Miracle-dz/UniFAR-FTUs huggingface dataset:
A comparison of modelling approaches for closed-loop decision making over discrete domains in manufacturing systems

The manufacturing industry is transforming towards smart, flexible, and energy-efficient systems. In this regard, control strategies based on optimisation have been proposed to improve the energy efficiency of manufacturing systems. Usually, the optimisation problem behind the controller design involves decision variables constrained to binary and discrete domains resulting in Mixed-Integer Linear Programming (MILP) problems and require a high computational burden to find their optimal solution. In this paper, three different approaches are proposed for the closed-loop decision making over discrete domains in manufacturing systems, including the constraints and limitations to their implementation. Thus, the number and nature of required variables as well as the additional constraints needed to represent the discrete domains of the decision variables are established. These approaches are tested and compared to solve the optimisation problem behind a predictive-like controller designed to minimise the energy consumption of a manufacturing process line.
Translocation of Labeled Assimilates into and Out of Bean Leaves as Affected by 2,4-D and Benzyl Adenine

When a primary leaf on a bean plant was treated with 2,4-D, it imported 14C-labeled assimilates from the opposite primary leaf; the greatest import occurred when the treated leaf was darkened. Benzyl adenine also caused treated leaves to import labeled assimilates when the leaves were darkened but usually not when the leaves were not darkened. Essentially no label was imported by untreated leaves, even when darkened for several days, unless the shoots were debudded; debudding caused darkened leaves to import label. Regardless of the type of treatment, the imported labeled assimilates were confined mainly to the veins. Leaves that were detached from plants following their exposure to 14CO2 translocated labeled assimilates from the mesophyll into the veins in a polar manner; this polar movement was not prevented by any treatment of these leaves, including prior applications of 10,000 ppm of 2,4-D or 7,900 ppm of benzyl adenine. After the veins were loaded, the labeled assimilates were transported basipetally into the petioles; the distribution of the label was relatively uniform from top to bottom of the petioles (except for the pulvinus-upper and lower). The basipetal movement in detached leaves was not prevented by benzyl adenine unless they were assimilate-starved (darkening them for 5 days); basipetal movement was reduced but not prevented by similar treatments with 2,4-D. Basipetal flow out of attached leaves treated with 2,4-D or benzyl adenine occurred in all tests. Labeled assimilate transport in the bean plant was greatly altered by treatment of a primary leaf with 2,4-D and to a lesser extent by benzyl adenine. The greatest alteration occurred when the 2,4-D-treated leaf was kept in the dark; this resulted in most of the exported 2,4-D being confined to the epicotyl. The bulk of the labeled assimilate then migrated into the 2,4-D-containing tissues.
ISOLATION, SEQUENCING AND IN SILICO CHARACTERIZATION OF PROMOTER REGION OF HSPA6 GENE FROM ARABIAN CAMEL (CAMELUS DROMEDARIES)

Heat-shock proteins, divided in numerous families, play an important role in cellular processes. The human Hsp70 (PDB ID 1HJO) performs multiple steps including protein folding, trafficking, remodeling and degradation. In addition, the Hsp70 is also involved in cell signaling, and tumor repression. We have previously sequenced the full-length cDNA encoding the putative stress induced heat shock protein HSPA6 from Arabian camel (NCBI Gene Bank accession number HQ214118.1). The sequence analysis of HSPA6 gene revealed a 1932bp-long open reading frame encoding 643 amino acids. In this work, we extended our study to explore the mechanism that manages the HSPA6 gene expression by isolating and characterizing the flanking 5’ region using online tools, expected to harbor the regulatory elements. The straight-walk method was used to isolate the upstream genomic DNA sequences of the HSPA6. The isolated fragment has a length of 1417bp and after verifying the sequence, it was deposited in GenBank under accession number JX888464. The sequence analysis revealed the presence of several heat shock elements (HSEs) that act as binding sites for the major heat shock factor. These HSE sites were dispersed in the isolated region and had consensus sequences identical to those found in eukaryotes. Other core promoter elements such as potential transcriptional start site for the HSPA6 gene were found including the canonical TATA-box, DPE, MTE, BRE, CpG island, palindromic and repeated sequences. Taken together, this is the first report on the promoter region of the HSPA6 gene from Arabian camel which will enhance our understanding of how such regulatory cis-elements are engaged in regulating the gene expression.
Value Investing: Cheapness, Quality, and Investor Confidence*

This paper investigates whether signals from changes in investors’ portfolio concentrations can be used to enhance the performance of value and value/quality stock portfolios. Using data on all the more than 1.3 million investor portfolios participating in the Finnish stock market, I find that increases in average shareholder portfolio concentration, a signal of investor confidence, can be used to improve the performance of value portfolios and portfolios selected based on value and quality signals. The results indicate that shareholder portfolio concentration can be used as an additional signal to improve the performance of value-oriented investing strategies.
Initial results of a high-speed spatial auditory BCI

Most P300 BCI approaches use the visual modality for stimulation. For use with ALS patients this might not be the preferable choice because of sight deterioration. Moreover, using a modality different from the visual one minimizes interference with possible visual feedback. Therefore, a multi-class brain-computer interface paradigm is proposed that uses spatially distributed, auditive cues. Ten subjects participated in an offline oddball task with the spatial location of the stimuli being a discriminating cue. Different inter-stimulus intervals of 1000 ms, 300 ms and 175 ms were tested. With averaging over multiple classifier outputs, selection scores went over 90% for most conditions; two subjects reached a 100% correct score. Corresponding information transfer rates were high, up to an average optimal score of 20.99 bits/minute for the 175 ms condition (best subject 37.80 bits/minute). We conclude that the proposed paradigm is successful for healthy subjects and shows promising results that may lead to a fast BCI that solely relies on the auditory sense.
Relationship between Board Characteristics and Firm Financial Diversification (Geographic Aasets) Among Listed Firms on Nairobi Securities Exchange, Kenya: Dynamic Panel Approach

BSTRACT Previous studies on board diversity and firm diversification have concentrated on large sized firms in America, Western Europe and Asia with no conclusive evidence on the relationship between board demographics and firm financial diversification and majorly utilized static panel multivariate regressions. Due to differences in country specific factors and level of market development, this study was an attempt to fill this gap by utilizing both static and dynamic multivariate panel regression analysis in Kenya. Agency Theory, free cash flow hypothesis and Resource Based View theory provided theoretical framework.The specific objectives of the study were: to determine the relationship between diversity of board and firm financial diversification. Longitudinal research design was used in the study.Research revealed that experience diversity positively and significantly influenced geographic assets while interlock directorship and nationality diversities negatively and significantly determined investments in geographic assets.Future studies could examine relationship between Board diversity and firm financial diversification mediated by EBIT. -------------------------------------------------------------------------------------------------------------------------------------Date of Submission: 27-04-2020 Date of Acceptance: 10-05-2020 --------------------------------------------------------------------------------------------------------------------------------------
SEED: Semantics Enhanced Encoder-Decoder Framework for Scene Text Recognition

Scene text recognition is a hot research topic in computer vision. Recently, many recognition methods based on the encoder-decoder framework have been proposed, and they can handle scene texts of perspective distortion and curve shape. Nevertheless, they still face lots of challenges like image blur, uneven illumination, and incomplete characters. We argue that most encoder-decoder methods are based on local visual features without explicit global semantic information. In this work, we propose a semantics enhanced encoder-decoder framework to robustly recognize low-quality scene texts. The semantic information is used both in the encoder module for supervision and in the decoder module for initializing. In particular, the state-of-the-art ASTER method is integrated into the proposed framework as an exemplar. Extensive experiments demonstrate that the proposed framework is more robust for low-quality text images, and achieves state-of-the-art results on several benchmark datasets. The source code will be available.
Secure and Differentially Private Bayesian Learning on Distributed Data

Data integration and sharing maximally enhance the potential for novel and meaningful discoveries. However, it is a non-trivial task as integrating data from multiple sources can put sensitive information of study participants at risk. To address the privacy concern, we present a distributed Bayesian learning approach via Preconditioned Stochastic Gradient Langevin Dynamics with RMSprop, which combines differential privacy and homomorphic encryption in a harmonious manner while protecting private information. We applied the proposed secure and privacy-preserving distributed Bayesian learning approach to logistic regression and survival analysis on distributed data, and demonstrated its feasibility in terms of prediction accuracy and time complexity, compared to the centralized approach.
Information-Theoretic Limits for the Matrix Tensor Product

This article studies a high-dimensional inference problem involving the matrix tensor product of random matrices. This problem generalizes a number of contemporary data science problems including the spiked matrix models used in sparse principal component analysis and covariance estimation and the stochastic block model used in network analysis. The main results are single-letter formulas (i.e., analytical expressions that can be approximated numerically) for the mutual information and the minimum mean-squared error (MMSE) in the Bayes optimal setting where the distributions of all random quantities are known. We provide non-asymptotic bounds and show that our formulas describe exactly the leading order terms in the mutual information and MMSE in the high-dimensional regime where the number of rows <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> and number of columns <inline-formula> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula> scale with <inline-formula> <tex-math notation="LaTeX">$d = O(n^{\alpha })$ </tex-math></inline-formula> for some <inline-formula> <tex-math notation="LaTeX">$\alpha < 1/20$ </tex-math></inline-formula>. On the technical side, this article introduces some new techniques for the analysis of high-dimensional matrix-valued signals. Specific contributions include a novel extension of the adaptive interpolation method that uses order-preserving positive semidefinite interpolation paths, and a variance inequality between the overlap and the free energy that is based on continuous-time I-MMSE relations.
Accelerating Antimicrobial Discovery with Controllable Deep Generative Models and Molecular Dynamics

De novo therapeutic design is challenged by a vast chemical repertoire and multiple constraints such as high broad-spectrum potency and low toxicity. We propose CLaSS (Controlled Latent attribute Space Sampling) - a novel and efficient computational method for attribute-controlled generation of molecules, which leverages guidance from classifiers trained on an informative latent space of molecules modeled using a deep generative autoencoder. We further screen the generated molecules by using a set of deep learning classifiers in conjunction with novel physicochemical features derived from high-throughput molecular simulations. The proposed approach is employed for designing non-toxic antimicrobial peptides (AMPs) with strong broad-spectrum potency, which are emerging drug candidates for tackling antibiotic resistance. Synthesis and wet lab testing of only twenty designed sequences identified two novel and minimalist AMPs with high potency against diverse Gram-positive and Gram-negative pathogens, including the hard-to-treat multidrug-resistant K. pneumoniae, as well as low in vitro and in vivo toxicity. The proposed approach thus presents a viable path for faster discovery of potent and selective broad-spectrum antimicrobials with a higher success rate than state-of-the-art methods.
A Survey of Information Cascade Analysis: Models, Predictions, and Recent Advances

The deluge of digital information in our daily life—from user-generated content, such as microblogs and scientific papers, to online business, such as viral marketing and advertising—offers unprecedented opportunities to explore and exploit the trajectories and structures of the evolution of information cascades. Abundant research efforts, both academic and industrial, have aimed to reach a better understanding of the mechanisms driving the spread of information and quantifying the outcome of information diffusion. This article presents a comprehensive review and categorization of information popularity prediction methods, from feature engineering and stochastic processes, through graph representation, to deep learning-based approaches. Specifically, we first formally define different types of information cascades and summarize the perspectives of existing studies. We then present a taxonomy that categorizes existing works into the aforementioned three main groups as well as the main subclasses in each group, and we systematically review cutting-edge research work. Finally, we summarize the pros and cons of existing research efforts and outline the open challenges and opportunities in this field.
On the Distance of SGR 1935+2154 Associated with FRB 200428 and Hosted in SNR G57.2+0.8

Owing to the detection of an extremely bright fast radio burst (FRB) 200428 associated with a hard X-ray counterpart from the magnetar soft gamma-ray repeater (SGR) 1935+2154, the distance of SGR 1935+2154 potentially hosted in the supernova remnant (SNR) G57.2+0.8 can be revisited. Under the assumption that the SGR and the SNR are physically related, in this Letter, by investigating the dispersion measure (DM) of the FRB contributed by the foreground medium of our Galaxy and the local environments and combining other observational constraints, we find that the distance of SGR 1935+2154 turns out to be 9.0 ± 2.5 kpc and the SNR radius falls into 10–18 pc since the local DM contribution is as low as 0–18 pc cm−3. These results are basically consistent with previous studies. In addition, an estimate for the Faraday rotation measure of the SGR and SNR is also carried out.
Intent Mining from past conversations for Conversational Agent

Conversational systems are of primary interest in the AI community. Organizations are increasingly using chatbot to provide round-the-clock support and to increase customer engagement. Many commercial bot building frameworks follow a standard approach that requires one to build and train an intent model to recognize user input. These frameworks require a collection of user utterances and corresponding intent to train an intent model. Collecting a substantial coverage of training data is a bottleneck in the bot building process. In cases where past conversation data is available, the cost of labeling hundreds of utterances with intent labels is time-consuming and laborious. In this paper, we present an intent discovery framework that can mine a vast amount of conversational logs and to generate labeled data sets for training intent models. We have introduced an extension to the DBSCAN algorithm and presented a density-based clustering algorithm ITER-DBSCAN for unbalanced data clustering. Empirical evaluation on one conversation dataset, six different intent dataset, and one short text clustering dataset show the effectiveness of our hypothesis.
Beyond the Mean-Field: Structured Deep Gaussian Processes Improve the Predictive Uncertainties

Deep Gaussian Processes learn probabilistic data representations for supervised learning by cascading multiple Gaussian Processes. While this model family promises flexible predictive distributions, exact inference is not tractable. Approximate inference techniques trade off the ability to closely resemble the posterior distribution against speed of convergence and computational efficiency. We propose a novel Gaussian variational family that allows for retaining covariances between latent processes while achieving fast convergence by marginalising out all global latent variables. After providing a proof of how this marginalisation can be done for general covariances, we restrict them to the ones we empirically found to be most important in order to also achieve computational efficiency. We provide an efficient implementation of our new approach and apply it to several regression benchmark datasets. We find that it yields more accurate predictive distributions, in particular for test data points that are distant from the training set.
Long-Time Correlations in Single-Neutron Interferometry Data

We present a detailed analysis of the time series of time-stamped neutron counts obtained by single-neutron interferometry. The neutron counting statistics display the usual Poissonian behavior, but the variance of the neutron counts does not. Instead, the variance is found to exhibit a dependence on the phase-shifter setting which can be explained by a probabilistic model that accounts for fluctuations of the phase shift. The time series of the detection events exhibit long-time correlations with amplitudes that also depend on the phase-shifter setting. These correlations appear as damped oscillations with a period of about 2.8 s. By simulation, we show that the correlations of the time differences observed in the experiment can be reproduced by assuming that, for a fixed setting of the phase shifter, the phase shift experienced by the neutrons varies periodically in time with a period of 2.8 s. The same simulations also reproduce the behavior of the variance. Our analysis of the experimental data suggests that time-stamped data of singleparticle interference experiments may exhibit transient features that require a description in terms of non-stationary processes, going beyond the standard quantum model of independent random events.
Constraints on dark energy models from the Horndeski theory

In light of the cosmological observations, we investigate dark energy models from the Horndeski theory of gravity. In particular, we consider cosmological models with the derivative self-interaction of the scalar field and the derivative coupling between the scalar field and gravity. We choose the self-interaction term to have an exponential function of the scalar field with both positive and negative exponents. For the function that has a positive exponent, our result shows that the derivative self-interaction term plays an important role in the late-time universe. On the other hand, to reproduce the right cosmic history, the derivative coupling between the scalar field and gravity must dominate during the radiation-dominated phase. However, the importance of such a coupling in the present universe found to be negligible due to its drastic decrease over time. Moreover, the propagation speed of gravitational waves estimated for our model is within the observational bounds, and our model satisfies the observational constraints on the dark energy equation of state.
Human-Like Decision Making for Autonomous Driving: A Noncooperative Game Theoretic Approach

Considering that human-driven vehicles and autonomous vehicles (AVs) will coexist on roads in the future for a long time, how to merge AVs into human drivers’ traffic ecology and minimize the effect of AVs and their misfit with human drivers, are issues worthy of consideration. Moreover, different passengers have different needs for AVs, thus, how to provide personalized choices for different passengers is another issue for AVs. Therefore, a human-like decision making framework is designed for AVs in this paper. Different driving styles and social interaction characteristics are formulated for AVs regarding driving safety, ride comfort and travel efficiency, which are considered in the modeling process of decision making. Then, Nash equilibrium and Stackelberg game theory are applied to the noncooperative decision making. In addition, potential field method and model predictive control (MPC) are combined to deal with the motion prediction and planning for AVs, which provides predicted motion information for the decision-making module. Finally, two typical testing scenarios of lane change, i.e., merging and overtaking, are carried out to evaluate the feasibility and effectiveness of the proposed decision-making framework considering different human-like behaviors. Testing results indicate that both the two game theoretic approaches can provide reasonable human-like decision making for AVs. Compared with the Nash equilibrium approach, under the normal driving style, the cost value of decision making using the Stackelberg game theoretic approach is reduced by over 20%.
Following up the afterglow: strategy for X-ray observation triggered by gravitational wave events

The multi-messenger observation of coalescing compact binary systems promises great scientific treasure. However, synthesising observations from both gravitational wave and electromagnetic channels remains challenging. In the context of the day-to-week long emission from a macronova, the binary neutron star merger GW170817 remains the only event with successful electromagnetic followup. In this manuscript, we explore the possibility of using the early stage X-ray afterglow to search for the electromagnetic counterpart of a gravitational wave event. Two algorithms, the simple and straightforward sequential observation (SO) and the step-wise optimizing local optimization are considered and applied to some simulated events. We consider the WXT from the proposed Einstein Probe as a candidate X-ray telescope, which has a very wide field of view of 3600 deg2. Benefiting from the large field of view and high sensitivity, we find that the SO algorithm not only is easy to implement, but also promises a good chance of actual detection.
The status of gould's petrel, Pterodroma leucoptera leucoptera, on Cabbage Tree Island, New South Wales

Gould's petrel, Pterodroma leucoptera leucoptera, breeds only on Cabbage Tree Island, New South Wales. Annual surveys to estimate the size of the breeding population and the reproductive output were conducted between 1989 and 1992. Annual estimates of the total number of pairs breeding in the two gullies containing the main population were between 122 +/- 20 (s.e.) and 202 +/- 26. Breeding success was less than 20% between 1989 and 1991, but rose to 24.7% in 1992. This marginal improvement may be the result of management action undertaken to reduce mortality of breeding adults and fledglings. The total population is estimated to have declined from 2004 (95% confidence limits: 1464,3185) in 1970 to 1479 (1250,1815) in 1992, a decline of 26% in 22 years. The reasons for this decline are unclear.