Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Short-Term Modified Fasting on Sleep Patterns and Daytime Vigilance in Non-Obese Subjects: Results of a Pilot Study

Background: Periodically repeated short-term fasting is a frequently practised tradition worldwide. Empirical reports suggest that during fasting periods the quality of sleep and daytime performance are improved. The effects of a home-based 1-week modified fasting on sleep patterns and daytime vigilance and performance were analysed in 15 healthy non-obese volunteers. Methods: Sleep was measured by polysomnography before and after a 7-day fasting period; sleep inventories with assessment of daytime performance were collected throughout the observation period. Blood samples and urine were drawn at the beginning and at the end of fasting. Results: 13 subjects (12 females, 1 male; age 41.2 ± 13.4 years; BMI 23.9 ± 4.2 kg/m2) completed the fasting period; weight decreased from 66.5 ± 11.7 kg to 63 ± 11.9 kg. Compared to baseline, a significant decrease in arousals, a decrease in periodic leg movements (PLM) and a non-significant increase in REM sleep were observed at the end of fasting. Subjective sleep ratings showed a fasting-induced increase in global quality of sleep, daytime concentration, vigour and emotional balance. Clinical laboratory tests showed a decrease in serum magnesium; urinary melatonin excretion decreased moderately. Conclusion: This open pilot study demonstrates that along with a decrease in sleep arousals a 1-week fasting period promotes the quality of sleep and daytime performance in non-obese subjects. The observed decrease in PLM might point to a nutritional modification of brain dopaminergic functions. In terms of evolutionary development, an improved daytime performance during periods of food deprivation could have been beneficial for the success in search for food.
Robust Boosting via Convex Optimization: Theory and Applications

In this work we consider statistical learning problems. A learning machine aims to extract information from a set of training examples such that it is able to predict the associated label on unseen examples. We consider the case where the resulting classification or regression rule is a combination of simple rules – also called base hypotheses. The so-called boosting algorithms iteratively find a weighted linear combination of base hypotheses that predict well on unseen data. We address the following issues: The statistical learning theory framework for analyzing boosting methods. We study learning theoretic guarantees on the prediction performance on unseen examples. Recently, large margin classification techniques emerged as a practical result of the theory of generalization, in particular Boosting and Support Vector Machines. A large margin implies a good generalization performance. Hence, we analyze how large the margins in boosting are and find an improved algorithm that is able to generate the maximum margin solution. How can boosting methods be related to mathematical optimization techniques? To analyze the properties of the resulting classification or regression rule, it is of high importance to understand whether and under which conditions boosting converges. We show that boosting can be used to solve large scale constrained optimization problems, whose solutions are well characterizable. To show this, we relate boosting methods to methods known from mathematical optimization, and derive convergence guarantees for a quite general family of boosting algorithms. How to make Boosting noise robust? One of the problems of current boosting techniques is that they are sensitive to noise in the training sample. In order to make boosting robust, we transfer the soft margin idea from support vector learning to boosting. We develop theoretically motivated regularized algorithms that exhibit a high noise robustness. How to adapt boosting to regression problems? Boosting methods are originally designed for classification problems. To extend the boosting idea to regression problems, we use the previous convergence results and relations to semi-infinite programming to design boosting-like algorithms for regression problems. We show that these leveraging algorithms have desirable theoretical and practical properties. Can boosting techniques be useful in practice? The presented theoretical results are guided by simulation results either to illustrate properties of the proposed algorithms or to show that they work well in practice. We report on successful applications in a non-intrusive power monitoring system, chaotic time series analysis and a drug discovery process. Preface I started my research …
Cooperative Jamming for Physical Layer Security Enhancement Using Deep Reinforcement Learning

Wireless data communications are always facing the risk of eavesdropping and interception. Conventional protection solutions which are based on encryption may not always be practical as is the case for wireless IoT networks or may soon become ineffective against quantum computers. In this regard, Physical Layer Security (PLS) presents a promising approach to secure wireless communications through the exploitation of the physical properties of the wireless channel. Cooperative Friendly Jamming (CFJ) is among the PLS techniques that have received attention in recent years. However, finding an optimal transmit power allocation that results in the highest secrecy is a complex problem that becomes more difficult to address as the size of the wireless network increases. In this paper, we propose an optimization approach to achieve CFJ in large Wi-Fi networks by using a Reinforcement Learning Algorithm. Obtained results show that our optimization approach offers better secrecy results and becomes more effective as the network size and the density of Wi-Fi access points increase.
Digit Position and Forces Covary during Anticipatory Control of Whole-Hand Manipulation

Theoretical perspectives on anticipatory planning of object manipulation have traditionally been informed by studies that have investigated kinematics (hand shaping and digit position) and kinetics (forces) in isolation. This poses limitations on our understanding of the integration of such domains, which have recently been shown to be strongly interdependent. Specifically, recent studies revealed strong covariation of digit position and load force during the loading phase of two-digit grasping. Here, we determined whether such digit force-position covariation is a general feature of grasping. We investigated the coordination of digit position and forces during five-digit whole-hand manipulation of an object with a variable mass distribution. Subjects were instructed to prevent object roll during the lift. As found in precision grasping, there was strong trial-to-trial covariation of digit position and force. This suggests that the natural variation of digit position that is compensated for by trial-to-trial variation in digit forces is a fundamental feature of grasp control, and not only specific to precision grasp. However, a main difference with precision grasping was that modulation of digit position to the object’s mass distribution was driven predominantly by the thumb, with little to no modulation of finger position. Modulation of thumb position rather than fingers is likely due to its greater range of motion and therefore adaptability to object properties. Our results underscore the flexibility of the central nervous system in implementing a range of solutions along the digit force-to-position continuum for dexterous manipulation.
Evaluation of an exposure assessment used in epidemiological studies of diesel exhaust and lung cancer in underground mines

NIOSH/NCI (National Institute of Occupational Safety and Health and National Cancer Institute) developed exposure estimates for respirable elemental carbon (REC) as a surrogate for exposure to diesel exhaust (DE) for different jobs in eight underground mines by year beginning in the 1940s—1960s when diesel equipment was first introduced into these mines. These estimates played a key role in subsequent epidemiological analyses of the potential relationship between exposure to DE and lung cancer conducted in these mines. We report here on a reanalysis of some of the data from this exposure assessment. Because samples of REC were limited primarily to 1998–2001, NIOSH/NCI used carbon monoxide (CO) as a surrogate for REC. In addition, because CO samples were limited, particularly in the earlier years, they used the ratio of diesel horsepower (HP) to the mine air exhaust rate as a surrogate for CO. There are considerable uncertainties connected with each of these surrogate-based steps. The estimates of HP appear to involve considerable uncertainty, although we had no data upon which to evaluate the magnitude of this uncertainty. A sizable percentage (45%) of the CO samples used in the HP to CO model was below the detection limit which required NIOSH/NCI to assign CO values to these samples. In their preferred REC estimates, NIOSH/NCI assumed a linear relation between C0 and REC, although they provided no credible support for that assumption. Their assumption of a stable relationship between HP and CO also is questionable, and our reanalysis found a statistically significant relationship in only one-half of the mines. We re-estimated yearly REC exposures mainly using NIOSH/NCI methods but with some important differences: (i) rather than simply assuming a linear relationship, we used data from the mines to estimate the CO—REC relationship; (ii) we used a different method for assigning values to nondetect CO measurements; and (iii) we took account of statistical uncertainty to estimate bounds for REC exposures. This exercise yielded significantly different exposure estimates than estimated by NIOSH/NCI. However, this analysis did not incorporate the full range of uncertainty in REC exposures because of additional uncertainties in the assumptions underlying the modeling and in the underlying data (e.g. HP and mine exhaust rates). Estimating historical exposures in a cohort is generally a very difficult undertaking. However, this should not prevent one from recognizing the uncertainty in the resulting estimates in any use made of them.
Uncultivated Phylotypes and Newly Named Species Associated with Primary and Persistent Endodontic Infections

ABSTRACT Endodontic infections have been traditionally studied by culture methods, but recent reports showing that over 50% of the oral microbiota is still uncultivable (B. J. Paster et al., J. Bacteriol. 183:3770-3783, 2001) raise the possibility that many endodontic pathogens remain unknown. This study intended to investigate the prevalence of several uncultivated oral phylotypes, as well as newly named species in primary or persistent endodontic infections associated with chronic periradicular diseases. Samples were taken from the root canals of 21 untreated teeth and 22 root-filled teeth, all of them with radiographic evidence of periradicular bone destruction. Genomic DNA was isolated directly from each sample, and 16S rRNA gene-based nested or heminested PCR assays were used to determine the presence of 13 species or phylotypes of bacteria. Species-specific primers had already been validated in the literature or were developed by aligning closely related 16S rRNA gene sequences. Species specificity for each primer pair was confirmed by running PCRs against a panel of several oral bacteria and by sequencing DNA from representative positive samples. All species or phylotypes were detected in at least one case of primary infections. The most prevalent species or phylotypes found in primary infections were Dialister invisus (81%), Synergistes oral clone BA121 (33%), and Olsenella uli (33%). Of the target bacteria, only these three species were detected in persistent infections. Detection of uncultivated phylotypes and newly named species in infected root canals suggests that there are previously unrecognized bacteria that may play a role in the pathogenesis of periradicular diseases.
Genomic Characterization of a Circovirus Associated with Fatal Hemorrhagic Enteritis in Dog, Italy

Dog circovirus (DogCV) was identified in an outbreak of enteritis in pups in Italy. The disease was observed in 6 young dachshunds pups of a litter from a breeding kennel and caused the death of 2 dogs. Upon full-genome analysis, the virus detected in one of the dead pups (strain Bari/411–13) was closely related to DogCVs that have been recently isolated in the USA. The present study, if corroborated by further reports, could represent a useful contribution to the knowledge of the pathogenic potential of DogCV and its association with enteritis in dogs.
L-functional analysis

Inspired by the theories of Kaplansky-Hilbert modules and probability theory in vector lattices, we generalise functional analysis by replacing the scalars $\mathbb{R}$ or $\mathbb{C}$ by a real or complex Dedekind complete unital $f$-algebra $\mathbb{L}$; such an algebra can be represented as a suitable space of continuous functions. We set up the basic theory of $\mathbb{L}$-normed and $\mathbb{L}$-Banach spaces and bounded operators between them, we discuss the $\mathbb{L}$-valued analogues of the classical $\ell^p$-spaces, and we prove the analogue of the Hahn-Banach theorem. We also discuss the basics of the theory of $\mathbb{L}$-Hilbert spaces, including projections onto convex subsets and the Riesz Representation theorem.
Tokamak H-mode edge-SOL global turbulence simulations with an electromagnetic, transcollisional drift-fluid model

The design of commercially feasible magnetic confinement fusion reactors strongly relies on the reduced turbulent transport in the plasma edge during operation in the high confinement mode (H-mode). We present first global turbulence simulations of the ASDEX Upgrade tokamak edge and scrape-off layer (SOL) in ITER baseline H-mode conditions. Reasonable agreement with the experiment is obtained for outboard mid-plane measurements of plasma density, electron and ion temperature, as well as the radial electric field. The radial heat transport is underpredicted by roughly a factor 2. These results were obtained with the GRILLIX code implementing a transcollisional, electromagnetic, global drift-fluid plasma model, coupled to diffusive neutrals. The transcollisional extensions include neoclassical corrections for the ion viscosity, as well as either a Landau-fluid or free-streaming limited model for the parallel heat conduction. Electromagnetic fluctuations are found to play a critical role in H-mode conditions. We investigate the structure of the significant $E \times B$ flow shear, finding both neoclassical components as well as zonal flows. But unlike in L-mode, geodesic acoustic modes are not observed. The turbulence mode structure is mostly that of drift-Alfv\'en waves. However, in the upper part of the pedestal, it is very weak and overshadowed by neoclassical transport. At the pedestal foot, on the other hand, we find instead the (electromagnetic) kinetic ballooning mode (KBM), most clearly just inside the separatrix. Our results pave the way towards predictive simulations of fusion reactors.
Estimation and removal of clock skew from network delay measurements

Packet delay and loss traces are frequently used by network engineers, as well as network applications, to analyze network performance. The clocks on the end-systems used to measure the delays, however, are not always synchronized, and this lack of synchronization reduces the accuracy of these measurements. Therefore, estimating and removing relative skews and offsets from delay measurements between sender and receiver clocks are critical to the accurate assessment and analysis of network performance. We introduce a linear programming-based algorithm to estimate the clock skew in network delay measurements and compare it with three other algorithms. We show that our algorithm has a time complexity of O(N), leaves the delay after the skew removal positive, and is robust in the sense that the error margin of the skew estimate is independent of the magnitude of the skew. We use traces of real Internet delay measurements to assess the algorithm, and compare its performance to that of three other algorithms. Furthermore, we show through simulation that our algorithm is unbiased, and that the sample variance of the skew estimate is better (smaller) than existing algorithms.
Novel rhythms of N1‐acetyl‐N2‐formyl‐5‐methoxykynuramine and its precursor melatonin in water hyacinth: importance for phytoremediation

N1‐acetyl‐N2‐formyl‐5‐methoxykynura‐mine (AMFK) is a major metabolite of melatonin in mammals. To investigate whether AFMK exists in plants, an aquatic plant, water hyacinth, was used. To achieve this, LC/MS/MS with a deuterated standard was employed. AFMK was identified in any plant for the first time. Both it and its precursor, melatonin, were rhythmic with peaks during the late light phase. These novel rhythms indicate that these molecules do not serve as the chemical signal of darkness as in animals but may relate to processes of photosynthesis or pho‐toprotection. These possibilities are supported by higher production of melatonin and AFMK in plants grown in sunlight (10, 000–15, 000 μW/cm2) compared to those grown under artificial light (400–450 μW/ cm2). Melatonin and AFMK, as potent free radical scavengers, may assist plants in coping with harsh environmental insults, including soil and water pollutants. High levels of melatonin and AFMK in water hyacinth may explain why this plant more easily tolerates environmental pollutants, including toxic chemicals and heavy metals and is successfully used in phytoremediation. These novel findings could lead to improvements in the phytoremediative capacity of plants by either stimulating endogenous melatonin synthesis or by adding melatonin to water/soil in which they are grown. —Dun‐Xian Tan, Lucien C. Manchester, Paolo Di Mascio, Glaucia R. Martinez, Fernanda M. Prado, and Russel J. Reiter. Novel rhythms of N1‐acetyl‐N2‐formyl‐5‐methoxykynuramine and its precursor mel‐atonin in water hyacinth: importance for phytoremedia‐tion. FASEB J. 21, 1724–1729 (2007)
Monte Carlo Study of Real Time Dynamics on the Lattice.

Monte Carlo studies involving real time dynamics are severely restricted by the sign problem that emerges from a highly oscillatory phase of the path integral. In this Letter, we present a new method to compute real time quantities on the lattice using the Schwinger-Keldysh formalism via Monte Carlo simulations. The key idea is to deform the path integration domain to a complex manifold where the phase oscillations are mild and the sign problem is manageable. We use the previously introduced "contraction algorithm" to create a Markov chain on this alternative manifold. We substantiate our approach by analyzing the quantum mechanical anharmonic oscillator. Our results are in agreement with the exact ones obtained by diagonalization of the Hamiltonian. The method we introduce is generic and, in principle, applicable to quantum field theory albeit very slow. We discuss some possible improvements that should speed up the algorithm.
The Potential Elimination of Plasmodium vivax Malaria by Relapse Treatment: Insights from a Transmission Model and Surveillance Data from NW India

Background With over a hundred million annual infections and rising morbidity and mortality, Plasmodium vivax malaria remains largely a neglected disease. In particular, the dependence of this malaria species on relapses and the potential significance of the dormant stage as a therapeutic target, are poorly understood. Methodology/Principal Findings To quantify relapse parameters and assess the population-wide consequences of anti-relapse treatment, we formulated a transmission model for P. vivax suitable for parameter inference with a recently developed statistical method based on routine surveillance data. A low-endemic region in NW India, whose strong seasonality demarcates the transmission season, provides an opportunity to apply this modeling approach. Our model gives maximum likelihood estimates of 7.1 months for the mean latency and 31% for the relapse rate, in close agreement with regression estimates and clinical evaluation studies in the area. With a baseline of prevailing treatment practices, the model predicts that an effective anti-relapse treatment of 65% of those infected would result in elimination within a decade, and that periodic mass treatment would dramatically reduce the burden of the disease in a few years. Conclusion/Significance The striking dependence of P. vivax on relapses for survival reinforces the urgency to develop more effective anti-relapse treatments to replace Primaquine (PQ), the only available drug for the last fifty years. Our methods can provide alternative and simple means to estimate latency times and relapse frequency using routine epidemiological data, and to evaluate the population-wide impact of relapse treatment in areas similar to our study area.
Benchmarking Zero-Shot Robustness of Multimodal Foundation Models: A Pilot Study

Pre-training image representations from the raw text about images enables zero-shot vision transfer to downstream tasks. Through pre-training on millions of samples collected from the internet, multimodal foundation models, such as CLIP, produce state-of-the-art zero-shot results that often reach competitiveness with fully supervised methods without the need for task-specific training. Besides the encouraging performance on classification accuracy, it is reported that these models close the robustness gap by matching the performance of supervised models trained on ImageNet under natural distribution shift. Because robustness is critical to real-world applications, especially safety-critical ones, in this paper, we present a comprehensive evaluation based on a large-scale robustness benchmark covering 7 natural, 3 synthetic distribution shifts, and 11 adversarial attacks. We use CLIP as a pilot study. We show that CLIP leads to a significant robustness drop compared to supervised ImageNet models on our benchmark, especially under synthetic distribution shift and adversarial attacks. Furthermore, data overlap analysis suggests that the observed robustness under natural distribution shifts could be attributed, at least in part, to data overlap. In summary, our evaluation shows a comprehensive evaluation of robustness is necessary; and there is a significant need to improve the robustness of zero-shot multimodal models.
Mild pro-p-groups and Galois groups of p-extensions of ℚ

Abstract In this paper we introduce a new class of finitely presented pro-p-groups G of cohomological dimension 2 called mild groups. If d(G), r(G) are respectively the minimal number of generators and relations of G, we give an infinite family of mild groups G with r(G) ≧ d(G) and d(G) ≧ 2 arbitrary. These groups can be constructed with G/[G, G] finite, answering a question of Kuzmin. If G = G S (p) is the Galois group of the maximal p-extension of ℚ unramified outside a finite set of primes S and p ≠ 2, we show that G is mild for a co-final class of sets S, even in the case p ∉ S.