Miracle-dz/UniFAR-FTUs huggingface dataset:
Where the Network ends: Infrastructure and Delay Tolerant Networks in a Hybrid Future Internet

Traffic carried by mobile networks doubles per year, resulting in congestion and high cost for network upgrades. With the widespread integration of sensing devices with the Internet—in the so-called “Internet of Things”—infrastructurebased networks are becoming an increasingly expensive way of keeping up with communication needs due to the high volume of sensing data. Delay Tolerant Networks provide new communication possibilities for the transport of sensing data, as they allow for communication without any infrastructure—based on mobileto-mobile communication and store-carry-forward mechanisms. Performing a cooperative hybrid approach of today’s networks with DTNs has empirically shown to be promising, but is scarcely understood. In this talk we give an overview on our work in hybrid networks, based on overlay networks and Delay Tolerant Networks. Our approach allows for the integration of opportunistic mobileto-mobile communication with partial infrastructure access to inexpensively route delay-tolerant data.
Development and Evaluation of a Wireless Interface for Inputting Characters Using Laplacian EMG

We developed a compact wireless electrode containing an amplifier, a filter and a transmitter to measure the Laplacian electromyogram (Laplacian EMG) using the source derivation method. We made a character input system by connecting a circuit converting Laplacian EMG signals to clicks, with a personal computer in which a software for scanning character input was installed. We evaluated the system by comparing two integrated EMGs (IEMGs) measured with a conventional amplifier: one measured when the Laplacian EMG was used as an input signal to the system during a character inputting task, and the other measured when the conventional EMG was used. The IEMG was smaller in all of five subjects when the Laplacian EMG was used as the input signal than when the conventional EMG was used
MAC games for distributed wireless network security with incomplete information of selfish and malicious user types

We consider game theoretic models of wireless medium access control (MAC) in which each transmitter makes individual decisions regarding their power level or transmission probability. This allows for scalable distributed operation; however, it can also enable users to pursue malicious objectives such as jamming other nodes to deny them service. We study games with two types of players: selfish and malicious transmitters. Each type is characterized by a utility function depending on throughput reward and energy cost. Furthermore, we focus on the setting where the transmitters have incomplete information regarding other transmitters' types, modeled as probabilistic beliefs. We first analyze a power-controlled MAC game in which the nodes select powers for continuous transmissions and then extend this to a random access MAC in which nodes choose transmission probabilities. For each case, the Bayesian Nash equilibrium strategies are derived for different degrees of uncertainty, and the resulting equilibrium throughput of selfish nodes is characterized. We identify conditions in which the throughput improves with increasing type uncertainty and introduce Bayesian learning mechanisms to update the type beliefs in repeated games. For unknown types and costs, we also specify the equilibrium cut-off thresholds for monotonic transmission decisions. The analysis provides insights into the optimal defense mechanisms against denial of service attacks at the MAC layer in wireless networks.
Initial Application and Evaluation of a Promising New Sampling Method for Response Surface Generation: Centroidal Voronoi Tessellation *

A recently developed Centroidal Voronoi Tessellation (CVT) sampling method is investigated here to assess its suitability for use in response surface generation. CVT is an unstructured sampling method that can generate nearly uniform point spacing over arbitrarily shaped Mdimensional parameter spaces. For rectangular parameter spaces (hypercubes), CVT appears to extend to higher dimensions more effectively and inexpensively than “Distributed” and “Improved Distributed” Latin Hypercube Monte Carlo methods, and CVT does not appear to suffer from spurious correlation effects in higher dimensions and at high sampling densities as quasi-Monte-Carlo methods such as Halton and Sobol sequences typically do. CVT is described briefly in this paper and its impact on response surface accuracy in a 2D test problem is compared to the accuracy yielded by Latin Hypercube Sampling (LHS) and a deterministic structured-uniform sampling method. To accommodate the different point patterns over the parameter space given by the different sampling methods, Moving Least Squares (MLS) for interpolation of arbitrarily located data points is used. It is found that CVT performs better than LHS in 11 of 12 test cases investigated here, and as often as not performs better than the structured sampling method with its deterministically uniform point placement over the 2-D parameter space.
The Prevalence and Distribution of Diagnosed and Undiagnosed Type Two Diabetes Mellitus Among Young Adults Aged 20-40 Years, Utilizing NHANES Data from 1999-2010

2 Acknowledgement I would like to personally thank Dr. Rothenberg and the entire epidemiology department within Georgia State University's School of Public Health for their continuous support and knowledge. I would also like to thank my wife for her continued support throughout my education. I could not have done it without you. Abstract Background Within the last century, Type 2 diabetes mellitus has become one of the major health concerns both in the US and globally. Overall prevalence and incidence of the disease within younger populations including adolescents and young adults has sharply increased since 1990 and is estimated to increase further if no actions are taken. It is estimated that 5.7 million people have a form of diabetes. Of the total estimated prevalence of T2DM, 25% of persons with diabetes are unaware they have the disease. This study aims to identify populations most at risk as well as to label important risk factors associated with the rise of T2DM within the young adult population. Objectives This study aimed to measure the prevalence of diagnosed and undiagnosed T2DM among different populations based on ethnicity, socioeconomic status, and weight classification among a representative sample of young adults within the United States. This study also aims to identify and quantify associated risk factors of T2DM among young adults, and address current preventative and treatment measures. Methods Six two-year samples of the National Health and Nutrition Examination Survey (NHANES) containing a total of 62,160 individuals were compiled into one dataset. Of these, 11,874 young adults aged 20-40 were selected into the study. Prevalence of diagnosed and undiagnosed T2DM was calculated for specific populations. The study also aimed to identify important risk factors through univariate and multivariate binomial logistic regression analysis. Results Among young adults aged 20-40, overall prevalence of T2DM was found to be 1.4% (CI: 1.2-1.6%). Of observed diabetes cases, an alarming 20.1% (N = 68) were categorized as undiagnosed. Significant increases in risk were observed within older age groups, overweight and obese weight categories, individuals living below poverty and within minority racial/ethnic groups. was identified as the most influential risk factor, and should be a main area of focus in development of interventions. Increases in activity levels, both at work and during leisure-time, were found to decrease one's risk of having T2DM. Discussion Type 2 diabetes has become a major health concern within the past decades, and will continue to be …
Topographic Distribution of Stimulus-Specific Adaptation across Auditory Cortical Fields in the Anesthetized Rat

Stimulus-specific adaptation (SSA) in single neurons of the auditory cortex was suggested to be a potential neural correlate of the mismatch negativity (MMN), a widely studied component of the auditory event-related potentials (ERP) that is elicited by changes in the auditory environment. However, several aspects on this SSA/MMN relation remain unresolved. SSA occurs in the primary auditory cortex (A1), but detailed studies on SSA beyond A1 are lacking. To study the topographic organization of SSA, we mapped the whole rat auditory cortex with multiunit activity recordings, using an oddball paradigm. We demonstrate that SSA occurs outside A1 and differs between primary and nonprimary cortical fields. In particular, SSA is much stronger and develops faster in the nonprimary than in the primary fields, paralleling the organization of subcortical SSA. Importantly, strong SSA is present in the nonprimary auditory cortex within the latency range of the MMN in the rat and correlates with an MMN-like difference wave in the simultaneously recorded local field potentials (LFP). We present new and strong evidence linking SSA at the cellular level to the MMN, a central tool in cognitive and clinical neuroscience.
The causal effects of retirement on mental health: Looking beyond the mean effects

We analyze the causal effect of retirement on mental health, exploiting differences in retirement eligibility ages across countries and over time using data from the Survey of Health, Ageing and Retirement in Europe. We estimate not only average effects, but also use distributional regression to examine whether these effects are unequally distributed across the mental health distribution. We find unequally distributed protective effects of retirement on mental health. These gains are larger among those above the clinically defined threshold of being at risk of depression. The preserving effects are larger for women, blue collar workers and those in social networks.
Consistent Propagation of Normal Orientations in Point Clouds

Many algorithms for point cloud processing especially surface reconstruction rely on normal information available at each point. Normal directions are typically taken from a local tangent plane approximation which is obtained by fitting a surface model to the neighboring point samples. While the direction can be estimated locally, finding a consistent normal orientation over the whole surface is only possible in a global context. Existing methods for this problem can be classified into volumetric and propagation based approaches. Volumetric methods are trying to divide the space into inside and outside regions which is often complicated to implement and have problems with open surfaces and large holes. Propagation based methods can deal with open surfaces but often fail on sharp features. This paper analyses the behavior of surficial orientation methods, gives a better understanding of the underlying model assumptions of existing techniques and proposes a novel and improved propagation heuristic.
Ethnic Identity and Familism Among Latino College Students

The present study examined the longitudinal associations among familism respect and obligations values, ethnic centrality and private regard, and ethnic self-identification. Data were drawn from a socioeconomically diverse sample of Latino students attending a predominantly White university. The selection of a White label was associated with less positive private regard, less ethnic centrality, and less strong endorsement of familism respect and obligation values at the start of the academic year compared to those students who selected a national origin label. There was a complex relationship between ethnic identity and familism values over time. Ethnic centrality supported the growth in familism respect values across time, but familism respect values also predicted later growth in ethnic private regard. Our results highlight the fact that ethnic identity processes and familial cultural values can mutually influence each other in emerging adulthood for Latinos.
A Nonlinear Scoring Framework for Peptide Identification via Tandem Mass Spectrometry

The problem of false positives in peptide identification via tandem mass spectrometry (MS/MS) by database searching remains unsatisfactorily resolved in the current proteomics research. The correlative information among fragment ions in the MS/MS spectrum can be very helpful for reducing the number of false positives. However, due to the computational difficulty, existing peptide-scoring algorithms usually assume the independence of the occurrences of fragment ions and employ linear scoring functions. In our earlier work, we have developed a nonlinear peptide-scoring function called Kernel Spectral Dot Product (KSDP, see Fu et al., 2004), in which co-occurring matches of consecutive fragment ions are emphasized by the locally improved polynomial kernel. In this paper, we extend the KSDP to a general framework that can readily accommodate different kernel functions. We show that by using the Radial Basis Function (RBF) kernel for consecutive fragment ions, ∗ To whom correspondence should be addressed. better performance can be achieved. Experiments on the KSDP for complementary and homologous fragment ions reveal that these two kinds of correlation are not necessarily useful for reducing false positives. Our software tool, pFind, obtains higher identification accuracy on a previously reported dataset than two popular software tools, SEQUEST and Sonar MS/MS.
A Concept Language Model for Ad-hoc Retrieval

We propose an extension to language models for information retrieval. Typically, language models estimate the probability of a document generating the query, where the query is considered as a set of independent search terms. We extend this approach by considering the concepts implied by both the query and words in the document. The model combines the probability of the document generating the concept embodied by the query, and the traditional language model probability of the document generating the query terms. We use a word embedding space to express concepts. The similarity between two vectors in this space is estimated using a weighted cosine distance. The weighting significantly enhances the discrimination between vectors. We evaluate our model on benchmark datasets (TREC 6--8) and empirically demonstrate it outperforms state-of-the-art baselines.
Mechanistic investigation of a highly active phosphite dehydrogenase mutant and its application for NADPH regeneration

NAD(P)H regeneration is important for biocatalytic reactions that require these costly cofactors. A mutant phosphite dehydrogenase (PTDH‐E175A/A176R) that utilizes both NAD and NADP efficiently is a very promising system for NAD(P)H regeneration. In this work, both the kinetic mechanism and practical application of PTDH‐E175A/A176R were investigated for better understanding of the enzyme and to provide a basis for future optimization. Kinetic isotope effect studies with PTDH‐E175A/A176R showed that the hydride transfer step is (partially) rate determining with both NAD and NADP giving DV values of 2.2 and 1.7, respectively, and DV/Km,phosphite values of 1.9 and 1.7, respectively. To better comprehend the relaxed cofactor specificity, the cofactor dissociation constants were determined utilizing tryptophan intrinsic fluorescence quenching. The dissociation constants of NAD and NADP with PTDH‐E175A/A176R were 53 and 1.9 µm, respectively, while those of the products NADH and NADPH were 17.4 and 1.22 µm, respectively. Using sulfite as a substrate mimic, the binding order was established, with the cofactor binding first and sulfite binding second. The low dissociation constant for the cofactor product NADPH combined with the reduced values for DV and kcat implies that product release may become partially rate determining. However, product inhibition does not prevent efficient in situ NADPH regeneration by PTDH‐E175A/A176R in a model system in which xylose was converted into xylitol by NADP‐dependent xylose reductase. The in situ regeneration proceeded at a rate approximately fourfold faster with PTDH‐E175A/A176R than with either WT PTDH or a NADP‐specific Pseudomonas sp.101 formate dehydrogenase mutant with a total turnover number for NADPH of 2500.
Glucose Metabolic Changes in the Brain and Muscles of Patients with Nonspecific Neck Pain Treated by Spinal Manipulation Therapy: A [18F]FDG PET Study

Objective. The aim of this study was to investigate changes in brain and muscle glucose metabolism that are not yet known, using positron emission tomography with [18F]fluorodeoxyglucose ([18F]FDG PET). Methods. Twenty-one male volunteers were recruited for the present study. [18F]FDG PET scanning was performed twice on each subject: once after the spinal manipulation therapy (SMT) intervention (treatment condition) and once after resting (control condition). We performed the SMT intervention using an adjustment device. Glucose metabolism of the brain and skeletal muscles was measured and compared between the two conditions. In addition, we measured salivary amylase level as an index of autonomic nervous system (ANS) activity, as well as muscle tension and subjective pain intensity in each subject. Results. Changes in brain activity after SMT included activation of the dorsal anterior cingulate cortex, cerebellar vermis, and somatosensory association cortex and deactivation of the prefrontal cortex and temporal sites. Glucose uptake in skeletal muscles showed a trend toward decreased metabolism after SMT, although the difference was not significant. Other measurements indicated relaxation of cervical muscle tension, decrease in salivary amylase level (suppression of sympathetic nerve activity), and pain relief after SMT. Conclusion. Brain processing after SMT may lead to physiological relaxation via a decrease in sympathetic nerve activity.
ZPD-Based Dynamic Assessment and Collaborative L2 Vocabulary Learning

Despite growing interest in the implementation of dynamic assessment in second or foreign language learning research, few studies have attempted to incorporate the notion into real second language classrooms. This study explored the effect of cumulative Group-Dynamic Assessment on depth of vocabulary knowledge in an EFL context. To this end, 50 EFL learners were selected from a junior high school in Iran after administering the Oxford Quick Placement Test. Then, they were randomly assigned to two equal groups, namely, experimental Group-Dynamic Assessment and control Non-Dynamic Assessment. The Group-Dynamic Assessment group was instructed through interactionist cumulative Group-Dynamic Assessment procedures while the control group was taught without providing any cumulative ZPD-sensitive feedback. Before and after the instruction, a vocabulary knowledge scale (Paribakh & Wesche, 1993, 1996) was administered to measure both groups’ depth of second language vocabulary knowledge. The split-plot ANOVA results revealed that the implementation of interactionist cumulative Group-Dynamic Assessment helped the learners outperform the Non-Dynamic Assessment group on both immediate and delayed posttests. Additionally, complementary qualitative analysis showed that cumulative Group-Dynamic Assessment had both diagnostic and developmental potentials to contribute not only to individual participants but also to the whole class to increase their depth of vocabulary knowledge. The findings pointed to the use of ZPD-based collaborative frameworks in teaching second language vocabulary in meaningful contexts and thereby helping learners develop deeper word knowledge.
Generalization performance of backpropagation learning on a syllabification task

We investigated the generalization capabilities of backpropagation learning in feed-forward and recurrent feed-forward connectionist networks on the assignment of syllable boundaries to ortho-graphic representations in Dutch (hyphenation). This is a di(cid:14)cult task because phonological and morphological constraints interact, leading to ambiguity in the input patterns. We compared the results to di(cid:11)erent symbolic pattern matching approaches, and to an exemplar-based generalization scheme, related to a k-nearest neighbour approach, but using a similarity metric weighed by the relative information entropy of positions in the training patterns. Our results indicate that the generalization performance of backpropagation learning for this task is not better than that of the best symbolic pattern matching approaches, and of exemplar-based generalization.
An edge detection algorithm for steel bar in hot rolling process (ICCAS 2014)

In hot rolling process, camber is one of the most significant defects, where camber is longitudinal curvature in the plan view of a steel bar. To obtain a camber value of the steel bar, the center line of the steel bar is necessary, where the center line can be calculated through the average of both edge lines. Therefore, the edge information of the overall shape in the steel bar is important to determine an exact camber value. In this paper, we propose a new edge detection algorithm for obtaining the exact edge information for the steel bar only using the image data. The proposed edge detection algorithm not only uses the mean filter and the gradient but also employs the least square fitting to get the edge information exactly. In this process, we use the width information of the steel bar to decide the edge information. Experimental results demonstrate that the proposed algorithm finds the exact edge information for the steel bar efficiently.
Transgenic Arabidopsis Plants Expressing a Fungal Cutinase Show Alterations in the Structure and Properties of the Cuticle and Postgenital Organ Fusions

A major structural component of the cuticle of plants is cutin. Analysis of the function of cutin in vivo has been limited because no mutants with specific defects in cutin have been characterized. Therefore, transgenic Arabidopsis plants were generated that express and secrete a cutinase from Fusarium solani f sp pisi. Arabidopsis plants expressing the cutinase in the extracellular space showed an altered ultrastructure of the cuticle and an enhanced permeability of the cuticle to solutes. In addition, pollen could germinate on fully differentiated leaves of cutinase-expressing plants but not on control leaves. These differences coincided with strong postgenital organ fusions. The junctions of the fusions contained pectic polysaccharides. As fused organs grew apart from each other, organ deformations and protrusions of epidermal cells developed at positions with high mechanical stress. These results demonstrate that an intact cutin layer not only is important for plant–environment interactions but also prevents fusions between different plant organs and is therefore necessary for normal epidermal differentiation and organ formation.
Biases in probabilistic category learning in relation to social anxiety

Instrumental learning paradigms are rarely employed to investigate the mechanisms underlying acquired fear responses in social anxiety. Here, we adapted a probabilistic category learning paradigm to assess information processing biases as a function of the degree of social anxiety traits in a sample of healthy individuals without a diagnosis of social phobia. Participants were presented with three pairs of neutral faces with differing probabilistic accuracy contingencies (A/B: 80/20, C/D: 70/30, E/F: 60/40). Upon making their choice, negative and positive feedback was conveyed using angry and happy faces, respectively. The highly socially anxious group showed a strong tendency to be more accurate at learning the probability contingency associated with the most ambiguous stimulus pair (E/F: 60/40). Moreover, when pairing the most positively reinforced stimulus or the most negatively reinforced stimulus with all the other stimuli in a test phase, the highly socially anxious group avoided the most negatively reinforced stimulus significantly more than the control group. The results are discussed with reference to avoidance learning and hypersensitivity to negative socially evaluative information associated with social anxiety.