Miracle-dz/UniFAR-FTUs huggingface dataset:
Inferring Gene Regulatory Networks from Time-Ordered Gene Expression Data of Bacillus Subtilis Using Differential Equations

We describe a new method to infer a gene regulatory network, in terms of a linear system of differential equations, from time course gene expression data. As biologically the gene regulatory network is known to be sparse, we expect most coefficients in such a linear system of differential equations to be zero. In previously proposed methods, the number of nonzero coefficients in the system was limited based on ad hoc assumptions. Instead, we propose to infer the degree of sparseness of the gene regulatory network from the data, where we use Akaike's Information Criterion to determine which coefficients are nonzero. We apply our method to MMGE time course data of Bacillus subtilis.
The Use of Simplified Learning Materials (SLM) in Physical Education as a Tool in Enhancing Students Learning Experiences

The pandemic has allowed teachers to apply their innate abilities, such as creativity and innovation, and to impart comprehensive knowledge about physical education with simplified learning materials (SLM) that are centered on the contextual needs of students. The objective of the study is to determine the effect of Simplified Learning Materials (SLM) on students' learning performance in Physical Education. The study specifically aimed to determine the following: (1) evaluate the learning performance of the students in Physical Education before and after using simplified learning materials, and (2) identify the said significant differences between the students' mean scores before and after the intervention. The researchers used a pre-experimental design, specifically the One-Group Pretest-Posttest Design. The study was participated by 52 Grade 8 students in one of the secondary schools in the Philippines. Purposive sampling was used in the study. Data analysis was performed using the JASP software (weighted mean and paired t-test). Data revealed that the Shapiro-Wilk test of normality can be used to determine that the data does not deviate from the assumption of normality with a p-value of 0.305, which is greater than 0.05. According to the descriptive table, the post-test result is higher than the pretest result. The mean difference of -2.538 in the Paired Samples T-Test results is significant, with a p-value of 0.002 for the test. The use of Simplified Learning Materials (SLM) helps grade 8 students perform better on tasks and grasp understanding to the lesson related to physical education. This shows that the intervention has a significant effect on the student’s learning performance in Physical Education. Future researchers can utilize the findings as a foundation to use simplified learning materials (SLM) for further contextualize and address what students need to accomplish. Keywords: JASP Software, Learning performance, Modality, Physical education, Simplified Learning Materials
A Scalable Frank-Wolfe-Based Algorithm for the Max-Cut SDP

We consider the problem of solving large-scale instances of the Max-Cut semidefinite program (SDP), i.e., optimizing a linear function over n × n positive semidefinite (PSD) matrices with unit diagonal. When the cost matrix is PSD, we show how to exactly reformulate the problem as maximizing a smooth concave function over PSD matrices with unit trace. By applying the Frank-Wolfe method, we obtain a simple algo-rithm that is compatible with recent sampling-based techniques to solve SDPs using low memory. We demonstrate the practical performance of our method on 10 6 × 10 6 instances of the max-cut SDP with costs having up to 5 × 10 6 non-zero entries. Theoretically, we show that our method solves problems with diagonally dominant costs to relative error ϵ in O ( nϵ − 1 ) calls to a randomized approximate largest eigenvalue subroutine, each of which succeeds with high probability after O (log( n ) ϵ − 1 / 2 ) matrix-vector multiplications with the cost matrix.
Stereotypes.

The stereotyping literature within psychology has grown considerably over the past decade. In large part, this growth can be attributed to progress in understanding the individual mechanisms that give rise to stereotypic thinking. In the current review, the recent psychological literature on stereotypes is reviewed, with particular emphasis given to the cognitive and motivational factors that contribute to stereotype formation, maintenance, application, and change. In addition, the context-dependent function of stereotypes is highlighted, as are the representational issues that various models of stereotypes imply.
Supplement to Objaverse: A Universe of Annotated 3D Objects

Model. The agent’s embodiment is a simulated LoCoBot [2]. The action space consists of six actions: MOVEAHEAD, ROTATELEFT, ROTATERIGHT, END, LOOKUP, and LOOKDOWN. Given the excellent exploration capabilities of EmbCLIP [3,7], we opt to keep the same overall architecture, just replacing the learned embedding for target types in prior work by a linear projection of the text branch output of CLIP for the target description, as shown in Fig. 1. Additionally, in order to provide more information about the target and the current visual input, we increase the respective internal representations for each modality from the a christmas tree
Implementing a Digital Twin of an Underground Utility Tunnel for Geospatial Feature Extraction Using a Multimodal Image Sensor

The increasing interest in digital twin technology, the digitalization of worn-out social overhead capital (SOC), and disaster management services has augmented the usage of 3D spatial models and information to manage infrastructure. In this study, a digital twin of a subterranean utility tunnel was created, and spatial objects were identified using inbuilt image sensors. The novelty lies in the development of a unique algorithm that breaks down the structure of the utility tunnel into points, lines, and planes, identifying objects using a multimodal image sensor that incorporates light detection and ranging (LiDAR) technology. The three main conclusions of this study are the following: First, a digital twin of the utility tunnel was constructed using building information modeling integrated with a geographic information system (BIM-GIS). Second, a method for extracting spatial objects was defined. Third, image-sensor-based segmentation and a random sample consensus (RANSAC) were applied. In this process, the supplementary algorithm for extracting and updating 3D spatial objects was analyzed and improved. The developed algorithm was tested using point cloud data, showing easier object classification with more precise LiDAR data.
Morphological classification method and data-driven estimation of the joint roughness coefficient by consideration of two-order asperity

Abstract The roughness of the joint surface plays a significant role in evaluating the shear strength of rock. The waviness (first-order) and unevenness (second-order) of natural joints have different effects on the characterization of joint surface roughness. To accurately quantify the influence of the two-order asperity on the joint roughness coefficient (JRC) prediction of joint surface profile curve, the optimal sampling interval of the asperity was determined through the change of the R p {R}_{{\rm{p}}} value of the joint surface profile curve. The separation of the two-order asperity of 48 joint surface profile curves was completed at the optimal sampling interval, and morphological parameters of the asperity such as i ave {i}_{{\rm{ave}}} , R max {R}_{{\rm{\max }}} , and R p {R}_{{\rm{p}}} were counted from three aspects: asperity angle of the profile curve, asperity degree, and the trace length. Based on the statistical results of the morphological parameters considering the two-order asperity, the new nonlinear prediction models were proposed. The results showed that the curve slope mutation point SI = 2 mm is the optimal separation distance of the two-order asperity of the joint surface profile curve. The refined separation method that considers the waviness and unevenness of morphological parameters can characterize the detailed morphological features of the joint surface in more dimensions. The support vector regression (SVR) and random forest (RF) models that take into account a two-order asperity separated results have higher accuracy than traditional models. The prediction accuracy has improved by 7–8% in SVR model compared with SVR(SO) and RF(SO). The SVR nonlinear model that considering separation of two-orders of joint surface roughness is more suitable for the prediction of JRC.
Phylogenetics Maximum likelihood of phylogenetic networks

Motivation:Horizontal gene transfer (HGT) is believed tobeubiquitous amongbacteria,andplaysamajor role in their genomediversificationas well as their ability to develop resistance to antibiotics. In light of its evolutionary significanceand implications for humanhealth, developing accurate and efficient methods for detecting and reconstructing HGT is
Inferring functional information from domain co-evolution

MOTIVATION Co-evolution is a powerful mechanism for understanding protein function. Prior work in this area has shown that co-evolving proteins are more likely to share the same function than those that do not because of functional constraints. Many of the efforts founded on this observation, however, are at the level of entire sequences, implicitly assuming that the complete protein sequence follows a single evolutionary trajectory. Since it is well known that a domain can exist in various contexts, this assumption is not valid for numerous multi-domain proteins. Motivated by these observations, we introduce a novel technique called Coevolutionary-Matrix that captures co-evolution between regions of two proteins. Instead of using existing domain information, the method exploits residue-level conservation to identify co-evolving regions that might correspond to domains. RESULTS We show that the Coevolutionary-Matrix method can detect greater number of known functional associations for the Escherichia coli proteins when compared with earlier implementations of phylogenetic profiles. Furthermore, co-evolving regions of proteins detected by our method enable us to make hypotheses about their specific functions, many of which are supported by existing biochemical studies.
Theoretical and practical advances in genome halving

Motivation: Duplication of an organism’s entire genome is a rare but spectacular event, enabling the rapid emergence of multiple new gene functions. Over time, the parallel linkage of duplicated genes across chromosomes may be disrupted by reciprocal translocations, while the intra-chromosomal order of genes may be shuffled by inversions and transpositions. Some duplicate genes may evolve unrecognizably or be deleted. As a consequence, the only detectable signature of an ancient duplication event in a modern genome may be the presence of various chromosomal segments containing parallel paralogous genes, with each segment appearing exactly twice in the genome. The problem of reconstructing the linkage structure of an ancestral genome before duplication is known as genome halving with unordered chromosomes. Results: In this paper, we derive a new upper bound on the genome halving distance that is tighter than the best known, and a new lower bound that is almost always tighter than the best known. We also define the notion of genome halving diameter, and obtain both upper and lower bounds for it. Our tighter bounds on genome halving distance yield a new algorithm for reconstructing an ancestral duplicated genome. We create a software package GenomeHalving based on this new algorithm and test it on the yeast genome, identifying a sequence of translocations for halving the yeast genome that is shorter than previously conjectured possible.
The Image of Tourism Destinations: A Case of Dracula Tourism

Destination image is a concept created by different demand and supply agents. The balance between what is expected and what is being delivered is essential in promoting the destination. The main goal of this study is to describe the different destination images related to Dracula tourism in Transylvania. This is accomplished by looking at the Western tourists’ expectations and demands as expressed in their travel blogs, and by analyzing the supply side through the elicitation of information from locals and Bran Castle tour guides through qualitative interviews. Qualitative content analysis was used to analyze the content, visual and audio information on sampled blogs and interviews. This method identified 11 different themes which captured the overall destination image. The themes focused primarily on historical and fictional images of the destination, on the type of tourists visiting Bran Castle, but also on the locals’ image and response to Dracula tourism. The findings of this study reveal that the majority of Western bloggers visit Bran in search for Count Dracula; however, the Bran Castle tour guides focus on presenting the historical truth. Local residents, although seeing Dracula as a national hero, sometimes adopt the mythical image as a marketing or product development tactic in selling to visitors. Nevertheless, the stakeholders involved in the creation of the image each fit into different destination image frameworks, allowing for the creation of an overall destination image based on historical and fictional elements.
The effects of elevated CO2 and ocean acidification on the production of marine biogenic trace gases

The human-induced increases in atmospheric carbon dioxide since the beginning of the industrial revolution have led to increasing oceanic carbon uptake and changes in seawater carbonate chemistry, resulting in lowering of surface water pH. To date, surface ocean acidity has increased by 30% compared with pre-industrial times. The aim of this study was to investigate the relationship between increasing pCO2, decreasing pH and changes in volatile dimethylsulphide (DMS) and halocarbon concentrations, through 70,000 litre, high pCO2 mesocosm experiments and laboratory culture studies. DMS is a climatically important trace gas produced by marine algae: it transfers sulphur into the atmosphere and is a major influence on biogeochemical climate feedbacks. Halocarbons are also important biogenic trace gases which undergo atmospheric photochemical degradation, releasing halide radicals to participate in atmospheric ozone cycling, and transfer halogens from sea to land. Evidence is presented from a Norwegian coastal study which showed a 60% DMS, 30% DMSP and 40% iodocarbon reduction in high pCO2 mesocosms, and in the Baltic Sea, known for its low-salinity, cyanobacterial dominated community, where DMS concentrations showed an 80% reduction under high pCO2 but halocarbon concentrations were unaffected. No decrease in DMS or DMSP concentrations were identified in high pCO2 laboratory cultures of the DMSproducing species Emiliania huxleyi RCC1229, and halocarbons were undetectable. Changes in trace gas concentrations may arise due to pH effects on the interactions between microbial producers and consumers. Other effects may arise from cell biochemistry due to long-term adaptation to increased ρCO2 and reduced pH on the enzymatic activity production of the compounds. Further studies should determine the nature of the pCO2 and pH effect on bacterial interactions with DMS, DMSP and halocarbon production and breakdown. There should also be attention given to the DMS source in the cyanobacterial-dominated community of the Baltic Sea.
Aquifer Recharge From Overbank Floods

Recharge from overbank floods is often neglected in water accounting as it is difficult to measure in the field, and highly variable between catchments. The physics of overbank flood recharge is not well understood, but it is becoming increasingly important for estimations of aquifer sustainable yield and accounting for artificial flooding used for improving riparian ecosystem health. Modelling of the overbank tlood recharge process in a sandy loam aquifer was undertaken using a fully-coupled, surface-subsurface flow model to determine the prevailing characteristics of piezometric response to overbank flood recharge. Groundwater response to floods was also monitored in bores on the flood plain of the Bremer River catchment, South Australia. Both modelling and field monitoring showed that the rise in water table due to flood inundation was more rapid and the total rise was higher than for those examples where the bore was not inundated. The decline in piezometric level following the flood was slower than the bank storage response to within-bank events. The modelling showed that rapid infiltration and associated bore response did not necessarily take place in areas at the extremities of the flood extent if the period of inundation was short, as the vadose zone did not become fully saturated.
A strategy for 2D MXenes as thermal management materials by laser shock nanoshaping

The two-dimensional (2D) titanium carbides ( Tin+1Cn ) belong to the MXene family, with carbon and titanium alternating in a flake structure, and are emerging options for nanoelectronics applications. In this study, the feasibility of nanoshaping of 2D titanium carbides for tunable thermal management materials was investigated. 2D titanium carbides demonstrate high degrees of formability on nanoscale and efficiency as thermal management systems in nanoelectronics components. The thermal conductivity of various MXene 2D flakes was studied using molecular dynamics simulations. A robust thermal management behavior has been predicted for 2D MXenes after nanoshaping on various nanomold patterns, which will facilitate the development of MXene-based metamaterials for thermal management in electric nanocomponents. The size dependence analysis shows that the MXenes thermal conductivity is highly influenced by the flake size leading to a variation in experimental values due to scale factors. Our model showed that Ti2C is more sensible to strain at both supported and suspended conditions, while the thicker MXenes are not too influenced by strain. When supported, the thermal conductivities of all simulated MXenes considerably decrease due to Z phonon modes suppression. Bending strain also showed an effect in the MXenes thermal conductivity by scattering phonon modes. This makes MXenes an attractive option for the management of thermal fields.
Synthetic high-resolution, volumetric, and wide field-of-view optical coherence tomography images with generative adversarial networks

Optical Coherence Tomography (OCT) has been widely used in many clinical apartments. The trade-off between Field-of-View (FOV) and transverse resolution has always become critical. High-Resolution (HR) image reconstruction of OCT has the potential to increase the resolution without reducing the FOV. Although it has been widely explored in these years, it is hard to find one-to-one paired high-resolution for reference. The SR methods are used to improve the cross-sectional OCT image only. Therefore, we build a custom-design, wide field-of-view, multi-scale optical coherence tomography that can allow us to produce excellent high- and low-resolution one-to-one mapping volumetric data set. With the SR methods, we can reduce the acquisition of synthetic high-resolution volumetric images by up to four times.
SDW-SWF: Speech Distortion Weighted Single-Channel Wiener Filter for Noise Reduction

Speech enhancement shows an important necessity in many audio applications, particularly in noisy environments, where the speech quality needs to be improved. In this work, we consider the single-channel noise reduction (NR) problem from the conventional signal processing perspective. As conventional single-channel NR filters suffer from a serious speech distortion (SD) problem, we propose an SD weighted single-channel Wiener filter (SDW-SWF) in the short-time Fourier transform domain, which is obtained by minimizing the mean-square error (MSE) of the clean speech plus a <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>-weighted residual noise variance. Based on the generalized eigenvalue decomposition (GEVD) and rank-<inline-formula><tex-math notation="LaTeX">$r$</tex-math></inline-formula> approximation of the speech correlation matrix, the SDW-SWF can be written as a linear combination of eigenpairs, from which some special cases reduce to existing single-channel NR filters. As such, the proposed SDW-SWF has two parameters (i.e., <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$r$</tex-math></inline-formula>) to tradeoff the MSE and SD. Then we theoretically analyze the impacts of the tradeoff parameters on the NR performance in SD, residual noise variance and the output signal-to-noise ratio (SNR). In addition, it is shown that the STFT-domain SDW-SWF can be further extended to the time domain, where the derived theorems still hold. Numerical results from several perspectives validate the effectiveness of the proposed method.
Borehole Shear Test in Geotechnical Investigations

The concept of Borehole Shear presents a unique alternative in shear strength testing of soil and rock in that tests may be conducted rapidly on the sides of the hole on relatively undisturbed material; therefore the inherent problems associated with obtaining equivalent information from laboratory tests are eliminated. In contrast to other methods of measuring in situ shear strength (that is, vane shear, cone penetrometer and pressuremeter) the Borehole Shear Test (BST) and Rock Borehole Shear Test (RBST) give discrete values of friction angle and cohesion; therefore more complete analyses may be made for such fundamental problems as bearing capacity and slope stability. In the past the BST has been considered a drained test and the effective parameters Φ' and c' have been reported. This may not always be the case. The general use, operation, assumptions, and problems of the test are examined and examples of the interpretation of results are given. Because of the time, economics, and accuracy of this method, the technique of Borehole Shear should see increased use in future in situ geotechnical testing.
Learning to Perturb for Contrastive Learning of Unsupervised Sentence Representations

Recently, contrastive learning has been shown effective in fine-tuning pre-trained language models (PLM) to learn sentence representations, which incorporates perturbations into unlabeled sentences to augment semantically related positive examples for training. However, previous works mostly adopt heuristic perturbation methods that are independent of the sentence representations. Since the perturbations are unaware of the goal or process of sentence representation learning during training, it is likely to lead to sub-optimal augmentations for conducting constrative learning. To address this issue, we propose a new framework L2P-CSR that adopts a learnable perturbation strategy for improving contrastive learning of sentence representations. In our L2P-CSR, we design a safer perturbation mechanism that only weakens the influence of tokens and features on the sentence representation, which avoids dramatically changing the semantics of the sentence representations. Besides, we devise a gradient-based algorithm to generate adaptive perturbations specially for the dynamically updated sentence representation during training. Such a way is more capable of augmenting high-quality examples that guide the sentence representation learning. Extensive experiments on diverse sentence-related tasks show that our approach outperforms competitive baselines.