Miracle-dz/UniFAR-FTUs huggingface dataset:
Microbiomes in an acidic rock-water cave system.

Belowground ecosystems are accessible by mining, where a specific microbial community can be discovered. The biodiversity of a former alum mine rich in carbon, but with a low pH of 2.6-3.7 was evaluated by DNA- and cultivation-dependent methods using samples of the black slate rock material, secondary mineralization phases and seepage water. Pyrite oxidation within the low grade metamorphic Silurian black slate established high concentrations of Fe and SO42- forming the extreme conditions visible with acidophilic and Fe-oxidizing microorganisms. In addition, an unexpected predominance of fungi in this C-rich and acidic cave ecosystem including high numbers of Mucoromycota and Mortierellomycota was detected. Therefore, fungal cultures were obtained; mainly form the secondary mineral phases that are iron phosphates. Hence, the fungi might well have been involved in phosphate mobilization there. The rock material itself is rich in organic carbon that can be used by oxidases activity. The cultivation setup mimicking the cave conditions (low temperature, low pH, oxic conditions) with one oligotrophic and one medium rich in nutrients, that does allow for isolation of different fungal (and eutrophic bacterial) groups. The acidic conditions prevented the occurrence of many basidiomycetes, while the isolated fungi could survive these adverse conditions.
Use What You Have: Video retrieval using representations from collaborative experts

The rapid growth of video on the internet has made searching for video content using natural language queries a significant challenge. Human-generated queries for video datasets `in the wild' vary a lot in terms of degree of specificity, with some queries describing specific details such as the names of famous identities, content from speech, or text available on the screen. Our goal is to condense the multi-modal, extremely high dimensional information from videos into a single, compact video representation for the task of video retrieval using free-form text queries, where the degree of specificity is open-ended. For this we exploit existing knowledge in the form of pre-trained semantic embeddings which include 'general' features such as motion, appearance, and scene features from visual content. We also explore the use of more 'specific' cues from ASR and OCR which are intermittently available for videos and find that these signals remain challenging to use effectively for retrieval. We propose a collaborative experts model to aggregate information from these different pre-trained experts and assess our approach empirically on five retrieval benchmarks: MSR-VTT, LSMDC, MSVD, DiDeMo, and ActivityNet. Code and data can be found at this http URL. This paper contains a correction to results reported in the previous version.
Gravitational waves triggered by $B-L$ charged hidden scalar and leptogenesis

We study the electroweak symmetry breaking in the framework of a classically conformal $U(1)_{B-L}$ theory, where three right-handed neutrinos (RHNs) and a hidden scalar are introduced, with the latter playing the role of dark matter (DM). It is found that the DM and RHN sectors are crucial for the spontaneous symmetry breaking of the $U(1)_{B-L}$ symmetry, strong first order phase transition in the conformal theory and the resultant gravitational wave (GW) prospects at future space-based interferometer LISA and other GW experiments. The baryon asymmetry of the Universe is addressed by the resonant leptogenesis mechanism, which is potentially disturbed by the hidden scalar. To make the GW spectra detectable by LISA and resonant leptogenesis work in the conformal $U(1)_{B-L}$ theory, the hidden scalar can not fully saturate the observed DM relic density.
Generative adversarial networks (GAN) for compact beam source modelling in Monte Carlo simulations

A method is proposed and evaluated to model large and inconvenient phase space files used in Monte Carlo simulations by a compact generative adversarial network (GAN). The GAN is trained based on a phase space dataset to create a neural network, called Generator (G), allowing G to mimic the multidimensional data distribution of the phase space. At the end of the training process, G is stored with about 0.5 million weights, around 10 MB, instead of a few GB of the initial file. Particles are then generated with G to replace the phase space dataset. This concept is applied to beam models from linear accelerators (linacs) and from brachytherapy seed models. Simulations using particles from the reference phase space on one hand and those generated by the GAN on the other hand were compared. 3D distributions of deposited energy obtained from source distributions generated by the GAN were close to the reference ones, with less than 1% of voxel-by-voxel relative difference. Sharp parts such as the brachytherapy emission lines in the energy spectra were not perfectly modeled by the GAN. Detailed statistical properties and limitations of the GAN-generated particles still require further investigation, but the proposed exploratory approach is already promising and paves the way for a wide range of applications.
A Reverse Shock in GRB 181201A

We present comprehensive multiwavelength radio to X-ray observations of GRB 181201A spanning from ≈150 s to ≈163 days after the burst, comprising the first joint ALMA–VLA–GMRT observations of a gamma-ray burst (GRB) afterglow. The radio and millimeter-band data reveal a distinct signature at ≈3.9 days, which we interpret as reverse-shock (RS) emission. Our observations present the first time that a single radio-frequency spectral energy distribution can be decomposed directly into RS and forward shock (FS) components. We perform detailed modeling of the full multiwavelength data set, using Markov Chain Monte Carlo sampling to construct the joint posterior density function of the underlying physical parameters describing the RS and FS synchrotron emission. We uncover and account for all discovered degeneracies in the model parameters. The joint RS–FS modeling reveals a weakly magnetized (σ ≈ 3 × 10−3), mildly relativistic RS, from which we derive an initial bulk Lorentz factor of Γ0 ≈ 103 for the GRB jet. Our results support the hypothesis that low-density environments are conducive to the observability of RS emission. We compare our observations to other events with strong RS detections and find a likely observational bias selecting for longer lasting, nonrelativistic RSs. We present and begin to address new challenges in modeling posed by the present generation of comprehensive, multifrequency data sets.
Agarwood resin production and resin quality of Gyrinops walla Gaertn

Agarwood is a resin produced by certain species of family Thymalaeaceae due to a self-defence mechanism. Most species of Aquilaria and a few species of Gyrinops, Aetoxylon and Gonystylus are capable of producing agarwood. Gyrinops walla, a member of the family Thymalaeceae is recorded only in the wet zone of Sri Lanka and very rarely in southwest India, has not been previously studied to identify its ability of producing agarwood. Therefore the present study was the first ever to conduct and identify the production of agarwood in G. walla and the quality of its resins. Six G. walla trees growing in two distinctive areas of the wet zone of Sri Lanka were used for the present study. All six trees had natural wounds occurred sometime before the sample collection due to abrasions or fallen branches. The dark coloured tissues of the affected areas were carefully collected without cutting the trees and resins were extracted by solvent extraction method. The extracted resins were analysed using gas chromatography to identify the different compounds. Finally these compounds were compared with that of selected Aquilaria species. The results revealed a strong similarity of resin compounds of G. walla with that of Aquilaria species which are commercially used to collect agarwood. Further studies should be conducted to identify the effects of artificial resin induction methods on G. walla that are already used on Aquilaria species.
Normalyzing Numeronyms - A NLP approach

This paper presents a method to apply Natural Language Processing for normalizing numeronyms to make them understandable by humans. We approach the problem through a two-step mechanism. We make use of the state of the art Levenshtein distance of words. We then apply Cosine Similarity for selection of the normalized text and reach greater accuracy in solving the problem. Our approach garners accuracy figures of 71\% and 72\% for Bengali and English language, respectively.
On conducting better validation studies of automatic metrics in natural language generation evaluation

Natural language generation (NLG) has received increasing attention, which has highlighted evaluation as a central methodological concern. Since human evaluations for these systems are costly, automatic metrics have broad appeal in NLG. Research in language generation often finds situations where it is appropriate to apply existing metrics or propose new ones. The application of these metrics are entirely dependent on validation studies - studies that determine a metric's correlation to human judgment. However, there are many details and considerations in conducting strong validation studies. This document is intended for those validating existing metrics or proposing new ones in the broad context of NLG: we 1) begin with a write-up of best practices in validation studies, 2) outline how to adopt these practices, 3) conduct analyses in the WMT'17 metrics shared task\footnote{Our jupyter notebook containing the analyses is available at \url{this https URL}}, and 4) highlight promising approaches to NLG metrics 5) conclude with our opinions on the future of this area.
Random-graph models and characterization of granular networks

Various approaches and measures from network analysis have been applied to granular and particulate networks to gain insights into their structural, transport, failure-propagation and other systems-level properties. In this article, we examine a variety of common network measures and study their ability to characterize various two-dimensional and three-dimensional spatial random-graph models and empirical two-dimensional granular networks. We identify network measures that are able to distinguish between physically plausible and unphysical spatial network models. Our results also suggest that there are significant differences in the distributions of certain network measures in two and three dimensions, hinting at important differences that we also expect to arise in experimental granular networks.
Wireless Networked Control Systems With Coding-Free Data Transmission for Industrial IoT

Wireless networked control systems for the Industrial Internet of Things (IIoT) require low-latency communication techniques that are very reliable and resilient. In this article, we investigate a coding-free control method to achieve ultralow latency communications in single-controller-multiplant networked control systems for both slow- and fast-fading channels. We formulate a power allocation problem to optimize the sum cost functions of multiple plants, subject to the plant stabilization condition and the controller’s power limit. Although the optimization problem is a nonconvex one, we derive a closed-form solution, which indicates that the optimal power allocation policy for stabilizing the plants with different channel conditions is reminiscent of the channel-inversion policy. We numerically compare the performance of the proposed coding-free control method and the conventional coding-based control methods in terms of the control performance (i.e., the cost function) of a plant, which shows that the coding-free method is superior in a practical range of signal-to-noise ratios.
WISE Discovery of Mid-infrared Variability in Massive Young Stellar Objects

We systematically investigate the mid-infrared (MIR; λ > 3 μm) time variability of uniformly selected ∼800 massive young stellar objects (MYSOs) from the Red Midcourse Space Experiment Source survey. Out of the 806 sources, we obtain reliable 9 yr long MIR magnitude variability data of 331 sources at the 3.4 μm (W1) and 4.6 μm (W2) bands by cross-matching the MYSO positions with ALLWISE and NEOWISE catalogs. After applying the variability selections using ALLWISE data, we identify five MIR-variable candidates. The light curves show various classes, with the periodic, plateau-like, and dipper features. Out of the obtained two color–magnitude diagram of W1 and W1−W2, one shows “bluer when brighter and redder when fainter” trends in variability, suggesting change in extinction or accretion rate. Finally, our results show that G335.9960−00.8532 (hereafter, G335) has a periodic light curve, with an ≈690 day cycle. Spectral energy density model fitting results indicate that G335 is a relatively evolved MYSO; thus, we may be witnessing the very early stages of a hyper- or ultra-compact H ii region, a key source for understanding MYSO evolution.
Hug and Hop: a discrete-time, nonreversible Markov chain Monte Carlo algorithm

We introduced the Hug and Hop Markov chain Monte Carlo algorithm for estimating expectations with respect to an intractable distribution. The algorithm alternates between two kernels: Hug and Hop. Hug is a non-reversible kernel that repeatedly applies the bounce mechanism from the recently proposed Bouncy Particle Sampler to produce a proposal point far from the current position, yet on almost the same contour of the target density, leading to a high acceptance probability. Hug is complemented by Hop, which deliberately proposes jumps between contours and has an efficiency that degrades very slowly with increasing dimension. There are many parallels between Hug and Hamiltonian Monte Carlo using a leapfrog integrator, including the order of the integration scheme, however Hug is also able to make use of local Hessian information without requiring implicit numerical integration steps, and its performance is not terminally affected by unbounded gradients of the log-posterior. We test Hug and Hop empirically on a variety of toy targets and real statistical models and find that it can, and often does, outperform Hamiltonian Monte Carlo.
Information Propagation in Time through Allosteric Signaling

Naively, one expects the information communicated by an enzyme downstream within a signaling network, in which the enzyme is embedded, to grow monotonically with the enzyme's rate of product formation. However, here we observe that this does not necessarily hold true for allosterically regulated enzymes, often observed in signaling networks. In particular, we find that the mutual information between the catalytic sites of an allosterically regulated enzyme and a receiver protein downstream in the signaling pathway depends on the transition kinetics between the different allosterically regulated states of the enzyme and their respective rates of product formation. Surprisingly, our work implies that allosteric down-regulation of an enzyme's rate of product formation may not only be used as a way to silence itself, as one would normally expect. Rather, down-regulation may also be used to increase the information communicated by this enzyme to a receiver protein downstream in a signaling pathway.
Fast Tensor Needlet Transforms for Tangent Vector Fields on the Sphere

This paper constructs a semi-discrete tight frame of tensor needlets associated with a quadrature rule for tangent vector fields on the unit sphere $\mathbb{S}^2$ of $\mathbb{R}^3$ --- tensor needlets. The proposed tight tensor needlets provide a multiscale representation of any square integrable tangent vector field on $\mathbb{S}^2$, which leads to a multiresolution analysis (MRA) for the field. From the MRA, we develop fast algorithms for tensor needlet transforms, including the decomposition and reconstruction of the needlet coefficients between levels, via a set of filter banks and scalar FFTs. The fast tensor needlet transforms have near linear computational cost proportional to $N\log \sqrt{N}$ for $N$ evaluation points or coefficients. Numerical examples for the simulated and real data demonstrate the efficiency of the proposed algorithm.
Competitive Co-evolution for Dynamic Constrained Optimisation

Dynamic constrained optimisation problems (DCOPs) widely exist in the real world due to frequently changing factors influencing the environment. Many dynamic optimisation methods such as diversity-driven methods, memory and prediction methods offer different strategies to deal with environmental changes. However, when DCOPs change very fast or have very limited time for the algorithm to react, the potential of these methods is limited due to time shortage for re-optimisation and adaptation. This is especially true for population-based dynamic optimisation methods, which normally need quite a few fitness evaluations to find a near-optimum solution. To address this issue, this paper proposes to tackle fast-changing DCOPs through a smart combination of offline and online optimisation. The offline optimisation aims to prepare a set of good solutions for all possible environmental changes beforehand. With this solution set, the online optimisation aims to react quickly to each truly happening environmental change by doing optimisation on the set. To find this solution set, this paper further proposes to use competitive co-evolution for offline optimisation by co-evolving candidate solutions and environmental parameters. The experimental studies on a well-known benchmark test set of DCOPs show that the proposed method outperforms existing methods significantly especially when the environment changes very fast
Development of a Fragment-Based Machine Learning Algorithm for Designing Hybrid Drugs Optimized for Permeating Gram-Negative Bacteria

Gram-negative bacteria are a serious health concern due to the strong multidrug resistance that they display, partly due to the presence of a permeability barrier comprising two membranes with active efflux. New approaches are urgently needed to design antibiotics effective against these pathogens. In this work, we present a novel topological fragment-based approach ("Hunting Fragments Of X" or "Hunting FOX") to rationally "hunt for" chemical fragments that promote compound ability to permeate the outer membrane. Our approach generalizes to other drug design applications. We measure minimum inhibitory concentrations of compounds in two strains of Pseudomonas aeruginosa with variable permeability barriers and use them as an input to the Hunting FOX algorithm to identify molecular fragments responsible for enhanced outer membrane permeation properties and candidate molecules from an external library that demonstrate good permeation ability. Overall, we present proof of concept for a novel method that is expected to be valuable for rational design of hybrid drugs.
Privately Answering Classification Queries in the Agnostic PAC Model

We revisit the problem of differentially private release of classification queries. In this problem, the goal is to design an algorithm that can accurately answer a sequence of classification queries based on a private training set while ensuring differential privacy. We formally study this problem in the agnostic PAC model and derive a new upper bound on the private sample complexity. Our results improve over those obtained in a recent work [BTT18] for the agnostic PAC setting. In particular, we give an improved construction that yields a tighter upper bound on the sample complexity. Moreover, unlike [BTT18], our accuracy guarantee does not involve any blow-up in the approximation error associated with the given hypothesis class. Given any hypothesis class with VC-dimension $d$, we show that our construction can privately answer up to $m$ classification queries with average excess error $\alpha$ using a private sample of size $\approx \frac{d}{\alpha^2}\,\max\left(1, \sqrt{m}\,\alpha^{3/2}\right)$. Using recent results on private learning with auxiliary public data, we extend our construction to show that one can privately answer any number of classification queries with average excess error $\alpha$ using a private sample of size $\approx \frac{d}{\alpha^2}\,\max\left(1, \sqrt{d}\,\alpha\right)$. When $\alpha=O\left(\frac{1}{\sqrt{d}}\right)$, our private sample complexity bound is essentially optimal.
EARLY CAROTID ENDARTERECTOMY AFTER ISCHEMIC STROKE IMPROVES DIFFUSION/PERFUSION MISMATCH

Accepted, August 12, 2002. OBJECTIVE AND IMPORTANCE: The functional magnetic resonance imaging techniques of diffusion-weighted imaging and perfusion-weighted imaging allow for ultraearly detection of brain infarction and concomitant identification of blood flow abnormalities in surrounding regions, which may represent brain “at risk. ” CLINICAL PRESENTATION: We report two patients with acute ischemic stroke associated with ipsilateral high-grade carotid stenosis. The first patient, a 64-year-old woman with a remote history of ischemic stroke and a vertebral artery aneurysm, presented with worsening of her preexisting right hemiparesis. The second patient, another 64-year-old woman with known multiple intracranial aneurysms and bilateral high-grade internal carotid artery stenosis, was admitted for the elective microsurgical clipping of an enlarging giant left carotid-ophthalmic artery aneurysm. Postoperatively, she developed right hemiparesis and mild aphasia. Both patients showed progressive worsening of their neurological deficits in the setting of small or undetected diffusionweighted imaging abnormalities and large perfusion-weighted imaging defects. INTERVENTION: After prompt carotid endarterectomy, symptoms in both patients resolved or improved. Follow-up magnetic resonance imaging scans demonstrated resolution or significant improvement in the perfusion abnormalities in both patients. CONCLUSION: Carotid endarterectomy in the setting of diffusion-weighted/perfusionweighted imaging mismatch can lead to improvement in cerebral perfusion as evidenced by resolution of the perfusion-weighted imaging lesion. Diffusion/perfusion magnetic resonance imaging may be useful in identifying patients with severe neurological deficits but without large territories of infarction who may safely undergo early surgical revascularization.
ENHANCED TEMPERATURE SENSING BY USING ONE-DIMENSIONAL TERNARY PHOTONIC BAND GAP STRUCTURES

This paper demonstrates a novel way to enhance the temperature sensitivity in one-dimensional (1D) photonic band gap (PBG) material by using a ternary periodic structure (i.e., three material layers constituting a period of lattice). The temperature sensitive wavelength band shift of (Si/SiO2) periodic structure was signiflcantly enhanced when the structure was modifled by sandwiching a thin layer of Bi4Ge3O12 between every two layers, constituting a period of lattice. When the thickness of sandwiched layer was increased further enhancement in temperature sensitivity was observed. These 1D ternary PBG structures can be substituted in place of 1D binary PBG structures for enhancing the temperature sensing performance.