Miracle-dz/UniFAR-FTUs huggingface dataset:
Duration of Untreated Psychosis, Referral Route, and Age of Onset in an Early Intervention in Psychosis Service and a Local CAMHS.

BACKGROUND The aim of this study was to investigate associations between demographic and clinical variables and duration of untreated psychosis (DUP) in a sample of cases of psychosis across an adult early intervention in psychosis service and a child and adolescent community team. METHOD Cross-sectional baseline data for cases of psychosis across the two teams on the caseload at a given time point were collected, including age of onset, gender, ethnicity, referral route, and DUP. RESULTS The median DUP across the entire sample was 91 days, while those patients with initial treatment for psychosis from the child and adolescent team had a median DUP of 69 days. Using multiple linear regression, there were two variables that showed a significant association with DUP: referral route (p < .001), and age of onset, with earlier age of onset associated with shorter DUP (p = .015). CONCLUSION These findings are discussed in relation to possible explanatory factors, with particular focus on service-level variables and pathways to care. It is suggested that the involvement of child and adolescent teams is vital to the work of early intervention in psychosis services.
Fine needle aspiration cytology in the diagnosis of Tuberculous lymphadenitis and utility of Ziehl Neelsen stain benefits and pitfalls

Introduction: In a developing country like India, tuberculous lymphadenitis is one of the most common presentations at OPDs. However, anti-tubercular treatment cannot be administered only on clinical suspicion. Cytomorphology with acid fast staining proves to be a valuable tool in diagnosing these cases along with culture. The study was undertaken to study the utility, limitations of fine needle aspiration cytology and various cytomorphological presentations in reference to Ziehl-Neelsen staining in tuberculous lymphadenitis and correlate the culture findings. Methods: The study was conducted for duration of two years with total of 170 cases at a tertiary care centre. The patients with clinically suspected lymphadenopathy were selected. Results: The incidence of tuberculous lymphadenitis was 68.8%. Overall AFB positivity was 64.1 %. Epithelioid cell granulomas with lymphocytes were the most common cytological picture and cases showing necrosis had highest AFB positivity. Maximum patients presented in second to fourth decade of life. Cervical region was the most common site of involvement with solitary lymphadenopathy as the most common presentation in contrast to matted lymph nodes as reported by others. Conclusion: Yet again Fine needle aspiration cytology is a safe, cheap and reliable procedure requiring minimal instrumentation and is highly sensitive to diagnose tuberculous lymphadenitis. The diagnostic index can be further increased by complementing cytomorphology with acid fast staining and culture techniques. However FNAC complimented with techniques like ELISA and PCR would give better dimensions to the current scenario of diagnosis and treatment modalities.
In-memory batch-normalization for resistive memory based binary neural network hardware

Binary Neural Network (BNN) has a great potential to be implemented on Resistive memory Crossbar Array (RCA)-based hardware accelerators because it requires only 1-bit precision for weights and activations. While general structures to implement convolution or fully-connected layers in RCA-based BNN hardware were actively studied in previous works, Batch-Normalization (BN) layer, which is another key layer of BNN, has not been discussed in depth yet. In this work, we propose in-memory batch-normalization schemes which integrate BN layers on RCA so that area/energy-efficiency of the BNN accelerators can be maximized. In addition, we also show that sense amp error due to device mismatch can be suppressed using the proposed in-memory BN design.
Learning the sparsity for ReRAM: mapping and pruning sparse neural network for ReRAM based accelerator

With the in-memory processing ability, ReRAM based computing gets more and more attractive for accelerating neural networks (NNs). However, most ReRAM based accelerators cannot support efficient mapping for sparse NN, and we need to map the whole dense matrix onto ReRAM crossbar array to achieve O(1) computation complexity. In this paper, we propose a sparse NN mapping scheme based on elements clustering to achieve better ReRAM crossbar utilization. Further, we propose crossbar-grained pruning algorithm to remove the crossbars with low utilization. Finally, since most current ReRAM devices cannot achieve high precision, we analyze the effect of quantization precision for sparse NN, and propose to complete high-precision composing in the analog field and design related periphery circuits. In our experiments, we discuss how the system performs with different crossbar sizes to choose the optimized design. Our results show that our mapping scheme for sparse NN with proposed pruning algorithm achieves 3 -- 5X energy efficiency and more than 2.5 -- 6X speedup, compared with those accelerators for dense NN. Also, the accuracy experiments show that our pruning method appears to have almost no accuracy loss.
TAD: time side-channel attack defense of obfuscated source code

Program obfuscation is widely used to protect commercial software against reverse-engineering. However, an adversary can still download, disassemble and analyze binaries of the obfuscated code executed on an embedded System-on-Chip (SoC), and by correlating execution times to input values, extract secret information from the program. In this paper, we show (1) the impact of widely-used obfuscation methods on timing leakage, and (2) that well-known software countermeasures to reduce timing leakage of programs, are not always effective for low-noise environments found in embedded systems. We propose two methods for mitigating timing leakage in obfuscated codes. The first is a compiler driven method, called TAD, which removes conditional branches with distinguishable execution times for an input program. In the second method (TADCI), TAD is combined with dynamic hardware diversity by replacing primitive instructions with Custom Instructions (CIs) that exhibit non-deterministic execution times at runtime. Experimental results on the RISC-V platform show that the information leakage is reduced by 92% and 82% when TADCI is applied to the original and obfuscated source code, respectively.
TNPU: an efficient accelerator architecture for training convolutional neural networks

Training large scale convolutional neural networks (CNNs) is an extremely computation and memory intensive task that requires massive computational resources and training time. Recently, many accelerator solutions have been proposed to improve the performance and efficiency of CNNs. Existing approaches mainly focus on the inference phase of CNN, and can hardly address the new challenges posed in CNN training: the resource requirement diversity and bidirectional data dependency between convolutional layers (CVLs) and fully-connected layers (FCLs). To overcome this problem, this paper presents a new accelerator architecture for CNN training, called TNPU, which leverages the complementary effect of the resource requirements between CVLs and FCLs. Unlike prior approaches optimizing CVLs and FCLs in separate way, we take an alternative by smartly orchestrating the computation of CVLs and FCLs in single computing unit to work concurrently so that both computing and memory resources will maintain high utilization, thereby boosting the performance. We also proposed a simplified out-of-order scheduling mechanism to address the bidirectional data dependency issues in CNN training. The experiments show that TNPU achieves a speedup of 1.5x and 1.3x, with an average energy reduction of 35.7% and 24.1% over comparably provisioned state-of-the-art accelerators (DNPU and DaDianNao), respectively.
Maximizing the Earned Benefit in an Incentivized Social Networking Environment: An Integer Programming-Based Approach

Given a social network with a predesignated set of target users, choosing a set of influential nodes (also known as seed nodes) for initial activation to maximize the influence among the target users is known as the Targeted Influence Maximization Problem. In this paper, we study this problem with a variant, where the nodes are associated with a cost and a fixed budget is allocated for the seed set selection. The target nodes are associated with a benefit value that can be earned by influencing that node, and the goal is to select a seed set within the budget, such that its initial activation leads to the maximum amount of earned benefit. For this problem, we formulate an integer program and solve it using PuLP1 module with Python 2.7. We compare the performance of our integer programming based approach with many baseline methods on real-world social network datasets and observe that the proposed approach can lead to more amount of earned benefit.
An Example-Based Facial Sketch Generation System

In this paper, an example-based facial sketch system is presented, which automatically generates a sketch from an input image. All the example sketches are drawn with a particular style. There are two key elements in this system, a non-parametric sampling method and a flexible sketch model. Given an input image pixel and its neighborhood, the conditional distribution of a sketch point is computed by querying the examples and finding all similar neighborhoods. An expected sketch image is then drawn from the distribution to reflect the drawing style. Finally, good quality facial sketches are obtained.
Can vigorous shaking cause extra-axial hemorrhage in newborns? A detailed human and porcine study.

Traumatic brain injury (TBI) is the most prominent cause of death and disability in infants. Extra-axial hemorrhage (EAH) is a common finding among infants diagnosed with abusive head trauma, but is confounded by similar findings in accidental injuries. We conducted detailed biomechanical studies to determine whether the kinematics of vigorous shaking could rupture the parasagittal bridging veins (BVs), one cause of EAH. We performed BV mechanical property tests, in situ and in vivo rapid head rotation animal experiments, infant surrogate head kinematic studies, and finite element (FE) model simulations of the neonatal porcine and infant human heads to reveal whether BV rupture may occur due to vigorous shaking. Under longitudinal tension, we found that BV mechanical properties and behavior do not depend on age, but vary with species, stretch rate, and a history of cyclic loading. In situ brain-skull displacements in the neonatal porcine head under rapid nonimpact sagittal rotations are lower than those observed in axial rotations, and altering BV properties yielded no appreciable difference in FE model brain-skull displacement. We correlated FE-predicted BV element failures during sagittal rapid nonimpact head rotations in the piglet with EAH pathology from corresponding animal studies, and identified and independently validated a threshold of 6 failed BV elements in our model were associated with detectable EAH. Finally, using this threshold and human infant BV mechanical properties in a human infant head FE model, we determined a response corridor for BV rupture, and found that rotational acceleration influences BV rupture. Our results suggest that it may be possible to generate a combination of sagittal head kinematics during vigorous shaking that could produce EAH in the human infant. Prior to legal application, additional analyses are required to validate these predictions of BV rupture against real-world data, such as torn BVs at autopsy in cases of admitted shaking without impact. Together, the integrated studies presented in this dissertation illuminate biomechanical factors that may cause BV rupture in the infant. Degree Type Dissertation Degree Name Doctor of Philosophy (PhD) Graduate Group Bioengineering First Advisor Susan S. Margulies Subject Categories Biomedical This dissertation is available at ScholarlyCommons: http://repository.upenn. edu/edissertations/1934 CAN VIGOROUS SHAKING CAUSE EXTRA-AXIAL HEMORRHAGE IN NEWBORNS? A DETAILED HUMAN AND PORCINE STUDY Stephanie Ann Pasquesi A DISSERTATION
BUOCA: Budget-Optimized Crowd Worker Allocation

Due to concerns about human error in crowdsourcing, it is standard practice to collect labels for the same data point from multiple internet workers. We here show that the resulting budget can be used more effectively with a flexible worker assignment strategy that asks fewer workers to analyze easy-to-label data and more workers to analyze data that requires extra scrutiny. Our main contribution is to show how the allocations of the number of workers to a task can be computed optimally based on task features alone, without using worker profiles. Our target tasks are delineating cells in microscopy images and analyzing the sentiment toward the 2016 U.S. presidential candidates in tweets. We first propose an algorithm that computes budget-optimized crowd worker allocation (BUOCA). We next train a machine learning system (BUOCA-ML) that predicts an optimal number of crowd workers needed to maximize the accuracy of the labeling. We show that the computed allocation can yield large savings in the crowdsourcing budget (up to 49 percent points) while maintaining labeling accuracy. Finally, we envisage a human-machine system for performing budget-optimized data analysis at a scale beyond the feasibility of crowdsourcing.
Quasi-potential as an implicit regularizer for the loss function in the stochastic gradient descent

We interpret the variational inference of the Stochastic Gradient Descent (SGD) as minimizing a new potential function named the \textit{quasi-potential}. We analytically construct the quasi-potential function in the case when the loss function is convex and admits only one global minimum point. We show in this case that the quasi-potential function is related to the noise covariance structure of SGD via a partial differential equation of Hamilton-Jacobi type. This relation helps us to show that anisotropic noise leads to faster escape than isotropic noise. We then consider the dynamics of SGD in the case when the loss function is non-convex and admits several different local minima. In this case, we demonstrate an example that shows how the noise covariance structure plays a role in "implicit regularization", a phenomenon in which SGD favors some particular local minimum points. This is done through the relation between the noise covariance structure and the quasi-potential function. Our analysis is based on Large Deviations Theory (LDT), and they are validated by numerical experiments.
SPARCs for Unsourced Random Access

Unsourced random-access (U-RA) is a type of grant-free random access with a virtually unlimited number of users, of which only a certain number <inline-formula> <tex-math notation="LaTeX">$K_{a}$ </tex-math></inline-formula> are active on the same time slot. Users employ exactly the same codebook, and the task of the receiver is to decode the list of transmitted messages. We present a concatenated coding construction for U-RA on the AWGN channel, in which a sparse regression code (SPARC) is used as an inner code to create an effective outer OR-channel. Then an outer code is used to resolve the multiple-access interference in the OR-MAC. We propose a modified version of the approximate message passing (AMP) algorithm as an inner decoder and give a precise asymptotic analysis of the error probabilities of the AMP decoder and of a hypothetical optimal inner MAP decoder. This analysis shows that the concatenated construction under optimal decoding can achieve a vanishing per-user error probability in the limit of large blocklength and a large number of active users at sum-rates up to the symmetric Shannon capacity, i.e. as long as <inline-formula> <tex-math notation="LaTeX">$K_{a}R < 0.5\log _{2}(1+K_{a} { \mathsf {SNR}})$ </tex-math></inline-formula>. This extends previous point-to-point optimality results about SPARCs to the unsourced multiuser scenario. Furthermore, we give an optimization algorithm to find the power allocation for the inner SPARC code that minimizes the <inline-formula> <tex-math notation="LaTeX">${\mathsf {SNR}}$ </tex-math></inline-formula> required to achieve a given target per-user error probability with the AMP decoder.
Computing Optimal Coarse Correlated Equilibria in Sequential Games

We investigate the computation of equilibria in extensive-form games where ex ante correlation is possible, focusing on correlated equilibria requiring the least amount of communication between the players and the mediator. Motivated by the hardness results on the computation of normal-form correlated equilibria, we introduce the notion of normal-form coarse correlated equilibrium, extending the definition of coarse correlated equilibrium to sequential games. We show that, in two-player games without chance moves, an optimal (e.g., social welfare maximizing) normal-form coarse correlated equilibrium can be computed in polynomial time, and that in general multi-player games (including two-player games with Chance), the problem is NP-hard. For the former case, we provide a polynomial-time algorithm based on the ellipsoid method and also propose a more practical one, which can be efficiently applied to problems of considerable size. Then, we discuss how our algorithm can be extended to games with Chance and games with more than two players.
Don't Panic! Better, Fewer, Syntax Errors for LR Parsers (Artifact)

Syntax errors are generally easy to fix for humans, but not for parsers in general nor LR parsers in particular. Traditional 'panic mode' error recovery, though easy to implement and applicable to any grammar, often leads to a cascading chain of errors that drown out the original. More advanced error recovery techniques suffer less from this problem but have seen little practical use because their typical performance was seen as poor, their worst case unbounded, and the repairs they reported arbitrary. In this paper we introduce the CPCT+ algorithm, and an implementation of that algorithm, that address these issues. First, CPCT+ reports the complete set of minimum cost repair sequences for a given location, allowing programmers to select the one that best fits their intention. Second, on a corpus of 200,000 real-world syntactically invalid Java programs, CPCT+ is able to repair 98.37% of files within a timeout of 0.5s. Finally, CPCT+ uses the complete set of minimum cost repair sequences to reduce the cascading error problem, where incorrect error recovery causes further spurious syntax errors to be identified. Across the test corpus, CPCT+ reports 435,812 error locations to the user, reducing the cascading error problem substantially relative to the 981,628 error locations reported by panic mode.
Optimal Space-Time Block Code Designs Based on Irreducible Polynomials of Degree Two

The main of this paper is to prove that in terms of normalized density, a space-time block code based on an irreducible quadratic polynomial over the Eisenstein integers is an optimal space-time block code compared with any quadratic space-time block code over the ring of integers of imaginary quadratic fields. In addition we find the optimal design of space-time block codes based on an irreducible quadratic polynomial over some rings of imaginary quadratic fields.
Prevalence and Associated Factors of Low Back Pain Among Teachers Working at Governmental Primary Schools in Addis Ababa, Ethiopia: A Cross Sectional Study

Introduction: Low back pain (LBP) is among the greatest public health threats that individuals face around the world. Teachers are recognized to denote an occupational cluster in which it appears to partake a great prevalence of LBP. Nevertheless, data about its extent and associated factors among school teachers is limited in Ethiopia. Therefore, the objective of this study was to assess the prevalence and associated factors of LBP among teachers working at governmental primary schools in Addis Ababa, Ethiopia, 2016. Materials and Methods: A school centered cross sectional study was conducted. The total number teachers participated in the study was 827. A self-administered questionnaire was used. Bivariate and multivariable logistic regression analyses were employed to assess the outcome of different factors on LBP. A statistical significance was assumed when, P value ≤0.05 at 95% CI. Results: The prevalence of LBP was found to be 340 (44%). The multivariable logistic regression showed that, ergonomics training, enough teachers in school, prolonged sitting posture and poor work place social environment were significantly associated with LBP (AOR=2.246, 3.786, 0.237 and 6.799 respectively). Those respondents who are female, aged 30-40 and aged >40 also showed a significant association (AOR=1.547, 1.96, 4.895 respectively). Conclusion: LBP is a thoughtful problem among governmental primary school teachers in Addis Ababa. Work place social environment, work posture, enough teachers in the school and ergonomic training are among the factors which have to be given a great attention in order to surmount the situation.
A study for determining computer programming students’ attitudes towards programming and their programming self-efficacy / Bilgisayar programcılığı öğrencilerinin programlamaya karşı tutum ve programlama öz-yeterliklerinin belirlenmesine yönelik bir çalı

This study aimed at examining computer programming students’ attitudes towards programming and their programming self-efficacy as well as the relationship between them. The study was conducted in a university located in the north of Turkey in the spring semester of the 2013-2014 academic year. The research sample consisted of 325 students studying in three different Computer Programming programs of the Department of Computer Technologies affiliated to a vocational school of the above-mentioned university. The Attitude Scale toward Computer Programming (AStCP) and the Computer Programming Self-Efficacy Inventory (CPSEI) were used for data collection. The descriptive survey model was employed in the study. The data obtained in the study were analyzed via the Mann Whitney U-test, the Kruskal-Wallis test, and the Spearman Brown rho correlation coefficient. The research results indicated that the students generally had positive attitudes towards computer programming, and they had a medium-level programming self-efficacy. The attitudes of the students towards programming varied statistically significantly by gender, grade, and education type. Similarly, programming self-efficacy varied statistically by the aforementioned three variables. Finally, a positive medium relationship was found between the students’ attitudes towards programming and their self-efficacy.
Possible benefits of micronutrient supplementation in the treatment and management of HIV infection and AIDS

Recently, several reports have indicated that individuals living with HIV/AIDS undergo a condition of chronic oxidative stress with a resultant decline in nutritional antioxidants and other micronutrients. It has also been reported that these micronutrient deficiencies interfere with immune functions, weaken epithelial integrity, contribute to oxidative stress and enhance HIV disease progression. Reports from observational studies have led to an increasing interest in the possible benefits of micronutrient supplementation as a cost-effective strategy for improving oxidative and nutritional status. Micronutrient supplementation may also assist in the possible prevention of vertical transmission of HIV from mother to child especially in low-income countries where antiretroviral therapy and prophylactic drugs are not readily available. However, there are conflicting reports from other observational studies as to the usefulness or benefits of micronutrient supplementation in the treatment and management of HIV/AIDS. In this review we examine possible benefits of micronutrient supplementation in the treatment and management of HIV infection and AIDS.