Miracle-dz/UniFAR-FTUs huggingface dataset:
Endogenous single-strand DNA breaks at RNA polymerase II promoters in Saccharomyces cerevisiae

Abstract Molecular combing and gel electrophoretic studies revealed endogenous nicks with free 3′OH ends at ∼100 kb intervals in the genomic DNA (gDNA) of unperturbed and G1-synchronized Saccharomyces cerevisiae cells. Analysis of the distribution of endogenous nicks by Nick ChIP-chip indicated that these breaks accumulated at active RNA polymerase II (RNAP II) promoters, reminiscent of the promoter-proximal transient DNA breaks of higher eukaryotes. Similar periodicity of endogenous nicks was found within the ribosomal rDNA cluster, involving every ∼10th of the tandemly repeated 9.1 kb units of identical sequence. Nicks were mapped by Southern blotting to a few narrow regions within the affected units. Three of them were overlapping the RNAP II promoters, while the ARS-containing IGS2 region was spared of nicks. By using a highly sensitive reverse-Southwestern blot method to map free DNA ends with 3′OH, nicks were shown to be distinct from other known rDNA breaks and linked to the regulation of rDNA silencing. Nicks in rDNA and the rest of the genome were typically found at the ends of combed DNA molecules, occasionally together with R-loops, comprising a major pool of vulnerable sites that are connected with transcriptional regulation.
Genetic Quality Affects the Rate of Male and Female Reproductive Aging Differently in Drosophila melanogaster

Males and females often maximize fitness by pursuing different reproductive strategies, with males commonly assumed to benefit more from increased resource allocation into current reproduction. Such investment should trade off with somatic maintenance and may explain why males frequently live shorter than females. It also predicts that males should experience faster reproductive aging. Here we investigate whether reproductive aging and life span respond to condition differently in male and female Drosophila melanogaster, as predicted if sexual selection has shaped male and female resource-allocation patterns. We manipulate condition through genetic quality by comparing individuals inbred or outbred for a major autosome. While genetic quality had a similar effect on condition in both sexes, condition had a much larger general effect on male reproductive output than on female reproductive output, as expected when sexual selection on vigor acts more strongly on males. We find no differences in reproductive aging between the sexes in low condition, but in high condition reproductive aging is relatively faster in males. No corresponding sex-specific change was found for life span. The sex difference in reproductive aging appearing in high condition was specifically due to a decreased aging rate in females rather than any change in males. Our results suggest that females age slower than males in high condition primarily because sexual selection has favored sex differences in resource allocation under high condition, with females allocating relatively more toward somatic maintenance than males.
Feedforward Neural Networks with a Hidden Layer Regularization Method

In this paper, we propose a group Lasso regularization term as a hidden layer regularization method for feedforward neural networks. Adding a group Lasso regularization term into the standard error function as a hidden layer regularization term is a fruitful approach to eliminate the redundant or unnecessary hidden layer neurons from the feedforward neural network structure. As a comparison, a popular Lasso regularization method is introduced into standard error function of the network. Our novel hidden layer regularization method can force a group of outgoing weights to become smaller during the training process and can eventually be removed after the training process. This means it can simplify the neural network structure and it minimizes the computational cost. Numerical simulations are provided by using K-fold cross-validation method with K = 5 to avoid overtraining and to select the best learning parameters. The numerical results show that our proposed hidden layer regularization method prunes more redundant hidden layer neurons consistently for each benchmark dataset without loss of accuracy. In contrast, the existing Lasso regularization method prunes only the redundant weights of the network, but it cannot prune any redundant hidden layer neurons.
Correcting acoustic measurements of scatterer density for extinction

Extinction is sometimes a major problem in acoustic surveys of fish stocks, as it often occurs when the fish are concentrated and easiest to survey. The same may be true of certain macrozooplankton, such as krill in swarms. This study aims to describe how to correct single‐ping measurements of the vertical distribution of scatterer density for extinction. The general case is considered in which the aggregation density is variable and the mean backscattering and extinction cross sections vary with depth. By dividing the water column into a finite number of layers, with constant properties within each, a closed‐form mean‐field solution is derived. Methods of applying this to single‐ping echo records and the quality of the solution are both examined. Extinction is discussed vis‐a‐vis multiple scattering. Application of the technique in other areas, e.g., in remote probing of the atmosphere by lidar, is mentioned.
Drawing of micro-structured fibres: circular and non-circular tubes

Abstract A general mathematical framework is presented for modelling the pulling of optical glass fibres in a draw tower. The only modelling assumption is that the fibres are slender; cross-sections along the fibre can have general shape, including the possibility of multiple holes or channels. A key result is to demonstrate how a so-called reduced time variable $\def \xmlpi #1{}\def \mathsfbi #1{\boldsymbol {\mathsf {#1}}}\let \le =\leqslant \let \leq =\leqslant \let \ge =\geqslant \let \geq =\geqslant \def \Pr {\mathit {Pr}}\def \Fr {\mathit {Fr}}\def \Rey {\mathit {Re}}\tau $ serves as a natural parameter in describing how an axial-stretching problem interacts with the evolution of a general surface-tension-driven transverse flow via a single important function of $\tau $ , herein denoted by $H(\tau )$ , derived from the total rescaled cross-plane perimeter. For any given preform geometry, this function $H(\tau )$ may be used to calculate the tension required to produce a given fibre geometry, assuming only that the surface tension is known. Of principal practical interest in applications is the ‘inverse problem’ of determining the initial cross-sectional geometry, and experimental draw parameters, necessary to draw a desired final cross-section. Two case studies involving annular tubes are presented in detail: one involves a cross-section comprising an annular concatenation of sintering near-circular discs, the cross-section of the other is a concentric annulus. These two examples allow us to exemplify and explore two features of the general inverse problem. One is the question of the uniqueness of solutions for a given set of experimental parameters, the other concerns the inherent ill-posedness of the inverse problem. Based on these examples we also give an experimental validation of the general model and discuss some experimental matters, such as buckling and stability. The ramifications for modelling the drawing of fibres with more complicated geometries, and multiple channels, are discussed.
Architecture of Computing with Words Based Information Technology for Proactive Aviation Security Control

Level of aviation security (AS) of an airport depends on the state of the system for countering acts of unlawful interference by intruders into airports (SCAUI), which includes airport security officers, as well as and technical devices for inspection of passengers, hand luggage, and baggage, security systems to protect the airport's controlled areas. In this article, we introduce basic principles of the information technology (IT), whose application allows to increase the airport AS by improving the quality of proactive control over the state of SCAUI. The developed IT is built on the basis of the client-server architecture and the SCAUI model. The authors propose to perform proactive control by consecutively solving direct and inverse problems. The direct problem is a problem of assessing the overall level of the AS based on the verbal assessments of individual factors. The inverse problem is the problem of identifying those factors, which have predominant influence on the AS. At the core of the SCAUI model and all computing involved in solving both direct an inverse problems lie ideas from the computing with words domain. Each word is represented as an interval type-2 fuzzy set, and their aggregation is performed using linguistic weighted average.
Does Google Content Degrade Google Search? Experimental Evidence

While Google is known primarily as a search engine, it has increasingly developed and promoted its own content as an alternative to results from other websites. By prominently displaying Google content in response to search queries, Google is able to use its dominance in search to gain customers for this content. This may reduce consumer welfare if the internal content is inferior to organic search results. In this paper, we provide a legal and empirical analysis of this practice in the domain of online reviews. We first identify the conditions under which universal search would be considered anticompetitive. We then empirically investigate the impact of this practice on consumer welfare. To investigate, we implement a randomized controlled trial in which we vary the search results that subjects are shown - comparing Google's current policy of favorable treatment of Google content to results in which external content is displayed. We find that users are roughly 40% more likely to engage with universal search results (which receive favored placement) when the results are organically determined relative to when they contain only Google content. To shed further light on the underlying mechanisms, we show that users are more likely to engage with the OneBox when there are more reviews, holding content constant. This suggests that Google is reducing consumer welfare by excluding reviews from other platforms in the OneBox.
Compact 5G MIMO Mobile Phone Antennas With Tightly Arranged Orthogonal-Mode Pairs

In this communication, the novel compact tightly arranged pairs are employed to form a <inline-formula> <tex-math notation="LaTeX">$4 \times 4$ </tex-math></inline-formula> multiple-input-multiple-output (MIMO) system and an <inline-formula> <tex-math notation="LaTeX">$8 \times 8$ </tex-math></inline-formula> MIMO system operating at 3.4–3.6 GHz for fifth-generation (5G) mobile phones. Each tightly arranged pair is composed of a bent monopole and an edge-fed dipole with a compact size of <inline-formula> <tex-math notation="LaTeX">$7 \times 12$ </tex-math></inline-formula> mm<sup>2</sup>. The orthogonal-mode method is presented to mitigate the mutual coupling of the tightly arranged pairs without any external decoupling structure. With the help of the orthogonal mode, isolation performances across the desired band of the <inline-formula> <tex-math notation="LaTeX">$4 \times 4$ </tex-math></inline-formula> MIMO system and the <inline-formula> <tex-math notation="LaTeX">$8 \times 8$ </tex-math></inline-formula> MIMO system are better than 20 and 17 dB, respectively, with the elements closely spaced. The measured efficiencies are 51.7%–84.5%/49%–72.9%, and the measured envelope correlation coefficients are less than 0.06/0.07 for the <inline-formula> <tex-math notation="LaTeX">$4 \times 4/ 8 \times 8$ </tex-math></inline-formula> MIMO system. The proposed MIMO systems provide a promising solution to compact 5G MIMO mobile phone antennas with good isolation and diversity performance.
Classical communication cost of quantum steering

Quantum steering is observed when performing appropriate local measurements on an entangled state. Here we discuss the possibility of simulating classically this effect, using classical communication instead of entanglement. We show that infinite communication is necessary for exactly simulating steering for any pure entangled state, as well as for a class of mixed entangled states. Moreover, we discuss the communication cost of steering for general entangled states, as well as approximate simulation. Our findings reveal striking differences between Bell nonlocality and steering, and provide a natural way of measuring the strength of the latter.
A Bayesian Nonparametric Approach to Clustering Data from Underwater Robotic Surveys

The use of robots for scientific mapping and exploration can result in large, rapidly growing data sets that make complete analysis by humans infeasible. This situation highlights the need for automated means of converting raw data into scientifically relevant information. This paper applies a Bayesian nonparametric model, the variational Dirichlet process, to clustering large quantities of seafloor imagery in an unsupervised manner. It has the attractive property that it does not require knowledge of the number of clusters a-priori, which enables truly autonomous sensor data abstraction. The underlying data representation uses descriptors for colour, texture and 3D structure that are obtained from stereo imagery. This approach consistently produces easily recognisable clusters that approximately correspond to different habitat types. These clusters are useful in observing spatial patterns, focusing expert analysis on subsets of seafloor imagery, aiding mission planning, and potentially informing real time adaptive sampling. We present results from two different surveys with large spatial extents, consisting of thousands of stereo image pairs. We use hand labelled observations from one of these surveys to compare the variational Dirichlet processes’ performance to other clustering algorithms.
Toward a general explanation for latitudinal clines in body size among chelonians

For many species, the population-mean body size increases with latitude. Although heat conservation and starvation resistance are frequently invoked to explain latitudinal clines, these explanations seem incompatible with the natural history of turtles, which also increase in size with latitude. We collate the population-mean body size for nearly 100 populations of North American freshwater turtles belonging to seven species. We test the hypothesis that temperature–size relationships in turtles are driven by strong seasonality in the north, which leads to maturation at a larger size and the production of relatively fewer, larger clutches per season. Our results, however, do not support the seasonality hypothesis. We present two new hypotheses that can explain the generality of temperature–size responses in chelonians. First, oxygen consumption is temperature sensitive, placing a premium on small size in warm aquatic environments, because smaller size reduces total oxygen demand during activities requiring submergence. Second, overwintering physiology of turtles might drive size clines, because a decrease in mass-specific metabolic rate with size might result in a disproportionate increase in the capacity of large individuals to buffer lactic acid in anoxic conditions. The pursuit of a general explanation for temperature–size relationships in chelonians would benefit from comparing temperature–size responses between aquatic and terrestrial species.
Modeling Volatility and Interdependencies of Thai Rubber Spot Price Return with Climatic Factors, Exchange Rate and Crude Oil Markets

Thailand is a leading producer and exporter of rubber in the world market. The interdependencies and volatility of Thai rubber price return with climatic factors (precipitation and temperature), exchange rate, and crude oil market returns are determined in this paper. Vector autoregressive moving average process with generalized autoregressive conditional heteroscedasticity (VARMA-GARCH), VARMA with generalized autoregressive conditional heteroscedasticity (VARMA-AGARCH), and copula-based GARCH models were employed for the analyses. The results demonstrated the interdependencies of Thai rubber price return with dollar and crude oil returns as well as with crude oil return and climatic factors in the VARMA-AGARCH and the copula-based GARCH models, respectively. We conclude that the volatility of Thai rubber price return is linked with volatility in the exchange rate and crude oil markets as well as climatic factors. Thus, stakeholders in the rubber industry should consider movements in those markets when forecasting Thai rubber price returns. Using a set of robust approaches is also recommended to obtain a complete picture of the volatilities and interdependencies of the asset markets.
Adaptive Natural Policy Gradient in Reinforcement Learning

In recent years, the policy gradient method in intensive learning has attracted wide attention with its good convergence performance. At the same time, regulation of hyper parameters is also a matter of concern. Based on the advantages of Actor-Critic structure (AC), the Natural-Gradient Actor-Critic algorithm (NAC) in the discount model is studied in this article. Then the Natural-Gradient Actor-Critic with ADADELTA (A-NAC) algorithm is proposed . The use of ADADELTA is adapted to adjust the learning rate in the actor network, and further improves the convergence speed of the NAC algorithm. Simulation results show that NAC/A-NAC have better learning efficiency and faster convergence rate than regular gradient AC methods.
Group Transfer to an Aliphatic Bond: A Biomimetic Study Inspired by Nonheme Iron Halogenases

In this work, we predict a group-transfer reaction to an aliphatic substrate on a biomimetic nonheme iron center based on the structural and functional properties of nonheme iron halogenases. Transferring groups other than halogens to C–H bonds on the same catalytic center would improve the versatility and applicability of nonheme iron halogenases and enhance their use in biotechnology; however, few studies have been reported on this matter. Furthermore, very few biomimetic models are known that are able to transfer halogens or other groups to aliphatic C–H bonds. To gain insight into group transfer to an aliphatic C–H bond, we performed a detailed computational study on a biomimetic nonheme iron complex and studied the reactivity patterns with a model substrate (ethylbenzene). In particular, we investigated the reaction mechanisms of [FeIV(O)(TPA)X]+, TPA = tris(2-pyridylmethy1)amine, and X = Cl, NO2, N3 with ethylbenzene leading to 1-phenylethanol and 1-phenyl-1-X-ethane products. Interestingly, we find.
A brief history and review of accelerators

ABSTRACT The history of accelerators is traced from three separate roots, through a rapid development to the present day. The well-known Livingston chart is used to illustrate how spectacular this development has been with, on average, an increase of one and a half orders of magnitude in energy per decade, since the early thirties. Several present-day accelerators are reviewed along with plans and hopes for the future.
On Fault Diagnosis using Bayesian Networks ; A Case Study of Combinational Adders.

In this paper, we use Bayesian networks to reduce the set of vectors for the test and the diagnosis of combinational circuits. We are able to integrate any fault model (such as bit-ﬂip and stuck-at models) and consider either single or multiple faults. We apply our method to adders and obtain a minimum set of vectors for a complete diagnosis in the case of the bit-ﬂip model. A very good diagnosis coverage for the stuck-at fault model is found with a minimum set of test vectors and a complete diagnosis by adding few vectors
Transmission of depressive symptoms: A study with couples undergoing assisted-reporduction treatment

Transmission of depressive symptoms among spouses is well documented. In accordance with the transactional stress model, cognitive appraisals were tested as indirect effects in transmission. In 82 couples (age range women: 23–44, men: 26–55), both partners stress appraisals and depressive symptoms were assessed at three measurement points throughout assisted-reproduction treatment. Relations among partners variables were tested using the actor-partner interdependence model. Findings indicated positive transmission effects of depressive symptoms from men to women across both measurement intervals. A positive transmission effect of stress appraisals from men to women was observed from before until after the pregnancy test. Womens stress appraisals mediated part of the transmission of depressive symptoms from men to women. Mens stress appraisals, however, were unrelated to womens earlier depressive symptoms. Mens earlier depressive symptoms might have operated as cues for womens adjustment of their ow.
Leveraging Supervised Learning and Automated Reasoning for Robot Sequential Decision-Making

Sequential decision-making (SDM) plays a key role in intelligent robotics, and can be realized in very different ways, such as supervised learning, automated reasoning, and probabilistic planning. The three families of methods follow different assumptions and have different (dis)advantages. In this work, we aim at a robot SDM framework that exploits the complementary features of learning, reasoning, and planning. We use long short-term memory (LSTM) for passive state estimation with streaming sensor data, and commonsense reasoning and probabilistic planning (CORPP) for active information collection and task accomplishment. In experiments, a mobile robot is tasked with estimating human intentions using human motion trajectories, declarative contextual knowledge, and human-robot interaction (dialog-based and motion-based). Results suggest that our framework performs better than its “no learning” and “no reasoning” versions in a real-world office environment.
A Joint Selective Mechanism for Abstractive Sentence Summarization

Sequence-to-sequence (Seq2Seq) learning framework has been widely used in many natural language processing (NLP) tasks, including abstractive summarization and machine translation (MT). However, abstractive summarization generates the output in a lossy manner, in comparison with MT which is almost loss-less. We model this by introducing a joint selective mechanism: (i) A selective gate is added after encoding phase of the Seq2Seq learning framework, which learns to tailor the original input information and generates a selected input representation. (ii) A selection loss function is also added to help our selective gate function well, which is computed by looking at the input and the output jointly. Experimental results show that our proposed model outperforms most of the baseline models and is comparable to the state-of-the-art model in automatic evaluations.