Miracle-dz/UniFAR-FTUs huggingface dataset:
Gun Violence News Information Retrieval using BERT as Sequence Tagging Task

The growth in both frequency and severity of gun violence in the United States has necessitated increased research into prevention, despite the lack of funding. Comprising more than 60k gun violence media articles with a total data size of 520 MB, the gun violence database (GVDB) was developed to assist natural language processing researchers in developing and testing prevention methods. Original research based on the GVDB utilized a span-selection model to extract shooter and victim information, but their works might potentially trim out important span candidates. We proposed a new approach to improve identification accuracy and recognize every token in a sentence using a sequence tagging technique. We implemented a BIO sequence tagging model at the token-level using BERT, then further classified each token using LSTM, BiLSTM, and CRF. We found that utilizing BERT as an embedding layer, and decoding word representation as a sequence tagging task, improved shooter/victim identification compared to a span-selection model. We believe that if this improved model is combined with gun violence related keywords, automated techniques could be implemented to identify precursors/risks to gun violence on social media, allowing for intervention by law enforcement or community agencies before escalation to deaths.
Re-Thinking Technology Transfer as Technology Translation: A Case Study of Health Information Systems in Mozambique

This thesis presents a theoretically and empirically informed analysis of “technology transfer” of a computer-based HIS within a South-North-South network involving Mozambique, South Africa and Norway. Technology transfer from the North to the South has historically been problematic, including the domain of ICTs more broadly and Health Information Systems (HIS) in particular. It is argued that the diffusion perspective, the dominant approach employed to technology transfer, is inherently problematic, and there is the urgent need to identify alternative conceptualizations. Against this backdrop, this thesis addresses two research aims: (i) to develop a theoretical conceptualization of technology transfer based on a “translation perspective”; and, (ii) to understand and address the (empirical) challenges shaping the translation process of a computer-based HIS in SouthNorth-South networks. The theoretical foundation of the thesis is informed by translation perspective offered by Actor Network Theory. The concept of translation is adapted in this thesis to reflect a process of incremental change involving a socio-technical heterogeneous network, which leads to the development of sustainable network, implying that they are both institutionalized and flexible enough to change as per evolving institutional needs. Given this conceptualization of translation, a theoretical framework is developed which identifies four influences on this translation process, (1) of history: legacy systems and installed base; (2) of the role of adaptation: how software is adapted to the local context; (3) of the role of participation: how users exercise control over HIS; and, (4) of the process of customization: the balance between localization and internationalization. The empirical foundation of the thesis is a South-North-South collaborative network case study of computer-based HIS adaptation in Mozambique, under an ongoing action research project called Health Information Systems Programme which was initiated in South Africa in 1994 and its subsequent “transfer” to Mozambique in 1998. The thesis examines the various challenges and opportunities that were involved in this process, based on empirical work carried out during the period from 2000 to 2003 in selected districts, provinces, and also at the national level. The research design is characterized by four key features, (1) action research; (2) longitudinal case study; (3) multiple level analysis; and, (4) interpretative approach. The research approach is primarily based on the principles of prototyping, which was applied as four iterative action research cycles: (1) situation analysis; (2) selecting the minimum functionality; (3) matching the prototype with the context; and, (4) scaling up the DHIS. The research findings are articulated in research papers presented as appendix of the thesis, which address the four themes identified above. In each of these four cases I have tried to
Compliance with Stay-at-Home Orders During COVID-19

Are stay-at-home orders effective during a global pandemic? Although stay-at-home orders should help to slow the spread of contagious diseases (like COVID-19) by reducing person-to-person contact outside a household, these orders are only effective if people actually stay at home. This study uses data on the mobility of (anonymized) smartphones within states before and after the enactment of stay-at-home orders to understand the effects of stay-at-home orders on mobility. The dataset contains over ten million observations on the movements of smartphones across all states within the US, which allows for an analysis of compliance with stay-at-home orders without worrying about the potential for self-reporting biases. By using a quasi-experimental method, this study overcomes biases that can come from comparing pre and post policy trends (due to unmeasured differences across states). The results from a difference-in-difference analysis suggest that stay-at-home orders are associated with a 4.6% increase in the percent of smartphones that remained at home during the late spring of 2020 across the US. Although there is a statistically significant difference across states with and without stay-at-home orders, it is important to note that the average percent of smartphones that remained at home was 40% in stay-at-home states compared to 36% in states without stay-at-home orders. The results also show that penalties (jail time and fines) had no significant effect on compliance with stay-at-home orders (considering all states with such orders), while compliance with stay at home orders in Republican controlled states was 5.3% lower than in Democratic controlled states. Further, states with higher population densities had the highest percent of smartphones that remained at home after stay-at-home orders went into effect (at 45%). Overall, the results in this paper suggest that stay-at-home orders had a small but significant impact on mobility, while also suggesting that studies of individual behaviors and choices will be necessary to understand when and why people may be more or less willing to shelter at home during a global pandemic.
Characterizing Soft Error Vulnerability of CPUs Across Compiler Optimizations and Microarchitectures

In this paper, we present a fine-grained characterization of the impact of transient faults (soft errors) on program execution for different compiler optimization levels and two out-of-order microarchitectures through extensive microarchitecture-level fault injection experiments. We evaluate how the different levels of compiler optimization impact the failure probability of the most important hardware structures in two different out-of-order Arm microarchitectures (Cortex-A15 and Cortex-A 72). We analyze 32 different executables: sources come from eight different benchmarks with large datasets, each one compiled with three different levels of compiler optimization (O1, O2, 03) and the baseline unoptimized code level (O0); execution times of the 32 binaries range from 72M cycles to 1.4B cycles. We show how the different compiler optimization levels affect the vulnerability of eight important hardware structures. We perform extensive soft error fault injection campaigns to measure with high statistical significance the Architectural Vulnerability Factor (AVF) of all hardware structures at each optimization level, and identify the structures whose vulnerability is more sensitive to compiler optimizations. Finally, we aggregate the vulnerabilities of the hardware structures into the overall failure rates of the microprocessor and complement with a performance-aware comparison of all optimization levels. The performance-aware vulnerability analysis shows that higher optimization levels counterbalance their increased vulnerability with the speedup the deliver. From the failure rates sole point of view, an unprotected design has variable behavior, however, when typical ECC protection is employed the O2 optimization level is consistently the most robust one, while for more recent microarchitectures, O1 can be equally robust to O2 which is not the case in older microarchitectures.
An Approach to Support Human-in-the-Loop Big Data Software Development Projects

There is a lack of approaches and tools to support the development of projects in which humans and machines (e.g., machine learning algorithms) need to collaborate to achieve a specified goal. Specifically, given a set of software development tasks to develop a project collaboratively, how can these tasks be assigned to humans or machines to perform each task most efficiently and effectively? Such understanding is essential to support new methodologies for developing human-in-the-loop approaches in which machine learning automated procedures assist software developers in achieving their tasks. This paper describes our work in progress towards providing an approach to guide the assignment of tasks in developing human-in-the-loop big data (science) software development projects. The paper provides several contributions, including the provision of (i) a human-in-the-loop approach for the development of big data software development projects; (ii) the application of the approach to two case studies; (iii) a discussion of implications and research opportunities.
By hybrid neural networks for prediction and interpretation of transcription factor binding sites based on multi-omics

Transcription factors (TFs) binding sites prediction and analysis are vital for comprehending cis-regulatory mechanisms. Recently, several deep learning-based methods have shown outstanding performance on TFs binding sites (TFBSs) recognition by leveraging solely base-pair arrangement of regulatory sequences. Except for the aforementioned genomic features, the epigenomics signature represented by the histone modification is also a critical factor related to TFs-DNA binding. We present a multi-omics based hybrid neural network, dubbed as BHSite, for TFBSs prediction by adaptively integrating base-pair arrangements and histone modification signatures. Experiments over 196 ChIP-seq datasets demonstrate that BHSite significantly outperforms several state-of-the-art methods in TFBSs prediction. Besides, studies of the relative importance of histone modification signatures prove that diverse signatures complement each other. Furthermore, visualization analysis of Squeeze-and-Excitation Network reveals the contribution of multi-omics latent features concerning different cell types to TFBS prediction. Thus, BHSite improves both performance and interpretability by combining the multi-omic features into deep learning architecture.
Optimizing Bike-Share Repositioning: Networked Inventory Management with Spatiotemporal Modeling

In a bike-sharing system, demand loss is primarily due to out-of-stock stations. One solution to tackle this problem is to rebalance the bike inventory of the stations through repositioning. In a large-scale bike-sharing system, bike repositioning typically works in two steps: The platform generates the reposition tasks, and then the operators execute those tasks. In this paper, we focus on the problem of determining the reposition tasks, which is a core problem for the platform operations. We model the bike-sharing system as a networked inventory system and propose a select-and-match method to generate optimal repositions, seamlessly combining inventory management and spatiotemporal value learning techniques. We compute the optimal (s, S) policy to select the stations with excess and deficit bike stocks. Subsequently, we solve a minimum cost maximum flow problem for rebalance assignment while considering the long-term effects of repositioning, characterized by a bike transition value. We design a spatiotemporal value network to learn the values. We evaluate the performance of the proposed method in a simulator based on real bike-sharing data, and the results demonstrate the superiority of our approach over the other alternatives.
Air-Water Communication and Sensing with Light

The ability to communicate and sense across the air-water boundary is essential for efficient exploration and monitoring of the underwater world. Existing wireless solutions for communication and sensing typically focus on a single physical medium and fall short in achieving high-bandwidth communication and accurate sensing across the air-water interface without any relays on the water surface. We study the use of laser light in this context given its ability to effectively pass the air-water boundary. We present a holistic system framework to address practical challenges such as ambient light interference and environmental dynamics. The proposed AmphiLight framework achieves 5 Mbps bi-directional throughput and zero bit error rate with ranges up to 6.1 m in strong ambient light conditions, and connection time improvements between 47.1 % and 29.5% during wave dynamics. Our ongoing effort extends to realizing robust air-water sensing that enables aerial drones to track multiple underwater robots for topology planning and coordination.
Applying self-normalizing neural networks to tackle data-driven soft sensing problems in manufacturing lines

In this paper we present the results of our participation in the IEEE BigData 2021 Cup: Soft Sensing at Scale. The goal of the contest is to classify eleven different tasks based on high-dimensional time series soft sensing data from a wafer manufacturing process. To achieve the classification, two different data-driven soft sensing approaches have been implemented. One approach is especially optimized towards cost and runtime, whereas the other achieves better accuracy but comes at a higher runtime and cost. Both methods can be deployed in the manufacturing line, e.g. a wafer manufacturing factory, by using incremental learning, thus making sure that the models are always up to date in an changing environment with different data distributions. Both models can be integrated for different soft sensing scenarios in a manufacturing line, i.e. the cost-optimized model in a timing-critical scenario and the accuracy-optimized model in a scenario requiring exactly that high accuracy. This guarantees low costs and high accuracy where they are needed.
Some Aspects of Internet Portal Market Competition

Stony Brook University Libraries. Department of Economics. Lawrence Martin (Dean of Graduate School), Hugo Benitez-Silva - Dissertation Supervisor Associate Professor, Department of Economics, Mark R. Montgomery - Chairperson of Defense Professor, Department of Economics, Wei Tan Assistant Professor, Department of Economics, Thomas Sexton Professor, College of Business.
Design of a Soft Robotic Glove for Hand Rehabilitation of Stroke Patients with Clenched Fist Deformity using Inflatable Plastic Actuators

In this paper, we present a soft robotic glove designed to augment hand rehabilitation for stroke patients with clenched fist deformity. The robotic glove provides active finger extension for hand rehabilitative training, through its embedded inflatable actuators that are fabricated by heat bonding of flexible plastic sheets. Upon pressurization, the actuators inflate, stiffen, and extend the fingers. The actuators were embedded in the finger pockets of a glove. In this work, the device was evaluated in terms of its extension torque on a hand model, a healthy subject and a stroke patient. Preliminary results showed that the device was able to generate significant torques to provide assistance in finger extension and hand opening on both healthy and stroke participants.
Differential recognition and cytokine induction by the peptidorhamnomannan from Sporothrix brasiliensis and S. schenckii

Sporotrichosis is a deep mycosis caused by dimorphic species of the genus Sporothrix, with differences in pathogenicity between S. schenckii and S. brasiliensis species. Recently, it was discovered that the cell wall peptidorhamnomannan (PRM) of Sporothrix spp. is a pathogen associated molecular pattern (PAMP). Interestingly, S. brasiliensis PRM has additional unknown rhamnose residues. We hypothesize that the structural differences of Sporothrix spp PRMs impact the host’s immune response and may explain the severity of sporotrichosis caused by S. brasiliensis. Here we demonstrate that S. brasiliensis yeasts and its PRM (S.b PRM) induced a strong inflammatory response in human PBMCs, with high production of TNF-α, IL-6 and IL-1β and induction of T-helper cytokines IFN-γ, IL-17 and IL-22. In contrast, S. schenckii yeasts and its PRM induced higher concentrations of interleukin-1 receptor antagonist (IL-1Ra), which resulted in low production of T-helper cytokines such as IL-17 and IL-22. CR3 and dectin-1 were required for cytokine induction by both PRMs, while TLR2 and TLR4 were required for the response of S.s PRM and S.b PRM, respectively. IL-1β and IL-1α production induced by S. brasiliensis yeasts and S.b PRM were dependent on inflammasome and caspase-1 activation. S. schenckii and S.s PRM were able to induce IL-1β independent of ROS. In conclusion, these findings improve our understanding of the pathogenesis of Sporothrix spp. by reporting differences of immunological responses induced by S. schenckii and S. brasiliensis. The study also opens the gateway for novel treatment strategies targeting local inflammation and tissue destruction induced by S. brasiliensis infection through IL-1 inhibition.
Mode selection mechanism in traveling and standing waves revealed by Min wave reconstituted in artificial cells

Reaction-diffusion coupling (RDc) generates spatiotemporal patterns, including two dynamic wave modes: traveling and standing waves. Although mode selection plays a significant role in the spatiotemporal organization of living cell molecules, the mechanism for selecting each wave mode remains elusive. Here, we investigated a wave mode selection mechanism using Min waves reconstituted in artificial cells, emerged by the RDc of MinD and MinE. Our experiments and theoretical analysis revealed that the balance of membrane binding and dissociation from the membrane of MinD determines the mode selection of the Min wave. We successfully demonstrated that the transition of the wave modes can be regulated by controlling this balance and found hysteresis characteristics in the wave mode transition. These findings highlight a novel role of the balance between activators and inhibitors as a determinant of the mode selection of waves by RDc and depict a novel mechanism in intracellular spatiotemporal pattern formations. Teaser Activator-inhibitor balance determines whether a nonlinear wave in live cells becomes a traveling wave or standing wave.
Doing Iterative and Adaptive Work

Many of the challenges in international development are complex in nature. They involve many actors in uncertain contexts and with unclear solutions. Our work has proposed an approach to addressing such challenges, called Problem Driven Iterative Adaptation (PDIA). This paper is the most recent in a series intended to show how one can do PDIA, building on the first paper, "Doing Problem Driven Work. " The current paper addresses a key part of the approach one moves to once a problem has been identified, performing real-time experimental iterations. This is intended as a practical paper that builds on experience and embeds exercises for readers who are actually involved in this kind of work.
Transition location effects on normal shock wave-boundary layer interactions

© 2015 by Todd Davidson and Holger Babinsky. Published by the American Institute of Aeronautics and Astronautics, Inc. This paper reports recent work undertaken to investigate the interaction between normal shocks and laminar/transitional boundary layers. Laminar ow is desirable for its low skin friction, but problems are expected with shock interactions as the boundary layer will be prone to separation. Experiments have been performed on a at plate to explore the fundamental interaction without complex geometries and ow fields, and on a transonic aerofoil to research the in uence of the imposed pressure field. The boundary layer state before the shock on the at plate does not appear to significantly affect the downstream ow development, contrary to expectations, as the laminar separation is so small. On the aerofoil, a similar result is observed, with no change in the buffet onset Mach number. This implies that normal shock wave-laminar boundary layer interactions are not as detrimental as originally feared, and are in fact quite benign.
Potential Impact of Serum Magnesium and Serum Calcium on Premenstrual Distress in Students: A Case-Control Study

Aim: To evaluate and compare the serum magnesium and calcium levels in students with premenstrual distress with those of normal students. Methodology: The present study was conducted at Gandhi Medical College, Telangana, India. Fifty medical and paramedical students of age 18-24years with regular cycles were included in the study and were given the PMS diagnostic questionnaire developed by the University of California, San Diego recommended by the American College of Obstetrics and Gynecology. Students showing premenstrual symptoms were considered as Study group (25) and those without symptoms as controls (25). 5ml of fasting venous blood samples were collected from both groups between the 15-28day (luteal phase) of the menstrual cycle. These samples were tested using Beckman Coulter for serum calcium and magnesium levels. Comparison between the groups was done by student’s t-test and a p-value<0.05 was considered significant. Results: Significant lower values for serum magnesium levels were observed in study group compared to controls. The prevalence of magnesium deficiency was significant in the study group. Conclusion: Low magnesium levels are probably an important cause of premenstrual distress. Based on serum values, more than half of students with premenstrual distress were found to be deficient in magnesium.
Empirical Correlation between Inelastic and Elastic Spectral Displacement Demands

Inelastic spectral displacement demand is arguably one of the most effective, simplified means of relating earthquake intensity to building damage. However, seismic hazard assessment is typically conducted using empirical ground-motion prediction equations (GMPEs) that only provide indications of elastic spectral response quantities, which an engineer subsequently relates to inelastic demands using empirical relationships such as the equal-displacement rule. An alternative approach is to utilize relationships for the inelastic spectral displacement demand directly within the seismic hazard assessment process. Such empirical relationships are developed in this work, as a function of magnitude, distance, building period, and yield strength coefficient, for four different hysteretic models that are representative of a wide range of possible structural typologies found in practice. The new relationships are likely to be particularly useful for performance-based seismic design and assessment.