Miracle-dz/UniFAR-FTUs huggingface dataset:
2-Chloro-3-nitro-5-(trifluoromethyl)benzoic acid and -benzamide: structural characterization of two precursors for antitubercular benzothiazinones

The crystal and molecular structures of 2-chloro-3-nitro-5-(trifluoromethyl)benzoic acid and 2-chloro-3-nitro-5-(trifluoromethyl)benzamide, two precursors for the synthesis of 8-nitro-1,3-benzothiazin-4-ones, a promising class of new antituberculosis drug candidates, are described.
Co-occurrences using Fasttext embeddings for word similarity tasks in Urdu

Urdu is a widely spoken language in South Asia. Though immoderate literature exists for the Urdu language still the data isn't enough to naturally process the language by NLP techniques. Very efficient language models exist for the English language, a high resource language, but Urdu and other under-resourced languages have been neglected for a long time. To create efficient language models for these languages we must have good word embedding models. For Urdu, we can only find word embeddings trained and developed using the skip-gram model. In this paper, we have built a corpus for Urdu by scraping and integrating data from various sources and compiled a vocabulary for the Urdu language. We also modify fasttext embeddings and N-Grams models to enable training them on our built corpus. We have used these trained embeddings for a word similarity task and compared the results with existing techniques.
Probabilistic Association of Transients to their Hosts (PATH)

We introduce a new method to estimate the probability that an extragalactic transient source is associated with a candidate host galaxy. This approach relies solely on simple observables: sky coordinates and their uncertainties, galaxy fluxes, and angular sizes. The formalism invokes Bayes’ rule to calculate the posterior probability from the galaxy prior P(O), observables x, and an assumed model for the true distribution of transients in/around their host galaxies. Using simulated transients placed in the well-studied Cosmic Evolution Survey field, we consider several agnostic and physically motivated priors and offset distributions to explore the method sensitivity. We then apply the methodology to the set of 13 fast radio bursts (FRBs) localized with an uncertainty of several arcseconds. Our methodology finds nine of these are securely associated to a single host galaxy, . We examine the observed and intrinsic properties of these secure FRB hosts, recovering distributions similar to those found in previous works. Furthermore, we find a strong correlation between the apparent magnitude of the securely identified host galaxies and the estimated cosmic dispersion measures of the corresponding FRBs, which results from the Macquart relation. Future work with FRBs will leverage this relation and other measures from the secure hosts as priors for future associations. The methodology is generic to transient type, localization error, and image quality. We encourage its application to other transients where host galaxy associations are critical to the science, e.g., gravitational wave events, gamma-ray bursts, and supernovae. We have encoded the technique in Python on GitHub: https://github.com/FRBs/astropath.
The likelihood-ratio test for multi-edge network models

The complexity underlying real-world systems implies that standard statistical hypothesis testing methods may not be adequate for these peculiar applications. Specifically, we show that the likelihood-ratio (LR) test’s null-distribution needs to be modified to accommodate the complexity found in multi-edge network data. When working with independent observations, the p-values of LR tests are approximated using a χ 2 distribution. However, such an approximation should not be used when dealing with multi-edge network data. This type of data is characterized by multiple correlations and competitions that make the standard approximation unsuitable. We provide a solution to the problem by providing a better approximation of the LR test null-distribution through a beta distribution. Finally, we empirically show that even for a small multi-edge network, the standard χ 2 approximation provides erroneous results, while the proposed beta approximation yields the correct p-value estimation.
Social Networks Analysis to Retrieve Critical Comments on Online Platforms

[1-2] Abstract—Social networks are rich source of data to analyze user habits in all aspects of life. User’s behaviour is decisive component of a health system in various countries. Promoting good behaviour can improve the public health significantly. In this work, we develop a new model for social network analysis by using text analysis approach. We define each user reaction to global pandemic with analysing his online behaviour. Clustering a group of online users with similar habits, help to find how virus spread in different societies. Promoting the healthy life style in the high risk online users of social media have significant effect on public health and reducing the effect of global pandemic. In this work, we introduce a new approach to clustering habits based on user activities on social media in the time of pandemic and recommend a machine learning model to promote health in the online platforms.
IMPACT OF INDUSTRIAL ACTIVITIES ON HEAVY METAL LOADING AND THEIR PHYSICO-CHEMICAL EFFECTS ON WETLANDS OF LAKE VICTORIA BASIN (UGANDA)

The diverse functions of wetlands are being adversely affected by human activities. This paper discusses the impact of these activities on heavy metal loading in different media within the wetlands. Water and sediment/soil samples were taken from areas with active industrial activities and from an area where there is no industrial activity. Sources of water pollution include effluents from a brewery (high pH) and areas associated with tanning activities, sewage treatment plant and former copper smelter (high electric conductivity values). Effluents from a battery assembly plant, water treatment plant, pharmaceutical industry and former copper smelter have relatively high Pb values (up to 1.4 ppm) otherwise most heavy metal concentrations are below maximum acceptable limits for water. This calls for mitigation measures. In sediments, high heavy metal values were associated with battery and metal fabricating industry (Pb), operations involving Zn scrap (Cd), former Cu smelter (Cu, Pb, Ni and Co), tannery and pharmaceutical industry (Cr), and soap and cosmetics industry (Hg). Fish have low levels of Hg. However, fish is known to bioaccumulate Hg through methylation, may cause increase in the Hg in the food chain. Industries which release effluents with high heavy metal contents should treat them before discharge.
CRAFTS for Fast Radio Bursts: Extending the Dispersion–Fluence Relation with New FRBs Detected by FAST

We report three new FRBs discovered by the Five-hundred-meter Aperture Spherical radio Telescope (FAST), namely FRB 181017.J0036+11, FRB 181118, and FRB 181130, through the Commensal Radio Astronomy FAST Survey (CRAFTS). Together with FRB 181123, which was reported earlier, all four FAST-discovered FRBs share the same characteristics of low fluence (≤0.2 Jy ms) and high dispersion measure (DM, >1000 pc cm−3), consistent with the anticorrelation between DM and fluence of the entire FRB population. FRB 181118 and FRB 181130 exhibit band-limited features. FRB 181130 is prominently scattered (τ s ≃ 8 ms) at 1.25 GHz. FRB 181017.J0036+11 has full-bandwidth emission with a fluence of 0.042 Jy ms, which is one of the faintest FRB sources detected so far. CRAFTS has started to build a new sample of FRBs that fills the region for more distant and fainter FRBs in the fluence–DME diagram, previously out of reach of other surveys. The implied all-sky event rate of FRBs is 1.24−0.90+1.94×105 sky−1 day−1 at the 95% confidence interval above 0.0146 Jy ms. We also demonstrate here that the probability density function of CRAFTS FRB detections is sensitive to the assumed intrinsic FRB luminosity function and cosmological evolution, which may be further constrained with more discoveries.
Multiple Kernel-Based Online Federated Learning

Online federated learning (OFL) becomes an emerging learning framework, in which edge nodes perform online learning with continuous streaming local data and a server constructs a global model from the aggregated local models. Online multiple kernel learning (OMKL), using a preselected set of P kernels, can be a good candidate for OFL framework as it has provided an outstanding performance with a low-complexity and scalability. Yet, an naive extension of OMKL into OFL framework suffers from a heavy communication overhead that grows linearly with P. In this paper, we propose a novel multiple kernel-based OFL (MK-OFL) as a non-trivial extension of OMKL, which yields the same performance of the naive extension with 1/P communication overhead reduction. We theoretically prove that MK-OFL achieves the optimal sublinear regret bound when compared with the best function in hindsight. Finally, we provide the numerical tests of our approach on real-world datasets, which suggests its practicality.
A Small-Uniform Statistic for the Inference of Functional Linear Regressions

We propose a “small-uniform” statistic for the inference of the functional PCA estimator in a functional linear regression model. The literature has shown two extreme behaviors: on the one hand, the FPCA estimator does not converge in distribution in its norm topology; but on the other hand, the FPCA estimator does have a pointwise asymptotic normal distribution. Our statistic takes a middle ground between these two extremes: after a suitable rate normalization, our small-uniform statistic is constructed as the maximizer of a fractional programming problem of the FPCA estimator over a finite-dimensional subspace, and whose dimensions will grow with sample size. We show the rate for which our scalar statistic converges in probability to the supremum of a Gaussian process. The small-uniform statistic has applications in hypothesis testing. Simulations show our statistic has comparable to slightly better power properties for hypothesis testing than the two statistics of Cardot, Ferraty, Mas and Sarda (2003).
Improving Lossless Compression Rates via Monte Carlo Bits-Back Coding

Latent variable models have been successfully applied in lossless compression with the bits-back coding algorithm. However, bits-back suffers from an increase in the bitrate equal to the KL divergence between the approximate posterior and the true posterior. In this paper, we show how to remove this gap asymptotically by deriving bits-back coding algorithms from tighter variational bounds. The key idea is to exploit extended space representations of Monte Carlo estimators of the marginal likelihood. Naively applied, our schemes would require more initial bits than the standard bits-back coder, but we show how to drastically reduce this additional cost with couplings in the latent space. When parallel architectures can be exploited, our coders can achieve better rates than bits-back with little additional cost. We demonstrate improved lossless compression rates in a variety of settings, especially in out-of-distribution or sequential data compression.
Towards Personalised and Document-level Machine Translation of Dialogue

State-of-the-art (SOTA) neural machine translation (NMT) systems translate texts at sentence level, ignoring context: intra-textual information, like the previous sentence, and extra-textual information, like the gender of the speaker. As a result, some sentences are translated incorrectly. Personalised NMT (PersNMT) and document-level NMT (DocNMT) incorporate this information into the translation process. Both fields are relatively new and previous work within them is limited. Moreover, there are no readily available robust evaluation metrics for them, which makes it difficult to develop better systems, as well as track global progress and compare different methods. This thesis proposal focuses on PersNMT and DocNMT for the domain of dialogue extracted from TV subtitles in five languages: English, Brazilian Portuguese, German, French and Polish. Three main challenges are addressed: (1) incorporating extra-textual information directly into NMT systems; (2) improving the machine translation of cohesion devices; (3) reliable evaluation for PersNMT and DocNMT.
Deep Learning-Based Frequency-Selective Channel Estimation for Hybrid mmWave MIMO Systems

Millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems typically employ hybrid mixed signal processing to avoid expensive hardware and high training overheads. However, the lack of fully digital beamforming at mmWave bands imposes additional challenges in channel estimation. Prior art on hybrid architectures has mainly focused on greedy optimization algorithms to estimate frequency-flat narrowband mmWave channels, despite the fact that in practice, the large bandwidth associated with mmWave channels results in frequency-selective channels. In this paper, we consider a frequency-selective wideband mmWave system and propose two deep learning (DL) compressive sensing (CS) based algorithms for channel estimation. The proposed algorithms learn critical apriori information from training data to provide highly accurate channel estimates with low training overhead. In the first approach, a DL-CS based algorithm simultaneously estimates the channel supports in the frequency domain, which are then used for channel reconstruction. The second approach exploits the estimated supports to apply a low-complexity multi-resolution fine-tuning method to further enhance the estimation performance. Simulation results demonstrate that the proposed DL-based schemes significantly outperform conventional orthogonal matching pursuit (OMP) techniques in terms of the normalized mean-squared error (NMSE), computational complexity, and spectral efficiency, particularly in the low signal-to-noise ratio regime. When compared to OMP approaches that achieve an NMSE gap of $\mathrm {\{4-10\}\,\,dB}$ with respect to the Cramer Rao Lower Bound (CRLB), the proposed algorithms reduce the CRLB gap to only $\mathrm {\{1-1.5\}\,\,dB}$ , while reducing complexity by two orders of magnitude.
Retrain or not retrain: Conformal test martingales for change-point detection

We argue for supplementing the process of training a prediction algorithm by setting up a scheme for detecting the moment when the distribution of the data changes and the algorithm needs to be retrained. Our proposed schemes are based on exchangeability martingales, i.e., processes that are martingales under any exchangeable distribution for the data. Our method, based on conformal prediction, is general and can be applied on top of any modern prediction algorithm. Its validity is guaranteed, and in this paper we make first steps in exploring its efficiency.
Comparison of Liquid and Gas Chromatographic Mass Spectrometry for the Determination of Multiclass Pesticides at Low Concentrations

ABSTRACT Two identical triple quadrupole mass spectrometers, operated in the Selected Reaction Monitoring mode, under exactly the same parameters' settings, were employed for the comparison of GC-MS/MS and LC-MS/MS techniques in the analysis of 221 pesticides at low-concentration levels. The comparison was performed with the use of analytical standards' solutions that contained no matrix. The evaluated parameters for the comparison were the applicability, that is, the number of analytes determined by each technique: linearity, sensitivity, repeatability, and confirmation capability. The results of the study indicate that both techniques are equally applicable, as each is capable of determining 167 of the 221 compounds, while 113 of the compounds can be determined by both techniques. The results also show that, although both techniques are very sensitive and selective, GC-MS/MS presents better sensitivity, selectivity, and repeatability at low-concentration levels. But, LC-MS/MS has better linearity characteristics. However, the behavior of certain pesticide classes seems to deviate from these general findings. These results indicate the potential for a wider use of GC-MS/MS in the near future and possibly a simultaneous decline in the use of single quadrupole techniques for the analysis of pesticides at low concentrations.
Towards Evidence-Based Testability Measurements

Evaluating Software testability can assist software managers in optimizing testing budgets and identifying opportunities for refactoring. In this paper, we abandon the traditional approach of pursuing testability measurements based on the correlation between software metrics and test characteristics observed on past projects, e.g., the size, the organization or the code coverage of the test cases. We propose a radically new approach that exploits automatic test generation and mutation analysis to quantify the amount of evidence about the relative hardness of identifying effective test cases. We introduce two novel evidence-based testability metrics, describe a prototype to compute them, and discuss initial findings on whether our measurements can reflect actual testability issues.
BSQ: Exploring Bit-Level Sparsity for Mixed-Precision Neural Network Quantization

Mixed-precision quantization can potentially achieve the optimal tradeoff between performance and compression rate of deep neural networks, and thus, have been widely investigated. However, it lacks a systematic method to determine the exact quantization scheme. Previous methods either examine only a small manually-designed search space or utilize a cumbersome neural architecture search to explore the vast search space. These approaches cannot lead to an optimal quantization scheme efficiently. This work proposes bit-level sparsity quantization (BSQ) to tackle the mixed-precision quantization from a new angle of inducing bit-level sparsity. We consider each bit of quantized weights as an independent trainable variable and introduce a differentiable bit-sparsity regularizer. BSQ can induce all-zero bits across a group of weight elements and realize the dynamic precision reduction, leading to a mixed-precision quantization scheme of the original model. Our method enables the exploration of the full mixed-precision space with a single gradient-based optimization process, with only one hyperparameter to tradeoff the performance and compression. BSQ achieves both higher accuracy and higher bit reduction on various model architectures on the CIFAR-10 and ImageNet datasets comparing to previous methods.
Temporal Reachability Minimization: Delaying vs. Deleting

We study spreading processes in temporal graphs, i. e. , graphs whose connections change over time. These processes naturally model real-world phenomena such as infectious diseases or information flows. More precisely, we investigate how such a spreading process, emerging from a given set of sources, can be contained to a small part of the graph. To this end we consider two ways of modifying the graph, which are (1) deleting connections and (2) delaying connections. We show a close relationship between the two associated problems and give a polynomial time algorithm when the graph has tree structure. For the general version, we consider parameterization by the number of vertices to which the spread is contained. Surprisingly, we prove W[1]-hardness for the deletion variant but fixed-parameter tractability for the delaying variant.
The effect of DNA bases permutation on surface-enhanced Raman scattering spectrum

Abstract Surface-enhanced Raman scattering (SERS) process results in a tremendous increase of Raman scattering cross section of molecules adsorbed to plasmonic metals and influenced by numerous physico-chemical factors such as geometry and optical properties of the metal surface, orientation of chemisorbed molecules and chemical environment. While SERS holds promise for single molecule sensitivity and optical sensing of DNA sequences, more detailed understanding of the rich physico-chemical interplay between various factors is needed to enhance predictive power of existing and future SERS-based DNA sensing platforms. In this work, we report on experimental results indicating that SERS spectra of adsorbed single-stranded DNA (ssDNA) isomers depend on the order on which individual bases appear in the 3-base long ssDNA due to intramolecular interaction between DNA bases. Furthermore, we experimentally demonstrate that the effect holds under more general conditions when the molecules do not experience chemical enhancement due to resonant charge transfer effect and also under standard Raman scattering without electromagnetic or chemical enhancements. Our numerical simulations qualitatively support the experimental findings and indicate that base permutation results in modification of both Raman and chemically enhanced Raman spectra.
Universal Structural Influence on the 2D Electron Gas at SrTiO3 Surfaces

The 2‐dimensional electron gas (2DEG) found at the surface of SrTiO3 and related interfaces has attracted significant attention as a promising basis for oxide electronics. In order to utilize its full potential, the response of this 2DEG to structural changes and surface modification must be understood in detail. Here, a study of the detailed electronic structure evolution of the 2DEG as a function of sample temperature and surface step density is presented. By comparing the experimental results with ab initio calculations, it is shown that local structure relaxations cause a metal‐insulator transition of the system around 135 K. This study presents a new and simple way of tuning the 2DEG via surface vicinality and identifies how the operation of prospective devices will respond to changes in temperature.
An Attention Ensemble Approach for Efficient Text Classification of Indian Languages

The recent surge of complex attention-based deep learning architectures has led to extraordinary results in various downstream NLP tasks in the English language. However, such research for resource-constrained and morphologically rich Indian vernacular languages has been relatively limited. This paper proffers team SPPU\_AKAH's solution for the TechDOfication 2020 subtask-1f: which focuses on the coarse-grained technical domain identification of short text documents in Marathi, a Devanagari script-based Indian language. Availing the large dataset at hand, a hybrid CNN-BiLSTM attention ensemble model is proposed that competently combines the intermediate sentence representations generated by the convolutional neural network and the bidirectional long short-term memory, leading to efficient text classification. Experimental results show that the proposed model outperforms various baseline machine learning and deep learning models in the given task, giving the best validation accuracy of 89.57\% and f1-score of 0.8875. Furthermore, the solution resulted in the best system submission for this subtask, giving a test accuracy of 64.26\% and f1-score of 0.6157, transcending the performances of other teams as well as the baseline system given by the organizers of the shared task.