Miracle-dz/UniFAR-FTUs huggingface dataset:
Analyzing a Tandem Fluid Queueing Model with Stochastic Capacity and Spillback

The tandem fluid queueing model is a useful tool for performance analysis and control design for a variety of transportation systems. In this article, we study the joint impact of stochastic capacity and spillback on the long-time properties of this model. Our analysis focuses on the system of two fluid queueing links in series. The upstream link has a constant capacity (saturation rate) and an infinite buffer size. The downstream link has a stochastic capacity and a finite buffer size. Queue spillback occurs when the the downstream link is full. We derive a necessary condition and a sufficient condition for the total queue length to be bounded on average. The necessary (resp. sufficient) condition leads to an upper (resp. lower) bound for the throughput of the two-link system. Using our results, we analyze the sensitivity of throughput of the two-link system with respect to the frequency and intensity of capacity disruptions, and to the buffer size. In addition, we discuss how our analysis can be extended to feedback-controlled systems and to networks consisting of merges and splits.
The Power of One Qubit in Machine Learning.

Kernel methods are used extensively in classical machine learning, especially in the field of pattern analysis. In this paper, we propose a kernel-based quantum machine learning algorithm that can be implemented on a near-term, intermediate scale quantum device. Our proposal is based on estimating classically intractable kernel functions, using a restricted quantum model known as deterministic quantum computing with one qubit. Our method provides a framework for studying the role of quantum correlations other than quantum entanglement for machine learning applications.
Induction of hairy roots in Arnica montana L. by Agrobacterium rhizogenes

The induction of hairy roots in Arnica montana L. by Agrobacterium rhizogenes mediated system was established. The frequency of genetic transformation varied from 4.8 to 12% depended on method of infection. The cefotaxime at concentration of 200 mg/l proved to suppress effectively the growth of A. rhizogenes after co-cultivation. Among the three tested nutrient media: Murashige and Skoog (MS), Gamborg’s (B5) and Schenk and Hildebrandt (SH), MS medium was superior for growth and high biomass production of transformed roots compared to other culture media. After culturing for 40 days the fresh weight of clone T4 increased 7.6 fold over the non-transformed roots. The transfer of rol A, rol B and rol C genes into Arnica genome was confirmed by PCR analysis. Established genetic transformation techniques in A. montana efficiently provided and generated a large number of transformed roots — an excellent system for studying gene function and could be used for the production of secondary metabolites synthesized in roots.
Cycles of Sums of Integers

We study the period of the linear map $T:\textbf{Z}_m^n\rightarrow \textbf{Z}_m^n:(a_0,\dots,a_{n-1})\mapsto(a_0+a_1,\dots,a_{n-1}+a_0)$ as a function of $m$ and $n$, where $\textbf{Z}_m$ stands for the ring of integers modulo $m$. This map being a variant of the Ducci map, several known results are adapted in the context of $T$. The main theorem of this paper states that the period modulo $m$ can be deduced from the prime factorization of $m$ and the periods of its prime factors. We give some other interesting properties.
Short GRB 160821B: A Reverse Shock, a Refreshed Shock, and a Well-sampled Kilonova

We report our identification of the optical afterglow and host galaxy of the short-duration gamma-ray burst sGRB 160821B. The spectroscopic redshift of the host is z = 0.162, making it one of the lowest redshift short-duration gamma-ray bursts (sGRBs) identified by Swift. Our intensive follow-up campaign using a range of ground-based facilities as well as Hubble Space Telescope, XMM-Newton, and Swift, shows evidence for a late-time excess of optical and near-infrared emission in addition to a complex afterglow. The afterglow light curve at X-ray frequencies reveals a narrow jet, deg, that is refreshed at >1 day post-burst by a slower outflow with significantly more energy than the initial outflow that produced the main GRB. Observations of the 5 GHz radio afterglow shows a reverse shock into a mildly magnetized shell. The optical and near-infrared excess is fainter than AT2017gfo associated with GW170817, and is well explained by a kilonova with dynamic ejecta mass Mdyn = (1.0 ± 0.6) × 10−3 M⊙ and a secular (post-merger) ejecta mass with Mpm = (1.0 ± 0.6) × 10−2 M⊙, consistent with a binary neutron star merger resulting in a short-lived massive neutron star. This optical and near-infrared data set provides the best-sampled kilonova light curve without a gravitational wave trigger to date.
Integrated Myocardial Perfusion Imaging Diagnostics Improve Detection of Functionally Significant Coronary Artery Stenosis by 13N-ammonia Positron Emission Tomography

Background—Recent evidence suggests that the diagnostic accuracy of myocardial perfusion imaging is improved by quantifying stress myocardial blood flow (MBF) in absolute terms. We evaluated a comprehensive quantitative 13N-ammonia positron emission tomography (13NH3-PET) diagnostic panel, including stress MBF, coronary flow reserve (CFR), and relative flow reserve (RFR) in conjunction with relative perfusion defect (PD) assessments to better detect functionally significant coronary artery stenosis. Methods and Results—A total of 130 patients (307 vessels) with coronary artery disease underwent both 13NH3-PET and invasive coronary angiography with fractional flow reserve (FFR) measurement. Diagnostic accuracy, optimal cut points, and discrimination indices of respective 13NH3-PET quantitative measures were compared, with FFR as standard reference. The capacity to discern disease with stepwise addition of stress MBF, CFR, and RFR to qualitatively assessed relative PD was also gauged, using the category-free net reclassification index. All quantitative measures showed significant correlation with FFR (PET-derived CFR, r=0.388; stress MBF, r=0.496; and RFR, r=0.780; all P<0.001). Optimal respective cut points for FFR ⩽0.8 and ⩽0.75 were 1.99 and 1.84 mL/min per g for stress MBF and 2.12 and 2.00 for PET-derived CFR. Discrimination indices of quantitative measures that correlated with FFR ⩽0.8 were all significantly higher than that of relative PD (area under the curve: 0.626, 0.730, 0.806, and 0.897 for relative PD, CFR, stress MBF, and RFR, respectively; overall comparison P<0.001). The capacity for functionally significant coronary stenosis was incrementally improved by the successive addition of CFR (net reclassification index=0.629), stress MBF (net reclassification index=0.950), and RFR (net reclassification index=1.253; all P<0.001) to relative PD. Conclusions—Integrating quantitative 13NH3-PET measures with qualitative myocardial perfusion assessment provides superior diagnostic accuracy and improves the capacity to detect functionally significant coronary artery stenosis. Clinical Trial Registration—URL: http://www.clinicaltrials. gov. Unique identifiers: NCT01621438 and NCT01366404.
MapReduce Meets Fine-Grained Complexity: MapReduce Algorithms for APSP, Matrix Multiplication, 3-SUM, and Beyond

Distributed processing frameworks, such as MapReduce, Hadoop, and Spark are popular systems for processing large amounts of data. The design of efficient algorithms in these frameworks is a challenging problem, as the systems both require parallelism---since datasets are so large that multiple machines are necessary---and limit the degree of parallelism---since the number of machines grows sublinearly in the size of the data. Although MapReduce is over a dozen years old~\cite{dean2008mapreduce}, many fundamental problems, such as Matrix Multiplication, 3-SUM, and All Pairs Shortest Paths, lack efficient MapReduce algorithms. We study these problems in the MapReduce setting. Our main contribution is to exhibit smooth trade-offs between the memory available on each machine, and the total number of machines necessary for each problem. Overall, we take the memory available to each machine as a parameter, and aim to minimize the number of rounds and number of machines. In this paper, we build on the well-known MapReduce theoretical framework initiated by Karloff, Suri, and Vassilvitskii ~\cite{karloff2010model} and give algorithms for many of these problems. The key to efficient algorithms in this setting lies in defining a sublinear number of large (polynomially sized) subproblems, that can then be solved in parallel. We give strategies for MapReduce-friendly partitioning, that result in new algorithms for all of the above problems. Specifically, we give constant round algorithms for the Orthogonal Vector (OV) and 3-SUM problems, and $O(\log n)$-round algorithms for Matrix Multiplication, All Pairs Shortest Paths (APSP), and Fast Fourier Transform (FFT), among others. In all of these we exhibit trade-offs between the number of machines and memory per machine.
A Benchmark API Call Dataset for Windows PE Malware Classification

The use of operating system API calls is a promising task in the detection of PE-type malware in the Windows operating system. This task is officially defined as running malware in an isolated sandbox environment, recording the API calls made with the Windows operating system and sequentially analyzing these calls. Here, we have analyzed 7107 different malicious software belonging to various families such as virus, backdoor, trojan in an isolated sandbox environment and transformed these analysis results into a format where different classification algorithms and methods can be used. First, we'll explain how we got the malware, and then we'll explain how we've got these software bundled into families. Finally, we will describe how to perform malware classification tasks using different computational methods for the researchers who will use the data set we have created.
Two-scale homogenization of a stationary mean-field game

In this paper, we characterize the asymptotic behavior of a first-order stationary mean-field game (MFG) with a logarithm coupling, a quadratic Hamiltonian, and a periodically oscillating potential. This study falls into the realm of the homogenization theory, and our main tool is the two-scale convergence. Using this convergence, we rigorously derive the two-scale homogenized and the homogenized MFG problems, which encode the so-called macroscopic or effective behavior of the original oscillating MFG. Moreover, we prove existence and uniqueness of the solution to these limit problems.
Accurate Face Detection for High Performance

Face detection has witnessed significant progress due to the advances of deep convolutional neural networks (CNNs). Its central issue in recent years is how to improve the detection performance of tiny faces. To this end, many recent works propose some specific strategies, redesign the architecture and introduce new loss functions for tiny object detection. In this report, we start from the popular one-stage RetinaNet approach and apply some recent tricks to obtain a high performance face detector. Specifically, we apply the Intersection over Union (IoU) loss function for regression, employ the two-step classification and regression for detection, revisit the data augmentation based on data-anchor-sampling for training, utilize the max-out operation for classification and use the multi-scale testing strategy for inference. As a consequence, the proposed face detection method achieves state-of-the-art performance on the most popular and challenging face detection benchmark WIDER FACE dataset.
Evolution of the disc atmosphere in the X-ray binary MXB 1659-298, during its 2015-2017 outburst

We report on the evolution of the X-ray emission of the accreting neutron star (NS) low mass X-ray binary (LMXB), MXB 1659-298, during its most recent outburst in 2015-2017. We detected 60 absorption lines during the soft state (of which 21 at more than 3 $\sigma$), that disappeared in the hard state (e.g., the Fe xxv and Fe xxvi lines). The absorbing plasma is at rest, likely part of the accretion disc atmosphere. The bulk of the absorption features can be reproduced by a high column density ($log(N_H/cm^{-2})\sim23.5$) of highly ionised ($log(\xi/erg~cm~s^{-1})\sim3.8$) plasma. Its disappearance during the hard state is likely the consequence of a thermal photo-ionisation instability. MXB 1659-298's continuum emission can be described by the sum of an absorbed disk black body and its Comptonised emission, plus a black body component. The observed spectral evolution with state is in line with that typically observed in atoll and stellar mass black hole LMXB. The presence of a relativistic Fe K$\alpha$ disk-line is required during the soft state. We also tentatively detect the Fe xxii doublet, whose ratio suggests an electron density of the absorber of $n_e>10^{13} cm^{-3}$, hence, the absorber is likely located at $<7\times10^4 r_g$ from the illuminating source, well inside the Compton and outer disc radii. MXB 1659-298 is the third well monitored atoll LMXB showcasing intense Fe xxv and Fe xxvi absorption during the soft state that disappears during the hard state.
Beyond Personalization: Research Directions in Multistakeholder Recommendation

Recommender systems are personalized information access applications; they are ubiquitous in today's online environment, and effective at finding items that meet user needs and tastes. As the reach of recommender systems has extended, it has become apparent that the single-minded focus on the user common to academic research has obscured other important aspects of recommendation outcomes. Properties such as fairness, balance, profitability, and reciprocity are not captured by typical metrics for recommender system evaluation. The concept of multistakeholder recommendation has emerged as a unifying framework for describing and understanding recommendation settings where the end user is not the sole focus. This article describes the origins of multistakeholder recommendation, and the landscape of system designs. It provides illustrative examples of current research, as well as outlining open questions and research directions for the field.
Follow-up of the Neutron Star Bearing Gravitational-wave Candidate Events S190425z and S190426c with MMT and SOAR

On 2019 April 25.346 and 26.640 UT the Laser Interferometer Gravitational-Wave Observatory (LIGO) and Virgo gravitational-wave (GW) observatory announced the detection of the first candidate events in Observing Run 3 that contained at least one neutron star (NS). S190425z is a likely binary neutron star (BNS) merger at dL = 156 ± 41 Mpc, while S190426c is possibly the first NS–black hole (BH) merger ever detected, at dL = 377 ± 100 Mpc, although with marginal statistical significance. Here we report our optical follow-up observations for both events using the MMT 6.5 m telescope, as well as our spectroscopic follow-up of candidate counterparts (which turned out to be unrelated) with the 4.1 m SOAR telescope. We compare to publicly reported searches, explore the overall areal coverage and depth, and evaluate those in relation to the optical/near-infrared (NIR) kilonova emission from the BNS merger GW170817, to theoretical kilonova models, and to short gamma-ray burst (SGRB) afterglows. We find that for a GW170817-like kilonova, the partial volume covered spans up to about 40% for S190425z and 60% for S190426c. For an on-axis jet typical of SGRBs, the search effective volume is larger, but such a configuration is expected in at most a few percent of mergers. We further find that wide-field γ-ray and X-ray limits rule out luminous on-axis SGRBs, for a large fraction of the localization regions, although these searches are not sufficiently deep in the context of the γ-ray emission from GW170817 or off-axis SGRB afterglows. The results indicate that some optical follow-up searches are sufficiently deep for counterpart identification to about 300 Mpc, but that localizations better than 1000 deg2 are likely essential.
concussive symptom reporting in OEF / OIF service members with mild traumatic brain injuries

Objective: The relationship between combat stress and post-concussive symptoms in service members with mild traumatic brain injuries (mTBI) is poorly understood. It was hypothesized that the co-occurrence of combat stress would have a significant effect on the severity of post-concussive complaints, specifically on emotional and cognitive symptoms. j\1ethods: Four hundred and seventy-two combat-deployed service members with mTBI completed self-report inventories of post-traumatic stress and post-concussive symptoms. Two groups were formed based on post-traumatic stress symptoms (High Combat Stress and Low Combat Stress). Results: A 3-8-fold increase in post-concussive symptoms was observed when comparing the High and Lmv Combat Stress Groups. Elevations in post-concussive symptom reporting were not limited to emotional andJor cognitive symptoms, but rather were inclusive of all measured post-concussive symptoms. Conclusions: The findings of the present study suggest that non-brain injury-related factors, such as high-levels of combat stress, may impact post-concussive symptom reporting in this population, further confounding the accuracy of the post-concussion syndrome (PCS) diagnosis. Considerable caution should be exercised in making the diagnosis of PCS in concussed service members with co-occurring combat-stress disorders.
High quality factor polariton resonators using van der Waals materials

We present high quality factor optical nanoresonators operating in the mid-IR to far-IR based on phonon polaritons in van der Waals materials. The nanoresonators are disks patterned from isotopically pure hexagonal boron nitride (isotopes 10B and 11B) and {\alpha}-molybdenum trioxide. We experimentally achieved quality factors of nearly 400, the highest ever observed in nano-resonators at these wavelengths. The excited modes are deeply subwavelength, and the resonators are 10 to 30 times smaller than the exciting wavelength. These results are very promising for the realization of nano-photonics devices such as optical bio-sensors and miniature optical components such as polarizers and filters.
Real-time Inference in Multi-sentence Tasks with Deep Pretrained Transformers

The use of deep pretrained bidirectional transformers has led to remarkable progress in learning multi-sentence representations for downstream language understanding tasks (Devlin et al., 2018). For tasks that make pairwise comparisons, e.g. matching a given context with a corresponding response, two approaches have permeated the literature. A Cross-encoder performs full self-attention over the pair; a Bi-encoder performs self-attention for each sequence separately, and the final representation is a function of the pair. While Cross-encoders nearly always outperform Bi-encoders on various tasks, both in our work and others' (Urbanek et al., 2019), they are orders of magnitude slower, which hampers their ability to perform real-time inference. In this work, we develop a new architecture, the Poly-encoder, that is designed to approach the performance of the Cross-encoder while maintaining reasonable computation time. Additionally, we explore two pretraining schemes with different datasets to determine how these affect the performance on our chosen dialogue tasks: ConvAI2 and DSTC7 Track 1. We show that our models achieve state-of-the-art results on both tasks; that the Poly-encoder is a suitable replacement for Bi-encoders and Cross-encoders; and that even better results can be obtained by pretraining on a large dialogue dataset.
The Missing Data Encoder: Cross-Channel Image Completion\\with Hide-And-Seek Adversarial Network

Image completion is the problem of generating whole images from fragments only. It encompasses inpainting (generating a patch given its surrounding), reverse inpainting/extrapolation (generating the periphery given the central patch) as well as colorization (generating one or several channels given other ones). In this paper, we employ a deep network to perform image completion, with adversarial training as well as perceptual and completion losses, and call it the “missing data encoder” (MDE). We consider several configurations based on how the seed fragments are chosen. We show that training MDE for “random extrapolation and colorization” (MDE-REC), i.e. using random channel-independent fragments, allows a better capture of the image semantics and geometry. MDE training makes use of a novel “hide-and-seek” adversarial loss, where the discriminator seeks the original non-masked regions, while the generator tries to hide them. We validate our models qualitatively and quantitatively on several datasets, showing their interest for image completion, representation learning as well as face occlusion handling.
Text2Node: a Cross-Domain System for Mapping Arbitrary Phrases to a Taxonomy

Electronic health record (EHR) systems are used extensively throughout the healthcare domain. However, data interchangeability between EHR systems is limited due to the use of different coding standards across systems. Existing methods of mapping coding standards based on manual human experts mapping, dictionary mapping, symbolic NLP and classification are unscalable and cannot accommodate large scale EHR datasets. In this work, we present Text2Node, a cross-domain mapping system capable of mapping medical phrases to concepts in a large taxonomy (such as SNOMED CT). The system is designed to generalize from a limited set of training samples and map phrases to elements of the taxonomy that are not covered by training data. As a result, our system is scalable, robust to wording variants between coding systems and can output highly relevant concepts when no exact concept exists in the target taxonomy. Text2Node operates in three main stages: first, the lexicon is mapped to word embeddings; second, the taxonomy is vectorized using node embeddings; and finally, the mapping function is trained to connect the two embedding spaces. We compared multiple algorithms and architectures for each stage of the training, including GloVe and FastText word embeddings, CNN and Bi-LSTM mapping functions, and node2vec for node embeddings. We confirmed the robustness and generalisation properties of Text2Node by mapping ICD-9-CM Diagnosis phrases to SNOMED CT and by zero-shot training at comparable accuracy. This system is a novel methodological contribution to the task of normalizing and linking phrases to a taxonomy, advancing data interchangeability in healthcare. When applied, the system can use electronic health records to generate an embedding that incorporates taxonomical medical knowledge to improve clinical predictive models.
Biological Applications of Organic Electrochemical Transistors: Electrochemical Biosensors and Electrophysiology Recording

Organic electrochemical transistors (OECTs) are recently developed high-efficient transducers not only for electrochemical biosensor but also for cell electrophysiological recording due to the separation of gate electrode from the transistor device. The efficient integration of OECTs with electrochemical gate electrode makes the as-prepared sensors with improved performance, such as sensitivity, limit of detection, and selectivity. We herein reviewed the recent progress of OECTs-based biosensors and cell electrophysiology recording, mainly focusing on the principle and chemical design of gate electrode and the channel. First, the configuration, work principle, semiconductor of OECT are briefly introduced. Then different kinds of sensing modes are reviewed, especially for the biosensing and electrophysiological recording. Finally, the challenges and opportunities of this research field are discussed.