Miracle-dz/UniFAR-FTUs huggingface dataset:
Influence of net freshwater supply on salinity in Florida Bay

An annual water budget for Florida Bay, the large, seasonally hypersaline estuary in the Everglades National Park, was constructed using physically based models and long‐term (31 years) data on salinity, hydrology, and climate. Effects of seasonal and interannual variations of the net freshwater supply (runoff plus rainfall minus evaporation) on salinity variation within the bay were also examined. Particular attention was paid to the effects of runoff, which are the focus of ambitious plans to restore and conserve the Florida Bay ecosystem. From 1965 to 1995 the annual runoff from the Everglades into the bay was less than one tenth of the annual direct rainfall onto the bay, while estimated annual evaporation slightly exceeded annual rainfall. The average net freshwater supply to the bay over a year was thus approximately zero, and interannual variations in salinity appeared to be affected primarily by interannual fluctuations in rainfall. At the annual scale, runoff apparently had little effect on the bay as a whole during this period. On a seasonal basis, variations in rainfall, evaporation, and runoff were not in phase, and the net freshwater supply to the bay varied between positive and negative values, contributing to a strong seasonal pattern in salinity, especially in regions of the bay relatively isolated from exchanges with the Gulf of Mexico and Atlantic Ocean. Changes in runoff could have a greater effect on salinity in the bay if the seasonal patterns of rainfall and evaporation and the timing of the runoff are considered. One model was also used to simulate spatial and temporal patterns of salinity responses expected to result from changes in net freshwater supply. Simulations in which runoff was increased by a factor of 2 (but with no change in spatial pattern) indicated that increased runoff will lower salinity values in eastern Florida Bay, increase the variability of salinity in the South Region, but have little effect on salinity in the Central and West Regions.
Fluid-flow pressure measurements and thermo-fluid characterization of a single loop two-phase passive heat transfer device

A Novel Single Loop Pulsating Heat Pipe (SLPHP), with an inner diameter of 2 mm, filled up with two working fluids (Ethanol and FC-72, Filling Ratio of 60%), is tested in Bottom Heated mode varying the heating power and the orientation. The static confinement diameter for Ethanol and FC-72, respectively 3.4 mm and 1.7mm, is above and slightly under the inner diameter of the tube. This is important for a better understanding of the working principle of the device very close to the limit between the Loop Thermosyphon and Pulsating Heat Pipe working modes. With respect to previous SLPHP experiments found in the literature, such device is designed with two transparent inserts mounted between the evaporator and the condenser allowing direct fluid flow visualization. Two highly accurate pressure transducers permit local pressure measurements just at the edges of one of the transparent inserts. Additionally, three heating elements are controlled independently, so as to vary the heating distribution at the evaporator. It is found that peculiar heating distributions promote the slug/plug flow motion in a preferential direction, increasing the device overall performance. Pressure measurements point out that the pressure drop between the evaporator and the condenser are related to the flow pattern. Furthermore, at high heat inputs, the flow regimes recorded for the two fluids are very similar, stressing that, when the dynamic effects start to play a major role in the system, the device classification between Loop Thermosyphon and Pulsating Heat Pipe is not that sharp anymore.
Improving the expressiveness of deep learning frameworks with recursion

Recursive neural networks have widely been used by researchers to handle applications with recursively or hierarchically structured data. However, embedded control flow deep learning frameworks such as TensorFlow, Theano, Caffe2, and MXNet fail to efficiently represent and execute such neural networks, due to lack of support for recursion. In this paper, we add recursion to the programming model of existing frameworks by complementing their design with recursive execution of dataflow graphs as well as additional APIs for recursive definitions. Unlike iterative implementations, which can only understand the topological index of each node in recursive data structures, our recursive implementation is able to exploit the recursive relationships between nodes for efficient execution based on parallel computation. We present an implementation on TensorFlow and evaluation results with various recursive neural network models, showing that our recursive implementation not only conveys the recursive nature of recursive neural networks better than other implementations, but also uses given resources more effectively to reduce training and inference time.
Studying chloroplast biogenesis in the basal land plant Marchantia polymorpha: Using CRISPR/Cas9 genome editing and forward genetics to assess functional conservation and seek novel components Primary supervisor

Chloroplasts are responsible for photosynthesis, and are the organelles that define plants [1]. They evolved as a result of an endosymbiotic relationship between a cyanobacterium and an algal progenitor, in a process that began over a billion years ago. Land plants emerged around 500 million years ago, by which time the chloroplast had already become a fully integrated component of the plant cell.
An auditory brain-computer interface using virtual sound field

Brain-computer interfaces (BCIs) exploring the auditory communication channel might be preferable for amyotrophic lateral sclerosis (ALS) patients with poor sight or with the visual system being occupied for other uses. Spatial attention was proven to be able to modulate the event-related potentials (ERPs); yet up to now, there is no auditory BCI based on virtual sound field. In this study, auditory spatial attention was introduced by using stimuli in a virtual sound field. Subjects attended selectively to the virtual location of the target sound and discriminated its relevant properties. The concurrently recorded ERP components and the users' performance were compared with those of the paradigm where all sounds were presented in the frontal direction. The early ERP components (100–250ms) and the simulated online accuracies indicated that spatial attention indeed added effective discriminative information for BCI classification. The proposed auditory paradigm using virtual sound field may lead to a high-performance and portable BCI system.
Autism Spectrum Disorders Diagnosis , Prevalence , and Services for Children and Families

R ecent prevalence rates for autism spectrum disorders (ASDs) are now estimated at about 1 in 110 children in the U.S. Increases in public awareness and research funding in response to the growing numbers of children and adults with this disorder have led to numerous important scientific advances over the last several years. Nevertheless, because ASD remains a diagnosis that is defined completely on the basis of behavior, diagnostic assessment is both complex and expensive. Appropriate interventions and services are also multi-faceted and costly, and because of the pervasive nature of the disorder, are often required in some form across the lifespan. In the absence of standard societal mechanisms to pay for appropriate assessment and treatment, families must personally shoulder many of the costs associated with securing appropriate services for their children. This Social Policy Report summarizes selected recent studies on diagnosis, prevalence, and intervention, and discusses strategies for designing social policies to help improve the outcomes and independence of children and
Experimental Studies in Software Inspection Process - A Systematic Mapping

Background: The interest in produce experimental knowledge about verification and validation techniques increases over the years. This kind of knowledge can be useful for researchers who develop studies in that area as well as for industry that can make decisions about verification and validation activities (V&V) on the basis of experimental results. Aim: This paper aims to map the empirical studies conducted in the software inspection process area. Method: Each step of the Systematic Mapping (SM) process was performed with the support of the StArt tool, and the papers from major databases, journals, conferences, and workshops were covered. Results: Seventy nine papers were accepted in this mapping and helped identifying the inspection processes, techniques and tools commonly referenced in that papers, as well as the artifacts usually inspected and the research groups and universities frequently involved in these papers. Conclusion: Different inspection processes have been investigated through experimental studies, and the Fagan’s process is the most investigated of them. To evaluate these different processes requirements document and source code were the artifacts more used. Besides, different tools and techniques have been used to support these processes. Some important lessons were learned, which are in accordance to explanations of others authors.
User-Perceived Source Code Quality Estimation Based on Static Analysis Metrics

The popularity of open source software repositories and the highly adopted paradigm of software reuse have led to the development of several tools that aspire to assess the quality of source code. However, most software quality estimation tools, even the ones using adaptable models, depend on fixed metric thresholds for defining the ground truth. In this work we argue that the popularity of software components, as perceived by developers, can be considered as an indicator of software quality. We present a generic methodology that relates quality with source code metrics and estimates the quality of software components residing in popular GitHub repositories. Our methodology employs two models: a one-class classifier, used to rule out low quality code, and a neural network, that computes a quality score for each software component. Preliminary evaluation indicates that our approach can be effective for identifying high quality software components in the context of reuse.
Hybrid Program Dependence Approximation for Effective Dynamic Impact Prediction

Impact analysis determines the effects that program entities of interest, or changes to them, may have on the rest of the program for software measurement, maintenance, and evolution tasks. Dynamic impact analysis could be one major approach to impact analysis that computes smaller impact setsthan static alternatives for concrete sets of executions. However, existing dynamic approaches often produce impact sets that are too large to be useful, hindering their adoption in practice. To address this problem, we propose to exploit static program dependencies to drastically prune false-positive impacts that are not exercised by the set of executions utilized by the analysis, via hybrid dependence approximation. Further, we present a novel dynamic impact analysis called Diver which leverages both the information provided by the dependence graph and method-execution events to identify runtime method-level dependencies, hence dynamic impact sets, much more precisely without reducing safety and at acceptable costs. We evaluate Diver on ten Java subjects of various sizes and application domains against both arbitrary queries covering entire programs and practical queries based on changes actually committed by developers to actively evolving software repositories. Our extensive empirical studies show that Diver can significantly improve the precision of impact prediction, with 100-186 percent increase, with respect to a representative existing alternative thus provide a far more effective option for dynamic impact prediction. Following a similar rationale to Diver, we further developed and evaluated an online dynamic impact analysis called DiverOnline which produces impact sets immediately upon the termination of program execution. Our results show that compared to the offline approach, for the same precision, the online approach can reduce the time by 50 percent on average for answering all possible queries in the given program at once albeit at the price of possibly significant increase in runtime overhead. For users interested in one specific query only, the online approach may compute the impact set for that query during runtime without much slowing down normal program operation. Further, the online analysis, which does not incur any space cost beyond the static-analysis phase, may be favored against the offline approach when trace storage and/or related file-system resource consumption becomes a serious challenge or even stopper for adopting dynamic impact prediction. Therefore, the online and offline analysis together offer complementary options to practitioners accommodating varied application/task scenarios and diverse budget constraints.
Unix Domain Sockets Applied in Android Malware Should Not Be Ignored

Increasingly, malicious Android apps use various methods to steal private user data without their knowledge. Detecting the leakage of private data is the focus of mobile information security. An initial investigation found that none of the existing security analysis systems can track the flow of information through Unix domain sockets to detect the leakage of private data through such sockets, which can result in zero-day exploits in the information security field. In this paper, we conduct the first systematic study on Unix domain sockets as applied in Android apps. Then, we identify scenarios in which such apps can leak private data through Unix domain sockets, which the existing dynamic taint analysis systems do not catch. Based on these insights, we propose and implement JDroid, a taint analysis system that can track information flows through Unix domain sockets effectively to detect such privacy leaks.
Inhibition and reversal of growth cone collapse in adult sensory neurons by enteric glia-induced neurotrophic factors

Aim: Previous studies show enteric glia (EG)-conditioned medium promotes neurite outgrowth in adult dorsal root ganglia (DRG) derived sensory neurons. This EG-conditioned medium contains various neurotrophic factors, including nerve growth factor (NGF), brainderived neurotrophic factor (BDNF), glial cell line-derived neurotropic factor (GDNF), and neurotrophin-3 (NT-3). This study attempts to determine the importance of these neurotrophic factors in enabling DRG-derived sensory neuron axons to overcome the inhibitory guidance cues released from the glial scar. Methods: A Semaphorin 3A (SEMA3A) growth cone collapse model was used on cultured rat DRG. Neutralizing antibodies to each neurotrophic growth factor in question (NGF, BDNF, GDNF and NT-3) were applied to the EG-conditioned medium to evaluate the factor’s individual importance in preventing growth cone collapse. Results: EG-conditioned medium inhibits and reverses growth cone collapse in adult DRG neurons when added either 1 h before or concurrently with SEMA3A. When administered 40 min after the initial SEMA3A-induced collapse, EG-conditioned medium was able to reverse the growth cone collapse. Individual inhibition of all the neurotrophic factors, except for BDNF in the co-treatment setting, resulted in increased growth cone collapse. Conclusion: NGF, BDNF, GDNF, and NT-3 are all variably involved in preventing or reversing SEMA3Ainduced growth cone collapse in pre-, co-, and post-treatment time settings. However, no individual neurotrophic factors appear to be essential to promoting neurite outgrowth.
Raman-based point and proximal detection and imaging

Despite massive international search efforts, the detection and identification of hazardous materials is still a persisting challenge. Here, a modular, compact Raman spectrometer for short distance detection and imaging of traces of explosives and related compounds was developed. It utilized green excitation sources (a laser pointer, or a hand-held laser) and the resultant beam was focused as point or line on particular samples. The ensuing signals were detected and employed for sensitive and selective detection of different liquids and individual particles, placed on a variety of substrates. Synchronization of sample movement with detector readout, allowed obtaining not only characteristic Raman signals but also Raman images of traces of different compounds. Imaging was carried out by point-mapping, photo-guided sampling/point-mapping and line-mapping, where the latter two approaches allowed detection and mapping of individual particles at considerably reduced sampling times. It is anticipated that realization of these operation modes can lead to improved performance and be applied for examination and identification of objects contaminated with harmful compounds. This suggests the possibility for future use of the setup as a fast, compact, and relatively low-cost system for detecting and mapping a variety of samples under lab conditions and, following additional improvements, for field measurements.
Segmentation techniques for the summarization of individual mobility data

Segmentation techniques partition a sequence of data points into a series of disjoint subsequences—segments—based on some criteria. Depending on the context and the nature of data themselves, segments return an approximate representation. The final result is a summarized representation of the sequence. This intuitive mechanism has been extensively studied, for example, for the summarization of time series in order to preserve the ‘shape’ of the sequence while omitting irrelevant details. This survey focuses on the use of segmentation methods for extracting behavioral information from individual mobility data, in particular from spatial trajectories. Such information can then be given a compact representation in the form of summarized trajectories, e.g., semantic trajectories and symbolic trajectories. Two major streams of research are discussed, spanning computational geometry and data mining respectively, that are emblematic of the multiplicity of views. WIREs Data Mining Knowl Discov 2017, 7:e1214. doi: 10.1002/widm.1214
It’s Not All About the Face: Variability Reveals Asymmetric Obligatory Processing of Faces and Bodies in Whole-Body Contexts

Researchers have sought to understand the specialized processing of faces and bodies in isolation, but recently they have considered how face and body information interact within the context of the whole body. Although studies suggest that face and body information can be integrated, it remains an open question whether this integration is obligatory and whether contributions of face and body information are symmetrical. In a selective attention task with whole-body stimuli, we focused attention on either the face or body and tested whether variation in the irrelevant part could be ignored. We manipulated orientation to determine the extent to which inversion disrupted obligatory face and body processing. Obligatory processing was evidenced as performance changes in discrimination that depended on stimulus orientation when the irrelevant region varied. For upright but not inverted face discrimination, participants could not ignore body posture variation, even when it was not diagnostic to the task. However, participants could ignore face variation for upright body posture discrimination but not for inverted posture discrimination. The extent to which face and body information necessarily influence each other in whole-body contexts appears to depend on both domain-general attentional and face- or body-specific holistic processing mechanisms.
Effects of Bacterial Dose and Fly Sex on Persistence and Excretion of Salmonella enterica serovar Typhimurium From Adult House Flies (Musca domestica L.; Diptera: Muscidae)

Abstract Salmonella Typhimurium (Le Minor and Popoff 1987; Enterobacteriales: Enterobacteriaceae) is a pathogen that causes gastroenteritis in humans and can be harbored by house flies. Factors influencing excretion of S. Typhimurium from infected flies have not been elucidated but are essential for assessing transmission potential. We determined the persistence and excretion of a green fluorescent protein (GFP) expressing strain of S.Typhimurium from house flies. Individual male and female flies were fed either sterile Luria-Bertani (LB) broth (controls) or cultures of “high” (~105 colony forming units [CFU]) or “low” (~104 CFU) doses of bacteria (treatments). Bacterial persistence was determined over 16 h by culturing whole-fly homogenate. Both sex and dose affected persistence between 6 and 12 h postingestion. In a separate experiment, fly excretion events were monitored during this time interval and excreta droplets were individually cultured for bacteria. Female flies had more excretion events than males across treatments. We observed interactions of fly sex and bacterial abundance (dose), both on the proportion of Salmonella-positive droplets and the CFU shed per droplet (CFU/droplet). In the low-dose treatment, males excreted a greater proportion of positive droplets than females. In the high-dose treatment, males excreted more CFU/droplet than females. High-dose male flies excreted more CFU/droplet than low-dose males, but low-dose females excreted more CFU/droplet than high-dose females. Irrespective of sex, low-dose flies excreted a greater dose-adjusted CFU (CFU droplet/CFU fed) than high-dose flies. This study demonstrates that both bacterial abundance and fly sex may influence excretion of bacteria from flies, and should be considered when assessing the risk of house fly transmission of pathogens.
Single-trial classification of feedback potentials within neurofeedback training with an EEG brain-computer interface

Neurofeedback therapies are an emerging technique used to treat neuropsychological disorders and to enhance cognitive performance. The feedback stimuli presented during the therapy are a key factor, serving as guidance throughout the entire learning process of the brain rhythms. Online decoding of these stimuli could be of great value to measure the compliance and adherence of the subject to the training. This paper describes the modeling and classification of performance feedback potentials with a Brain-Computer Interface (BCI), under a real neurofeedback training with five subjects. LDA and SVM classification techniques are compared and are both able to provide an average performance of approximately 80%.
Partial differential equations for training deep neural networks

This paper establishes a connection between non-convex optimization and nonlinear partial differential equations (PDEs). We interpret empirically successful relaxation techniques motivated from statistical physics for training deep neural networks as solutions of a viscous Hamilton-Jacobi (HJ) PDE. The underlying stochastic control interpretation allows us to prove that these techniques perform better than stochastic gradient descent. Our analysis provides insight into the geometry of the energy landscape and suggests new algorithms based on the non-viscous Hamilton-Jacobi PDE that can effectively tackle the high dimensionality of modern neural networks.
Parametric analysis of a fractional-order Newton-Leipnik system

In this paper, the influences of parameters on the dynamics of a fractional-order Newton-Leipnik system are numerically studied. The ranges of the parameters used in this study are relatively broad. The system displays comprehensive dynamic behaviors, such as fixed points, periodic motion (including periodic-3 motion), chaotic motion, and transient chaos. A period-doubling route to chaos is also found.