Miracle-dz/UniFAR-FTUs huggingface dataset:
Recent Highlights of ATVB

Macrophages are the building blocks of intimal atherosclerotic lesions where they scavenge and accumulate modified lipoproteins, profoundly affecting lesion development and progression through their roles in lipid metabolism and as major sensors and effectors of the innate immune system. Lesional macrophages originate from circulating monocytes, although more recent work indicates that a subpopulation may also derive from dedifferentiated vascular smooth muscle cells. Resident macrophages are found in the heart at steady state and along with newly recruited monocyte-derived macrophages accumulate after myocardial infarction. Macrophage turnover in atherosclerotic lesions and ischemic myocardium is under the control of several biological processes, including monocyte recruitment, macrophage proliferation, and apoptosis, as well as macrophage egress. Moreover, local microenvironment whether in health or disease profoundly affects macrophage phenotype and consequently, both metabolic and immune-related responses. Here, I summarize recent work published in ATVB on the roles of monocytes/macrophages in cardiovascular diseases (CVDs) and review it in light of the most recent advances in this research area.
Myelofibrosis in a 24-Year Old Nigerian

Myelofibrosis (MF) is a chronic myeloproliferative disorder that is uncommon and for which transfusion of red cells is an integral part of management. Treatment with cytoreductive agents, corticosteroids and androgens has so far been unrewarding. Stem cell transplantation may improve treatment outcome. We hereby report MF in a young Nigerian illustrating the diagnostic and therapeutic difficulties encountered in the management of myelofibrosis in resource poor settings.
Recent Highlights of ATVB

Endothelial cells form a subtle monolayer covering the inner surface of the vascular tree. This unique localization allows them to integrate physical and neurohumoral signals from the blood and surrounding tissues to regulate vascular tone, cellular adhesion, inflammation, smooth muscle phenotype and proliferation as well as thromboresistance. Alterations of endothelial atheroprotective functions pave the way for the development of atherosclerotic lesions and their clinical manifestations. Therefore, better understanding the molecular mechanisms shaping up endothelial biology will help design future therapeutic strategies to prevent the development of vascular diseases. Recent publications in Arteriosclerosis, Thrombosis, and Vascular Biology have contributed to understanding several of these important processes.
A New Operator for Egyptian Fractions

This paper introduces a new equation for rewriting two unit fractions to another two unit fractions. This equation is useful for optimizing the elements of an Egyptian Fraction. Parity of the elements of the Egyptian Fractions are also considered. And lastly, the statement that all rational numbers can be represented as Egyptian Fraction is re-established.
An analysis of carrier dynamics in methylammonium lead triiodide perovskite solar cells using cross correlation noise spectroscopy

Using cross-correlation current noise spectroscopy, we have investigated carrier dynamics in methylammonium lead triiodide solar cells. This method provides a space selectivity for devices with planar multi-layered structure, effectively amplifying current noise contributions coming from the most resistive element of the stack. In the studied solar cells, we observe near full-scale shot noise, indicating the dominance of noise generation by a single source, likely the interface between the perovskite and the spiro-OMeTAD hole-transport layer. We argue that the strong 1/f noise term has contributions both from the perovskite layer and interfaces. It displays non-ideal dependence on photocurrent, $S \propto I^{1.4}$ (instead of usual $S \propto I^2$ ), which is likely due to current-induced halide migration. Finally, we observe generation-recombination noise. The relaxation time of this process grows linearly with photocurrent, which allows to attribute this contribution to bimolecular recombination in the perovskite bulk absorption layer. Extrapolating our results, we estimate that at the standard 1 sun illumination the electron-hole recombination time is 5 microseconds.
Joint Computation Offloading, SFC Placement, and Resource Allocation for Multi-Site MEC Systems

Network function Virtualization (NFV) and Mobile Edge Computing (MEC) are promising 5G technologies to support resource-demanding mobile applications. In NFV, one must process the service function chain (SFC) in which a set of network functions must be executed in a specific order. Moreover, the MEC technology enables computation offloading of service requests from mobile users to remote servers to potentially reduce energy consumption and processing delay for the mobile application. This paper considers the optimization of the computation offloading, resource allocation, and SFC placement in the multi-site MEC system. Our design objective is to minimize the weighted normalized energy consumption and computing cost subject to the maximum tolerable delay constraint. To solve the underlying mixed integer and non-linear optimization problem, we employ the decomposition approach where we iteratively optimize the computation offloading, SFC placement and computing resource allocation to obtain an efficient solution. Numerical results show the impacts of different parameters on the system performance and the superior performance of the proposed algorithm compared to benchmarking algorithms.
Hypergraph Simplification: Linking the Path-sum Approach to the ZH-calculus

The ZH-calculus is a complete graphical calculus for linear maps between qubits that admits, for example, a straightforward encoding of hypergraph states and circuits arising from the Toffoli+Hadamard gate set. In this paper, we establish a correspondence between the ZH-calculus and the path-sum formalism, a technique recently introduced by Amy to verify quantum circuits. In particular, we find a bijection between certain canonical forms of ZH-diagrams and path-sum expressions. We then introduce and prove several new simplification rules for the ZH-calculus, which are in direct correspondence to the simplification rules of the path-sum formalism. The relatively opaque path-sum rules are shown to arise naturally as the convergence of two consequences of the ZH-calculus. The first is the extension of the familiar graph-theoretic simplifications based on local complementation and pivoting to their hypergraph-theoretic analogues: hyper-local complementation and hyper-pivoting. The second is the graphical Fourier transform introduced by Kuijpers et al., which enables effective simplification of ZH-diagrams encoding multi-linear phase polynomials with arbitrary real coefficients.
Cryptocurrency Address Clustering and Labeling

Anonymity is one of the most important qualities of blockchain technology. For example, one can simply create a bitcoin address to send and receive funds without providing KYC to any authority. In general, the real identity behind cryptocurrency addresses is not known, however, some addresses can be clustered according to their ownership by analyzing behavioral patterns, allowing those with known attribution to be assigned labels. These labels may be further used for legal and compliance purposes to assist in law enforcement investigations. In this document, we discuss our methodology behind assigning attribution labels to cryptocurrency addresses.
Deriving AC OPF Solutions via Proximal Policy Optimization for Secure and Economic Grid Operation

Optimal power flow (OPF) is a very fundamental but vital optimization problem in the power system, which aims at solving a specific objective function (ex.: generator costs) while maintaining the system in the stable and safe operations. In this paper, we adopted the start-of-the-art artificial intelligence (AI) techniques to train an agent aiming at solving the AC OPF problem, where the nonlinear power balance equations are considered. The modified IEEE-14 bus system were utilized to validate the proposed approach. The testing results showed a great potential of adopting AI techniques in the power system operations.
THE CHARACTERIZATION OF WEIHRAUCH REDUCIBILITY IN SYSTEMS CONTAINING 
$E-PA^{\omega } + QF-AC^{0,0}$

Abstract We characterize Weihrauch reducibility in $ \operatorname {\mathrm {E-PA^{\omega }}} + \operatorname {\mathrm {QF-AC^{0,0}}}$ and all systems containing it by the provability in a linear variant of the same calculus using modifications of Gödel’s Dialectica interpretation that incorporate ideas from linear logic, nonstandard arithmetic, higher-order computability, and phase semantics.
Data-Driven Optimization of Personalized Reserve Prices

We study the problem of computing data-driven personalized reserve prices in eager second price auctions without having any assumption on valuation distributions. Here, the input is a data-set that contains the submitted bids of $n$ buyers in a set of auctions and the problem is to return personalized reserve prices $\textbf r$ that maximize the revenue earned on these auctions by running eager second price auctions with reserve $\textbf r$. For this problem, which is known to be NP-hard, we present a novel LP formulation and a rounding procedure which achieves a $(1+2(\sqrt{2}-1)e^{\sqrt{2}-2})^{-1} \approx 0.684$-approximation. This improves over the $\frac{1}{2}$-approximation algorithm due to Roughgarden and Wang. We show that our analysis is tight for this rounding procedure. We also bound the integrality gap of the LP, which shows that it is impossible to design an algorithm that yields an approximation factor larger than $0.828$ with respect to this LP.
Tunable interlayer magnetism and band topology in van der Waals heterostructures of 
MnBi2Te4
-family materials

Manipulating the interlayer magnetic coupling in van der Waals magnetic materials and heterostructures is the key to tailoring their magnetic and electronic properties for various electronic applications and fundamental studies in condensed matter physics. By utilizing the MnBi2Te4-family compounds and their heterostructures as a model system, we systematically studied the dependence of the sign and strength of interlayer magnetic coupling on constituent elements by using first-principles calculations. It was found that the coupling is a long-range superexchange interaction mediated by the chains of p orbitals between the magnetic atoms of neighboring septuple-layers. The interlayer exchange is always antiferromagnetic in the pure compounds, but can be tuned to ferromagnetic in some combinations of heterostructures, dictated by d orbital occupations. Strong interlayer magnetic coupling can be realized if the medial p electrons are delocalized and the d bands of magnetic atoms are near the Fermi level. The knowledge on the interlayer coupling mechanism enables us to engineer magnetic and topological properties of MnBi2Te4-family materials as well as many other insulating van der Waals magnetic materials and heterostructures.
Efficient Black-box Optimization of Adversarial Windows Malware with Constrained Manipulations

Windows malware detectors based on machine learning are vulnerable to adversarial examples, even if the attacker is only given black-box access to the model. The main drawback of these attacks is that they require executing the adversarial malware sample in a sandbox at each iteration of its optimization process, to ensure that its intrusive functionality is preserved. In this paper, we present a novel black-box attack that leverages a set of semantics-preserving, constrained malware manipulations to overcome this computationally-demanding validation step. Our attack is formalized as a constrained minimization problem which also enables optimizing the trade-off between the probability of evading detection and the size of the injected adversarial payload. We investigate this trade-off empirically, on two popular static Windows malware detectors, and show that our black-box attack is able to bypass them with only few iterations and changes. We also evaluate whether our attack transfers to other commercial antivirus solutions, and surprisingly find that it can increase the probability of evading some of them. We conclude by discussing the limitations of our approach, and its possible future extensions to target malware classifiers based on dynamic analysis.
Named Entities in Medical Case Reports: Corpus and Experiments

We present a new corpus comprising annotations of medical entities in case reports, originating from PubMed Central’s open access library. In the case reports, we annotate cases, conditions, findings, factors and negation modifiers. Moreover, where applicable, we annotate relations between these entities. As such, this is the first corpus of this kind made available to the scientific community in English. It enables the initial investigation of automatic information extraction from case reports through tasks like Named Entity Recognition, Relation Extraction and (sentence/paragraph) relevance detection. Additionally, we present four strong baseline systems for the detection of medical entities made available through the annotated dataset.
Investigating Language Impact in Bilingual Approaches for Computational Language Documentation

For endangered languages, data collection campaigns have to accommodate the challenge that many of them are from oral tradition, and producing transcriptions is costly. Therefore, it is fundamental to translate them into a widely spoken language to ensure interpretability of the recordings. In this paper we investigate how the choice of translation language affects the posterior documentation work and potential automatic approaches which will work on top of the produced bilingual corpus. For answering this question, we use the MaSS multilingual speech corpus (Boito et al., 2020) for creating 56 bilingual pairs that we apply to the task of low-resource unsupervised word segmentation and alignment. Our results highlight that the choice of language for translation influences the word segmentation performance, and that different lexicons are learned by using different aligned translations. Lastly, this paper proposes a hybrid approach for bilingual word segmentation, combining boundary clues extracted from a non-parametric Bayesian model (Goldwater et al., 2009a) with the attentional word segmentation neural model from Godard et al. (2018). Our results suggest that incorporating these clues into the neural models’ input representation increases their translation and alignment quality, specially for challenging language pairs.
Re-purposing heterogeneous generative ensembles with evolutionary computation

Generative Adversarial Networks (GANs) are popular tools for generative modeling. The dynamics of their adversarial learning give rise to convergence pathologies during training such as mode and discriminator collapse. In machine learning, ensembles of predictors demonstrate better results than a single predictor for many tasks. In this study, we apply two evolutionary algorithms (EAs) to create ensembles to re-purpose generative models, i.e., given a set of heterogeneous generators that were optimized for one objective (e.g., minimize Fréchet Inception Distance), create ensembles of them for optimizing a different objective (e.g., maximize the diversity of the generated samples). The first method is restricted by the exact size of the ensemble and the second method only restricts the upper bound of the ensemble size. Experimental analysis on the MNIST image benchmark demonstrates that both EA ensembles creation methods can re-purpose the models, without reducing their original functionality. The EA-based demonstrate significantly better performance compared to other heuristic-based methods. When comparing both evolutionary, the one with only an upper size bound on the ensemble size is the best.
Optimal Periodic Dividend Strategies for Spectrally Positive Lévy Risk Processes With Fixed Transaction Costs

Maximising dividends is one classical stability criterion in actuarial risk theory. Motivated by the fact that dividends are paid periodically in real life, $\textit{periodic}$ dividend strategies were recently introduced (Albrecher, Gerber and Shiu, 2011). In this paper, we incorporate fixed transaction costs into the model and study the optimal periodic dividend strategy with fixed transaction costs for spectrally negative L\'evy processes. The value function of a periodic $(b_u,b_l)$ strategy is calculated by means of exiting identities and It\^o's excusion when the surplus process is of unbounded variation. We show that a sufficient condition for optimality is that the L\'evy measure admits a density which is completely monotonic. Under such assumptions, a periodic $(b_u,b_l)$ strategy is confirmed to be optimal. Results are illustrated.
Agent57: Outperforming the Atari Human Benchmark

Atari games have been a long-standing benchmark in the reinforcement learning (RL) community for the past decade. This benchmark was proposed to test general competency of RL algorithms. Previous work has achieved good average performance by doing outstandingly well on many games of the set, but very poorly in several of the most challenging games. We propose Agent57, the first deep RL agent that outperforms the standard human benchmark on all 57 Atari games. To achieve this result, we train a neural network which parameterizes a family of policies ranging from very exploratory to purely exploitative. We propose an adaptive mechanism to choose which policy to prioritize throughout the training process. Additionally, we utilize a novel parameterization of the architecture that allows for more consistent and stable learning.
Data-Driven Neuromorphic DRAM-based CNN and RNN Accelerators

The energy consumed by running large deep neural networks (DNNs) on hardware accelerators is dominated by the need for lots of fast memory to store both states and weights. This large required memory is currently only economically viable through DRAM. Although DRAM is high-throughput and low-cost memory (costing 20X less than SRAM), its long random access latency is bad for the unpredictable access patterns in spiking neural networks (SNNs). In addition, accessing data from DRAM costs orders of magnitude more energy than doing arithmetic with that data. SNNs are energy-efficient if local memory is available and few spikes are generated. This paper reports on our developments over the last 5 years of convolutional and recurrent deep neural network hardware accelerators that exploit either spatial or temporal sparsity similar to SNNs but achieve SOA throughput, power efficiency and latency even with the use of DRAM for the required storage of the weights and states of large DNNs.
Planck intermediate results. LVI. Detection of the CMB dipole through modulation of the thermal Sunyaev-Zeldovich effect: Eppur si muove II.

The largest temperature anisotropy in the cosmic microwave background (CMB) is the dipole, which has been measured with increasing accuracy for more than three decades, particularly with the Planck satellite. The simplest interpretation of the dipole is that it is due to our motion with respect to the rest frame of the CMB. Since current CMB experiments infer temperature anisotropies from angular intensity variations, the dipole modulates the temperature anisotropies with the same frequency dependence as the thermal Sunyaev-Zeldovich (tSZ) effect. We present the first, and significant, detection of this signal in the tSZ maps and find that it is consistent with direct measurements of the CMB dipole, as expected. The signal contributes power in the tSZ maps, which is modulated in a quadrupolar pattern, and we estimate its contribution to the tSZ bispectrum, noting that it contributes negligible noise to the bispectrum at relevant scales.
Semantically Multi-Modal Image Synthesis

In this paper, we focus on semantically multi-modal image synthesis (SMIS) task, namely, generating multi-modal images at the semantic level. Previous work seeks to use multiple class-specific generators, constraining its usage in datasets with a small number of classes. We instead propose a novel Group Decreasing Network (GroupDNet) that leverages group convolutions in the generator and progressively decreases the group numbers of the convolutions in the decoder. Consequently, GroupDNet is armed with much more controllability on translating semantic labels to natural images and has plausible high-quality yields for datasets with many classes. Experiments on several challenging datasets demonstrate the superiority of GroupDNet on performing the SMIS task. We also show that GroupDNet is capable of performing a wide range of interesting synthesis applications. Codes and models are available at: https://github.com/Seanseattle/SMIS.
Elastic Coupled Co-clustering for Single-Cell Genomic Data

The recent advances in single-cell technologies have enabled us to profile genomic features at unprecedented resolution and datasets from multiple domains are available, including datasets that profile different types of genomic features and datasets that profile the same type of genomic features across different species. These datasets typically have different powers in identifying the unknown cell types through clustering, and data integration can potentially lead to a better performance of clustering algorithms. In this work, we formulate the problem in an unsupervised transfer learning framework, which utilizes knowledge learned from auxiliary dataset to improve the clustering performance of target dataset. The degree of shared information among the target and auxiliary datasets can vary, and their distributions can also be different. To address these challenges, we propose an elastic coupled co-clustering based transfer learning algorithm, by elastically propagating clustering knowledge obtained from the auxiliary dataset to the target dataset. Implementation on single-cell genomic datasets shows that our algorithm greatly improves clustering performance over the traditional learning algorithms. The source code and data sets are available at this https URL.
SmartCert: Redesigning Digital Certificates with Smart Contracts

The Transport Layer Security (TLS) protocol and its public-key infrastructure (PKI) are widely used in the Internet to achieve secure communication. Validating domain ownership by trusted certification authorities (CAs) is a critical step in issuing digital certificates, but unfortunately, this process provides a poor security level. In this work, we present SmartCert, a novel approach based on smart contracts to improve digital certificates. A certificate in SmartCert conveys detailed information about its validation state which is constantly changing but only with respect to the specified smart contract code and individual domain policies. CAs issuing and updating certificates are kept accountable and their actions are transparent and monitored by the code. We present the implementation and evaluation of SmartCert, and discuss its deployability.