Miracle-dz/UniFAR-FTUs huggingface dataset:
A novel mass spectrometer for atmospheric measurements & halocarbons during the CARIBIC and SAMBBA aircraft campaigns

Identification and monitoring of halocarbons in the atmosphere remains important for the purposes of regulation and prediction of stratospheric ozone depletion. Measurements of these and other compounds have created a demand for techniques that improve the number of atmospheric compounds analysable. A prototype time-of-flight gas chromatography-mass spectrometer (GCMS) was characterised for atmospheric measurements. Instrument performance was found to be at the lower end of expectations. A comparison to a quadrupole GC-MS indicated that the TOF GC-MS would be suitable for measurements in polluted environments. As part of this comparison, a number of halocarbons were analysed in London, U.K. as part of the ClearfLo campaign. HCFCs were found at higher concentration than their Northern Hemispheric (NH) baseline values. Furthermore, HFC-134a and HFC-227ea were almost double their NH baseline. CH2Cl2, C2Cl4 and C2HCl3 were encountered at high concentrations and sources of very short-lived bromomethanes (VSLB) were identified close to London. As part of the CARIBIC project, five VSLB were measured in the mid-latitude upper troposphere/ lower stratosphere and tropical troposphere. Higher mixing ratios were encountered over Southeast Asia, likely due to a locally longer CH2Br2 lifetime. Total bromine derived from these five VSLB is at the lower end of the quantity required to balance the stratospheric bromine budget. During the SAMBBA project, biomass burning and natural sources of COS, methyl halides and other halocarbons were assessed over Brazil. Methyl halide emissions from rainforest and savannah fires were quantified. The Cerrado savannah was found to be a strong source of COS. Regional biomass burning emission estimates indicate that this is an important region for emissions of these compounds. Terrestrial, natural sources of CH3Cl, CH3Br and CHCl3 were confirmed over the Amazon rainforest. Emissions from a localised source of CHCl3 were identified and wetlands or agricultural soil disturbance were hypothesised as a likely cause.
Provably Correct Controller Synthesis of Switched Stochastic Systems with Metric Temporal Logic Specifications: A Case Study on Power Systems

In this paper, we present a provably correct controller synthesis approach for switched stochastic control systems with metric temporal logic (MTL) specifications with provable probabilistic guarantees. We first present the stochastic control bisimulation function for switched stochastic control systems, which bounds the trajectory divergence between the switched stochastic control system and its nominal deterministic control system in a probabilistic fashion. We then develop a method to synthesize the optimal control input signals for the nominal deterministic system with calculated robustness margins, and the same input signals can be applied to the switched stochastic control system with a lower bound guarantee for satisfying the MTL specifications. We implement our robust stochastic controller synthesis approach on both a four-bus power system and a nine-bus power system under generation loss disturbances, with MTL specifications expressing requirements for the grid frequency deviations, wind turbine generator rotor speed variations and the power flow constraints at different power lines.
Interior-point methods for second-order stationary points of nonlinear semideﬁnite optimization problems using negative curvature

We propose a primal-dual interior-point method (IPM) with convergence to second-order stationary points (SOSPs) of nonlinear semideﬁnite optimization problems, abbreviated as NSDPs. As far as we know, the current algorithms for NSDPs only ensure convergence to ﬁrst-order stationary points such as Karush-Kuhn-Tucker points. The proposed method generates a sequence approximating SOSPs while minimizing a primal-dual merit function for NSDPs by using scaled gradient directions and directions of negative curvature. Under some assumptions, the generated sequence accumulates at an SOSP with a worst-case iteration complexity. This result is also obtained for a primal IPM with slight modiﬁcation. Finally, our numerical experiments show the beneﬁts of using directions of negative curvature in the proposed method.
Distilling a Powerful Student Model via Online Knowledge Distillation

Existing online knowledge distillation approaches either adopt the student with the best performance or construct an ensemble model for better holistic performance. However, the former strategy ignores other students’ information, while the latter increases the computational complexity during deployment. In this article, we propose a novel method for online knowledge distillation, termed feature fusion and self-distillation (FFSD), which comprises two key components: FFSD, toward solving the above problems in a unified framework. Different from previous works, where all students are treated equally, the proposed FFSD splits them into a leader student set and a common student set. Then, the feature fusion module converts the concatenation of feature maps from all common students into a fused feature map. The fused representation is used to assist the learning of the leader student. To enable the leader student to absorb more diverse information, we design an enhancement strategy to increase the diversity among students. Besides, a self-distillation module is adopted to convert the feature map of deeper layers into a shallower one. Then, the shallower layers are encouraged to mimic the transformed feature maps of the deeper layers, which helps the students to generalize better. After training, we simply adopt the leader student, which achieves superior performance, over the common students, without increasing the storage or inference cost. Extensive experiments on CIFAR-100 and ImageNet demonstrate the superiority of our FFSD over existing works. The code is available at https://github.com/SJLeo/FFSD.
Online learning with exponential weights in metric spaces

This paper addresses the problem of online learning in metric spaces using exponential weights. We extend the analysis of the exponentially weighted average forecaster, traditionally studied in a Euclidean settings, to a more abstract framework. Our results rely on the notion of barycenters, a suitable version of Jensen's inequality and a synthetic notion of lower curvature bound in metric spaces known as the measure contraction property. We also adapt the online-to-batch conversion principle to apply our results to a statistical learning framework.
FDLP-Spectrogram: Capturing Speech Dynamics in Spectrograms for End-to-end Automatic Speech Recognition

We propose a technique to compute spectrograms using Frequency Domain Linear Prediction (FDLP) that uses all-pole models to ﬁt the Hilbert envelope of speech in different frequency sub-bands. The spectrogram of a complete speech utter-ance is computed by overlap-add of contiguous all-pole model responses. The long context window of 1.5 seconds allows us to capture the low frequency temporal modulations of speech in the spectrogram. For an end-to-end automatic speech recognition task, the FDLP-spectrogram performs at-par with the standard mel-spectrogram features for clean read speech training and test data. For more realistic mismatched train-test situations and noisy, reverberated training data, the FDLP-spectrogram shows up to 25% and 22% WER improvements over mel-spectrogram respectively.
Predominance of pressure transport in spatial energy budget for a mixing layer approaching absolute instability

In this letter, we report the outcome of a spatial energy budget performed for the linear convective instability of the plane incompressible mixing layer within the inviscid framework. We ﬁnd that, as the critical condition for the onset of absolute instability is approached, the integrated pressure-transport term becomes increasingly more prominent as compared to the integrated production term and it dominates the energy budget completely at the critical condition. This implies that, near the threshold of absolute instability, the growth of disturbances is almost entirely due to the pressure transport mechanism (rather than the production mechanism), which is a striking result. The part of the pressure-transport term that represents the work done by the ﬂuctuating pressure forces is seen to be primarily responsible for the observed shift in the energy balance. These results can help us better understand the physical processes causing absolute instability in a mixing layer. In particular, the redistribution of disturbance energy in streamwise direction by ﬂuctuating pressure, which is “non-local” in character for incompressible ﬂows, seems to play a key role in this respect.
Quantum theory of two-dimensional materials coupled to electromagnetic resonators

Emil V. Denning, 2, 3, ∗ Martijn Wubs, 2, 4 Nicolas Stenger, 2, 4 Jesper Mørk, 2 and Philip Trøst Kristensen 2 Department of Photonics Engineering, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark NanoPhoton Center for Nanophotonics, Technical University of Denmark, Ørsteds Plads 345A, DK-2800 Kgs. Lyngby, Denmark Nichtlineare Optik und Quantenelektronik, Institut für Theoretische Physik, Technische Universität Berlin, Berlin, Germany Centre for Nanostructured Graphene, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark (Dated: March 29, 2021)
Bellman: A Toolbox for Model-Based Reinforcement Learning in TensorFlow

In the past decade, model-free reinforcement learning (RL) has provided solutions to challenging domains such as robotics. Model-based RL shows the prospect of being more sample-efficient than model-free methods in terms of agent-environment interactions, because the model enables to extrapolate to unseen situations. In the more recent past, model-based methods have shown superior results compared to model-free methods in some challenging domains with non-linear state transitions. At the same time, it has become apparent that RL is not market-ready yet and that many real-world applications are going to require model-based approaches, because model-free methods are too sample-inefficient and show poor performance in early stages of training. The latter is particularly important in industry, e.g. in production systems that directly impact a company's revenue. This demonstrates the necessity for a toolbox to push the boundaries for model-based RL. While there is a plethora of toolboxes for model-free RL, model-based RL has received little attention in terms of toolbox development. Bellman aims to fill this gap and introduces the first thoroughly designed and tested model-based RL toolbox using state-of-the-art software engineering practices. Our modular approach enables to combine a wide range of environment models with generic model-based agent classes that recover state-of-the-art algorithms. We also provide an experiment harness to compare both model-free and model-based agents in a systematic fashion w.r. t. user-defined evaluation metrics (e.g. cumulative reward). This paves the way for new research directions, e.g. investigating uncertainty-aware environment models that are not necessarily neural-network-based, or developing algorithms to solve industrially-motivated benchmarks that share characteristics with real-world problems.
LRez: a C++ API and toolkit for analyzing and managing Linked-Reads data

Abstract Motivation Linked-Reads technologies combine both the high quality and low cost of short-reads sequencing and long-range information, through the use of barcodes tagging reads which originate from a common long DNA molecule. This technology has been employed in a broad range of applications including genome assembly, phasing and scaffolding, as well as structural variant calling. However, to date, no tool or API dedicated to the manipulation of Linked-Reads data exist. Results We introduce LRez, a C++ API and toolkit that allows easy management of Linked-Reads data. LRez includes various functionalities, for computing numbers of common barcodes between genomic regions, extracting barcodes from BAM files, as well as indexing and querying BAM, FASTQ and gzipped FASTQ files to quickly fetch all reads or alignments containing a given barcode. LRez is compatible with a wide range of Linked-Reads sequencing technologies, and can thus be used in any tool or pipeline requiring barcode processing or indexing, in order to improve their performances. Availability and implementation LRez is implemented in C++, supported on Unix-based platforms and available under AGPL-3.0 License at https://github.com/morispi/LRez, and as a bioconda module. Supplementary information Supplementary data are available at Bioinformatics Advances online.
Thermodynamics of Enzyme‐Catalyzed Reactions: Part 5. Isomerases and Ligases

Equilibrium constants and enthalpy changes for reactions catalyzed by the isomerase and ligase classes of enzymes have been compiled. For each reaction the following information is given: the reference for the data; the reaction studied; the name of the enzyme used and its Enzyme Commission number; the method of measurement; the conditions of measurement (temperature, pH, ionic strength, and the buffer(s) and cofactor(s) used); the data and an evaluation of it; and, sometimes, commentary on the data and on any corrections which have been applied to it or any calculations for which the data have been used. The data from 176 references have been examined and evaluated. Chemical Abstract Service registry numbers are given for the substances involved in these various reactions. There is a cross reference between the substances and the Enzyme Commission numbers of the enzymes used to catalyze the reactions in which the substances participate.
Mismatch Negativity and Stimulus-Preceding Negativity in Paradigms of Increasing Auditory Complexity: A Possible Role in Predictive Coding

The auditory mismatch negativity (MMN) has been considered a preattentive index of auditory processing and/or a signature of prediction error computation. This study tries to demonstrate the presence of an MMN to deviant trials included in complex auditory stimuli sequences, and its possible relationship to predictive coding. Additionally, the transfer of information between trials is expected to be represented by stimulus-preceding negativity (SPN), which would possibly fit the predictive coding framework. To accomplish these objectives, the EEG of 31 subjects was recorded during an auditory paradigm in which trials composed of stimulus sequences with increasing or decreasing frequencies were intermingled with deviant trials presenting an unexpected ending. Our results showed the presence of an MMN in response to deviant trials. An SPN appeared during the intertrial interval and its amplitude was reduced in response to deviant trials. The presence of an MMN in complex sequences of sounds and the generation of an SPN component, with different amplitudes in deviant and standard trials, would support the predictive coding framework.
Ophthalmologic Manifestations of Primary Sjögren’s Syndrome

Sjögren’s syndrome (SS) is a chronic, progressive, inflammatory, autoimmune disease, characterized by the lymphocyte infiltration of exocrine glands, especially the lacrimal and salivary, with their consequent destruction. The onset of primary SS (pSS) may remain misunderstood for several years. It usually presents with different types of severity, e.g., dry eye and dry mouth symptoms, due to early involvement of the lacrimal and salivary glands, which may be associated with parotid enlargement and dry eye; keratoconjunctivitis sicca (KCS) is its most common ocular manifestation. It is still doubtful if the extent ocular surface manifestations are secondary to lacrimal or meibomian gland involvement or to the targeting of corneal and conjunctival autoantigens. SS is the most representative cause of aqueous deficient dry eye, and the primary role of the inflammatory process was evidenced. Recent scientific progress in understanding the numerous factors involved in the pathogenesis of pSS was registered, but the exact mechanisms involved still need to be clarified. The unquestionable role of both the innate and adaptive immune system, participating actively in the induction and evolution of the disease, was recognized. The ocular surface inflammation is a central mechanism in pSS leading to the decrease of lacrimal secretion and keratoconjunctival alterations. However, there are controversies about whether the ocular surface involvement is a direct autoimmune target or secondary to the inflammatory process in the lacrimal gland. In this review, we aimed to present actual knowledge relative to the pathogenesis of the pSS, considering the role of innate immunity, adaptive immunity, and genetics.
Hunting for Familial Parkinson’s Disease Mutations in the Post Genome Era

Parkinson’s disease (PD) is typically sporadic; however, multi-incident families provide a powerful platform to discover novel genetic forms of disease. Their identification supports deciphering molecular processes leading to disease and may inform of new therapeutic targets. The LRRK2 p.G2019S mutation causes PD in 42.5–68% of carriers by the age of 80 years. We hypothesise similarly intermediately penetrant mutations may present in multi-incident families with a generally strong family history of disease. We have analysed six multiplex families for missense variants using whole exome sequencing to find 32 rare heterozygous mutations shared amongst affected members. Included in these mutations was the KCNJ15 p.R28C variant, identified in five affected members of the same family, two elderly unaffected members of the same family, and two unrelated PD cases. Additionally, the SIPA1L1 p.R236Q variant was identified in three related affected members and an unrelated familial case. While the evidence presented here is not sufficient to assign causality to these rare variants, it does provide novel candidates for hypothesis testing in other modestly sized families with a strong family history. Future analysis will include characterisation of functional consequences and assessment of carriers in other familial cases.
Diet, Obesity, and Depression: A Systematic Review

Background: Obesity and depression co-occur in a significant proportion of the population. Mechanisms linking the two disorders include the immune and the endocrine system, psychological and social mechanisms. The aim of this systematic review was to ascertain whether weight loss through dietary interventions has the additional effect of ameliorating depressive symptoms in obese patients. Methods: We systematically searched three databases (Pubmed, Medline, Embase) for longitudinal clinical trials testing a dietary intervention in people with obesity and depression or symptoms of depression. Results: Twenty-four longitudinal clinical studies met the eligibility criteria with a total of 3244 included patients. Seventeen studies examined the effects of calorie-restricted diets and eight studies examined dietary supplements (two studies examined both). Only three studies examined people with a diagnosis of both obesity and depression. The majority of studies showed that interventions using a calorie-restricted diet resulted in decreases in depression scores, with effect sizes between ≈0.2 and ≈0.6. The results were less clear for dietary supplements. Conclusions: People with obesity and depression appear to be a specific subgroup of depressed patients in which calorie-restricted diets might constitute a promising personalized treatment approach. The reduction of depressive symptoms may be related to immunoendocrine and psychosocial mechanisms.
Farm Animals Are Long Away from Natural Behavior: Open Questions and Operative Consequences on Animal Welfare

Simple Summary Animal welfare is a very important issue. One of the tasks of researchers is to provide explanations and possible solutions to questions arising from non-experts. This work analyzes part of the extensive literature on relationships between selection and domestic, mainly farm, animals’ behavior and deals with some very important themes, such as the role of regulations, domestication, and selection. Abstract The concept of welfare applied to farm animals has undergone a remarkable evolution. The growing awareness of citizens pushes farmers to guarantee the highest possible level of welfare to their animals. New perspectives could be opened for animal welfare reasoning around the concept of domestic, especially farm, animals as partial human artifacts. Therefore, it is important to understand how much a particular behavior of a farm animal is far from the natural one of its ancestors. This paper is a contribution to better understand the role of genetics of the farm animals on their behavior. This means that the naïve approach to animal welfare regarding returning animals to their natural state should be challenged and that welfare assessment should be considered.
Glatiramer Acetate Treatment in Multiple Sclerosis-Associated Fatigue—Beneficial Effects on Self-Assessment Scales But Not on Molecular Markers

Although fatigue is a common symptom in multiple sclerosis (MS), its pathomechanisms are incompletely understood. Glatiramer acetate (GA), an immunomodulatory agent approved for treatment of relapsing-remitting MS (RRMS), possesses unique mechanisms of action and has been shown to exhibit beneficial effects on MS fatigue. The objective of this study was to correlate clinical, neuropsychological, and immunological parameters in RRMS patients with fatigue before and during treatment with GA. In a prospective, open-label, multicenter trial, 30 patients with RRMS and fatigue were treated with GA for 12 months. Inclusion criterion was the presence of fatigue as one of the most frequent and disabling symptoms. Before and during treatment, fatigue was assessed using the Fatigue Severity Scale (FSS), the MS-FSS, and the Modified Fatigue Impact Scale (MFIS). In addition, fatigue and quality of life were assessed using the Visual Analog Scales (VAS). Laboratory assessments included screening of 188 parameters using real-time PCR microarrays followed by further analysis of several cytokines, chemokines, and neurotrophic factors. Fatigue self-assessments were completed in 25 patients. After 12 months of treatment with GA, 13 of these patients improved in all three scales (with the most prominent effects on the MFIS), whereas 5 patients had deteriorated. The remaining 7 patients exhibited inconsistent effects within the three scales. Fatigue and overall quality of life had improved, as assessed via VAS. Laboratory assessments revealed heterogeneous mRNA levels of cytokines, chemokines, and neurotrophic factors. In conclusion, we were not able to correlate clinical and molecular effects of GA in patients with RRMS and fatigue.