Miracle-dz/UniFAR-FTUs huggingface dataset:
On Constructing Primitive Roots in Finite Fields With Advice

Finding primitive roots in a finite field of <inline-formula> <tex-math notation="LaTeX">$p^{n}$ </tex-math></inline-formula> elements of characteristic <inline-formula> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> remains to be a hard computational problem with the bottlenecks coming from both locating a small set of possible candidates and also factoring <inline-formula> <tex-math notation="LaTeX">$p^{n}-1$ </tex-math></inline-formula> in order to test these candidates. Kopparty <italic>et al.</italic> (2016) have introduced a question of designing a fast algorithm to find primitive roots with a short advice from an oracle. Trivially, for an <inline-formula> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula>-bit prime <inline-formula> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula>, such a primitive root can be fully described by about <inline-formula> <tex-math notation="LaTeX">$mn$ </tex-math></inline-formula> bits of information received from an all-powerful oracle. Here, we have shown that one can achieve this in polynomial time and with about <inline-formula> <tex-math notation="LaTeX">$(1/2+o(1))m + O(\log n)$ </tex-math></inline-formula> bits of advice.
A Modern Take on the Bias-Variance Tradeoff in Neural Networks

The bias-variance tradeoff tells us that as model complexity increases, bias falls and variances increases, leading to a U-shaped test error curve. However, recent empirical results with over-parameterized neural networks are marked by a striking absence of the classic U-shaped test error curve: test error keeps decreasing in wider networks. This suggests that there might not be a bias-variance tradeoff in neural networks with respect to network width, unlike was originally claimed by, e.g., Geman et al. (1992). Motivated by the shaky evidence used to support this claim in neural networks, we measure bias and variance in the modern setting. We find that both bias and variance can decrease as the number of parameters grows. To better understand this, we introduce a new decomposition of the variance to disentangle the effects of optimization and data sampling. We also provide theoretical analysis in a simplified setting that is consistent with our empirical findings.
Novel Approach for Optimizing the Transformer’s Critical Power Limit

Massive penetration of plug-in electric vehicles (EVs) may create challenges in the near future for the distribution network. Moreover, this may lead to an increase of the transformers’ aging rate and a reduction of the financial profits. In this paper, a novel approach is proposed, in which the operational margin of the transformer is optimized based on the transformer’s internal characteristics, its loss of life, and the variation of the ambient temperature. This operational power limit should not be exceeded to guarantee that the loss of life of the transformer is equal to or less than the one provided by the manufacturer. For validation purposes, a comparative study between the conventional method and the suggested one is presented. This paper is applied to a parking lot for charging EVs, which is supplied by a distribution transformer. In contrary to the conventional method, the one suggested in this paper can guarantee a predefined transformer loss of life. Simulation results show that the proposed method increases the transformer lifetime, reduces the loss of life, and reduces its depreciation cost by 63% in certain conditions. In addition, it increases the financial profit for the parking lot’s owner up to 10% during cold weather.
The Research of G- expansive Map and Devaney’s G-Chaos Condition on Metric G-Space

The expansive map and chaos have an important significance in terms of theory and application. According to the definition of expansive map and chaos, we give the concept of G-expansive map and Devaney's G-Chaos in this paper. By inference, some conclusions of the metric space were extended to the metric G-space. We have the following result:(1) Let X be a metric G-space, G be a commutative topological group and f be a equivariant self-map on X . Then we have that the map f satisfies the first condition and second condition of Devaney's G-Chaos if and only if the sets U and V have the same periodic orbit where U and V be any nonempty open sets; (2) Let X be a metric G-space and f be a homeomorphism map of X into itself. Then we have that f is a G-expansive map if and only if f^m is a Gexpansive map for any nonzero integer m; (3) Let (X,d_1) and (Y,d_2 ) be a metric G-space. Let f be a homeomorphism and equivariant map from X to Y. If h is a G-expansive map of X into itself, then fhf^-11 is a Gexpansive map of Y into itself; (4) Let X be a metric G-space, A be a invariant subset of G and X X-A be finite. Let f be a homeomorphism map on X. If f is G-expansive on A, then f is G-expansive on X; (5) Let X be a compact metric G-space and G be compact. If f is a G-expansive map with G-expansive constant Î´, then for each Îµ > 0 satifying 0 < Îµ <Î´ there exists a positive integer k such that d(G(x),G(y)) Îµ implies d( f^n(G(x)), f^n(G(y))) > Î´ for some integer n where |n| <Tâ‰¤ k. These results enriched the theory of the G-expansive map and Devaney's G-Chaos.
Recombinant adiponectin inhibits inflammation processes via NF-kB pathway in acute pancreatitis.

PURPOSE Adiponectin is a protein stemming from adipose tissue and having strong anti-inflammatory properties. We aimed to assess the damage diminishing effects of recombinant adiponectin (rAD) through NF-kB in the experimental acute pancreatitis (AP) model. MATERIALS AND METHODS Acute pancreatitis was created by applying 50 µg/kg dose of intraperitoneal cerulean. The rats were randomised and divided into 3 groups as control, AP and rAD groups. Moreover, the rats in each group were divided into two sub-groups as 24th and 48th hour subgroups. rAD was injected in the study group intraperitoneally. Tissue and blood samples were taken after 24 and 48 hours. Histopathological assessment and NF-kB activity were investigated in pancreatic tissue. RESULTS Serum TNF-a, IL-1b and IL-6 levels were found to be statistically significant in the AP group compared to the rAD group in the 24th and 48th hour (p < 0.05). Similarly, NF-kB activity was also found to be significant in the AP group both in the 24th and 48th hour (p < 0.05). There were significant differences in the AP and the rAD groups histopathologically in terms of edema, inflammation, vacuolisation and necrosis (p < 0.001). CONCLUSION rAD has significantly reduced NF-kB activity, cytokine levels and tissue damage (Tab. 1, Fig. 1, Ref. 51).
Theoretical and Cold-Test Investigation of a Four-Port High-Frequency System for a 0.14-THz Dual-Sheet-Beam Backward-Wave Oscillator

A four-port high-frequency system for a 0.14-THz dual-sheet-beam hole-grating backward-wave oscillator (BWO) is presented in this paper. The hole-grating slow-wave structure consists of two back-to-back gratings and a diaphragm with a two-hole array, which ensures the electric fields in two electron beam tunnels symmetrical. S-parameter results indicate the four-port system has good transmission characteristics, which could demonstrate the coupling effect of the hole array and the symmetrical properties of the electric fields at two output/input ports. Particle-in-cell simulations show that this high-frequency system could produce stable output signals as long as the current density of one electron beam is higher than the minimum starting current density. It indicates that the four-port high-frequency system can help to reduce the requirement of the dual-beam electron gun and increase the feasibility of the dual-beam hole-grating BWO. The proof-of-principle experiment shows an excellent transmission performance of the system, which agrees well with the theoretical results. All analyses reveal that the four-port hole-grating system could be a feasible high-frequency system of a subterahertz backward-wave radiation source.
Reduction of Parameter Redundancy in Biaffine Classifiers with Symmetric and Circulant Weight Matrices

Currently, the biaffine classifier has been attracting attention as a method to introduce an attention mechanism into the modeling of binary relations. For instance, in the field of dependency parsing, the Deep Biaffine Parser by Dozat and Manning has achieved state-of-the-art performance as a graph-based dependency parser on the English Penn Treebank and CoNLL 2017 shared task. On the other hand, it is reported that parameter redundancy in the weight matrix in biaffine classifiers, which has O(n^2) parameters, results in overfitting (n is the number of dimensions). In this paper, we attempted to reduce the parameter redundancy by assuming either symmetry or circularity of weight matrices. In our experiments on the CoNLL 2017 shared task dataset, our model achieved better or comparable accuracy on most of the treebanks with more than 16% parameter reduction.
Cerebral Autoregulation Is Disrupted Following a Season of Contact Sports Participation

Repetitive subconcussive head impacts across a season of contact sports participation are associated with a number of deficits in brain function. To date, no research has investigated the effect of such head impact exposure on dynamic cerebral autoregulation (dCA). To address this issue, 179 elite, junior-level (age 19.6 ± 1.5 years) contact sport (ice hockey, American football) athletes were recruited for pre-season testing. Fifty-two non-concussed athletes returned for post-season testing. Fifteen non-contact sport athletes (age 20.4 ± 2.2) also completed pre- and postseason testing. dCA was assessed via recordings of beat-by-beat mean arterial pressure (MAP) and middle cerebral artery blood velocity (MCAv) using finger photoplethysmography and transcranial Doppler ultrasound, respectively, during repetitive squat-stand maneuvers at 0.05 and 0.10 Hz. Transfer function analysis was used to determine Coherence (correlation), Gain (response amplitude), and Phase (response latency) of the MAP-MCAv relationship. Results showed that in contact sport athletes, Phase was reduced (p = 0.027) and Gain increased (p < 0.001) at post-season compared to pre-season during the 0.10 Hz squat-stand maneuvers, indicating cerebral autoregulatory impairment in both the latency and magnitude of the response. Changes in Phase were greater in athletes experiencing higher numbers and severity of head impacts. By contrast, no changes in dCA were observed in non-contact sport controls. Taken together, these results demonstrate that repetitive subconcussive head impacts occurring across a season of contact sports participation are associated with exposure-dependent impairments in the cerebrovascular pressure-buffering system capacity. It is unknown how long these deficits persist or if they accumulate year-over-year.
Jumper's Knee: A Prospective Evaluation of Risk Factors in Volleyball Players Using a Novel Measure of Injury.

OBJECTIVES To examine potential intrinsic risk factors that may contribute to the onset of jumper's knee in elite level-male volleyball players. DESIGN Prospective Cohort Study. SETTING Varsity and National team volleyball gymnasiums. PARTICIPANTS Sixty elite adult male volleyball players from Canada. ASSESSMENT OF RISK FACTORS Players completed a series of risk factor assessments at the commencement of their seasons, including vertical jump (cm), ankle dorsiflexion range (degrees), dynamic balance (normalized distance reached; cm), dynamic knee alignment (degrees), and landing mechanics (degrees). MAIN OUTCOME MEASURE Self-reported knee problems, captured via short message service. RESULTS Knee problem prevalence was 75% [95% confidence intervals (CIs): 62.2-84.6] and the incidence rate for substantial injuries over the study period was 30 injuries/100 players/season (95% CI: 19.5-43.1). No risk factor was found to significantly predict the future occurrence of developing jumper's knee. The odds ratios were close to unity (range: 0.94-1.07) with narrow confidence intervals and P > 0.05. CONCLUSIONS A more sensitive capture of overuse knee problems did not result in the identification of distinct risk factors for the development of jumper's knee. These findings highlight a lack of available methodology to accurately assess risk factors for overuse injuries.
Natural Worlds

The colonial encounter with the tropics and the global hunt for resources between 1450 and 1650 under the mercantile capitalism of European empires transformed the natural worlds of the Renaissance leading to a cultural and intellectual efflorescence. This was a period when, as the historian Donald Worster puts it, ‘capitalism became the pioneering, and … most important, architect of that new integrated world economy’, the environmental impact of European colonial expansion on so much of the world territory became apparent and humans as environmental agents begin to dominate over nature. The idea of a threshold or turning point is key to the debate on changing human nature interactions in the early modern period and defining that moment is a major problem both in terms of the dating and also in terms of a new culture of valuation of the landscape when people became aware of the importance of human agency and its possible damaging effects.
Hypoxia and TGF-β3 Synergistically Mediate Inner Meniscus-Like Matrix Formation by Fibrochondrocytes.

IMPACT STATEMENT The interactions of hypoxia and TGF-β3 in aggregates of human meniscus fibrochondrocytes are synergistic in nature, suggesting combinatorial strategies using these factors are promising for tissue engineering the inner meniscus regions. Hypoxia alone in the absence of TGF-β supplementation may be insufficient to initiate an inner meniscus-like extracellular matrix-forming response in this model.
Improved Converses and Gap Results for Coded Caching

Improved lower bounds are derived on the average and worst case rate-memory tradeoffs of the Maddah-Ali and Niesen-coded caching scenario. For any number of users and files and for arbitrary cache sizes, the multiplicative gap between the exact rate-memory tradeoff and the new lower bound is shown to be less than 2.315 in the worst case scenario and 2.507 in the average-case scenario.
Evaluation of GAN Applicability for Intrusion Detection in Self-Organizing Networks of Cyber Physical Systems

Wireless self-organizing adhoc network of cyber physical systems, a kind of machine-to-machine (m2m) communication network connecting different cyber devices, e.g. networks of moving vehicles and mobile facilities (VANET, FANET, MARINET, MANET), industrial Internet of Things (IoT), sensor network of smart building, has become a concern for cyber security for last decade. Every device in this network is a cyber physical system that can independently get commands, do operations, move in the space, and it becomes possible to manage it remotely. For its security sensitivity, this ITC system is required to be protected with an adequate and effective intrusion detection mechanism. Traditional artificial neural networks (ANNs) applied to solve this issue become unhelpful for their computational constrains for big data processing and lack of comprehensive training datasets. The paper explores generative adversarial ANNs to detect security intrusions in large-scale networks of cyber devices. Based on the results of the experiments, an assessment is made of applicability of generative adversarial ANNs to detect security anomalies, and also practical recommendations are discussed for their utilization in the connected cyber physical networks.
The Role of Sex Hormones in Multiple Sclerosis

Multiple sclerosis (MS) is a chronic inflammatory demyelination disorder with an immune-mediated pathophysiology that affects the central nervous system (CNS). Like other autoimmune conditions, it has a predilection for female gender. This suggests a gender bias and a possible hormonal association. Inflammation and demyelination are hallmarks of MS. Oligodendrocytes are the myelinating cells of the CNS and these continue to be generated by oligodendrocyte precursor cells (OPCs). The process of remyelination represents a major form of plasticity in the developing adult CNS. Remyelination does occur in MS, but the process is largely inadequate and/or incomplete. Current treatment strategies primarily focus on reducing inflammation or immunosuppression, but there is a need for more extensive research on re-myelination as a possible mechanism of treatment. Previous studies have shown that pregnancy leads to an increase in OPC proliferation, oligodendrocyte generation and the number of myelinated axons in the maternal CNS. Studies have also suggested that this remyelination is possibly mediated by estriol. Sex hormones in particular have been shown to have an immuno-protective effect in TH1-driven autoimmunity diseases. The aim of our article is to review the available research on sex hormone-specific immune modulatory effects, assess its remyelination potential in MS, and suggest a future path for more extensive research on sex hormone as a target for therapeutics in MS.
SegGAN: Semantic Segmentation with Generative Adversarial Network

Semantic segmentation is a long standing challenging issue in computer vision. In this paper, a novel method named SegGAN is proposed, in which a pre-trained deep semantic segmentation network is fitted into a generative adversarial framework for computing better segmentation masks. The composited networks are jointly fine-tuned end-to-end to get better segmentation masks. In the pre-training of Generative Adversarial Network (GAN), we try to minimize the loss between the generated images from the generator with the ground truth masks as input and the original images. Our motivation is that the learned GAN shows the relationship between the ground truth masks and the original images, thus the predicted masks of the semantic segmentation model should have the same distribution or relationship with the original images. Concretely, GAN is treated as a kind of loss for semantic segmentation to achieve better performance. Numerous experiments conducted on two publicly available datasets demonstrate the effectiveness of the proposed SegGAN.
Generalized Variability Model for Speaker Verification

In this letter, we propose a generalized variability model as an extension to the total variability model. While the total variability model employs a standard normal prior distribution in its typical setup, the proposed generalized variability model relaxes this assumption and allows the latent variable distribution to be a mixture of Gaussians. The conventional total variability model can then be viewed as a special case of this generalized version where the number of mixture components is constrained to one. This proposed model is validated in the context of speaker verification tasks on both the standard and extended NIST SRE 2010 datasets. Experimental results show that modeling the distribution of the latent variables as a mixture of Gaussians leads to a better performance under all conditions and a greater gain can be expected for speaker verification using short utterances.
Agricultural Transformation, Nutrition Transition and Food Policy in Africa: Preston Curves Reveal New Stylised Facts

Abstract This paper uses a Preston Curve approach to test for changes over time in agriculture, nutrition and food policy, comparing national averages in Africa and elsewhere at each level of national income per capita from the 1990s to the 2010s. Our statistical tests and data visualisations reveal that, at each level of income, African countries have faster rural population growth, a larger share of workers in agriculture and lower agricultural labour productivity than countries elsewhere, with no significant shift in these patterns from the 1990s to the 2010s. In contrast, there have been structural shifts towards less child stunting everywhere, and towards more adult obesity in high-income countries. The overall pattern of African governments’ food policies and government expenditures have not shifted, however, as they continue price interventions and low investment levels characteristic of low-income countries around the world.
Improving Fine-Grained Object Classification Using Adversarial Generated Unlabelled Samples

Recently Convolutional Neural Networks(CNNs) models have achieved remarkable results for fine-grained image classification. However, CNNs require a large amount of training data during supervised learning and labeling so much data is expensive in many cases. To address this issue, this paper innovatively presents a semi-supervised pipeline to improve fine-grained classification tasks without any extra data. We carefully combine CNNs with Generative Adversarial Nets(GANs) for classification, which shows that the result is affirmative. In addition, we propose a multi dimension label regularization(MDLR) method to train labeled images and unlabeled images simultaneously. First we use a pre-trained Yolo v2 object detection model to detect coarse-grained object on the original dataset. Second we feed cropped images to the generator of GAN to produce more generated data and assign a uniform label distribution to the generated images. Third we mix these origin real images and generated images. Then these mixed images are fed to a baseline CNN classifier and a feature-fused CNN classifier. We obtain competitive or state-of-the-art results: using feature-fused CNN model on Stanford Dogs dataset we set a new state-of-the-art result of 90.7%; on Oxford 102 Flowers dataset, we show consistent improvements over baseline.
Assessing Youth Perceptions and Knowledge of Ethics at 4-H Horse Shows in Louisiana

ABSTRACT Due to the increased awareness of animal welfare issues and sensitized attitudes throughout society, youth animal projects provide a unique opportunity to assess and impact youth understanding and attitudes regarding animal welfare. Animal ethics training is not an uncommon requirement for participants in 4-H livestock programs. However, participants in 4-H horse projects are not universally held to the same standard. The purpose of this study was to assess youth knowledge, attitudes, and confidence in understanding of equine welfare at the 4-H state horse show, as well as to determine the beliefs and observations of unethical practices, and if differences exist between those parameters in youth competitors. The results suggest that 4-H competitors are interested in ethics and believe that unethical behavior rarely occurs within the state 4-H horse project. However, youth report observing common unethical behaviors at the show, suggesting a disconnect between what they believe is unethical and what they actually observe. Educational opportunities exist within these animal projects and should be enhanced to address the gap in understanding.