Miracle-dz/UniFAR-FTUs huggingface dataset:
One Group’s Advantage or Another Group’s Disadvantage? How Comparative Framing Shapes Explanations of, and Reactions to, Workplace Gender Inequality

Gender inequality is usually described as women’s disadvantage, only rarely as men’s advantage. Moreover, it is often illustrated by metaphors such as the glass ceiling—an invisible barrier to women’s career advancement—metaphors that often also focus on women’s disadvantage. Two studies (N = 228; N = 495) examined effects of these different ways of framing gender inequality. Participants read about gender inequality in leadership with a focus on either women or men, and either without a metaphor (women underrepresented vs. men overrepresented) or with a women-focused or men-focused metaphor (glass ceiling/labyrinth vs. old boys’ club). Metaphors caused participants to perceive gender inequality as (somewhat) more important. Regardless of metaphor use, women-focused descriptions led to more explanations of inequality focusing on women relative to explanations focusing on men, as well as to more suggestions of interventions targeting women at the expense of interventions aimed at systemic changes.
Lost and Found: Remnants of the Big Savannah and Their Relationship to Wet Savannas in North Carolina

Abstract Conversion to agriculture and plantations, development, and fire suppression have reduced the extent of savannas in the southeastern United States, and there is a need to catalog and classify the remaining savannas for both restoration and resource management purposes. The Big Savannah was a wet savanna in North Carolina that was destroyed in the 1950s, and subsequent vegetation classifications have generally not accommodated well the unique natural plant community of the Big Savannah. Vegetation reminiscent of that described for the Big Savannah was discovered north of the original site and designated as Wells Savannah. To evaluate the uniqueness of the savanna vegetation at Wells Savannah, we compiled a data set from permanent quadrats with information on vegetation and environmental variables from other Outer Coastal Plain savannas to compare with similar data from the natural community at Wells Savannah. We also inventoried an additional 26 quadrats on a tract adjacent to Wells Savannah that had experienced fire suppression. Results from multivariate analyses demonstrated clear differences between the Wells Savannah quadrats and other regional wet savanna quadrats based on both vegetation and soils. A number of species and several soil characteristics (higher clay percentage, and higher available iron and boron) distinguish Wells Savannah from other wet savannas. Although the fire suppressed quadrats near Wells Savannah had lower species richness, typical savanna species such as Ctenium aromaticum and Calamovilfa brevipilis were still present. Further exploration of fire-suppressed tracts in the area may yield more wet savanna inclusions similar to the former Big Savannah.
High speed DRAM transceiver design for low voltage applications with process and temperature variation-aware calibration

of the Dissertation High Speed DRAM Transceiver Design for Low Voltage Applications with Process and Temperature Variation-Aware Calibration by Yongsuk Choi Doctor of Philosophy in Electrical and Computer Engineering Northeastern University, Nov 2016 Dr. Yong-Bin Kim, Adviser Scaling down of integrated circuits (ICs) enables continuous improvement of the operation frequency on many processor and memory capacity. On the contrary, bus performance and I/O channel speed have not been followed the growth. The link speed is limited by the channel bandwidth due to its low pass filtering characteristics. To extend the channel bandwidth and increase signal integrity with low power consumption at multi-Gb/s date rates, new low-voltage signaling is adopted and channel equalization is an essential technique in mobile DRAM I/O part. The impedance calibration for output driver of transmitter is a widely used and effective way to improve signal integrity against various chip variations such as process related parameters and operating temperature. As a fast and efficient impedance calibration solution, a novel on-chip impedance calibration methodology for the LVSTL (low voltage swing
A Brief Comparison Between An Automated Testing And Manual Testing

--Software testing is a huge domain, but it can be broadly categorized into two areas which are manual testing and automated testing. Testing is an integral part of any successful software project. The type of testing (manual or automated) depends on various factors like including project requirements, budget, timeline, expertise, and suitability. Three vital factors of any project are of course time, cost, and quality – the goal of any successful project is to reduce the cost and time required to complete it successfully while maintaining quality output. When it comes to testing, one type may accomplish this goal better than the other. Both manual and automated testing offers benefits and disadvantages. It is worth knowing the difference and when to use one or the other for best results. This paper discusses the difference between both technique and how these techniques are implemented.
SmartFork: Partitioned Multicast Allocation and Switching in Network-on-Chip Routers

Multicast on-chip communication is encountered in various cache-coherence protocols targeting multi-core processors, and its pervasiveness is increasing due to the proliferation of machine learning accelerators. In-network handling of multicast traffic imposes additional switching-level restrictions to guarantee deadlock freedom, while it stresses the allocation efficiency of Network-on-Chip (NoC) routers. In this work, we propose a novel NoC router microarchitecture, called SmartFork, which employs a versatile and cost-efficient multicast packet replication scheme that allows the design of high-throughput and low-cost NoCs. The design is adapted to the average branch splitting observed in real-world multicast routing algorithms. Compared to state-of-the-art NoC multicast approaches, SmartFork is demonstrated to yield higher performance in terms of latency and throughput, while still offering a cost-effective implementation.
Influence of Neighbouring Tree Species on AFLP Variants of Endangered Picea chihuahuana Martínez Populations on the Sierra Madre Occidental, North-Western Mexico

ABSTRACT: Plant-plant interactions, which are defined as the effect of one plant on another of the same or different species, are inherently local in nature. Plants interact in both positive and negative ways, which are governed by the same underlying ecological dynamic processes. The Mexican endemic Picea chihuahuana Martínez is a largely untouched tree species and it is therefore an excellent model for estimating genetic potential of plant-plant interactions under natural conditions. Because of the scarce research examining plant-plant interactions at the genetic level, we tested the hypothesis that specific genetic variants of P. chihuahuana are associated with (and likely selected by) the presence of particular neighbouring tree species. To explore this question, we applied AFLP genotyping to five P. chihuahuana populations in the State of Durango, North-western Mexico. Under the assumption that the variables genetic variant of P. chihuahuana and nearest-neighbour tree species are directly related to each other, the results show a statistically significant association between such genetic variants - at one AFLP locus in one Picea population - and two nearest-neighbour conifer species (P. chihuahuana Martínez and Pinus arizonica Engelm.). The findings indicate that interactions between genetic variants and species diversity may be crucial in shaping plant communities. However, further studies are required for a better understanding of the possible roles that such associations between genetic variants might play.
The +1 Method: Model-Free Adaptive Repositioning Policies for Robotic Multi-Agent Systems

Robotic multi-agent systems can efficiently handle spatially distributed tasks in dynamic environments. Problem instances of particular interest, and generality are the dynamic traveling repairman problem, and the dynamic vehicle routing problem. Operational policies for robotic fleets solving these two problems take decisions in an online setting with continuously arriving demands to optimize service level, and efficiency, and can be classified along several lines. First, some require a model of the demand, e.g., based on historical information, while others work model-free. Second, they are designed for different operating conditions from light to heavy system load. Third, they work in a time-invariant or time-varying setting. We present a novel class of model-free operational policies for time-varying demands, which have performance independent of the load factor, a combination of properties not achieved by other operational policies in the literature. The underlying principle of the introduced policies is to send available robots to recent service request locations. In simple terms, they rely on sending more than one robot for every service request arriving to the system. This leads to an advantage in scenarios where demand is non-uniformly distributed, and correlated in space, and time. We provide performance guarantees for both the time-invariant, and the time-varying cases as well as for correlated demand. We verify our theoretical results numerically. Finally, we apply our operational policy to the problem of mobility-on-demand fleet operation, and demonstrate that it outperforms model-based, and complex algorithms across all load ranges, despite its simplicity.
Effects of Social Media on Purchase Intention and Brand Equity of Marketplace Sites in Indonesia

Marketplace is a popular term used in Indonesia online shopping experience, which a third party site who consists of many sellers (companies or individuals) selling their products to their customers. The purpose of this study was to analyze the effect of social media on the purchase intention of men's fashion item on the Marketplace sites. This sample in this study was the customers of men's fashion items in five largest marketplace sites in Indonesia, they are Tokopedia, Shopee, Lazada, Bukalapak and OLX. The results showed that all hypotheses were significantly positive, except for brand knowledge and perceived persuasiveness on marketplace brand equity; perceived in formativeness on purchase intention; and perceived usefulness as moderator between perceived persuasiveness and marketplace brand equity which are showed insignificant results. These results verify that companies must realize the enormity of the social media impact on the value of the company's equity, which is a big thrust to trigger purchase intention.
Identification of novel antibiotic resistance genes through the exploration of mobile genetic elements

Backgrounds and aims: The evolution of multi-resistant pathogens is seriously threatening our ability to provide modern healthcare. Many of the mobile resistance factors in clinics appear to originate from environmental bacteria. In this thesis, strategies are developed and applied to explore and identify novel antibiotic resistance genes (ARGs) captured and carried by different mobile genetic elements on a largescale. The primary aim is to identify novel, mobile ARGs that could become, or that already are, a threat to the public health. Method: We used targeted amplicon sequencing (Paper I) and functional metagenomics of amplified gene cassettes (Papers II and III) that were recovered from two polluted environments. In Paper IV, we studied the associations between insertion sequences (ISs) and ARGs by analyzing all sequenced bacterial genomes. Moreover, several thousand metagenomic runs were analyzed to estimate the abundance of ARGs (including novel ARGs) and ISs. Results and discussions: In Paper I, we found a novel mobile sulfonamide resistance gene providing a high level of resistance when expressed in E. coli. By using functional metagenomics (Papers II and III), we identified a completely new integronborne aminoglycoside resistance gene that was already present, but previously not identified, in multi-resistant clinical isolates collected from patients in Italy, as well as in two food-borne Salmonella enterica isolates from the USA. Moreover, we described and characterized the first ampCs encoded as integron gene cassettes with increased transmission opportunities to move between different bacterial species. Metagenomic analysis showed that all three genes are spread in different geographical locations and were abundant in wastewater environments. In Paper IV, ISs and tentative composite transposons with strong associations with ARGs were identified, and we proposed that these could be explored further to discover novel ARGs, for example with an amplicon sequencing approach. Finally, metagenomic analyses shed light on the environments that potentially contain such ISs. Conclusions: Targeted amplicon sequencing and its integration with functional metagenomics were successful in finding novel resistance gene cassettes that have already accumulated in pathogens or have the potential to do so. With a well-designed strategy, the content of ISs could be explored to identify unknown mobile ARGs in addition to those associated with integron gene cassettes. Finally, the information produced in this thesis is the initial seed for an accessible web application useful in studying the association between ISs and ARGs.
Learning Energy-Based Approximate Inference Networks for Structured Applications in NLP

Structured prediction in natural language processing (NLP) has a long history. The complex models of structured application come at the difficulty of learning and inference. These difficulties lead researchers to focus more on models with simple structure components (e.g., local classifier). Deep representation learning has become increasingly popular in recent years. The structure components of their method, on the other hand, are usually relatively simple. We concentrate on complex structured models in this dissertation. We provide a learning framework for complicated structured models as well as an inference method with a better speed/accuracy/search error trade-off. The dissertation begins with a general introduction to energy-based models. In NLP and other applications, an energy function is comparable to the concept of a scoring function. In this dissertation, we discuss the concept of the energy function and structured models with different energy functions. Then, we propose a method in which we train a neural network to do argmax inference under a structured energy function, referring to the trained networks as"inference networks"or"energy-based inference networks". We then develop ways of jointly learning energy functions and inference networks using an adversarial learning framework. Despite the inference and learning difficulties of energy-based models, we present approaches in this thesis that enable energy-based models more easily to be applied in structured NLP applications.
Autonomous Cinematography with Teams of Drones

Aerial cinematography with drones is becoming quite popular, with many new commercial platforms for both amateurs and professionals. The main reasons for this breakout are their reduced cost, their maneuverability and their ability to produce unique shots, mainly when compared to static cameras or dollies. Moreover, using multiple drones to film the same event concurrently widen the range of possibilities available for the production team, specially in outdoor, largescale scenarios. For instance, several action points could be covered simultaneously, or multi-camera shots could be carried out, achieving more artistic views. However, there are multiple issues to consider when operating an multi-drone system for cinematography, which increases considerable the operator’s load. To name a few, moving targets have to be tracked, smooth trajectories are needed for the drones, different shots should be assigned to the available drones taking into account their remaining battery, collisions between the drones should be prevented, drones should not access no-fly zones, etc. There are multiple works addressing camera motion planning for aerial filming [1], [2]. The common idea is to formulate some kind of optimization problem to generate smooth camera trajectories that fulfill aesthetic and cinematographic constraints. There are also end-to-end solutions for aerial cinematographers [3], [4] where high-level commands can be specified. However, the focus of these previous works is on static scenes and single-drone settings. Moreover, there are works filming dynamic targets in outdoor scenarios and coping with obstacle avoidance [5], [6]. There is no much work considering multi-drone shots for cinematography. Recently, some authors have proposed optimization techniques based on receding horizon to film with several drones in indoor settings [7], [8]. In this work, we propose a distributed system for autonomous execution of cinematography missions. We implement a set of flight controllers in order to perform with autonomous drones canonical shots described in the literature. Then, we devise a system that allows the drones to execute concurrent shots in a distributed manner, by means of synchronization events. Our work has been done within the framework of the EU-funded project MULTIDRONE 1, which addresses the problem of building drone teams for autonomous media production. The project studies all aspects in the complete system: how to define shooting missions
Dyanamic Load Balancing Strategies For A Distributed Computer System – A Survey

A distributed system consists of independent workstations connected usually by a local area network. The IT infrastructure is playing an increasingly important role in the success of a business. Market share, customer satisfaction and company image are all intertwined with the consistent availability of a company’s web site. Network servers are now frequently used to host ERP, e-commerce and a myriad of other applications. The foundation of these sites, the e-business infrastructure is expected to provide high performance, high availability, secure and scalable solutions to support all applications at all times. However, the availability of these applications is often threatened by network overloads as well as server and application failures. Resource utilization is often out of balance, resulting in the low-performance resources being overloaded with requests while the high-performance resources remain idle. Server load balancing is a widely adopted solution to performance and availability problems.
Boss fights in lectures! - A longitudinal study on a gamified application for testing factual knowledge

Gamification is used to influence the motivation and behavior of users. In research, the effect of gamification on motivation and other psychological outcomes has been confirmed in various application contexts. Gamification critics state that a sustained success is unlikely, because of the implementation of extrinsic motivation drivers like rewards. Current studies, however, have shown that gamification possesses the potential to support intrinsic motivation. In this article, we introduce a gamified app for testing factual knowledge, which we developed based on current empirical findings and recommendations and analyzed in a longitudinal study for novelty effects. In contrast to other contributions, our app takes up the boss fight concept to support a gameful framing and uses various game elements to provide feedback to students in lectures. Overall, students evaluated the app to be very useful and fun, and additionally reported positive outcomes concerning the experiences of autonomy and competence.
Design and reliability-based optimization of the piezoelectric flex transducer

In recent years, the rapid development of low power consuming devices has resulted in a high demand for mobile energy harvesters. The main contribution of this thesis is to optimize the novel piezoelectric energy harvesting device called the piezoelectric flex transducer, which was developed by other researchers for the purpose of harvesting biokinetic energy from human gait. The optimization uses both conventional and reliability-based optimization approaches in order to improve the electrical power generation from the device. First, the piezoelectric flex transducer is modeled by using the finite element method with the finite element analysis software ANSYS APDL. Seven geometric parameters of the piezoelectric energy harvester are considered as design variables. A set of designs with different design variables are generated by the Design of Experiment technique, the generated designs are analyzed by the finite element model and the surrogate models that representing the behavior of the FEM are built by these inputs and the results of the FEA. Conventional optimization, taking into consideration different safety factors, is driven by the von mises stress of the device and is then searched by a mathematical algorithm with the assistance of surrogate models. To improve the efficiency of the surrogate modeling, a multi-level surrogate modeling approach for fast convergence will be introduced and the method will be demonstrated by optimizing the PFT device. As the optimal design is subject to a low stress safety factor, which may be unreliable with the uncertainties of the real-world, the reliability and sensitivity of the optimal design are analyzed. A Monte Carlo simulation is employed to analyse how the electrical power output has been affected by the input parameters with parametric uncertainties. The design parameters of a set of designs are perturbed around the optimal design parameters in order to imitate the optimal design under parametric uncertainties. The effects of parametric uncertainties are then evaluated by the constructed surrogate models. The method for improving the product reliability will be demonstrated.
FOREST COVER–RAINFALL RELATIONSHIPS IN A BIODIVERSITY HOTSPOT: THE ATLANTIC FOREST OF BRAZIL

It is now generally accepted that the relationship between vegetation and climate is dynamic: vegetation is influenced by climate, but feedbacks between terrestrial ecosystems and the atmosphere mean that vegetation also affects climate. From this it follows that land-use changes may have climatic consequences. Specifically, it is widely believed that forest clearance may inhibit rainfall. Although models often support this view, this is not universally the case, and empirical evidence is scarce. We have compiled a database of forest cover and precipitation for the state of Sao Paulo, which lies within the diverse and highly endangered Atlantic forest region of Brazil. We do not find a strong relationship between forest cover and total rainfall, which appears to be influenced primarily by factors such as distance to the coast; but significant positive relationships between tree cover and the number of rain days consistently emerge. The degree of forest fragmentation seems to influence this relationship, w.