Miracle-dz/UniFAR-FTUs huggingface dataset:
Is open access affordable? Why current models do not work and why we need internet‐era transformation of scholarly communications

Progress to open access (OA) has stalled, with perhaps 20% of new papers ‘born‐free’, and half of all versions of record pay‐walled; why? In this paper, I review the last 12 months: librarians showing muscle in negotiations, publishers’ Read and Publish deals, and funders determined to force change with initiatives like Plan S. I conclude that these efforts will not work. For example, flipping to supply‐side business models, such as article processing charges, simply flips the pay‐wall to a ‘play‐wall’ to the disadvantage of authors without financial support. I argue that the focus on OA makes us miss the bigger problem: today’s scholarly communications is unaffordable with today’s budgets. OA is not the problem, the publishing process is the problem. To solve it, I propose using the principles of digital transformation to reinvent publishing as a two‐step process where articles are published first as preprints, and then, journal editors invite authors to submit only papers that ‘succeed’ to peer review. This would reduce costs significantly, opening a sustainable pathway for scholarly publishing and OA. The catalyst for this change is for the reputation economy to accept preprints as it does articles in minor journals today.
Joint 3-D Shape Estimation and Landmark Localization From Monocular Cameras of Intelligent Vehicles

3-D reconstruction is at the core for many driving applications of Internet of Intelligent Vehicles. Previous works on reconstruction of a 3-D point shape commonly use a two-step framework. Precisely localizing a series of feature points in an image is performed on the first step. Then the second procedure attempts to fit the 3-D data to the observations to get the real 3-D shape. Such an approach has high time consumption, and easily gets stuck into local minimum. To address this problem, we propose a method to jointly estimate the global 3-D geometric structure of car and localize 2-D landmarks from a single viewpoint image. First, we represent the 3-D shape with a set of predefined shape bases, while parametrizing it by the coefficients of the linear combination of them. Second, we adopt a cascaded regression framework to regress the global shape encoded by the prior bases, by jointly minimizing the appearance and shape fitting differences. The position fitting item can help cope with the description ambiguity of local appearance, and provide more information for 3-D reconstruction. We apply the proposed approach on a multiview car dataset. Experimental results demonstrate favorable improvements on pose estimation and shape prediction, compared with some previous methods.
Medications, Deep Brain Stimulation, and Other Factors Influencing Impulse Control Disorders in Parkinson's Disease

Impulse control disorders (ICDs) in Parkinson's disease (PD) have a high cumulative incidence and negatively impact quality of life. ICDs are influenced by a complex interaction of multiple factors. Although it is now well-recognized that dopaminergic treatments and especially dopamine agonists underpin many ICDs, medications alone are not the sole cause. Susceptibility to ICD is increased in the setting of PD. While causality can be challenging to ascertain, a wide range of modifiable and non-modifiable risk factors have been linked to ICDs. Common characteristics of PD patients with ICDs have been consistently identified across many studies; for example, males with an early age of PD onset and dopamine agonist use have a higher risk of ICD. However, not all cases of ICDs in PD can be directly attributable to dopamine, and studies have concluded that additional factors such as genetics, smoking, and/or depression may be more predictive. Beyond dopamine, other ICD associations have been described but remain difficult to explain, including deep brain stimulation surgery, especially in the setting of a reduction in dopaminergic medication use. In this review, we will summarize the demographic, genetic, behavioral, and clinical contributions potentially influencing ICD onset in PD. These associations may inspire future preventative or therapeutic strategies.
OPTiC: Optimizing Collaborative CPU–GPU Computing on Mobile Devices With Thermal Constraints

The CPU-graphic processing unit (GPU) co-execution of computation kernels on heterogeneous multiprocessor system-on-chip can significantly boost performance compared to the execution on either the CPU or the GPU alone. However, engaging multiple on-chip compute elements concurrently at the highest frequency may not provide the optimal performance in a mobile system with stringent thermal constraints. The system may repeatedly exceed the temperature threshold necessitating frequency throttling and hence performance degradation. We present OPTiC, an analytical framework that given a computation kernel can automatically select the partitioning point and the operating frequencies for optimal CPU–GPU co-execution under thermal constraints. OPTiC estimates, through modeling, CPU and GPU power, performance at different frequency points as well as the performance impact of thermal throttling and memory contention. Experimental evaluation on a commercial mobile platform shows that OPTiC achieves an average 13.68% performance improvement over existing schemes that enable co-execution without thermal considerations.
Learning Latent Hierarchical Structures via Probabilistic Models and Deep Learning

Author(s): Arabshahi, Forough | Advisor(s): Singh, Sameer | Abstract: Hierarchical structures arise in many real world applications and domains. For example, in social networks people’s relationships and the groups to which they belong form a hierarchy. In natural language and computer programs, parse trees (which have a hierarchical structure) are used to represent the compositionality of expressions. These hierarchies strongly affect the statistics and the behavior of the data. Hence, it is important to develop algorithms that take these structures into account when modeling such data. Apart from these hierarchical structures, some datasets are best explained with hierarchical models even though there is no apparent hierarchy in the data itself. For instance when modeling the occurrence of words in a document, it is more realistic to assume that the words are drawn in a hierarchical manner from a topic distribution rather than independently from a single topic. In this dissertation, we focus on capturing these hierarchies and leveraging them for modeling high dimensional datasets.Hierarchical structures underlying the data are either observed or latent. For example in the context of computer programs, the syntax tree is inherent to the program and is therefore observed. On the other hand, the statistical dependence of a social network’s users is latent. In this dissertation, we study both types of hierarchies and develop models under both struc- tures because they both arise in many applications and are equally important. Nevertheless, capturing latent hierarchical structures is more challenging. We develop novel probabilistic models to capture latent hierarchies and present statistically efficient and provably consistent parameter learning algorithms for them. When capturing observed hierarchical structures we develop deep learning models that learn low-dimensional continuous representations for the discrete symbols and variables.
Transcriptome Analysis Reveals Common and Differential Response to Low Temperature Exposure Between Tolerant and Sensitive Blue Tilapia (Oreochromis aureus)

Tilapias are very important to the world's aquaculture. As befitting fish of their tropical origin, their distribution, and culture practices are highly affected by low temperatures. In this study, we used genetic and genomic methodologies to reveal pathways involved in the response and tolerance of blue tilapia (Oreochromis aureus) to low temperature stress. Cold tolerance was characterized in 66 families of blue tilapia. Fish from cold-tolerant and cold-sensitive families were sampled at 24 and 12°C, and the transcriptional responses to low-temperature exposure were measured in the gills and liver by high-throughput mRNA sequencing. Four hundred and ninety four genes displayed a similar temperature-dependent expression in both tolerant and sensitive fish and in the two tissues, representing the core molecular response to low temperature exposure. KEGG pathway analysis of these genes revealed down-regulation of focal-adhesion and other cell-extracellular matrix (ECM) interactions, and up-regulation of proteasome and various intra-cellular proteolytic activities. Differential responses between cold-tolerant and cold-sensitive fish were found with genes and pathways that were up-regulated in one group and down-regulated in the other. This reverse response was characterized by genes involved in metabolic pathways such as glycolysis/gluconeogenesis in the gills and biosynthesis of amino-acids in the liver, with low temperature down-regulation in tolerant fish and up-regulation in sensitive fish.
The Relationship Between Heart Rate Variability and Electroencephalography Functional Connectivity Variability Is Associated With Cognitive Flexibility

The neurovisceral integration model proposes a neuronal network that is related to heart rate activity and cognitive performance. The aim of this study was to determine whether heart rate variability (HRV) and variability in electroencephalographic (EEG) functional connectivity in the resting state are related to cognitive flexibility. Thirty-eight right-handed students completed the CAMBIOS test, and their heart and EEG activity was recorded during 6 min in the resting state with their eyes open. We calculated correlations, partial correlations and multiple linear regressions among HRV indices, functional brain connectivity variability and CAMBIOS scores. Furthermore, the sample was divided into groups according to CAMBIOS performance, and one-way ANOVA was applied to evaluate group differences. Our results show direct and inverse correlations among cognitive flexibility, connectivity (positive and negative task networks) and heartbeat variability. Partial correlations and multiple linear regressions suggest that the relation between HRV and CAMBIOS performance is mediated by neuronal oscillations. ANOVA confirms that HRV and variability in functional brain connectivity is related to cognitive performance. In conclusion, the levels of brain signal variability might predict cognitive flexibility in a cognitive task, while HRV might predict cognitive flexibility only when it is mediated by neuronal oscillations.
A U-Net Deep Learning Framework for High Performance Vessel Segmentation in Patients With Cerebrovascular Disease

Brain vessel status is a promising biomarker for better prevention and treatment in cerebrovascular disease. However, classic rule-based vessel segmentation algorithms need to be hand-crafted and are insufficiently validated. A specialized deep learning method—the U-net—is a promising alternative. Using labeled data from 66 patients with cerebrovascular disease, the U-net framework was optimized and evaluated with three metrics: Dice coefficient, 95% Hausdorff distance (95HD) and average Hausdorff distance (AVD). The model performance was compared with the traditional segmentation method of graph-cuts. Training and reconstruction was performed using 2D patches. A full and a reduced architecture with less parameters were trained. We performed both quantitative and qualitative analyses. The U-net models yielded high performance for both the full and the reduced architecture: A Dice value of ~0.88, a 95HD of ~47 voxels and an AVD of ~0.4 voxels. The visual analysis revealed excellent performance in large vessels and sufficient performance in small vessels. Pathologies like cortical laminar necrosis and a rete mirabile led to limited segmentation performance in few patients. The U-net outperfomed the traditional graph-cuts method (Dice ~0.76, 95HD ~59, AVD ~1.97). Our work highly encourages the development of clinically applicable segmentation tools based on deep learning. Future works should focus on improved segmentation of small vessels and methodologies to deal with specific pathologies.
Choosing the number of groups in a latent stochastic blockmodel for dynamic networks

Abstract Latent stochastic blockmodels are flexible statistical models that are widely used in social network analysis. In recent years, efforts have been made to extend these models to temporal dynamic networks, whereby the connections between nodes are observed at a number of different times. In this paper, we propose a new Bayesian framework to characterize the construction of connections. We rely on a Markovian property to describe the evolution of nodes' cluster memberships over time. We recast the problem of clustering the nodes of the network into a model-based context, showing that the integrated completed likelihood can be evaluated analytically for a number of likelihood models. Then, we propose a scalable greedy algorithm to maximize this quantity, thereby estimating both the optimal partition and the ideal number of groups in a single inferential framework. Finally, we propose applications of our methodology to both real and artificial datasets.
Probiotic Properties of Enterococcus Isolated From Artisanal Dairy Products

The present study focused on probiotic characterization and safety evaluation of Enterococcus isolates from different artisanal dairy products. All the isolates exhibited inhibitory activity against several food spoilage bacteria and food-borne pathogens, including Shigella flexneri, Staphylococcus aureus, Listeria monocytogenes, Yersinia enterocolitica, Klebsiella pneumoniae, Escherichia coli, and Bacillus subtilis. The PCR results indicated the presence of at least one enterocin structural gene in all the tested strains. The Enterococcus isolates were further evaluated regarding their safety properties and functional features. The isolates were susceptible to vancomycin, gentamycin, and chloramphenicol. The results of PCR amplification revealed that all the tested isolates harbored none of the tested virulence genes except E. faecalis (ES9), which showed the presence of esp gene. The Enterococcus isolates showed cholesterol lowering properties. The selected isolates showed a high tolerance to low pH, and toward bile salts. They also demonstrated hydrophobicity activity, auto-aggregation, and adhesion ability to the human intestinal Caco-2 cell line. These properties may contribute the bacteria colonizing the gut. This study revealed that the Enterococcus isolates, especially E. durans ES11, ES20 and ES32, might be excellent candidates for production of functional foods to promote health benefits.
Assessing Seasonality and Density From Passive Acoustic Monitoring of Signals Presumed to be From Pygmy and Dwarf Sperm Whales in the Gulf of Mexico

Pygmy sperm whales (Kogia breviceps) and dwarf sperm whales (Kogia sima) are deep diving cetaceans that commonly strand along the coast of the southeast US, but that are difficult to study visually at sea because of their elusive behavior. Conventional visual surveys are thought to significantly underestimate the presence of Kogia and they have proven difficult to approach for tracking and tagging. An approach is presented for density estimation of signals presumed to be from Kogia spp. based on passive acoustic monitoring data collected at sites in the Gulf of Mexico (GOM) from the period following the Deepwater Horizon oil spill (2010-2013). Both species of Kogia are known to inhabit the GOM, although it is not possible to acoustically separate the two based on available knowledge of their echolocation clicks. An increasing interannual density trend is suggested for animals near the primary zone of impact of the oil spill, and to the southeast of the spill. Densities were estimated based on both counting individual echolocation clicks and counting the presence of groups of animals during one-min time windows. Densities derived from acoustic monitoring at three sites are all substantially higher (4–16 animals /1000 km2) than those that have been derived for Kogia from line transect visual surveys in the same region (0.5 animals/1000 km2). The most likely explanation for the observed discrepancy is that the visual surveys are underestimating Kogia spp. density, due to the assumption of perfect detectability on the survey trackline. We present an alternative approach for density estimation, one that derives echolocation and behavioral parameters based on comparison of modeled and observed sound received levels at sites of varying depth.
A Comprehensive Reliability Analysis Framework for NTC Caches: A System to Device Approach

Near threshold computing (NTC) has significant role in reducing the energy consumption of modern very large scale integrated circuits designs. However, NTC designs suffer from functional failures and performance loss. Understanding the characteristics of the functional failures and variability effects is of decisive importance in order to mitigate them, and get the utmost NTC benefits. This paper presents a comprehensive cross-layer reliability analysis framework to assess the effect of soft error, aging, and process variation in the operation of near threshold voltage caches. The objective is to quantify the reliability of different SRAM designs, evaluate voltage scaling potential of caches, and to find a reliability-performance optimal cache organization for an NTC microprocessor. In this paper, the soft error rate (SER) and static noise margin (SNM) of 6T and 8T SRAM cells and their dependencies on aging and process variation are investigated by considering device, circuit, and architecture level analysis. Their experimental results show that in NTC, process variation and aging-induced SNM degradation is $2.5{\boldsymbol \times }$ higher than in the super threshold domain while SER is $8{\boldsymbol \times }$ higher. At NTC, the use of 8T instead of 6T SRAM cells can reduce the system-level SNM and SER by 14% and 22%, respectively. Besides, we observe that we can find the right balance between performance and reliability by using an appropriate cache organization at NTC which is different from the super threshold.
A Hybrid Approach to Side-Slip Angle Estimation With Recurrent Neural Networks and Kinematic Vehicle Models

This paper presents a supervised machine learning state estimation scheme that is able to estimate the current side-slip angle of a vehicle. It consists of a recurrent neural network with gated recurrent units, an additional input projection and a regression head. This structure has been chosen to limit the computational complexity of the model while preserving the expressiveness of the overall system. It will also be shown how equations of a simplified vehicle model can be incorporated to make use of existing domain knowledge. The results show that the neural network is able to reach an excellent estimation quality while generalizing over different tires, surfaces, and driving situations. Comparisons of different model variants on selected data subsets allow us to draw conclusions on the adaptation to varying parameters and show a quality improvement through the physical model. Evaluations with the mean squared error are complemented by more expressive fit and error plots to give a better understanding of the model behavior. All data for this paper have been collected with a Porsche 911 Turbo (Type 991 II) with a series sensor setup and an additional reference sensor.
The Relationship Between Workplace Ostracism and Sleep Quality: A Mediated Moderation Model

Extant research suggests that workplace ostracism has a detrimental impact on the outcomes of employees. However, very little is known about the impact of workplace ostracism on sleep quality. Therefore, this study aimed to address this gap in the literature. By employing the extended stressor-detachment model, we investigated the mediating role of psychological detachment and the moderating role of coping humor. We used a self-report questionnaire and a time-lagged research design to assess employees’ workplace ostracism, coping humor, psychological detachment, and sleep quality. A total of 403 valid questionnaires were obtained from participants in full-time employment in China. As predicted, the results showed that workplace ostracism is negatively associated with psychological detachment, which in turn, impairs sleep quality. We further found that high levels of coping humor buffer the negative effect of workplace ostracism on psychological detachment and sleep quality. In addition, the moderation effect of coping humor on the relationship between workplace ostracism and sleep quality is mediated by psychological detachment. Finally, based upon the results, we discuss the theoretical implications, provide practical proposals for managers and policymakers, and make suggestions for the direction of further research.
Immune Tumor Microenvironment in Breast Cancer and the Participation of Estrogen and Its Receptors in Cancer Physiopathology

Breast cancer is characterized by cellular and molecular heterogeneity. Several molecular events are involved in controlling malignant cell processes. In this sense, there is an overriding importance to study the multiple cell alterations within this pathology. That the immune response can vary depending on sex is a widely identified fact. Steroid hormones and their receptors may regulate different functions and the responses of several subpopulations of the immune system. Few reports are focused on the function of estrogen receptors (ERs) on immune cells and their roles in different breast cancer subtypes. Thus, the aim of this review is to investigate the immune infiltrating tumor microenvironment and the prognosis conferred by it in different breast cancer subtypes, to discuss the current knowledge and to point out the roles of estrogen and its receptors on the infiltrating immune cells, as well as to identify how different immune subsets are modulated after anti-hormonal treatments in breast cancer patients.
GnRH-1 Neural Migration From the Nose to the Brain Is Independent From Slit2, Robo3 and NELL2 Signaling

Gonadotropin releasing hormone-1 (GnRH-1) neurons play a pivotal role in controlling pubertal onset and fertility once they reach their hypothalamic location. During embryonic development, GnRH-1 neurons migrate from the nasal area to the hypothalamus where they modulate gonadotropin release from the pituitary gland. Defective migration of the GnRH-1 neurons to the brain, lack of GnRH-1 secretion or signaling cause hypogonadotropic hypogonadism (HH), a pathology characterized by delayed or absence of puberty. Binding of the guidance cue Slit2 to the receptor roundabout 3 (Robo3) has been proposed to modulate GnRH-1 cell motility and basal forebrain (bFB) access during migration. However, evidence suggests that Neural EGFL Like 2 (NELL2), not Slit2, binds to Robo3. To resolve this discrepancy, we analyzed GnRH-1 neuronal migration in NELL2, Robo3, and Slit2 knock-out mouse lines. Our data do not confirm a negative effect for monogenic Robo3 and Slit2 mutations on GnRH-1 neuronal migration from the nasal area to the brain. Moreover, we found no changes in GnRH-1 neuronal migration in the brain after NELL2 loss-of-function. However, we found that Slit2 loss-of-function alters the patterning of GnRH-1 cells in the brain, suggesting that Slit2 loss-of-function affects GnRH-1 cell positioning in the brain in a Robo3 independent fashion. Our results challenge previous theories on GnRH-1 neuronal migration mechanisms and provide a new impetus to identify and understand the complex genetic mechanisms causing disorders like Kallmann syndrome (KS) and HH.