Miracle-dz/UniFAR-FTUs huggingface dataset:
Open Set Domain Adaptation with Multi-Classifier Adversarial Network

Domain adaptation aims to transfer knowledge from a domain with adequate labeled samples to a domain with scarce labeled samples. The majority of existing domain adaptation methods rely on the assumption of having identical label spaces across the source and target domains, which limits their application in real-world scenarios. To get rid of such an assumption, prior research has introduced various open set domain adaptation settings in the literature. This paper focuses on the type of open set domain adaptation setting where the target domain has both private (`unknown classes') label space beside the shared (`known classes') label space. However, the source domain only has the `known classes' label space. Prevalent distribution-matching domain adaptation methods are inadequate in such a setting that demands adaptation from a smaller source domain to a larger and diverse target domain with more classes. For addressing this specific open set domain adaptation setting, prior research introduces a domain adversarial model with an empirical fixed threshold which lacks at handling false-negative transfers. We propose a multi-classifier based weighting scheme for the adversarial domain adaptation model to address this issue and improve performance. Our proposed method assigns distinguishable weights to target samples belonging to the known and unknown classes to limit false-negative transfers, and simultaneously reduce the domain gap between shared classes of the source and target domains. A thorough evaluation shows that our proposed method outperforms existing domain adaptation methods for a number of domain adaptation datasets.
Uncertainty-Aware Lookahead Factor Models for Quantitative Investing

On a periodic basis, publicly traded companies report fundamentals, financial data including revenue, earnings, debt, among others. Quantitative finance research has identified several factors, functions of the reported data that historically correlate with stock market performance. In this paper, we first show through simulation that if we could select stocks via factors calculated on future fundamentals (via oracle), that our portfolios would far outperform standard factor models. Motivated by this insight, we train deep nets to forecast future fundamentals from a trailing 5-year history. We propose lookahead factor models which plug these predicted future fundamentals into traditional factors. Finally, we incorporate uncertainty estimates from both neural heteroscedastic regression and a dropout-based heuristic, improving performance by adjusting our portfolios to avert risk. In retrospective analysis, we leverage an industry-grade portfolio simulator (backtester) to show simultaneous improvement in annualized return and Sharpe ratio. Specifically, the simulated annualized return for the uncertainty-aware model is 17.7% (vs 14.0% for a standard factor model) and the Sharpe ratio is 0.84 (vs 0.52).
Double Prioritized State Recycled Experience Replay

Experience replay enables online reinforcement learning agents to store and reuse the previous experiences of interacting with the environment. In the original method, the experiences are sampled and replayed uniformly at random. A prior work called prioritized experience replay was developed where experiences are prioritized, so as to replay experiences seeming to be more important more frequently. In this paper, we develop a method called double-prioritized state-recycled (DPSR) experience replay, prioritizing the experiences in both training stage and storing stage, as well as replacing the experiences in the memory with state recycling to make the best of experiences that seem to have low priorities temporarily. We used this method in Deep Q-Networks (DQN), and achieved a state-of-the-art result, outperforming the original method and prioritized experience replay on many Atari games.
Seasonal dynamics and age structure of drifting cyprinid fishes: an interspecific comparison

We investigated interspecific differences in the downstream drift of seven species of young-of-the-year cyprinids over a 2-year period in two central European rivers, using a comparison of drifting fish with fish collected from nurseries at the same time and the same site as the drift samples. Species varied in the utilisation of the drift; Rutilus rutilus, Gobio spp. , Abramis brama and Carassius auratus gibelio drifted abundantly, while Leuciscus cephalus and Alburnus alburnus were abundant in nurseries, but drifted at low densities. Abramis bjoerkna was equally represented among drifting and resident fish. Generally, fish drifted at two major developmental intervals; L3 stage and the stage at transition between larval and juvenile periods (L6/Juv), with interspecific differences in the ontogenetic timing of drifting. The propensity to drifting of certain developmental stages was also reflected in body size comparisons between drifting and resident fish. The stage-dependent drifting may be attributed to major changes in fish morphology, behaviour and swimming ability.
A study of Neural networks point source extraction on simulated Fermi/LAT Telescope images

Astrophysical images in the GeV band are challenging to analyze due to the strong contribution of the background and foreground astrophysical diffuse emission and relatively broad point spread function of modern space-based instruments. In certain cases, even finding of point sources on the image becomes a non-trivial task. We present a method for point sources extraction using a convolution neural network (CNN) trained on our own artificial data set which imitates images from the Fermi Large Area Telescope. These images are raw count photon maps of 10x10 degrees covering energies from 1 to 10 GeV. We compare different CNN architectures that demonstrate accuracy increase by ~15% and reduces the inference time by at least the factor of 4 accuracy improvement with respect to a similar state of the art models.
Using Online Implicit Association Tests in Opinion Polling

Opinion polls have now become a very important component of society because they are now a defacto component of our daily news cycle and because their results influence governments and business in ways which are not always obvious to us. However, polling is not always accurate and there have been some really inaccurate polling results which have had major influences on the world going back to the 1930s but also as recently as just the last 3 or 4 years. In this paper we analyse the phenomenon of socially desirable responding (shy voters) which has emerged as one of the reasons for modern day inaccurate polling. We describe how it can be exposed through implicit association tests (IATs) and we demonstrate the shy voter effect in a small survey on opinions in Ireland towards the United Kingdom. We argue for inclusion of IATs in traditional polling and point to the fact that these can be conducted accurately online, which also allows polling to reach a larger and more diverse sample of respondents in the days of Covid-19 restrictions which restricts the opportunities for poll sampling from the general public.
The Causal Relationship Between Foreign Direct Investment And Exchange Rate In Nigeria (1986-2017)

This study examined the causal relationship between foreign direct investment (FDI) and exchange rate in Nigeria between 1986 and 2017. Secondary data were obtained from Statistical Bulletin of Central Bank of Nigeria (CBN). Annual data on Foreign Direct Investment (FDI), Nominal Exchange Rate (EXCH) and Economic growth proxied by Real Gross Domestic Product (RGDP) were used. A Descriptive and econometrics technique of Granger Causality (The Block Exogeneity Wald tests) was employed in the analysis of the data. The results revealed the existence of a unidirectional causality between FDI and exchange rate at 5% level of significance which flows from EXCH (F stat=3.86 and p-value 0.04) to FDI (EXCH→FDI). No causality exists between EXCH and RGDP. A bidirectional causality which flows from FDI (F stat= 4.35 and p-value 0.03) to RGDP at 5% level of significance and from RGDP (F stat= 9.12 and p-value 0.00) to FDI also at 5% level of significance. The study revealed that exchange rate better predict FDI and a bi-directional causal relationship exist between FDI and economic growth which implies that Nigerian economy experienced an enhanced economic growth through the attraction FDI (Growth-driven FDI) and that FDI improves the rate of growth in Nigeria (FDI-led growth). This study therefore recommends the adoption and implementation of effective policies that will attract and retain FDI in Nigeria as well as ensuring some level of stability in exchange rate to ensure consistency in economic growth in Nigeria. Keyword: FDI, Exchange rate, Economic growth, VEC Granger causality/Wald Test
KQA Pro: A Large Diagnostic Dataset for Complex Question Answering over Knowledge Base

Complex question answering over knowledge base (Complex KBQA) is challenging because it requires the compositional reasoning capability. Existing benchmarks have three shortcomings that limit the development of Complex KBQA: 1) they only provide QA pairs without explicit reasoning processes; 2) questions are either generated by templates, leading to poor diversity, or on a small scale; and 3) they mostly only consider the relations among entities but not attributes. To this end, we introduce KQA Pro, a large-scale dataset for Complex KBQA. We generate questions, SPARQLs, and functional programs with recursive templates and then paraphrase the questions by crowdsourcing, giving rise to around 120K diverse instances. The SPARQLs and programs depict the reasoning processes in various manners, which can benefit a large spectrum of QA methods. We contribute a unified codebase and conduct extensive evaluations for baselines and state-of-the-arts: a blind GRU obtains 31.58\%, the best model achieves only 35.15\%, and humans top at 97.5\%, which offers great research potential to fill the gap.
Interpreting Hierarchical Linguistic Interactions in DNNs

This paper proposes a method to disentangle and quantify interactions among words that are encoded inside a DNN for natural language processing. We construct a tree to encode salient interactions extracted by the DNN. Six metrics are proposed to analyze properties of interactions between constituents in a sentence. The interaction is defined based on Shapley values of words, which are considered as an unbiased estimation of word contributions to the network prediction. Our method is used to quantify word interactions encoded inside the BERT, ELMo, LSTM, CNN, and Transformer networks. Experimental results have provided a new perspective to understand these DNNs, and have demonstrated the effectiveness of our method.
Security Apps under the Looking Glass: An Empirical Analysis of Android Security Apps

Third-party security apps are an integral part of the Android app ecosystem. Many users install them as an extra layer of protection for their devices. By installing security apps, the smartphone users place a significant amount of trust on them allowing access to many smartphone resources that contain personal information such as the storage, text messages, email, and browser history. As such, it is essential to understand the mobile security apps ecosystem. In this paper, we present the first empirical study of Android security apps. We analyse 100 Android security apps from multiple aspects and offer insights to their operations and behaviours. Our results show that 20% of the security apps resell the data they collect to third parties; in some cases, even without the user consent. Also, we show that around 50% of the security apps fail to identify known malware.
Single femtosecond laser pulse excitation of individual cobalt nanoparticles

Laser-induced manipulation of magnetism at the nanoscale is a rapidly growing research topic with potential for applications in spintronics. In this work, we address the role of the scattering cross section, thermal effects, and laser fluence on the magnetic, structural, and chemical stability of individual magnetic nanoparticles excited by single femtosecond laser pulses. We find that the energy transfer from the fs laser pulse to the nanoparticles is limited by the Rayleigh scattering cross section, which in combination with the light absorption of the supporting substrate and protective layers determines the increase in the nanoparticle temperature. We investigate individual Co nanoparticles (8 to 20 nm in size) as a prototypical model system, using x-ray photoemission electron microscopy and scanning electron microscopy upon excitation with single femtosecond laser pulses of varying intensity and polarization. In agreement with calculations, we find no deterministic or stochastic reversal of the magnetization in the nanoparticles up to intensities where ultrafast demagnetization or all-optical switching is typically reported in thin films. Instead, at higher fluences, the laser pulse excitation leads to photo-chemical reactions of the nanoparticles with the protective layer, which results in an irreversible change in the magnetic properties. Based on our findings, we discuss the conditions required for achieving laser-induced switching in isolated nanomagnets.
Cloning, Sequencing and Expression of a GroEL-like Protein Gene of Vibrio vulnificus

Vibrio vulnificus causes wound infection and septicemia in humans with high mortality via the consumption of contaminated seafood. This study cloned, sequenced and analyzed the general properties of the GroEL-like protein gene of this pathogen. A 1.6 kb amplified DNA fragment from V. vulnificus ATCC27562 was cloned into pGEM-T Vector and nucleotide sequenced. The open reading frame consisted of 1,644bp, and encoded for a 57.7kDa GroEL-like protein of 547 amino acids. The amino acid sequence of the V. vulnificus GroEL-like protein showed high identity (82-92%) with other GroEL proteins, especially those of vibrios, E. coli and S. enterica Typhi. The amino acid residues 269 to 282 of the V. vulnificus GroEL-like protein were significantly more hydrophobic than others, with two basic lysine residues replaced by hydrophobic isoleucine residues. The groEL-like gene of V. vulnificus was cloned into the pQE-30 expression vector and expression of a His-tagged GroEL-like protein was rapidly induced by isopropyl-β-D- thiogalactopyranoside and confirmed by immunoblotting with anti-GroEL and Penta-His antibodies. The over-produced GroEL-like protein of V. vulnificus was present largely in an insoluble form. Results of this report may facilitate the study of responses of V. vulnificus to environmental stresses.
EVALUATION OF FORMULAE TO ESTIMATE THE CAPTURE AREA AND MESH HEIGHT OF ORB WEBS (ARANEOIDEA, ARANEAE)

Abstract We evaluated several formulae to estimate the capture area (the area of the web covered by capture spirals) and the mesh height (the distance between capture spirals) of orb webs constructed by Argiope keyserlingi Karsch. The accuracy of the various formulae was estimated through regression analyses. Accordingly, we propose two new formulae specifically suited for asymmetric orb webs, which provide accurate estimates of capture area and mesh height.
Charge-Collection Efficiency in Back-Illuminated Charge-Coupled Devices

Low noise CCDs fully-depleted up to 675 micrometers have been identified as a unique tool for Dark Matter searches and low energy neutrino physics. The charge collection efficiency (CCE) for these detectors is a critical parameter for the performance of future experiments. We present here a new technique to characterize CCE in back-illuminated CCDs based on soft X-rays. This technique is used to characterize two different detector designs. The results demonstrate the importance of the backside processing for detection near threshold, showing that a recombination layer of a few microns significantly distorts the low energy spectrum. The studies demonstrate that the region of partial charge collection can be reduced to less than 1 micrometer thickness with adequate backside processing.
Use of Hydrogen Peroxide to Treat Experimentally Induced Bacterial Gill Disease in Rainbow Trout

Abstract Bacterial gill disease, experimentally produced in rainbow trout Oncorhynchus mykiss exposed to Flavobacterium branchiophilum, was effectively treated by using 1-h static baths containing hydrogen peroxide as an alternative to treatment with chloramine-T. The optimal concentration of hydrogen peroxide depended on the number of treatments, the time intervals between them, and the stage of the disease. When administered as two treatments at 48-h intervals, 250 mg hydrogen peroxide/L was more effective than 10 mg chloramine-T/L. The effectiveness of 100 mg hydrogen peroxide/L was more variable, but treatment consistently and significantly reduced percent cumulative mortality (PCM) compared with untreated control groups; treatment with 25 mg/L was less effective. Clearance of gill-associated F. branchiophilum antigen was greatest with chloramine-T or 250 mg hydrogen peroxide/L; 100 mg hydrogen peroxide/L was again more variable. Three 1-h static baths at 24-h intervals reduced the concentration of hy.
Low-Dimensional Manifold-Constrained Disentanglement Network for Metal Artifact Reduction

Deep neural network-based methods have achieved promising results for computed tomography (CT) metal artifact reduction (MAR), most of which use many synthesized paired images for supervised learning. As synthesized metal artifacts in CT images may not accurately reflect the clinical counterparts, an artifact disentanglement network (ADN) was proposed with unpaired clinical images directly, producing promising results on clinical datasets. However, as the discriminator can only judge if large regions semantically look artifact-free or artifact-affected, it is difficult for ADN to recover small structural details of artifact-affected CT images based on adversarial losses only without sufficient constraints. To overcome the illposedness of this problem, here we propose a low-dimensional manifold (LDM)-constrained disentanglement network (DN), leveraging the image characteristics that the patch manifold of CT images is generally low dimensional. Specifically, we design an LDM-DN learning algorithm to empower the DN through optimizing the synergistic loss functions used in ADN while constraining the recovered images to be on a low-dimensional patch manifold. Moreover, learning from both paired and unpaired data, an efficient hybrid optimization scheme is proposed to further improve the MAR performance on clinical datasets. Extensive experiments demonstrate that the proposed LDM-DN approach can consistently improve the MAR performance in paired and/or unpaired learning settings, outperforming competing methods on synthesized and clinical datasets.
The rise and regulation of thermal facial recognition technology during the COVID-19 pandemic

Harvard Law School, Oxford University Press, and Stanford Law School. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com. This is an Open Access article distributed under the terms of the Creative Commons Attribution NonCommercial-NoDerivs licence (http://creativecommons.org/licenses/bync-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.com Journal of Law and the Biosciences, 1–17 doi:10.1093/jlb/lsaa038 Advance Access Publication 18 June 2020 Essay
A novel protocol for micropropagation of Rauvolfia serpentina: in low concentration of growth regulators with sucrose and phenolic acid.

Rauvolfia serpentina (Indian snake root), an important medicinal shrub, belongs to the Apocynaceae family. Rauvolfia root is bitter, acrid, laxative anthelmic, diuretic and sedative . The alkaloids are classified in to 3 groups, viz., reserpine, ajmaline and serpentine groups. Reserpine group comprising reserpine, rescinnamine, deserpine group etc. Ajmaline group comprising ajmaline, ajmalicine, ajmalinine, isoajmaline etc. Serpentine group comprising serpentine, alsotonine etc. (Husain, 1993; Iyengar, 1985). The root is a sedative and is used to control high blood pressure and certain forms of insanity (Ghani, 1998). In Ayurveda it is also used for the treatment of insomnia, epilepsy, asthma, acute stomachache and painful delivery. It is used in snake bite, insect stings and mental disorders. Reserpine is a potent hypotensive and tranquillizer but its prolonged usage stimulates prolactine release and cause breast cancer. The juice of leaves is used as a remedy for the removal of opacities of cornea. (Baksha et al., 2007). The present communication report is use of low concentration of growth regulators, sucrose and phenolic acid for regeneration of Rauvolfia serpentina.
COMPARATIVE GENOMIC ANALYSIS OF RESPIRATION SWITCH IN GAMMA-PROTEOBACTERIA

Summary Motivation: Selection of respiration pathways in prokaryotes appears to be one of the most important aspects of energy metabolism. Bacteria have a great number of respiratory enzymes and demonstrate then complicated regulatory pattern. Thus, it is of in interest to analyze the regulation of respiration in different species. Here, we studied the group of gamma-proteobacteria. Results: A large number of gamma-proteobacteria genomes was studied by the methods of comparative genomics. Putative ArcA, FNR and NarP regulons were described in detail for a number of microorganisms. After that different regulons for each organism were compared. We also confirmed known FNR-binding signal, improved the recognition profile for the ArcA-signal and created a new profile for the NarP-signal.
Demography of a Southern Population of the Spotted Turtle (Clemmys guttata)

Abstract The Spotted Turtle (Clemmys guttata) is declining throughout its range in eastern North America as a result of habitat loss and collection for the pet trade. Although the species has been relatively well-studied in the northern part of its range, little is known about southern populations. We conducted a four-year study on a population of Spotted Turtles in South Carolina. A total of 44 turtles were captured: 21 females, 17 males, 5 juveniles, and 1 hatchling. There was no size dimorphism with respect to carapace length. However, females had longer plastrons, greater shell heights, and heavier body masses than males, likely as a result of the concavity of the male plastron. The adult sex ratio did not differ from 1:1. Estimated adult population size varied annually from 31 to 36 turtles, and density was estimated at 0.36 turtles/ha. This information is important for the creation of management plans to conserve populations across the Spotted Turtle's range.
PSO BASED DISPATCH OF GENERATION AND LOAD IN RESTRUCTURED POWER SYSTEM

The role of the power system operator or independent system operator in restructured power system is to maintain a balance between the generation and demand. Traditionally, generation rescheduling is carried out in power system to maintain the power balance ignoring the demand side balancing. A PSO based optimization algorithm for dispatch of generation and load (DGL) is proposed in this paper. The dispatchable resources considered are generating units and demands. A framework for DGL is presented which is based on load shifting cost bid by the customer. The operating cost of the power system decreases as the costlier peaking units are off. The proposed algorithm is tested on IEEE 30 bus system, 10, 20, 40, 60, 80 and 100 unit systems. The results validate the efficiency and applicability of the proposed algorithm in deriving economic benefits.