Miracle-dz/UniFAR-FTUs huggingface dataset:
A G D I Working Paper

This study improves the African Regional Integration Index (ARII) proposed by the African Union, the African Development Bank and the United Nations Economic Commission for Africa by providing a theoretical framework and addressing shortcomings related to weighting and aggregation of the indicator. This paper measures monetary integration in the eight African Regional Economic Communities (RECs) by constructing an Index of African Monetary Integration (IAMI). It proposes an Optimal Currency Area as theoretical framework and uses a panel approach to appreciate the dynamics of the index over different periods of time. The findings show that: (i) inflation and finance (trade and mobility) present the highest (lowest) score while ECOWAS is (EAC and IGAD are) the highest (least) performing. (ii) Surprisingly, in most RECs, the highest contributors to wealth creation are not the top performers in regional monetary integration. (iii) The RECs in Africa are characterized by a stable monetary integration which is different from the gradual process usually observed in monetary integration because with the exception of the EAC and UMA, the dynamics of IAMI show a steady trend in the overall index across time. Policy implications are discussed.
Generalisation process for Top100: research in generalisation brought to fruition

IGN (the French National Mapping Agency) is working on a new production line to derive from a reference database a new 1:100.000 scaled map. The generalisation process designed has taken advantages from the last research in generalisation available with the multi-agent systems and elastic beams displacement algorithms. This paper reports on choices made by the Carto2001 project to design a production line using automated cartographic generalisation.
Chondroid chordoma of the sacrococcygeal region.

: This is a unique case of chondroid chordoma arising in the sacrococcygeal area of an asymptomatic man. S100 protein was detected in both chondroid and chordoid tissues; epithelial membrane antigen (EMA) and cytokeratin were present in abundant amounts in the cytoplasm of chordoma cells but not in chondroid cells. The presence of cytokeratin and EMA in chordoma implies the epithelial nature of the tumor and is extremely helpful as a differential marker for chordoma. I suggest that EMA, cytokeratin, and S100 protein should be used conjunctively to provide a resolution for difficult diagnostic problems when dealing with chordomas, especially with their variants, such as chondroid chordomas, which often cause further confusion.
Gravitational waves from primordial magnetic fields via photon-graviton conversion

We explore a novel process in the early Universe in which thermalized photons are converted into gravitons in the presence of strong primordial magnetic fields. It is found that the frequency of generated gravitational waves (GWs) is typically of order of GHz and their amplitude can be up to $ \Omega_\mathrm{GW}h^2 \sim 10^{-10}$. If detected with future developments of the technology to explore this frequency region, the produced stochastic GW background enables us to know when and how strong the primordial magnetic fields are generated. From the peak frequency of the GWs, we can also probe the number of relativistic degrees of freedom at that time.
Constraining Temporal Relationship for Action Localization

Recently, temporal action localization (TAL), i.e., finding specific action segments in untrimmed videos, has attracted increasing attentions of the computer vision community. State-of-the-art solutions for TAL involves predicting three values at each time point, corresponding to the probabilities that the action starts, continues and ends, and post-processing these curves for the final localization. This paper delves deep into this mechanism, and argues that existing approaches mostly ignored the potential relationship of these curves, and results in low quality of action proposals. To alleviate this problem, we add extra constraints to these curves, e.g., the probability of ''action continues'' should be relatively high between probability peaks of ''action starts'' and ''action ends'', so that the entire framework is aware of these latent constraints during an end-to-end optimization process. Experiments are performed on two popular TAL datasets, THUMOS14 and ActivityNet1.3. Our approach clearly outperforms the baseline both quantitatively (in terms of the AR@AN and mAP) and qualitatively (the curves in the testing stage become much smoother). In particular, when we build our constraints beyond TSA-Net and PGCN, we achieve the state-of-the-art performance especially at strict high IoU settings. The code will be available.
Schemes for Privacy Data Destruction in a NAND Flash Memory

We propose schemes for efficiently destroying privacy data in a NAND flash memory. Generally, even if privacy data is erased from NAND flash memories, there is a high probability that the data will remain in an invalid block. This is a management problem arising from characteristics of a program operation and an erase operation of NAND flash memories. When updating pages or performing a garbage collection, there is a problem that valid data remains in at least one unmapped memory block. Is it possible to impose an obligation to delete privacy data from an existing NAND flash memory? This paper is the answer to this question. We propose a partial overwriting scheme, an SLC programming scheme, and a deletion duty pulse application scheme for invalid pages to effectively address privacy data destruction issues caused by the remaining data. Such privacy data destruction schemes basically utilize at least one state in which data can be written to programmed cells based on a multi-level cell program operation. Our privacy data destruction schemes have advantages in terms of block management as compared with conventional erasing schemes, and are significantly economical in terms of time and cost. The proposed privacy data destruction schemes may be easily applied to many storage devices and data centers using a NAND flash memory.
Log-concavity results for a biparametric and an elliptic extension of the q-binomial coefficients

We establish discrete and continuous log-concavity results for a biparametric extension of the $q$-numbers and of the $q$-binomial coefficients. By using classical results for the Jacobi theta function we are able to lift some of our log-concavity results to the elliptic setting. One of our main ingredients is a putatively new lemma involving a multiplicative analogue of Turan's inequality.
Model based fractional order controller design for process plants satisfying desired robustness criteria

This paper contributes to the design of a fractional order (FO) internal model controller (IMC) for a first order plus time delay (FOPTD) process model to satisfy a given set of desired robustness specifications in terms of gain margin (Am) and phase margin (Pm). The highlight of the design is the choice of a fractional order (FO) filter in the IMC structure which has two parameters (lambda and beta) to tune as compared to only one tuning parameter (lambda) for traditionally used integer order (IO) filter. These parameters are evaluated for the controller, so that Am and Pm can be chosen independently. A new methodology is proposed to find a complete solution for controller parameters, the methodology also gives the system gain cross-over frequency (wg) and phase cross-over frequency (wp). Moreover, the solution is found without any approximation of the delay term appearing in the controller.
The Teukolsky equations in Kerr-Newman spacetime

The equations governing the gravitational and electromagnetic perturbations of Kerr-Newman spacetime are here derived. They generalize the Teukolsky equation in Kerr and the Teukolsky-like system of equations in Reissner-Nordstrom spacetime. Through their Chandrasekhar transformation, we obtain a system of physical-space coupled wave equations. In particular, the physical-space analysis of this system will solve the issue of the "apparent indissolubility of the coupling between the spin-1 and spin-2 fields in the perturbed spacetime", as put by Chandrasekhar. The derivation of the equations here obtained makes use of the formalism introduced in a recent work by Giorgi-Klainerman-Szeftel for Kerr, and represents the first step towards an analytical proof of the stability of the Kerr-Newman black hole.
D2C 2.0: Decoupled Data-Based Approach for Learning to Control Stochastic Nonlinear Systems via Model-Free ILQR

In this paper, we propose a structured linear parameterization of a feedback policy to solve the model-free stochastic optimal control problem. This parametrization is corroborated by a decoupling principle that is shown to be near-optimal under a small noise assumption, both in theory and by empirical analyses. Further, we incorporate a model-free version of the Iterative Linear Quadratic Regulator (ILQR) in a sample-efficient manner into our framework. Simulations on systems over a range of complexities reveal that the resulting algorithm is able to harness the superior second-order convergence properties of ILQR. As a result, it is fast and is scalable to a wide variety of higher dimensional systems. Comparisons are made with a state-of-the-art reinforcement learning algorithm, the Deep Deterministic Policy Gradient (DDPG) technique, in order to demonstrate the significant merits of our approach in terms of training-efficiency.
Improved Optimistic Algorithms for Logistic Bandits

The generalized linear bandit framework has attracted a lot of attention in recent years by extending the well-understood linear setting and allowing to model richer reward structures. It notably covers the logistic model, widely used when rewards are binary. For logistic bandits, the frequentist regret guarantees of existing algorithms are $\tilde{\mathcal{O}}(\kappa \sqrt{T})$, where $\kappa$ is a problem-dependent constant. Unfortunately, $\kappa$ can be arbitrarily large as it scales exponentially with the size of the decision set. This may lead to significantly loose regret bounds and poor empirical performance. In this work, we study the logistic bandit with a focus on the prohibitive dependencies introduced by $\kappa$. We propose a new optimistic algorithm based on a finer examination of the non-linearities of the reward function. We show that it enjoys a $\tilde{\mathcal{O}}(\sqrt{T})$ regret with no dependency in $\kappa$, but for a second order term. Our analysis is based on a new tail-inequality for self-normalized martingales, of independent interest.
Quasi-majority Functional Voting on Expander Graphs

Consider a distributed graph where each vertex holds one of two distinct opinions. In this paper, we are interested in synchronous voting processes where each vertex updates its opinion according to a predefined common local updating rule. For example, each vertex adopts the majority opinion among 1) itself and two randomly picked neighbors in best-of-two or 2) three randomly picked neighbors in best-of-three. Previous works intensively studied specific rules including best-of-two and best-of-three individually. In this paper, we generalize and extend previous works of best-of-two and best-of-three on expander graphs by proposing a new model, quasi-majority functional voting. This new model contains best-of-two and best-of-three as special cases. We show that, on expander graphs with sufficiently large initial bias, any quasi-majority functional voting reaches consensus within $O(\log n)$ steps with high probability. Moreover, we show that, for any initial opinion configuration, any quasi-majority functional voting on expander graphs with higher expansion (e.g., Erdős-Renyi graph $G(n,p)$ with $p=\Omega(1/\sqrt{n})$) reaches consensus within $O(\log n)$ with high probability. Furthermore, we show that the consensus time is $O(\log n/\log k)$ of best-of-$(2k+1)$ for $k=o(n/\log n)$.
ELM-Based Superimposed CSI Feedback for FDD Massive MIMO System

In frequency-division duplexing (FDD) massive multiple-input multiple-output (MIMO), deep learning (DL)-based superimposed channel state information (CSI) feedback has presented promising performance. However, it is still facing many challenges, such as the high complexity of parameter tuning, large number of training parameters, and long training time, etc. To overcome these challenges, an extreme learning machine (ELM)-based superimposed CSI feedback is proposed in this paper, in which the downlink CSI is spread and then superimposed on uplink user data sequence (UL-US) to feed back to base station (BS). At BS, an ELM-based network is constructed to recover both downlink CSI and UL-US. In the constructed ELM-based network, we employ the simplified versions of ELM-based subnets to replace the subnets of DL-based superimposed feedback, yielding less training parameters. Besides, the input weights and hidden biases of each ELM-based subnet are loaded from the same matrix by using its full or partial entries, which significantly reduces the memory requirement. With similar or better recovery performances of downlink CSI and UL-US, the proposed ELM-based method has less training parameters, storage space, offline training and online running time than those of DL-based superimposed CSI feedback.
Coreset-based Strategies for Robust Center-type Problems

Given a dataset $V$ of points from some metric space, the popular $k$-center problem requires to identify a subset of $k$ points (centers) in $V$ minimizing the maximum distance of any point of $V$ from its closest center. The \emph{robust} formulation of the problem features a further parameter $z$ and allows up to $z$ points of $V$ (outliers) to be disregarded when computing the maximum distance from the centers. In this paper, we focus on two important constrained variants of the robust $k$-center problem, namely, the Robust Matroid Center (RMC) problem, where the set of returned centers are constrained to be an independent set of a matroid of rank $k$ built on $V$, and the Robust Knapsack Center (RKC) problem, where each element $i\in V$ is given a positive weight $w_i 0$, the algorithms return solutions featuring a $(3+\epsilon)$-approximation ratio, which is a mere additive term $\epsilon$ away from the 3-approximations achievable by the best known polynomial-time sequential algorithms for the two problems. Moreover, the algorithms obliviously adapt to the intrinsic complexity of the dataset, captured by its doubling dimension $D$. For wide ranges of the parameters $k,z,\epsilon, D$, we obtain a sequential algorithm with running time linear in $|V|$, and MapReduce/Streaming algorithms with few rounds/passes and substantially sublinear local/working memory.
Statistical mechanics of a chiral active fluid

Statistical mechanics provides the foundation for describing complex materials using only a few thermodynamic variables. No such framework currently exists far from equilibrium. In this Letter, we demonstrate how thermodynamics emerges far from equilibrium, using fluids composed of active spinners as a case study. Activity gives rise to a single effective temperature that parameterizes both the equation of state and the emergent Boltzmann statistics. The same effective temperature, renormalized by velocity correlations, controls the linear response through canonical Green-Kubo relations for both the familiar shear viscosity and the odd (or Hall) viscosity observed in chiral fluids. The full frequency dependence of these viscosities can be derived analytically by modeling the active-spinner fluid as a random walker undergoing cyclotron motion in shear-stress space. More generally, we provide a first-principles derivation of the Green-Kubo relations valid for a broader class of fluids far from equilibrium. Besides advancing non-equilibrium thermodynamics, our work demonstrates in silico a non-invasive microrheology of active fluids.
A New Clustering neural network for Chinese word segmentation

In this article I proposed a new model to achieve Chinese word segmentation(CWS),which may have the potentiality to apply in other domains in the this http URL is a new thinking in CWS compared to previous works,to consider it as a clustering problem instead of a labeling this http URL this model,LSTM and self attention structures are used to collect context also sentence level features in every layer,and after several layers,a clustering model is applied to split characters into groups,which are the final segmentation results.I call this model CLNN.This algorithm can reach 98 percent of F score (without OOV words) and 85 percent to 95 percent F score (with OOV words) in training data sets.Error analyses shows that OOV words will greatly reduce performances,which needs a deeper research in the future.
A note on maximizing the difference between a monotone submodular function and a linear function

Motivated by team formation applications, we study discrete optimization problems of the form $\max_{S\in\mathcal{S}}\left(f(S)-w(S)\right)$, where $f:2^{V}\to\mathbb{R_{+}}$ is a non-negative monotone submodular function, $w:2^{V}\to\mathbb{R}_{+}$ is a non-negative linear function, and $\mathcal{S}\subseteq2^{V}$. We give very simple and efficient algorithms for classical constraints, such as cardinality and matroid, that work in a variety of models, including the offline, online, and streaming. Our algorithms use a very simple scaling approach: we pick an absolute constant $c\geq1$ and optimize the function $f(S)-c\cdot w(S)$ using a black-box application of standard algorithms, such as the classical Greedy algorithm and the single-threshold Greedy algorithm. These algorithms are based on recent works that use (time varying) scaling combined with classical algorithms such as the discrete and continuous Greedy algorithms (Feldman, WADS'19; Harshaw \emph{et al.}, ICML'19).
A Practical Phase Field Method for an Elliptic Surface PDE

We consider a diffuse interface approach for solving an elliptic PDE on a given closed hypersurface. The method is based on a (bulk) finite element scheme employing numerical quadrature for the phase field function and hence is very easy to implement compared to other approaches. We estimate the error in natural norms in terms of the spatial grid size, the interface width and the order of the underlying quadrature rule. Numerical test calculations are presented, which confirm the form of the error bounds.
How Impersonators Exploit Instagram to Generate Fake Engagement?

Impersonators on Online Social Networks such as Instagram are playing an important role in the propagation of the content. These entities are the type of nefarious fake accounts that intend to disguise a legitimate account by making similar profiles. In addition to having impersonated profiles, we observed a considerable engagement from these entities to the published posts of verified accounts. Toward that end, we concentrate on the engagement of impersonators in terms of active and passive engagements which is studied in three major communities including “Politician”, “News agency and “Sports star” on Instagram. Inside each community, four verified accounts have been selected. Based on implemented approach in our previous studies [1], we have collected 4. 8K comments, and 2. 6K likes across 566 posts created from 3. 8K impersonators during 7 months. Our study shed light into this interesting phenomena and provides a surprising observation that can help us to understand better how impersonators engaging themselves inside Instagram in terms of writing Comments and leaving Likes.
Artificial Intelligent Ethics in the Digital Era: an Engineering Ethical Framework Proposal

Nowadays technology is being adopted on every aspect of our lives and it is one of most important transformation driver in industry. Moreover, many of the systems and digital services that we use daily rely on artificial intelligent technology capable of modeling social or individual behaviors that in turns also modify personal decisions and actions. In this paper, we briefly discuss, from a technological perspective, a number of critical issues including the purpose of promoting trust and ensure social benefit by the proper use of Artificial Intelligent Systems. To achieve this goal we propose a generic ethical technological framework as a first attempt to define a common context towards developing real engineering ethical by design. We hope that this initial proposal to be useful for early adopters and especially for standardization teams.
Failout: Achieving Failure-Resilient Inference in Distributed Neural Networks

When a neural network is partitioned and distributed across physical nodes, failure of physical nodes causes the failure of the neural units that are placed on those nodes, which results in a significant performance drop. Current approaches focus on resiliency of training in distributed neural networks. However, resiliency of inference in distributed neural networks is less explored. We introduce ResiliNet, a scheme for making inference in distributed neural networks resilient to physical node failures. ResiliNet combines two concepts to provide resiliency: skip connection in residual neural networks, and a novel technique called failout, which is introduced in this paper. Failout simulates physical node failure conditions during training using dropout, and is specifically designed to improve the resiliency of distributed neural networks. The results of the experiments and ablation studies using three datasets confirm the ability of ResiliNet to provide inference resiliency for distributed neural networks.
Hierarchical Classification of Enzyme Promiscuity Using Positive, Unlabeled, and Hard Negative Examples

Despite significant progress in sequencing technology, there are many cellular enzymatic activities that remain unknown. We develop a new method, referred to as SUNDRY (Similarity-weighting for UNlabeled Data in a Residual HierarchY), for training enzyme-specific predictors that take as input a query substrate molecule and return whether the enzyme would act on that substrate or not. When addressing this enzyme promiscuity prediction problem, a major challenge is the lack of abundant labeled data, especially the shortage of labeled data for negative cases (enzyme-substrate pairs where the enzyme does not act to transform the substrate to a product molecule). To overcome this issue, our proposed method can learn to classify a target enzyme by sharing information from related enzymes via known tree hierarchies. Our method can also incorporate three types of data: those molecules known to be catalyzed by an enzyme (positive cases), those with unknown relationships (unlabeled cases), and molecules labeled as inhibitors for the enzyme. We refer to inhibitors as hard negative cases because they may be difficult to classify well: they bind to the enzyme, like positive cases, but are not transformed by the enzyme. Our method uses confidence scores derived from structural similarity to treat unlabeled examples as weighted negatives. We compare our proposed hierarchy-aware predictor against a baseline that cannot share information across related enzymes. Using data from the BRENDA database, we show that each of our contributions (hierarchical sharing, per-example confidence weighting of unlabeled data based on molecular similarity, and including inhibitors as hard-negative examples) contributes towards a better characterization of enzyme promiscuity.