Miracle-dz/UniFAR-FTUs huggingface dataset:
VDM-DA: Virtual Domain Modeling for Source Data-Free Domain Adaptation

Domain adaptation aims to leverage a label-rich domain (the source domain) to help model learning in a label-scarce domain (the target domain). Most domain adaptation methods require the co-existence of source and target domain samples to reduce the distribution mismatch. However, access to the source domain samples may not always be feasible in real-world applications due to different problems (e.g., storage, transmission, and privacy issues). In this work, we deal with the source data-free unsupervised domain adaptation problem and propose a novel approach referred to as Virtual Domain Modeling for Domain Adaptation (VDM-DA), in which the virtual domain acts as a bridge between the source and target domains. Specifically, based on the pre-trained source model, we generate the virtual domain samples by using an approximated Gaussian Mixture Model (GMM) in the feature space, such that the virtual domain maintains a similar distribution with the source domain without access to the original source data. Moreover, we also design an effective distribution alignment method to reduce the distribution divergence between the virtual domain and the target domain by gradually improving the compactness of the target domain distribution through model learning. In this way, we successfully achieve the goal of distribution alignment between the source and target domains when training deep networks without access to the source domain data. We conduct extensive experiments on four benchmark datasets for both 2D image-based and 3D point cloud-based cross-domain object recognition tasks, where the proposed method referred to as Virtual Domain Modeling for Domain Adaptation (VDM-DA) achieves the promising performance on all datasets.
Training Neural Networks Using the Property of Negative Feedback to Inverse a Function

With high forward gain, a negative feedback system has the ability to perform the inverse of a linear or non linear function that is in the feedback path. This property of negative feedback systems has been widely used in analog circuits to construct precise closed-loop functions. This paper describes how the property of a negative feedback system to perform inverse of a function can be used for training neural networks. This method does not require that the cost or activation functions be differentiable. Hence, it is able to learn a class of non-differentiable functions as well where a gradient descent-based method fails. We also show that gradient descent emerges as a special case of the proposed method. We have applied this method to the MNIST dataset and obtained results that shows the method is viable for neural network training. This method, to the best of our knowledge, is novel in machine learning.
Molecular packing analysis of the crystal smectic E phase of a benzothieno-benzothiophene derivative by a combined experimental / computational approach

ABSTRACT The molecule 2-decyl-7-phenyl[1]benzothieno[3,2-b][1]benzothiophene has gained a lot of attention since high charge carrier mobility was observed in thin film transistors. Its thermotropic liquid crystalline states may play an important role in the thin film formation since the smectic A and the crystal smectic E phase (SmE) are claimed to be pre-stages of the final bulk structure. To understand the phase diversity, structural characterisation of solution processed thin films is performed by X-ray diffraction in the complete temperature range up to the isotropic state at 240°C. The diffraction pattern of the SmE phase is analysed in detail. Peak broadening analysis reveals that the crystallographic order across the smectic layers is larger than the order along the smectic layers. A combined experimental and computational approach is used to determine the molecular packing within the SmE phase. It leads to a number of different packing motifs. A comparison of the calculated diffraction pattern with the experimental results reveals that nano-segregation is present within the SmE phase. Energy consideration clearly favours a herringbone arrangement of the aromatic units. The nano-segregation within the SmE phase with herringbone packing of the aromatic units is accompanied with interdigitation of side chains from neighbouringherringbone layers. Graphical abstract
UMAP does not reproduce high-dimensional similarities due to negative sampling

UMAP has supplanted t-SNE as state-of-the-art for visualizing high-dimensional datasets in many disciplines, while the reason for its success is not well understood. In this work, we investigate UMAP's sampling based optimization scheme in detail. We derive UMAP's effective loss function in closed form and find that it differs from the published one. As a consequence, we show that UMAP does not aim to reproduce its theoretically motivated high-dimensional UMAP similarities. Instead, it tries to reproduce similarities that only encode the shared k nearest neighbor graph, thereby challenging the previous understanding of UMAP's effectiveness. Instead, we claim that the key to UMAP's success is its implicit balancing of attraction and repulsion resulting from negative sampling. This balancing in turn facilitates optimization via gradient descent. We corroborate our theoretical findings on toy and single cell RNA sequencing data.
ShellCore: Automating Malicious IoT Software Detection Using Shell Commands Representation

The Linux shell is a command-line interpreter that provides users with a command interface to the operating system, allowing them to perform various functions. Although very useful in building capabilities at the edge, the Linux shell can be exploited, giving adversaries a prime opportunity to use them for malicious activities. With access to Internet of Things (IoT) devices, malware authors can abuse the Linux shell of those devices to propagate infections and launch large-scale attacks, e.g., Distributed Denial of Service. In this work, we provide a first look at the tasks managed by shell commands in Linux-based IoT malware toward detection. We analyze malicious shell commands found in IoT malware and build a neural network-based model, ShellCore, to detect malicious shell commands. Namely, we collected a large data set of shell commands, including malicious commands extracted from 2891 IoT malware samples and benign commands collected from real-world network traffic analysis and volunteered data from Linux users. Using conventional machine and deep learning-based approaches trained with a term- and character-level features, ShellCore is shown to achieve an accuracy of more than 99% in detecting malicious shell commands and files (i.e., binaries).
Incorporating Connections Beyond Knowledge Embeddings: A Plug-and-Play Module to Enhance Commonsense Reasoning in Machine Reading Comprehension

Conventional Machine Reading Comprehension (MRC) has been well-addressed by pattern matching, but the ability of commonsense reasoning remains a gap between humans and machines. Previous methods tackle this problem by enriching word representations via pre-trained Knowledge Graph Embeddings (KGE). However, they make limited use of a large number of connections between nodes in Knowledge Graphs (KG), which could be pivotal cues to build the commonsense reasoning chains. In this paper, we propose a Plug-and-play module to IncorporatE Connection information for commonsEnse Reasoning (PIECER). Beyond enriching word representations with knowledge embeddings, PIECER constructs a joint query-passage graph to explicitly guide commonsense reasoning by the knowledge-oriented connections between words. Further, PIECER has high generalizability since it can be plugged into suitable positions in any MRC model. Experimental results on ReCoRD, a large-scale public MRC dataset requiring commonsense reasoning, show that PIECER introduces stable performance improvements for four representative base MRC models, especially in low-resource settings.
Optical vortices in nanophotonics

In the last two decades, optical vortices carried by twisted light wavefronts have attracted a great deal of interest, providing not only new physical insights into light-matter interactions, but also a transformative platform for boosting optical information capacity. Meanwhile, advances in nanoscience and nanotechnology lead to the emerging field of nanophotonics, offering an unprecedented level of light manipulation via nanostructured materials and devices. Many exciting ideas and concepts come up when optical vortices meet nanophotonic devices. Here, we provide a mini review on recent achievements made in nanophotonics for the generation and detection of optical vortices and some of their applications.
Realistic Differentially-Private Transmission Power Flow Data Release

For the modeling, design and planning of future energy transmission networks, it is vital for stakeholders to access faithful and useful power flow data, while provably maintaining the privacy of business confidentiality of service providers. This critical challenge has recently been somewhat addressed in [1]. This paper significantly extends this existing work. First, we reduce the potential leakage information by proposing a fundamentally different post-processing method, using public information of grid losses rather than power dispatch, which achieve a higher level of privacy protection. Second, we protect more sensitive parameters, i.e., branch shunt susceptance in addition to series impedance (complete pi-model). This protects power flow data for the transmission high-voltage networks, using differentially private transformations that maintain the optimal power flow consistent with, and faithful to, expected model behaviour. Third, we tested our approach at a larger scale than previous work, using the PGLib-OPF test cases [10]. This resulted in the successful obfuscation of up to a 4700-bus system, which can be successfully solved with faithfulness of parameters and good utility to data analysts. Our approach addresses a more feasible and realistic scenario, and provides higher than state-of-the-art privacy guarantees, while maintaining solvability, fidelity and feasibility of the system.
NonlinearSchrodinger: Higher-Order Algorithms and Darboux Transformations for Nonlinear Schrödinger Equations

NonlinearSchrodinger.jl is a Julia package with a simple interface for studying solutions of nonlinear Schrödinger equations (NLSEs). In approximately ten lines of code, one can perform a simulation of the cubic NLSE using one of 32 algorithms, including symplectic and Runge-Kutta-Nyström integrators up to eighth order. Furthermore, it is possible to compute analytical solutions via a numerical implementation of the Darboux transformation for extended NLSEs up to ﬁfth order, with an equally simple interface. In what follows, we review the funda-mentals of solving this class of equations numerically and analytically, discuss the implementation, and provide several examples.
Supervised Chorus Detection for Popular Music Using Convolutional Neural Network and Multi-Task Learning

This paper presents a novel supervised approach to detecting the chorus segments in popular music. Traditional approaches to this task are mostly unsupervised, with pipelines designed to target some quality that is assumed to define "chorusness," which usually means seeking the loudest or most frequently repeated sections. We propose to use a convolutional neural network with a multi-task learning objective, which simultaneously fits two temporal activation curves: one indicating "chorusness" as a function of time, and the other the location of the boundaries. We also propose a post-processing method that jointly takes into account the chorus and boundary predictions to produce binary output. In experiments using three datasets, we compare our system to a set of public implementations of other segmentation and chorus-detection algorithms, and find our approach performs significantly better.
Determinants of the Essential Elements and Vitamins Intake and Status during Pregnancy: A Descriptive Study in Polish Mother and Child Cohort

The study objective was to identify determinants of essential elements and vitamins intake, and microelements and vitamins concentration in blood among pregnant women from Poland. Based on the data from food frequency questionnaires and information about supplements taken (n = 1252), daily supply of six elements (calcium, magnesium, iron, zinc, copper, selenium) and nine vitamins (folate, vitamins A, E, C, B1, B2, B3, B6, B12) was calculated. Zinc, copper, selenium (n = 340), vitamin A and E (n = 358) concentration was determined in blood collected during pregnancy. Most of the women did not meet the demand for essential elements and vitamins with a diet. About 94% of the respondents declared supplements use. The women with higher education, indicating leisure-time, physical activity and multiparity had a higher chance of meeting the average demand for the majority of the analyzed nutrients. On the other hand, factors such as BMI < 18.5kg/m2, a higher level of stress, and late first medical-care visit were associated with a lower chance of meeting the recommendations. Higher socio-economic status was a determinant of a higher selenium concentration in plasma (β = 3.1; 95%CI: 0.2–5.9), whereas BMI ≥ 25 kg/m2, and multiparity of a higher copper concentration in plasma (β = 0.2; 95%CI: 0.03-0.4; β = 0.2; 95%CI: 0.1–0.4). Higher plasma concentration of vitamin E was noted among women older than 30 years of age comparing to those who were 30 or younger (β = 1.5; 95%CI: 0.6–2.4). Although more studies are required, especially such based on laboratory measures, our results indicate target groups for dietary interventions during pregnancy for children’s optimal health and development.
Body Size and Behavioural Plasticity Interact to Influence the Performance of Free-Foraging Bumble Bee Colonies

Simple Summary Behavioural specialisation of individuals may improve the performance of groups, but could also limit the ability to switch tasks (behavioural ‘plasticity’) in response to changing group needs. In bumble bee colonies, body size, which is fixed once the bees reach adulthood, influences the tasks that bees perform, meaning that large and small bees often act as specialists. We found that when we experimentally reduced the body-size variation of colonies, some performed less well than normal. Nonetheless, in other colonies, individuals increased task specialisation or effort, which apparently compensated for the absence of large and small workers. These results suggest that both behavioural specialisation and plasticity can be important in collective group performance. Abstract Specialisation and plasticity are important for many forms of collective behaviour, but the interplay between these factors is little understood. In insect societies, workers are often developmentally primed to specialise in different tasks, sometimes with morphological or physiological adaptations, facilitating a division of labour. Workers may also plastically switch between tasks or vary their effort. The degree to which developmentally primed specialisation limits plasticity is not clear and has not been systematically tested in ecologically relevant contexts. We addressed this question in 20 free-foraging bumble bee (Bombus terrestris) colonies by continually manipulating colonies to contain either a typically diverse, or a reduced (“homogeneous”), worker body size distribution while keeping the same mean body size, over two trials. Pooling both trials, diverse colonies produced a larger comb mass, an index of colony performance. The link between body size and task was further corroborated by the finding that foragers were larger than nurses even in homogeneous colonies with a very narrow body size range. However, the overall effect of size diversity stemmed mostly from one trial. In the other trial, homogeneous and diverse colonies showed comparable performance. By comparing behavioural profiles based on several thousand observations of individuals, we found evidence that workers in homogeneous colonies in this trial rescued colony performance by plastically increasing behavioural specialisation and/or individual effort, compared to same-sized individuals in diverse colonies. Our results are consistent with a benefit to colonies of large and small specialists under certain conditions, but also suggest that plasticity or effort can compensate for reduced (size-related) specialisation. Thus, we suggest that an intricate interplay between specialisation and plasticity is functionally adaptive in bumble bee colonies.
A Brief Executive Language Screen for Frontal Aphasia

Aphasia assessment tools have primarily focused on classical aphasia type and severity, with minimal incorporation of recent findings that suggest a significant role of executive control operations in language generation. Assessment of the interface between language and executive functions is needed to improve detection of spontaneous speech difficulties. In this study we develop a new Brief Executive Language Screen (BELS), a brief tool specifically designed to assess core language and executive functions shown to be involved in spontaneous generation of language. Similar to other measures of aphasia, the BELS assesses articulation and core language skills (repetition, naming and comprehension). Unique additions to the BELS include assessments of spontaneous connected speech, word fluency (phonemic/semantic) and sentence completion (verbal initiation, inhibition and selection). One-hundred and eight healthy controls and 136 stroke patients were recruited. Confirmatory factor analysis was used to determine construct validity and logistic regression was used to evaluate the discriminative validity, informing the final version of the BELS. The results showed that the BELS is sensitive for articulation and nominal language deficits, and it measures executive aspects of spontaneous language generation, which is a hallmark of frontal dynamic aphasia. The results have encouraging theoretical and practical implications.
Impact of Covid-19 Lockdown on Availability of Drinking Water in the Arsenic-Affected Ganges River Basin

The 2020 COVID-19 pandemic has not only resulted in immense loss of human life, but it also rampaged across the global economy and socio-cultural structure. Worldwide, countries imposed stringent mass quarantine and lockdowns to curb the transmission of the pathogen. While the efficacy of such lockdown is debatable, several reports suggest that the reduced human activities provided an inadvertent benefit by briefly improving air and water quality. India observed a 68-days long, nation-wide, stringent lockdown between 24 March and 31 May 2020. Here, we delineate the impact of the lockdown on groundwater and river sourced drinking water sustainability in the arsenic polluted Ganges river basin of India, which is regarded as one of the largest and most polluted river basins in the world. Using groundwater arsenic measurements from drinking water wells and water quality data from river monitoring stations, we have studied ~700 km stretches of the middle and lower reaches of the As (arsenic)-polluted parts of the river for pre-lockdown (January–March 2020), syn-lockdown (April–May), and post-lockdown periods (June–July). We provide the extent of As pollution-free groundwater vis-à-vis river water and examine alleviation from lockdown as an opportunity for sustainable drinking water sources. The overall decrease of biochemical oxygen demand (BOD) and chemical oxygen demand (COD) concentrations and increase of pH suggests a general improvement in Ganges water quality during the lockdown in contrast to pre-and-post lockdown periods, potentially caused by reduced effluent. We also demonstrate that land use (agricultural/industrial) and land cover (urban-periurban/rural) in the vicinity of the river reaches seems to have a strong influence on river pollutants. The observations provide a cautious optimistic scenario for potentially developing sustainable drinking water sources in the arsenic-affected Ganges river basin in the future by using these observations as the basis of proper scientifically prudent, spatially adaptive strategies, and technological interventions.
Chondrogenic Commitment of Human Bone Marrow Mesenchymal Stem Cells in a Perfused Collagen Hydrogel Functionalized with hTGF-β1-Releasing PLGA Microcarrier

Tissue engineering strategies can be relevant for cartilage repair and regeneration. A collagen matrix was functionalized with the addition of poly-lactic-co-glycolic acid microcarriers (PLGA-MCs) carrying a human Transforming Growth Factor β1 (hTFG-β1) payload, to provide a 3D biomimetic environment with the capacity to direct stem cell commitment towards a chondrogenic phenotype. PLGA-MCs (mean size 3 ± 0.9 μm) were prepared via supercritical emulsion extraction technology and tailored to sustain delivery of payload into the collagen hydrogel for 21 days. PLGA-MCs were coseeded with human Bone Marrow Mesenchymal Stem Cells (hBM-MSCs) in the collagen matrix. Chondrogenic induction was suggested when dynamic perfusion was applied as indicated by transcriptional upregulation of COL2A1 gene (5-fold; p < 0.01) and downregulation of COL1A1 (0.07-fold; p < 0.05) and COL3A1 (0.11-fold; p < 0.05) genes, at day 16, as monitored by qRT-PCR. Histological and quantitative-immunofluorescence (qIF) analysis confirmed cell activity by remodeling the synthetic extracellular matrix when cultured in perfused conditions. Static constructs lacked evidence of chondrogenic specific gene overexpression, which was probably due to a reduced mass exchange, as determined by 3D system Finite Element Modelling (FEM) analysis. Proinflammatory (IL-6, TNF, IL-12A, IL-1β) and anti-inflammatory (IL-10, TGF-β1) cytokine gene expression by hBM-MSC was observed only in dynamic culture (TNF and IL-1β 10-fold, p < 0.001; TGF-β1 4-fold, p < 0.01 at Day 16) confirming the cells’ immunomodulatory activity mainly in relation to their commitment and not due to the synthetic environment. This study supports the use of 3D hydrogel scaffolds, equipped for growth factor controlled delivery, as tissue engineered models for the study of in vitro chondrogenic differentiation and opens clinical perspectives for injectable collagen-based advanced therapy systems.
Transcriptional Repression of Raf Kinase Inhibitory Protein Gene by Metadherin during Cancer Progression

Raf kinase inhibitory protein (RKIP), also known as a phosphatidylethanolamine-binding protein 1 (PEBP1), functions as a tumor suppressor and regulates several signaling pathways, including ERK and NF-κB. RKIP is severely downregulated in human malignant cancers, indicating a functional association with cancer metastasis and poor prognosis. The transcription regulation of RKIP gene in human cancers is not well understood. In this study, we suggested a possible transcription mechanism for the regulation of RKIP in human cancer cells. We found that Metadherin (MTDH) significantly repressed the transcriptional activity of RKIP gene. An analysis of publicly available datasets showed that the knockdown of MTDH in breast and endometrial cancer cell lines induced the expression RKIP. In addition, the results obtained from qRT-PCR and ChIP analyses showed that MTDH considerably inhibited RKIP expression. In addition, the RKIP transcript levels in MTDH-knockdown or MTDH-overexpressing MCF-7 cells were likely correlated to the protein levels, suggesting that MTDH regulates RKIP expression. In conclusion, we suggest that MTDH is a novel factor that controls the RKIP transcription, which is essential for cancer progression.