Miracle-dz/UniFAR-FTUs huggingface dataset:
Mixed Size Crossbar based RRAM CNN Accelerator with Overlapped Mapping Method

Convolutional Neural Networks (CNNs) play a vital role in machine learning. CNNs are typically both computing and memory intensive. Emerging resistive random-access memories (RRAMs) and RRAM crossbars have demonstrated great potentials in boosting the performance and energy efficiency of CNNs. Compared with small crossbars, large crossbars show better energy efficiency with less interface overhead. However, conventional workload mapping methods for small crossbars cannot make full use of the computation ability of large crossbars. In this paper, we propose an Overlapped Mapping Method (OMM) and MIxed Size Crossbar based RRAM CNN Accelerator (MISCA) to solve this problem. MISCA with OMM can reduce the energy consumption caused by the interface circuits, and improve the parallelism of computation by leveraging the idle RRAM cells in crossbars. The simulation results show that MISCA with OMM can achieve 2.7× speedup, 30% utilization rate improvement, and 1.2× energy efficiency improvement on average compared with fixed size crossbars based accelerator using the conventional mapping method. In comparison with GPU platform, MISCA with OMM can perform 490.4× higher on average in energy efficiency and 20× higher on average in speedup. Compared with PRIME, an existing RRAM based accelerator, MISCA has 26.4× speedup and 1.65× energy efficiency improvement.
The effects of the PARTY program on youths' risk-taking behaviours and beliefs

The PARTY (Prevent Alcohol Related Trauma in Youth) program is a one-day hospitalbased program that targets youth in Grade 10. The programs' intention is to educate youth in the dangers of irresponsible alcohol use and how this contributes to preventable trauma. The study of the PARTY program used a 12-item questionnaire to evaluate the effectiveness of the program in changing youths' risk-taking behaviors. The main objectives were to (a) carry out psychometric analysis of the questionnaire, (b) assess cognitive changes in regards to risk-taking behaviors and beliefs of Grade 10 students three months after completing the PARTY program, and (c) assess gender, location, and school structure differences in risk-taking behaviors and beliefs. The study utilized a mixed design as the program is using a pre-and post-program survey for data collection. The study sample consisted of Grade 10 students from senior and junior secondary schools in the School District 57 region that included one rural senior secondary school, three urban senior secondary schools, one urban junior secondary school and a private senior secondary school. Two of the schools had an insufficient number of participants completing all three times of measurement and were not included in analyses involving individual schools. A total of280 students completed the first questionnaire and ofthose 162 (57.8%) completed all three times of measure and were used in the study. Results indicate that the PARTY program has a positive effect on the risk-taking behaviors and beliefs of urban girls and boys, and rural girls attending senior secondary schools. The PARTY program showed no significant effect on the risk-taking behaviors and beliefs of junior secondary boys and girls, or rural boys. Table of
How Much is 'Too Much'?

In a mixed methods study of parents and teens (n=200), 87% (n=174) of participants used language consistent with smartphone addiction narratives when asked about their smartphone feelings and use. Mental health researchers and clinicians do not consistently agree about whether smartphone addiction exists nor what it would look like if it does. Our goal in this study was to explore the patterns of responses that people invoked when talking about the role of smartphones in their lives and the lives of those around them. Responses suggested that both parents and teens are aware of and potentially influenced by a narrative that smartphones are addictive and can lead to negative, though largely undefined, consequences. We examine potential origins of this narrative, including media coverage, and examine the critical need for a deeper examination in the CSCW community of how this narrative could be influencing well-being, sense of self, and sensemaking around smartphone use.
Classification of 12-Lead ECG Signals with Bi-directional LSTM Network

We propose a recurrent neural network classifier to detect pathologies in 12-lead ECG signals and train and validate the classifier with the Chinese physiological signal challenge dataset (this http URL). The recurrent neural network consists of two bi-directional LSTM layers and can train on arbitrary-length ECG signals. Our best trained model achieved an average F1 score of 74.15% on the validation set. Keywords: ECG classification, Deep learning, RNN, Bi-directional LSTM, QRS detection.
Revealing Fine Structures of the Retinal Receptive Field by Deep-Learning Networks

Deep convolutional neural networks (CNNs) have demonstrated impressive performance on many visual tasks. Recently, they became useful models for the visual system in neuroscience. However, it is still not clear what is learned by CNNs in terms of neuronal circuits. When a deep CNN with many layers is used for the visual system, it is not easy to compare the structure components of CNNs with possible neuroscience underpinnings due to highly complex circuits from the retina to the higher visual cortex. Here, we address this issue by focusing on single retinal ganglion cells with biophysical models and recording data from animals. By training CNNs with white noise images to predict neuronal responses, we found that fine structures of the retinal receptive field can be revealed. Specifically, convolutional filters learned are resembling biological components of the retinal circuit. This suggests that a CNN learning from one single retinal cell reveals a minimal neural network carried out in this cell. Furthermore, when CNNs learned from different cells are transferred between cells, there is a diversity of transfer learning performance, which indicates that CNNs are cell specific. Moreover, when CNNs are transferred between different types of input images, here white noise versus natural images, transfer learning shows a good performance, which implies that CNNs indeed capture the full computational ability of a single retinal cell for different inputs. Taken together, these results suggest that CNNs could be used to reveal structure components of neuronal circuits, and provide a powerful model for neural system identification.
The University of Maryland’s Chinese-English Neural Machine Translation Systems at WMT18

This paper describes the University of Maryland’s submission to the WMT 2018 Chinese↔English news translation tasks. Our systems are BPE-based self-attentional Transformer networks with parallel and backtranslated monolingual training data. Using ensembling and reranking, we improve over the Transformer baseline by +1.4 BLEU for Chinese→English and +3.97 BLEU for English→Chinese on newstest2017. Our best systems reach BLEU scores of 24.4 for Chinese→English and 39.0 for English→Chinese on newstest2018.
HIV-Related Stigma, Motivation to Adhere to Antiretroviral Therapy, and Medication Adherence Among HIV-Positive Methadone-Maintained Patients

Background: Opioid agonist therapies with methadone are associated with higher levels of adherence to antiretroviral therapy (ART); yet, no studies have explored factors associated with optimal ART levels in HIV-positive patients on methadone maintenance treatment, including explanatory pathways using mediation analysis. Setting: Participants included 121 HIV-positive, methadone-maintained patients who reported HIV-risk behaviors and were taking ART. Methods: Participants were assessed using an audio computer-assisted self-interview. Multivariable logistic regression was used to identify significant correlates and PROCESS macro to test the explanatory pathway (ie, mediational effect) for optimal ART adherence. Results: Among 121 participants, almost 40% reported suboptimal adherence to ART. Optimal ART adherence was significantly associated with being virally suppressed [adjusted odds ratio (aOR) = 6.470, P = 0.038], higher motivation to adhere to ART (aOR = 1.171, P = 0.011), and lower anticipated HIV-related stigma (aOR = 0.384, P = 0.015). Furthermore, results revealed an indirect effect of motivation on the relationship between HIV stigma and ART adherence (effect = −0.121, P = 0.043), thus supporting the mediation effect. Conclusions: Our findings underscore the complexities surrounding ART adherence, even in patients on methadone maintenance treatment. These findings provide insights on how to more effectively intervene to optimize HIV treatment outcomes, including HIV treatment-as-prevention initiatives, in methadone-maintained patients.
Streaming Graph Neural Networks

Graphs are used to model pairwise relations between entities in many real-world scenarios such as social networks. Graph Neural Networks(GNNs) have shown their superior ability in learning representations for graph structured data, which leads to performance improvements in many graph related tasks such as link prediction, node classification and graph classification. Most of the existing graph neural networks models are designed for static graphs while many real-world graphs are inherently dynamic with new nodes and edges constantly emerging. Existing graph neural network models cannot utilize the dynamic information, which has been shown to enhance the performance of many graph analytic tasks such as community detection. Hence, in this paper, we propose DyGNN, a Dynamic Graph Neural Network model, which can model the dynamic information as the graph evolving. In particular, the proposed framework keeps updating node information by capturing the sequential information of edges (interactions), the time intervals between edges and information propagation coherently. Experimental results on various dynamic graphs demonstrate the effectiveness of the proposed framework.
On lattices, learning with errors, random linear codes, and cryptography

Our main result is a reduction from worst-case lattice problems such as SVP and SIVP to a certain learning problem. This learning problem is a natural extension of the 'learning from parity with error' problem to higher moduli. It can also be viewed as the problem of decoding from a random linear code. This, we believe, gives a strong indication that these problems are hard. Our reduction, however, is quantum. Hence, an efficient solution to the learning problem implies a <i>quantum</i> algorithm for SVP and SIVP. A main open question is whether this reduction can be made classical.Using the main result, we obtain a public-key cryptosystem whose hardness is based on the worst-case quantum hardness of SVP and SIVP. Previous lattice-based public-key cryptosystems such as the one by Ajtai and Dwork were only based on unique-SVP, a special case of SVP. The new cryptosystem is much more efficient than previous cryptosystems: the public key is of size <i>Õ</i>(<i>n</i><sup>2</sup>) and encrypting a message increases its size by <i>Õ</i>(<i>n</i>)(in previous cryptosystems these values are <i>Õ</i>(<i>n</i><sup>4</sup>) and <i>Õ</i>(<i>n</i><sup>2</sup>), respectively). In fact, under the assumption that all parties share a random bit string of length <i>Õ</i>(<i>n</i><sup>2</sup>), the size of the public key can be reduced to <i>Õ</i>(<i>n</i>).
A generalized linear model for cardiovascular complications prediction in PD patients

This study was conducted using machine learning models to identify patient non-invasive information for cardiovascular complications prediction in peritoneal dialysis patients. Nowadays is well known that cardiovascular diseases are the key to mortality in patients undergoing peritoneal dialysis as the risk of cardiovascular disease increases with the progression of renal failure. Primary aim is to establish variables most associated with cardiovascular complications. To achieve this goal four different machine learning techniques were used. We found that the best classification algorithm was a Generalized Linear Model, which achieved AUC values above 96% using a small subset of the original variables following a feature selection approach. Our approach allows us to increase the interpretability of the combinations of traditional factors, advanced chronic kidney disease factors and peritoneal dialysis factors all related with cardiovascular risk profile. The final model is based primarily in the traditional factors.
Blind Two-Dimensional Super-Resolution and Its Performance Guarantee

We study the problem of identifying the parameters of a linear system from its response to multiple unknown waveforms. We assume that the system response is a scaled superposition of time-delayed and frequency-shifted versions of the unknown waveforms. Such kind of problem is severely ill-posed and does not yield a unique solution without introducing further constraints. To fully characterize the system, we assume that the unknown waveforms lie in a common known low-dimensional subspace that satisfies certain properties. Then, we develop a blind two-dimensional (2D) super-resolution framework that applies to a large number of applications. In this framework, we show that under a minimum separation between the time-frequency shifts, all the unknowns that characterize the system can be recovered precisely and with high probability provided that a lower bound on the number of the observed samples is satisfied. The proposed framework is based on a 2D atomic norm minimization problem, which is shown to be reformulated and solved via semidefinite programming. Simulation results that confirm the theoretical findings of the paper are provided.
LTE radio analytics made easy and accessible

Despite the rapid growth of next-generation cellular networks, researchers and end-users today have limited visibility into the performance and problems of these networks, particularly indoors. This paper introduces LTEye, the first open platform to monitor and analyze LTE radio performance at a fine temporal and spatial granularity. LTEye accesses the LTE PHY layer without requiring private user information or provider support. LTEye's analytics enable researchers and policy makers to uncover serious deficiencies in these networks due to inefficient spectrum utilization and inter-cell interference. In addition, LTEye extends synthetic aperture radar, widely used for radar and backscatter signals, to operate over cellular signals. This enables businesses and end-users to localize mobile users and study the distribution of LTE performance across spatial locations in their facility. We implement LTEye on USRP software radios, and present empirical insights and analytics from multiple AT&T and Verizon base stations in our locality.
Kidney transplantation using expanded criteria deceased donors with terminal acute kidney injury: a single center experience in Korea

Purpose We investigated the clinical outcomes of deceased donor kidney transplantation (KT) using kidneys with terminal acute kidney injury (AKI). Methods Between February 2000 and December 2013, we performed 202 deceased donor renal transplants from 159 brain dead donors. According to the expanded criteria donor (ECD) and AKI network criteria, we divided 202 recipients into 4 groups: Group I: Non-AKI & standard criteria donor (SCD) (n = 97); group II: Non-AKI & ECD (n = 15); group III: AKI & SCD (n = 52); and group IV: AKI & ECD (n = 38). Results The incidence of delayed graft function (DFG) was significantly higher in patients with AKI than it was in the non-AKI group (P = 0.008). There were no significant differences among the 4 groups in graft survival (P = 0.074) or patient survival (P = 0.090). However, the long-term allograft survival rate was significantly lower in group IV than it was in other groups (P = 0.024). Conclusion Allografts from deceased donors with terminal AKI had a higher incidence of DGF than did those from donors without AKI. However, there is no significant difference in graft and patient survival rates among the groups. So, the utilization of renal grafts from ECDs with terminal AKI is a feasible approach to address the critical organ shortage.
Verification of linear duration properties over continuous-time markov chains

Stochastic modeling and algorithmic verification techniques have been proved useful in analyzing and detecting unusual trends in performance and energy usage of systems such as power management controllers and wireless sensor devices. Many important properties are dependent on the cumulated time that the device spends in certain states, possibly intermittently. We study the problem of verifying continuous-time Markov chains (CTMCs) against linear duration properties (LDP), i.e. properties stated as conjunctions of linear constraints over the total duration of time spent in states that satisfy a given property. We identify two classes of LDP properties, eventuality duration properties (EDP) and invariance duration properties (IDP), respectively referring to the reachability of a set of goal states, within a time bound; and the continuous satisfaction of a duration property over an execution path. The central question that we address is how to compute the probability of the set of infinite timed paths of the CTMC that satisfy a given LDP. We present algorithms to approximate these probabilities up to a given precision, stating their complexity and error bounds. The algorithms mainly employ an adaptation of uniformization and the computation of volumes of multi-dimensional integrals under systems of linear constraints, together with different mechanisms to bound the errors.
Weighted Clustering of Sparse Educational Data

Clustering as an unsupervised technique is predominantly used in unweighted settings. In this paper, we present an efficient version of a robust clustering algorithm for sparse educational data that takes the weights, aligning a sample with the corresponding population, into account. The algorithm is utilized to divide the Finnish student population of PISA 2012 (the latest data from the Programme for International Student Assessment) into groups, according to their attitudes and perceptions towards mathematics, for which one third of the data is missing. Furthermore, necessary modifications of three cluster indices to reveal an appropriate number of groups are proposed and demonstrated.
Scale-free Networks Well Done

We bring rigor to the vibrant activity of detecting power laws in empirical degree distributions in real-world networks. We first provide a rigorous definition of power-law distributions, equivalent to the definition of regularly varying distributions that are widely used in statistics and other fields. This definition allows the distribution to deviate from a pure power law arbitrarily but without affecting the power-law tail exponent. We then identify three estimators of these exponents that are proven to be statistically consistent—that is, converging to the true value of the exponent for any regularly varying distribution—and that satisfy some additional niceness requirements. In contrast to estimators that are currently popular in network science, the estimators considered here are based on fundamental results in extreme value theory, and so are the proofs of their consistency. Finally, we apply these estimators to a representative collection of synthetic and real-world data. According to their estimates, real-world scale-free networks are definitely not as rare as one would conclude based on the popular but unrealistic assumption that real-world data come from power laws of pristine purity, void of noise, and deviations.
A musculoskeletal model customized for squatting task

Abstract Most musculoskeletal models (MSKM) are designed to evaluate gait and running, which have limited range of motion (ROM). The purpose of this study was to examine the effect of wrapping surfaces (WS) at the knee and hip joints in a MSKM, on the muscle moment arms (MA) and activations during squatting. The MSKM was then customized by changing parameters of the original WS and by implementing additional WS. The WS prevent muscles from crossing into the bones, providing realistic muscle MA for large ROM. The modified MSKM is suitable for analysis up to 138° hip and 145° knee flexions.
Content, Exposure, and Effects of Public Discourses about Marijuana: A Systematic Review

This review presents a comprehensive picture of research studies about marijuana-related content in news, social media, and advertisements. Studies that examined the extent of people’s exposure to the messages and its effects were also included. A total of 27 peer-reviewed journal articles and one thesis were located, of which 16 analyzed the content, four contained information about people’s exposure to the messages, and ten examined the effects. Over time, news coverage shifted from negative to slightly positive toward marijuana use. Social media and advertisements were overwhelmingly favorable toward marijuana use and legalization by claiming social, romantic, and health benefits of marijuana use while completely downplaying risks. A majority of social media accounts were also run by advocacy groups and industry interests, rather than individuals who were simply sharing opinions or experiences. Furthermore, absence of health authorities among the highly popular social media accounts about marijuana was notable. The news, social media, and advertising messages about marijuana were widely used by the members of public, which in turn generated some demonstrable effects on the audience’s marijuana-related perceptions and behaviors. The public and policymakers need to become aware of this information environment for potential marijuana users and take more proactive measures to protect vulnerable populations.
Cloudy Dialysate as the Initial Presentation for Lymphoma

Turbid dialysate in a patient on peritoneal dialysis is usually due to peritonitis and almost all these patients are started on empirical antibiotics pending cultures. However, in few of them with culture negative fluid, this could represent other etiologies like chyle, which requires more intensive investigations, and analysis of fluid itself reveals some rare diagnosis. We present one such report of chylous ascites with prompt investigation leading to a diagnosis of malignancy in a peritoneal dialysis patient.
Understanding Mixup Training Methods

Mixup is a neural network training method that generates new samples by linear interpolation of multiple samples and their labels. The mixup training method has better generalization ability than the traditional empirical risk minimization method (ERM). But there is a lack of a more intuitive understanding of why mixup will perform better. In this paper, several different sample mixing methods are used to test how neural networks learn and infer from mixed samples to illustrate how mixups work as a data augmentation method and how it regularizes neural networks. Then, a method of weighting noise perturbation was designed to visualize the loss functions of mixup and ERM training methods to analyze the properties of their high-dimensional decision surfaces. Finally, by analyzing the mixture of samples and their labels, a spatial mixup approach was proposed that achieved the state-of-the-art performance on the CIFAR and ImageNet data sets. This method also enables the generative adversarial nets to have more stable training process and more diverse sample generation ability.