Miracle-dz/UniFAR-FTUs huggingface dataset:
Long Short-Term Memory Neural Network for Financial Time Series

Performance forecasting is an age-old problem in economics and finance. Recently, developments in machine learning and neural networks have given rise to non-linear time series models that provide modern and promising alternatives to traditional methods of analysis. In this paper, we present an ensemble of independent and parallel long short-term memory (LSTM) neural networks for the prediction of stock price movement. LSTMs have been shown to be especially suited for time series data due to their ability to incorporate past information, while neural network ensembles have been found to reduce variability in results and improve generalization. A binary classification problem based on the median of returns is used, and the ensemble’s forecast depends on a threshold value, which is the minimum number of LSTMs required to agree upon the result. The model is applied to the constituents of the smaller, less efficient Stockholm OMX30 instead of other major market indices such as the DJIA and S&P500 commonly found in literature. With a straightforward trading strategy, comparisons with a randomly chosen portfolio and a portfolio containing all the stocks in the index show that the portfolio resulting from the LSTM ensemble provides better average daily returns and higher cumulative returns over time. Moreover, the LSTM portfolio also exhibits less volatility, leading to higher risk-return ratios.
Learning with latent group sparsity via heat flow dynamics on networks

Group or cluster structure on explanatory variables in machine learning problems is a very general phenomenon, which has attracted broad interest from practitioners and theoreticians alike. In this work we contribute an approach to learning under such group structure, that does not require prior information on the group identities. Our paradigm is motivated by the Laplacian geometry of an underlying network with a related community structure, and proceeds by directly incorporating this into a penalty that is effectively computed via a heat flow-based local network dynamics. In fact, we demonstrate a procedure to construct such a network based on the available data. Notably, we dispense with computationally intensive pre-processing involving clustering of variables, spectral or otherwise. Our technique is underpinned by rigorous theorems that guarantee its effective performance and provide bounds on its sample complexity. In particular, in a wide range of settings, it provably suffices to run the heat flow dynamics for time that is only logarithmic in the problem dimensions. We explore in detail the interfaces of our approach with key statistical physics models in network science, such as the Gaussian Free Field and the Stochastic Block Model. We validate our approach by successful applications to real-world data from a wide array of application domains, including computer science, genetics, climatology and economics. Our work raises the possibility of applying similar diffusion-based techniques to classical learning tasks, exploiting the interplay between geometric, dynamical and stochastic structures underlying the data.
Episodic Gaseous Outflows and Mass Loss from Red Supergiants

The red hypergiant VY CMa and the more typical red supergiant (RSG) Betelgeuse provide clear observational evidence for discrete, directed gaseous outflows in their optical and infrared imaging, spectra, and light curves. In the very luminous VY CMa, mass-loss estimates from the infrared-bright knots and clumps not only dominate its measured overall mass loss, but explain it. In the less luminous Betelgeuse, similar mass estimates of its circumstellar condensations show that they contribute significantly to its measured mass-loss rate. We present new measurements for both stars and discuss additional evidence for gaseous ejections in other RSGs. Gaseous outflows are the dominant mechanism for the most luminous RSGs and an important contributor to the more typical RSGs like Betelgeuse. We conclude that gaseous outflows, related to magnetic fields and surface activity, comparable to coronal mass ejections, are a major contributor to mass loss from RSGs and the missing component in discussions of their mass-loss mechanism.
Construction of a Quality Estimation Dataset for Automatic Evaluation of Japanese Grammatical Error Correction

In grammatical error correction (GEC), automatic evaluation is considered as an important factor for research and development of GEC systems. Previous studies on automatic evaluation have shown that quality estimation models built from datasets with manual evaluation can achieve high performance in automatic evaluation of English GEC. However, quality estimation models have not yet been studied in Japanese, because there are no datasets for constructing quality estimation models. In this study, therefore, we created a quality estimation dataset with manual evaluation to build an automatic evaluation model for Japanese GEC. By building a quality estimation model using this dataset and conducting a meta-evaluation, we verified the usefulness of the quality estimation model for Japanese GEC.
Planet populations inferred from debris discs. Insights from 178 debris systems in the ISPY, LEECH, and LIStEN planet-hunting surveys

We know little about the outermost exoplanets in planetary systems, because our detection methods are insensitive to moderate-mass planets on wide orbits. However, debris discs can probe the outer-planet population, because dynamical modelling of observed discs can reveal properties of perturbing planets. We use four sculpting and stirring arguments to infer planet properties in 178 debris-disc systems from the ISPY, LEECH and LIStEN planet-hunting surveys. Similar analyses are often conducted for individual discs, but we consider a large sample in a consistent manner. We aim to predict the population of wide-separation planets, gain insight into the formation and evolution histories of planetary systems, and determine the feasibility of detecting these planets in the near future. We show that a `typical' cold debris disc likely requires a Neptune- to Saturn-mass planet at 10-100 au, with some needing Jupiter-mass perturbers. Our predicted planets are currently undetectable, but modest detection-limit improvements (e.g. from JWST) should reveal many such perturbers. We find that planets thought to be perturbing debris discs at late times are similar to those inferred to be forming in protoplanetary discs, so these could be the same population if newly formed planets do not migrate as far as currently thought. Alternatively, young planets could rapidly sculpt debris before migrating inwards, meaning that the responsible planets are more massive (and located further inwards) than debris-disc studies assume. We combine self-stirring and size-distribution modelling to show that many debris discs cannot be self-stirred without having unreasonably high masses; planet- or companion-stirring may therefore be the dominant mechanism in many (perhaps all) debris discs. Finally, we provide catalogues of planet predictions, and identify promising targets for future planet searches.
Antennal transcriptome analysis of chemosensory genes in the cowpea beetle, Callosobruchus maculatus (F.)

Olfaction, one of the most important sensory systems governing insect behavior, is a possible target for pest management. Therefore, in this study, we analyzed the antennal transcriptome of the cowpea beetle, Callosobruchus maculatus (F.) (Coleoptera: Chrysomelidae: Bruchinae), which is a major pest of stored pulses and legumes. The de novo antennal RNA-seq assembly results identified 17 odorant, 2 gustatory, and 10 ionotropic receptors, 1 sensory neuron membrane protein, and 12 odorant-binding and 7 chemosensory proteins. Moreover, differential gene expression analysis of virgin male and female antennal samples followed by qRT-PCR revealed 1 upregulated and 4 downregulated odorant receptors in males. We also performed homology searches using the coding sequences built from previously proposed amino acid sequences derived from genomic data and identified additional chemosensory-related genes.
Insights into the genetic diversity of an underutilized Indian legume, Vigna stipulacea (Lam.) Kuntz., using morphological traits and microsatellite markers

Vigna stipulacea (Lam.) Kuntz. , commonly known as Minni payaru is an underutilized legume species and has a great potential to be utilized as food crop. To evaluate and select the best germplasm to be harnessed in the breeding programme, we assessed the genetic diversity of V. stipulacea (94 accessions) conserved in the Indian National Genebank, based on morphological traits and microsatellite markers. Significant variation was recorded for the morphological traits studied. Euclidean distance using UPGMA method grouped all accessions into two major clusters. Accessions were identified for key agronomic traits such as, early flowering (IC331436, IC251436, IC331437); long peduncle length (IC553518, IC550531, IC553557, IC553540, IC550532, IC553564); and more number of seeds per pod (IC553529, IC622865, IC622867, IC553528). To analyse the genetic diversity among the germplasm 33 SSR primers were used anda total of 116 alleles were detected. The number of alleles varied from two to seven, with an average of 3.52 per loci. The polymorphic information content values varied from 0.20 to 0.74, with a mean of 0.40. The high number of alleles per locus and the allelic diversity in the studied germplasm indicated a relatively wider genetic base of V. stipulacea. Phylogenetic analysis clustered accessions into seven clades. Population structure analysis grouped them into five genetic groups, which were partly supported by PCoA and phylogenetic tree. Besides, PCoA and AMOVA also decoded high genetic diversity among the V. stipulacea accessions. Thus, morphological and microsatellite markers distinguished V. stipulacea accessions and assessed their genetic diversity efficiently. The identified promising accessions can be utilized in Vigna improvement programme through introgression breeding and/or can be used for domestication and enhanced utilization of V. stipulacea.
Effects of a 12% carbohydrate beverage on tackling technique and running performance during rugby league activity: A randomised, placebo-controlled trial

The purpose of this study was to investigate the effects of a 12% carbohydrate (CHO) beverage on tackling technique and running performance during rugby league activity. Using a double-blind, placebo-controlled, randomised, crossover design, 15 academy rugby league players ingested a 250 ml bolus of a 12% CHO solution (30 g maltodextrin and 30 g sucrose in 500 ml) 15 minutes before two bouts of rugby activity. The rugby league match simulation for interchange players was used to standardise the movement patterns of activity and provide reliable outcome measures, whilst also reflecting the duration of a typical field-based conditioning session. Measures of tackling technique, external responses (e.g., fatigue index from sprint data) and rating of perceived exertion (RPE) were recorded throughout. Gut discomfort was measured before each bout. The interaction effect was largely compatible with the hypothesis for relative distance (P<0.001, η2 = 0.217) and fairly compatible for tackling technique (P = 0.068, η2 = 0.0640). The time effect for tackling technique, relative and high-intensity distance, sprint, and sprint to contact velocity, time at high metabolic power, PlayerLoad™, and RPE (all P<0.05; η2 = 0.131–0.701) was compatible with the hypothesis. Data for tackling technique, relative and high-intensity distance, sprint, and sprint to contact velocity, sprint, and sprint to contact fatigue index (all P<0.05; η2 = 0.189–0.612) was compatible with a supplement effect overall despite few differences in the pattern of change (interaction). Minimal gut discomfort was reported for the CHO (bout 1 = 27 ± 17; bout 2 = 23 ± 17 AU) and placebo (bout 1 = 23 ± 18 AU; bout 2 = 24 ± 13) trials. This study shows that a 12% CHO beverage before two bouts of standardised rugby activity is a practical and effective strategy for retaining tackling technique, increasing external responses, and reducing RPE without compromising gut comfort.
Transforming and comparing data between standard SQUID and OPM-MEG systems

Optically pumped magnetometers (OPMs) have recently become so sensitive that they are suitable for use in magnetoencephalography (MEG). These sensors solve operational problems of the current standard MEG, where superconducting quantum interference device (SQUID) gradiometers and magnetometers are being used. The main advantage of OPMs is that they do not require cryogenics for cooling. Therefore, they can be placed closer to the scalp and are much easier to use. Here, we measured auditory evoked fields (AEFs) with both SQUID- and OPM-based MEG systems for a group of subjects to better understand the usage of a limited sensor count OPM-MEG. We present a theoretical framework that transforms the within subject data and equivalent simulation data from one MEG system to the other. This approach works on the principle of solving the inverse problem with one system, and then using the forward model to calculate the magnetic fields expected for the other system. For the source reconstruction, we used a minimum norm estimate (MNE) of the current distribution. Two different volume conductor models were compared: the homogeneous conducting sphere and the three-shell model of the head. The transformation results are characterized by a relative error and cross-correlation between the measured and the estimated magnetic field maps of the AEFs. The results for both models are encouraging. Since some commercial OPMs measure multiple components of the magnetic field simultaneously, we additionally analyzed the effect of tangential field components. Overall, our dual-axis OPM-MEG with 15 sensors yields similar information to a 62-channel SQUID-MEG with its field of view restricted to the right hemisphere.
Virtual Healthcare Center for COVID-19 Patient Detection Based on Artificial Intelligence Approaches

At the end of 2019, the infectious coronavirus disease (COVID-19) was reported for the first time in Wuhan, and, since then, it has become a public health issue in China and even worldwide. This pandemic has devastating effects on societies and economies around the world, and poor countries and continents are likely to face particularly serious and long-lasting damage, which could lead to large epidemic outbreaks because of the lack of financial and health resources. The increasing number of COVID-19 tests gives more information about the epidemic spread, and this can help contain the spread to avoid more infection. As COVID-19 keeps spreading, medical products, especially those needed to perform blood tests, will become scarce as a result of the high demand and insufficient supply and logistical means. However, technological tests based on deep learning techniques and medical images could be useful in fighting this pandemic. In this perspective, we propose a COVID-19 disease diagnosis (CDD) tool that implements a deep learning technique to provide automatic symptoms checking and COVID-19 detection. Our CDD scheme implements two main steps. First, the patient's symptoms are checked, and the infection probability is predicted. Then, based on the infection probability, the patient's lungs will be diagnosed by an automatic analysis of X-ray or computerized tomography (CT) images, and the presence of the infection will be accordingly confirmed or not. The numerical results prove the efficiency of the proposed scheme by achieving an accuracy value over 90% compared with the other schemes.
A Complex-LASSO Approach for Localizing Forced Oscillations in Power Systems

We study the problem of localizing multiple sources of forced oscillations (FOs) and estimating their characteristics, including frequency, phase, and amplitude, using noisy PMU data. We assume sparsity in the number of locations, and for each location, we model the input FO as a sum of a few unknown sinusoids. This allows us to obtain a sparse linear model in the frequency domain that relates measurements and the unknown input locations at frequencies of the unknown sinusoidal terms. We determine these frequencies by thresholding the empirical spectrum of the noisy data. Finally, we cast the location recovery problem as an $\ell_{1}$-regularized least squares problem in the complex domain-i.e., complex-LASSO (linear shrinkage and selection operator). We numerically solve this optimization problem using the complex-valued coordinate descent method and show its efficiency on the IEEE 68-bus, 16 machine and WECC 179-bus, 29-machine systems.
Cool circumgalactic gas in galaxy clusters: connecting the DESI legacy imaging survey and SDSS DR16 MgII absorbers

We investigate the cool gas absorption in galaxy clusters by cross-correlating Mgii absorbers detected in quasar spectra from Data Release 16 of the Sloan Digital Sky Survey (SDSS) with galaxy clusters identiﬁed in the Dark Energy Spectroscopic Instrument (DESI) survey. We ﬁnd signiﬁcant covering fractions (1 − 5 % within 𝑟 500 , depending on the chosen redshift interval), ∼ 4 − 5 times higher than around random sightlines. While the covering fraction of cool gas in clusters decreases with increasing mass of the central galaxy, the total Mgii mass within 𝑟 500 is nonetheless ∼ 10 times higher than for SDSS luminous red galaxies (LRGs). The Mgii covering fraction versus impact parameter is well described by a power law in the inner regions and an exponential function at larger distances. The characteristic scale of the transition between these two regimes is smaller for large equivalent width absorbers. Cross-correlating Mgii absorption with photo − 𝑧 selected cluster member galaxies from DESI reveals a statistically signiﬁcant connection. The median projected distance between Mgii absorbers and the nearest cluster member is ∼ 200 kpc, compared to ∼ 500 kpc in random mocks with the same galaxy density proﬁles. We do not ﬁnd a correlation between Mgii strength and the star formation rate of the closest cluster neighbour. This suggests that cool gas in clusters, as traced by Mgii absorption, is: (i) associated with satellite galaxies, (ii) dominated by cold gas clouds in the intracluster medium, rather than by the interstellar medium of galaxies, and (iii) may originate in part from gas stripped from these cluster satellites in the past.
The Elements of Temporal Sentence Grounding in Videos: A Survey and Future Directions

—Temporal sentence grounding in videos (TSGV), a.k. a. , natural language video localization (NLVL) or video moment retrieval (VMR), aims to retrieve a temporal moment that semantically corresponds to a language query from an untrimmed video. Connecting computer vision and natural language, TSGV has drawn signiﬁcant attention from researchers in both communities. This survey attempts to provide a summary of fundamental concepts in TSGV and current research status, as well as future research directions. As the background, we present a common structure of functional components in TSGV, in a tutorial style: from feature extraction from raw video and language query, to answer prediction of the target moment. Then we review the techniques for multimodal understanding and interaction, which is the key focus of TSGV for effective alignment between the two modalities. We construct a taxonomy of TSGV techniques and elaborate methods in different categories with their strengths and weaknesses. Lastly, we discuss issues with the current TSGV research and share our insights about promising research directions.
Distribution of Therapeutic Efficacy of Ranunculales Plants Used by Ethnic Minorities on the Phylogenetic Tree of Chinese Species

The medicinal properties of plants can be evolutionarily predicted by phylogeny-based methods, which, however, have not been used to explore the regularity of therapeutic effects of Chinese plants utilized by ethnic minorities. This study aims at exploring the distribution law of therapeutic efficacy of Ranunculales plants on the phylogenetic tree of Chinese species. We collected therapeutic efficacy data of 551 ethnomedicinal species belonging to five species-rich families of Ranunculales; these therapeutic data were divided into 15 categories according to the impacted tissues and organs. The phylogenetic tree of angiosperm species was used to analyze the phylogenetic signals of ethnomedicinal plants by calculating the net relatedness index (NRI) and nearest taxon index (NTI) in R language. The NRI results revealed a clustered structure for eight medicinal categories (poisoning/intoxication, circulatory, gastrointestinal, eyesight, oral, pediatric, skin, and urinary disorders) and overdispersion for the remaining seven (neurological, general, hepatobiliary, musculoskeletal, otolaryngologic, reproductive, and respiratory disorders), while the NTI metric identified the clustered structure for all. Statistically, NRI and NTI values were significant in 5 and 11 categories, respectively. It was found that Mahonia eurybracteata has therapeutic effects on all categories. iTOL was used to visualize the distribution of treatment efficacy on species phylogenetic trees. By figuring out the distribution of therapeutic effects of Ranunculales medicinal plants, the importance of phylogenetic methods in finding potential medicinal resources is highlighted; NRI, NTI, and similar indices can be calculated to help find taxonomic groups with medicinal efficacy based on the phylogenetic tree of flora in different geographic regions.
Signature Construction and Molecular Subtype Identification Based on Pyroptosis-Related Genes for Better Prediction of Prognosis in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is one of the most common malignancies worldwide. However, there is a lack of adequate means of treatment prognostication for HCC. Pyroptosis is a newly discovered way of programmed cell death. However, the prognostic role of pyroptosis in HCC has not been thoroughly investigated. Here, we generated a novel prognostic signature to evaluate the prognostic value of pyroptosis-related genes (PRGs) using the data from The Cancer Genome Atlas (TCGA) database. The accuracy of the signature was validated using survival analysis through the International Cancer Genome Consortium cohort (n = 231) and the First Affiliated Hospital of Wenzhou Medical University cohort (n = 180). Compared with other clinical factors, the risk score of the signature was found to be associated with better patient outcomes. The enrichment analysis identified multiple pathways related with pyroptosis in HCC. Furthermore, drug sensitivity testing identified six potential chemotherapeutic agents to provide possible treatment avenues. Interestingly, patients with low risk were confirmed to be associated with lower tumor mutation burden (TMB). However, patients at high risk were found to have a higher count of immune cells. Consensus clustering was performed to identify two main molecular subtypes (named clusters A and B) based on the signature. It was found that compared with cluster B, better survival outcomes and lower TMB were observed in cluster A. In conclusion, signature construction and molecular subtype identification of PRGs could be used to predict the prognosis of HCC, which may provide a specific reference for the development of novel biomarkers for HCC treatment.