Miracle-dz/UniFAR-FTUs huggingface dataset:
Digital Predistortion for Multiuser Hybrid MIMO at mmWaves

Efficient mitigation of power amplifier (PA) nonlinear distortion in multi-user hybrid precoding based broadband mmWave systems is an open research problem. In this article, we carry out detailed signal and distortion modeling in broadband multi-user hybrid MIMO systems, with a bank of nonlinear PAs in each subarray, while also take the inevitable crosstalk between the antenna/PA branches into account. Building on the derived models, we adopt and describe an efficient closed-loop (CL) digital predistortion (DPD) solution that utilizes only a single-input DPD unit per transmit chain or subarray, despite crosstalk, providing thus substantial complexity-benefit compared to the state-of-the art multi-dimensional DPD solutions. We show that under spatially correlated multipath propagation, each single-input DPD unit can provide linearization towards every intended user, or more generally, towards all spatial directions where coherent propagation is taking place, and that the adopted CL DPD system is robust against crosstalk. Extensive numerical results building on practical measurement-based mmWave PA models are provided, demonstrating and verifying the excellent linearization performance of the overall DPD system in different evaluation scenarios.
High-Risk Indicators of Renal Involvement in Primary Sjogren's Syndrome: A Clinical Study of 1002 Cases

Objective A retrospective analysis of clinical characteristics and immunological manifestations of primary Sjogren's syndrome (pSS) patients with or without renal involvement was conducted in order to elucidate the potential risk factors of renal damage in pSS and evaluate the condition. Methods A total of 1002 patients, who fulfilled the 2002 classification criteria for pSS from the Second Affiliated Hospital of Shanxi Medical University, were enrolled in the cross-sectional study. Clinical, immunological, and histological characteristics were compared between pSS patients with and without renal involvement, and potential risk factors of renal involvements in pSS patients were examined by multivariate analysis. Results Among these pSS patients, there were 162 cases (16.17%) with and 840 cases (83.83%) without renal damage. Serious edema of both lower limbs, interstitial nephritis, and renal tubular acidosis were found in the pSS with renal damage group. Compared with simple pSS patients, the levels of creatinine, cystatin C, and alpha-1-microglobulin (α1-MG) in the pSS with renal damage group were significantly increased. The difference between the two groups was statistically significant (P < 0.05). The AUC of the combination of creatinine and α1-MG and creatinine, α1-MG, and creatinine was statistically larger than that of creatinine, and the biomarker of the biggest AUC is the combination of creatinine and α1-MG. Conclusion The main clinical manifestations of pSS with renal damage were edema of the lower limbs, interstitial nephritis, and renal tubular acidosis. Creatinine and α1-MG are effective indicators for renal function in pSS, which may provide a better understanding for clinical decision-making.
Influence of Economic Indicators on Supply Chain: Evidence from Indonesian Fishing Industry

Abstract - The supply chain dynamics has been broadly examined and experienced by the academes, industrialists as well as economists. However, still the literature is missing with the effect of economic indicators on supply chain practices, particularly in fishing industry. Therefore, the objective of the current study is to examine the role of economic indicators on supply chain in fishing industry of Indonesia. Fishing industry is one of the industries which is working from many centuries and now growing rapidly and considered to be the important element of economic growth in different countries like Indonesia. Six hypotheses were formulated with the help of previous studies, concerning the relationship between inflation rate, interest rate, human development index (HDI), gross domestic product (GDP) and supply chain. GDP was considered as the mediating variable. Managerial employees of fishing companies were selected as the respondents of this study. Primary data was collected by conducting the questionnaire survey. Total number of one hundred and ninety-six (196) response were received. These responses were analysed with the help of statistical software namely; Partial Least Square (PLS). It was found that economic indicators have influence on supply chain. Increases in inflation rate and interest rate decreases the supply chain. However, increases in HDI enhances the GDP and promote supply chain activities. Additionally, GDP is a mediating variable between HDI and supply chain which positively enhances the supply chain through HDI. Thus, study provides the clues for government to promote supply chain by controlling inflation and interest rate.
Reasons to Engage in and Learning Experiences From Different Play Strategies in a Web-Based Serious Game on Delirium for Medical Students: Mixed Methods Design

Background Although many studies have recently been published on the value of serious games for medical education, little attention has been given to the role of dark play (choosing unacceptable actions in games). Objective This study aimed to investigate potential differences in the characteristics of medical students who have the opportunity to choose normal or dark play in a serious game. This study also aimed to compare their reasons for choosing a play strategy and their perceptions of what they learned from their game play. Methods We asked undergraduate medical students to play a serious game in which they had to take care of a patient with delirium (The Delirium Experience). After getting acquainted with the game, students could opt for normal or dark play. Student characteristics (age, gender, experience with caring for older or delirious patients, and number of completed clerkships) were collected, and the Delirium Attitude Scale and Learning Motivation and Engagement Questionnaire were administered. Reasons for choosing normal or dark play were evaluated with an open-ended question. Information on lessons they had learned from the game was collected using an open-ended question and self-reported knowledge on delirium. Results This study had 160 participants (89 normal play, 71 dark play). Male students (26/160, 56.5%) chose dark play significantly more often than female students (45/160, 39.5%; P=.049). We did not find significant differences in student characteristics or measurement outcomes between play strategies. Participants’ main reason for choosing normal play was to learn how to provide care to delirious patients, and the main reason for dark play was to gain insight into what a delirious patient has to endure during delirious episodes. All participants learned what to do when taking care of a delirious patient and gained insight into how a patient experiences delirium. We found no differences in self-reported knowledge. Conclusions When medical students have the opportunity to choose dark play in a serious game, half of them will probably choose this play strategy. Male students will more likely opt for dark play than female students. Choice of play strategy is not affected by any other student characteristic or measurement outcome. All students learned the same lessons from playing the game, irrespective of their learning strategy.
Mixer-First Receiver with wide-RF Range

In the Passive Mixer first receiver, four mosfet, and four baseband impedance can synthesize High-Q bandpass filter. This on-chip High-Q bandpass filter can replace bulky, expensive and off-chip SAW filters. The impedance which is seen by the antenna is tuned by switch resistance of the Passive Mixer and the input impedance of the Trans-impedance amplifier (baseband impedance) to match antenna impedance. The gain of the feedback loop of the TIA controls the input impedance of the TIA. Further, M baseband impedance can be replaced by M/4 complex impedance. This complex impedance provides a wide RF range by changing the transconductance.
On total claim amount for marked Poisson cluster models.

We study the asymptotic distribution of the total claim amount for marked Poisson cluster models. The marks determine the size and other characteristics of the individual claims and potentially influence arrival rate of the future claims. We find sufficient conditions under which the total claim amount satisfies the central limit theorem or alternatively tends in distribution to an infinite variance stable random variable. We discuss several Poisson cluster models in detail, paying special attention to the marked Hawkes processes as our key example.
Constraining the Size of the Corona with Fully Relativistic Calculations of Spectra of Extended Coronae. I. The Monte Carlo Radiative Transfer Code

The size and geometry of the X-ray-emitting corona in an active galactic nucleus (AGN) are still not well constrained. Dovčiak & Done proposed a method based on calculations assuming a point-like lamp-post corona. To perform more self-consistent calculations of energy spectra of extended coronae, we develop monk, a Monte Carlo radiative transfer code dedicated to calculations of Comptonized spectra in the Kerr spacetime. In monk we assume a Klein–Nishina scattering cross section and include all general relativistic effects. We find that for a corona located above the disk, the spectrum is not isotropic, but has harder and less luminous spectra toward observers at lower inclinations, owing to anisotropic illumination of the seed photons. This anisotropy also leads to an underestimated size for the corona if we assume it to be a point-like, isotropic source located on the rotation axis of the black hole, demonstrating the necessity for more self-consistent calculations. We also study the effect of motion and geometry of the corona on the emergent spectrum. Finally, we discuss the implication of anisotropic coronal emission for the reflection spectrum in AGNs as well as black hole X-ray binaries (BHXRBs). We find that by assuming the coronal emission to be isotropic, one may underestimate the soft excess in AGNs, and the reflection continuum and iron K fluorescent line flux in BHXRBs.
Multi-Person Articulated Tracking With Spatial and Temporal Embeddings

We propose a unified framework for multi-person pose estimation and tracking. Our framework consists of two main components, i.e. SpatialNet and TemporalNet. The SpatialNet accomplishes body part detection and part-level data association in a single frame, while the TemporalNet groups human instances in consecutive frames into trajectories. Specifically, besides body part detection heatmaps, SpatialNet also predicts the Keypoint Embedding (KE) and Spatial Instance Embedding (SIE) for body part association. We model the grouping procedure into a differentiable Pose-Guided Grouping (PGG) module to make the whole part detection and grouping pipeline fully end-to-end trainable. TemporalNet extends the spatial grouping of keypoints to temporal grouping of human instances. Given human proposals from two consecutive frames, TemporalNet exploits both appearance features encoded in Human Embedding (HE) and temporally consistent geometric features embodied in Temporal Instance Embedding (TIE) for robust tracking. Extensive experiments demonstrate the effectiveness of our proposed model. Remarkably, we demonstrate substantial improvements over the state-of-the-art pose tracking method from 65.4% to 71.8% Multi-Object Tracking Accuracy (MOTA) on the ICCV'17 PoseTrack Dataset.
Polynomial mathematical programs with equilibrium constraints and semidefinite programming relaxations.

This paper focuses on the study of a mathematical program with equilibrium constraints, where the objective and the constraint functions are all polynomials. We present a method for finding its global minimizers and global minimum using a hierarchy of semidefinite programming (SDP) relaxations and prove the convergence result for the method. Numerical experiments are presented to show the efficiency of the proposed algorithm.
CityFlow: A City-Scale Benchmark for Multi-Target Multi-Camera Vehicle Tracking and Re-Identification

Urban traffic optimization using traffic cameras as sensors is driving the need to advance state-of-the-art multi-target multi-camera (MTMC) tracking. This work introduces CityFlow, a city-scale traffic camera dataset consisting of more than 3 hours of synchronized HD videos from 40 cameras across 10 intersections, with the longest distance between two simultaneous cameras being 2.5 km. To the best of our knowledge, CityFlow is the largest-scale dataset in terms of spatial coverage and the number of cameras/videos in an urban environment. The dataset contains more than 200K annotated bounding boxes covering a wide range of scenes, viewing angles, vehicle models, and urban traffic flow conditions. Camera geometry and calibration information are provided to aid spatio-temporal analysis. In addition, a subset of the benchmark is made available for the task of image-based vehicle re-identification (ReID). We conducted an extensive experimental evaluation of baselines/state-of-the-art approaches in MTMC tracking, multi-target single-camera (MTSC) tracking, object detection, and image-based ReID on this dataset, analyzing the impact of different network architectures, loss functions, spatio-temporal models and their combinations on task effectiveness. An evaluation server is launched with the release of our benchmark at the 2019 AI City Challenge (https://www.aicitychallenge.org/) that allows researchers to compare the performance of their newest techniques. We expect this dataset to catalyze research in this field, propel the state-of-the-art forward, and lead to deployed traffic optimization(s) in the real world.
Unsupervised Speech Enhancement Based on Multichannel NMF-Informed Beamforming for Noise-Robust Automatic Speech Recognition

This paper describes multichannel speech enhancement for improving automatic speech recognition (ASR) in noisy environments. Recently, the minimum variance distortionless response (MVDR) beamforming has widely been used because it works well if the steering vector of speech and the spatial covariance matrix (SCM) of noise are given. To estimating such spatial information, conventional studies take a supervised approach that classifies each time-frequency (TF) bin into noise or speech by training a deep neural network (DNN). The performance of ASR, however, is degraded in an unknown noisy environment. To solve this problem, we take an unsupervised approach that decomposes each TF bin into the sum of speech and noise by using multichannel nonnegative matrix factorization (MNMF). This enables us to accurately estimate the SCMs of speech and noise not from observed noisy mixtures but from separated speech and noise components. In this paper, we propose online MVDR beamforming by effectively initializing and incrementally updating the parameters of MNMF. Another main contribution is to comprehensively investigate the performances of ASR obtained by various types of spatial filters, i.e., time-invariant and variant versions of MVDR beamformers and those of rank-1 and full-rank multichannel Wiener filters, in combination with MNMF. The experimental results showed that the proposed method outperformed the state-of-the-art DNN-based beamforming method in unknown environments that did not match training data.
Impulse Response and Granger Causality in Dynamical Systems with Autoencoder Nonlinear Vector Autoregressions

Sometimes knowing the future given the present is not enough. Predicting possible futures given different defined scenarios can be more important. However, the workhorse for causality detection and impulse response, the Vector Autoregression (VAR), assumes linearity and has produced poor forecasts (Reis, 2018). Here, we introduce a vector autoencoder nonlinear autoregression neural network (VANAR) capable of both automatic time series feature extraction for its inputs and automatic functional form estimation. We evaluate VANAR in three ways: first in terms of pure forecast accuracy, second in terms of detecting the correct causality between variables, and lastly in terms of impulse response where we model trajectories given external shocks. These tests were performed on datasets with different underlying dynamics: a simulated nonlinear chaotic system, a simulated linear system, and an empirical system using Philippine macroeconomic data. Results show that VANAR significantly outperforms VAR in the forecast and causality tests. For the macroeconomic forecast, VANAR has consistently superior accuracy even over state of the art models such as SARIMA and TBATS (Hyndman et al., 2011). For the impulse response test, VANAR outperforms VAR in the linear system but both models fail to predict the shocked trajectories of the nonlinear chaotic system. VANAR was robust in its ability to model a wide variety of dynamics, from chaotic, high noise, and low data environments to complex macroeconomic systems, thus illustrating its potential usefulness in modeling more real world dynamical systems.
High-drag Interstellar Objects and Galactic Dynamical Streams

The nature of 1I/’Oumuamua (henceforth, 1I), the first interstellar object (ISO) known to pass through the solar system, remains mysterious. Feng & Jones noted that the incoming 1I velocity vector “at infinity” ( ) is close to the motion of the Pleiades dynamical stream (or Local Association), and suggested that 1I is a young object ejected from a star in that stream. Micheli et al. subsequently detected nongravitational acceleration in the 1I trajectory; this acceleration would not be unusual in an active comet, but 1I observations failed to reveal any signs of activity. Bialy & Loeb hypothesized that the anomalous 1I acceleration was instead due to radiation pressure, which would require an extremely low mass-to-area ratio (or area density). Here I show that a low area density can also explain the very close kinematic association of 1I and the Pleiades stream, as it renders 1I subject to drag capture by interstellar gas clouds. This supports the radiation pressure hypothesis and suggests that there is a significant population of low area density ISOs in the Galaxy, leading, through gas drag, to enhanced ISO concentrations in the galactic dynamical streams. Any ISO entrained in a dynamical stream will have a predictable incoming targeted deep surveys using this information should be able to find dynamical stream objects from months to as much as a year before their perihelion, providing the lead time needed for fast-response missions for the future in situ exploration of such objects.
Effects of post-deposition annealing on the structure and magnetization of PLD grown yttrium iron garnet films

We report on the recrystallization of 200 nm thick as-grown Yttrium Iron Garnet ( Y 3.4 Fe 4.6 O 12 ) films on the (111) face of gadolinium gallium garnet single crystals by post-deposition annealing. Epitaxial conversion of the as-grown microcrystalline yttrium iron garnet films was seen after annealing at 800 °C for more than 30 min both in ambient oxygen and in air. The as-grown oxygen annealed samples at 800 °C for 60 min crystallize epitaxially and show excellent figure-of-merit for saturation magnetization (MS = 3.3 μB/f.u., comparable to the bulk value) and coercivity (HC ∼ 1.1 Oe). The ambient air annealing at 800 °C with a very slow rate of cooling (2 °C/min) results in a double layer structure with a thicker unstrained epitaxial top layer having the MS and HC of 2.9 μB/f.u. and 0.12 Oe, respectively. The symmetric and asymmetric reciprocal space maps of both the samples reveal a locking of the in-plane lattice of the film to the in-plane lattice of the substrate, indicating a pseudomorphic gro.
Sampling Acquisition Functions for Batch Bayesian Optimization

This paper presents Acquisition Thompson Sampling (ATS), a novel algorithm for batch Bayesian Optimization (BO) based on the idea of sampling multiple acquisition functions from a stochastic process. We define this process through the dependency of the acquisition functions on a set of model parameters. ATS is conceptually simple, straightforward to implement and, unlike other batch BO methods, it can be employed to parallelize any sequential acquisition function. In order to improve performance for multi-modal tasks, we show that ATS can be combined with existing techniques in order to realize different explore-exploit trade-offs and take into account pending function evaluations. We present experiments on a variety of benchmark functions and on the hyper-parameter optimization of a popular gradient boosting tree algorithm. These demonstrate the competitiveness of our algorithm with two state-of-the-art batch BO methods, and its advantages to classical parallel Thompson Sampling for BO.
EMTk - The Emotion Mining Toolkit

The Emotion Mining Toolkit (EMTk) is a suite of modules and datasets offering a comprehensive solution for mining sentiment and emotions from technical text contributed by developers on communication channels. The toolkit is written in Java, Python, and R, and is released under the MIT open source license. In this paper, we describe its architecture and the benchmark against the previous, standalone versions of our sentiment analysis tools. Results show large improvements in terms of speed.
Understanding and taming SSD read performance variability: HDFS case study

In this paper we analyze the influence that lower layers (file system, OS, SSD) have on HDFS' ability to extract maximum performance from SSDs on the read path. We uncover and analyze three surprising performance slowdowns induced by lower layers that result in HDFS read throughput loss. First, intrinsic slowdown affects reads from every new file system extent for a variable amount of time. Second, temporal slowdown appears temporarily and periodically and is workload-agnostic. Third, in permanent slowdown, some files can individually and permanently become slower after a period of time. We analyze the impact of these slowdowns on HDFS and show significant throughput loss. Individually, each of the slowdowns can cause a read throughput loss of 10-15%. However, their effect is cumulative. When all slowdowns happen concurrently, read throughput drops by as much as 30%. We further analyze mitigation techniques and show that two of the three slowdowns could be addressed via increased IO request parallelism in the lower layers. Unfortunately, HDFS cannot automatically adapt to use such additional parallelism. Our results point to a need for adaptability in storage stacks. The reason is that an access pattern that maximizes performance in the common case is not necessarily the same one that can mask performance fluctuations.
Doubly stochastic models for replicated spatio-temporal point processes.

This paper proposes a log-linear model for the latent intensity functions of a replicated spatio-temporal point process. By simultaneously fitting correlated spatial and temporal Karhunen-Loeve expansions, the model produces spatial and temporal components that are usually easy to interpret and capture the most important modes of variation and spatio-temporal correlation of the process. The asymptotic distribution of the estimators is derived. The finite sample properties are studied by simulations. As an example of application, we analyze bike usage patterns on the Divvy bike sharing system of the city of Chicago.