood) between ca. 1.1 and 0.8 ka BP (850–1150 AD) is linked to intensive pastoral activity, associated with heightened interannual and/or seasonal climatic instability. A complete reshaping of the landscape is recorded during the 20th century. The remarkable expansion of the olive tree, and the deterioration of regional ecosystems with the spread of desert species, is linked to recent local socio-economic changes in Tunisia.