Miracle-dz/UniFAR-FTUs huggingface dataset:
A Doubly Enhanced EM Algorithm for Model-Based Tensor Clustering

Abstract Modern scientific studies often collect datasets in the form of tensors. These datasets call for innovative statistical analysis methods. In particular, there is a pressing need for tensor clustering methods to understand the heterogeneity in the data. We propose a tensor normal mixture model approach to enable probabilistic interpretation and computational tractability. Our statistical model leverages the tensor covariance structure to reduce the number of parameters for parsimonious modeling, and at the same time explicitly exploits the correlations for better variable selection and clustering. We propose a doubly enhanced expectation–maximization (DEEM) algorithm to perform clustering under this model. Both the expectation-step and the maximization-step are carefully tailored for tensor data in order to maximize statistical accuracy and minimize computational costs in high dimensions. Theoretical studies confirm that DEEM achieves consistent clustering even when the dimension of each mode of the tensors grows at an exponential rate of the sample size. Numerical studies demonstrate favorable performance of DEEM in comparison to existing methods.
Dynamics of Ultracold Bosons in Artificial Gauge Fields—Angular Momentum, Fragmentation, and the Variance of Entropy

We consider the dynamics of two-dimensional interacting ultracold bosons triggered by suddenly switching on an artificial gauge field. The system is initialized in the ground state of a harmonic trapping potential. As a function of the strength of the applied artificial gauge field, we analyze the emergent dynamics by monitoring the angular momentum, the fragmentation as well as the entropy and variance of the entropy of absorption or single-shot images. We solve the underlying time-dependent many-boson Schrödinger equation using the multiconfigurational time-dependent Hartree method for indistinguishable particles (MCTDH-X). We find that the artificial gauge field implants angular momentum in the system. Fragmentation—multiple macroscopic eigenvalues of the reduced one-body density matrix—emerges in sync with the dynamics of angular momentum: the bosons in the many-body state develop non-trivial correlations. Fragmentation and angular momentum are experimentally difficult to assess; here, we demonstrate that they can be probed by statistically analyzing the variance of the image entropy of single-shot images that are the standard projective measurement of the state of ultracold atomic systems.
An Experimental Study of the Transferability of Spectral Graph Networks

Spectral graph convolutional networks are generalizations of standard convolutional networks for graph-structured data using the Laplacian operator. A common misconception is the instability of spectral filters, i.e. the impossibility to transfer spectral filters between graphs of variable size and topology. This misbelief has limited the development of spectral networks for multi-graph tasks in favor of spatial graph networks. However, recent works have proved the stability of spectral filters under graph perturbation. Our work complements and emphasizes further the high quality of spectral transferability by benchmarking spectral graph networks on tasks involving graphs of different size and connectivity. Numerical experiments exhibit favorable performance on graph regression, graph classification, and node classification problems on two graph benchmarks. The implementation of our experiments is available on GitHub for reproducibility.
An Empirical Study of Using Pre-trained BERT Models for Vietnamese Relation Extraction Task at VLSP 2020

In this paper, we present an empirical study of using pre-trained BERT models for relation extraction task at VLSP 2020 Evaluation Campaign. We applied two state-of-the-art BERT-based models: R-BERT and BERT model with entity starts. For each model, we compared two pre-trained BERT models: FPTAI/vibert and NlpHUST/vibert4news. We found that NlpHUST/vibert4news model significantly outperforms FPTAI/vibert for Vietnamese relation extraction task. Finally, we proposed a simple ensemble model which combines R-BERT and BERT with entity starts. Our proposed ensemble model slightly improved against two single models on the development data provided by the task organizers.
The Simons Observatory: overview of data acquisition, control, monitoring, and computer infrastructure

The Simons Observatory (SO) will be a cosmic microwave background (CMB) survey experiment with three small-aperture telescopes and one large-aperture telescope, which will observe from the Atacama Desert in Chile. In total, SO will field over 60,000 transition-edge sensor (TES) bolometers in six spectral bands centered between 27 and 280 GHz in order to achieve the sensitivity necessary to measure or constrain numerous cosmological quantities, as outlined in The Simons Observatory Collaboration et al. (2019). To achieve these goals we have built an open-sourced platform, called OCS (Observatory Control System), which orchestrates distributed hardware systems. We provide an overview of the SO software and computer infrastructure.
Probing frequency-dependent half-wave plate systematics for CMB experiments with full-sky beam convolution simulations

We study systematic effects from half-wave plates (HWPs) for cosmic microwave background (CMB) experiments using full-sky time-domain beam convolution simulations. Using an optical model for a fiducial spaceborne two-lens refractor telescope, we investigate how different HWP configurations optimized for dichroic detectors centred at 95 and 150 GHz impact the reconstruction of primordial B-mode polarization. We pay particular attention to possible biases arising from the interaction of frequency dependent HWP non-idealities with polarized Galactic dust emission and the interaction between the HWP and the instrumental beam. To produce these simulations, we have extended the capabilities of the publicly available beamconv code. To our knowledge, we produce the first time-domain simulations that include both HWP non-idealities and realistic full-sky beam convolution. Our analysis shows how certain achromatic HWP configurations produce significant systematic polarization angle offsets that vary for sky components with different frequency dependence. Our analysis also demonstrates that once we account for interactions with HWPs, realistic beam models with non-negligible cross-polarization and sidelobes will cause significant B-mode residuals that will have to be extensively modelled in some cases.
Small Business Classification By Name: Addressing Gender and Geographic Origin Biases

Small business classification is a difficult and important task within many applications, including customer segmentation. Training on small business names introduces gender and geographic origin biases. A model for predicting one of GG business types based only upon the busi¬ ness name was developed in this work (top-1 fl-score = 60.2% ) . Two approaches to removing the bias from this model are explored: replac¬ ing given names with a placeholder token, and augmenting the training data with gender-swapped examples. The results for these approaches is reported, and the bias in the model was reduced by hiding given names from the model. However , bias reduction was accomplished at the ex¬ pense of classification performance (top-1 fl-score = 56.6%). Augmen¬ tation of the training data with gender-swapping samples proved less effective at bias reduction than the name hiding approach on the evalu¬ ated dataset . 1
Interpretable Model Summaries Using the Wasserstein Distance

Statistical and machine learning models are changing many fields including medicine, politics, finance, and technology. Increasingly, these models diagnose diseases, predict elections, purchase stocks, and drive cars; however, interpreting these models can be difficult. This difficulty is problematic because these models are used to make high stake decisions, sometimes to disastrous results (Angwin et al., 2016). Because of their increasing use, understanding how these models function, and diagnosing why they fail, is important. Such models can be hard to understand because they function as black boxes, which translate input data into predictions, but does “not explain [its] predictions in a way that humans can understand” (Rudin, 2018). Models fall into this black-box territory because of the method chosen—e.g. the coefficients in the third layer of an eight layer neural network do not have an intuitive interpretation—or because the model is so large as to be uninterpretable—like the logistic regression model discussed by Sur et al. (2019) that has 1,200 parameters. To remedy these failures, we propose summarizing black-box models with low-dimensional and interpretable models that we call Sparse Local Interpretable Model (SLIM) summaries. Our main contributions in this work are as follows.
RAICC: Revealing Atypical Inter-Component Communication in Android Apps

Inter-Component Communication (ICC) is a key mechanism in Android. It enables developers to compose rich functionalities and explore reuse within and across apps. Unfortunately, as reported by a large body of literature, ICC is rather "complex and largely unconstrained", leaving room to a lack of precision in apps modeling. To address the challenge of tracking ICCs within apps, state of the art static approaches such as EPICC, ICCTA and AMANDROID have focused on the documented framework ICC methods (e.g., startActivity) to build their approaches. In this work we show that ICC models inferred in these state of the art tools may actually be incomplete: the framework provides other atypical ways of performing ICCs. To address this limitation in the state of the art, we propose RAICC a static approach for modeling new ICC links and thus boosting previous analysis tasks such as ICC vulnerability detection, privacy leaks detection, malware detection, etc. We have evaluated RAICC on 20 benchmark apps, demonstrating that it improves the precision and recall of uncovered leaks in state of the art tools. We have also performed a large empirical investigation showing that Atypical ICC methods are largely used in Android apps, although not necessarily for data transfer. We also show that RAICC increases the number of ICC links found by 61.6% on a dataset of real-world malicious apps, and that RAICC enables the detection of new ICC vulnerabilities.
Flame‐retardant properties of phenol–formaldehyde‐type resins and triphenyl phosphate in styrene–acrylonitrile copolymers

The thermal and flame-retardant properties of phenol–formaldehyde-type resins (crosslinked and noncrosslinked) in mixtures with triphenyl phosphate and styrene–acrylonitrile resins were evaluated. The mixtures show a synergistic effect between triphenyl phosphate and novolacs. Those containing phenol–formaldehyde novolac resins are found to be most flame retardant. There does not seem to be a relationship between the oxygen index (OI) and UL 94 tests. Scanning electron microscopy analysis show a surface structure with cavities and stratification, very similar to that of intumescent additives. Evidence was found indicating that this flame-retardant system works in both the gas and condensed phase. © 1998 John Wiley & Sons, Inc. J Appl Polym Sci 68: 1067–1076, 1998
Emotion Recognition for Human-Robot Interaction: Recent Advances and Future Perspectives

A fascinating challenge in the field of human–robot interaction is the possibility to endow robots with emotional intelligence in order to make the interaction more intuitive, genuine, and natural. To achieve this, a critical point is the capability of the robot to infer and interpret human emotions. Emotion recognition has been widely explored in the broader fields of human–machine interaction and affective computing. Here, we report recent advances in emotion recognition, with particular regard to the human–robot interaction context. Our aim is to review the state of the art of currently adopted emotional models, interaction modalities, and classification strategies and offer our point of view on future developments and critical issues. We focus on facial expressions, body poses and kinematics, voice, brain activity, and peripheral physiological responses, also providing a list of available datasets containing data from these modalities.
Expression and Prognostic Significance of Metastasis-Associated Protein 1 in Gastrointestinal Cancer

Background Metastasis-associated protein 1 (MTA1) has been considered as a transcriptional regulator, which is significantly related to the prognosis in various types of tumors. However, whether MTA1 is a potential prognostic index of gastrointestinal cancer (GIC) remains controversial. The current meta-analysis was performed to evaluate the role of MTA1 expression in the prediction of the clinicopathological features and survival in GIC cases. And the results of gastric cancer were verified by immunohistochemistry (IHC). Methods Eligible studies assessing the relationship between MTA1 and GIC by IHC were searched in the PubMed, Cochrane, Ovid, Web of Science and CNKI databases by various search strategies. The STATA 16.0 software was applied to gather data and to analyze the potential relationship between MTA1 and GIC. The expression level of MTA1 was examined in 80 GC samples by IHC assay. SPSS 20.0 was applied for statistical analysis, and the survival curves were calculated by the Kaplan-Meier method. The data of 95% CI was displayed as “[a-b]”. Results According to the meta-analysis, the expression level of MTA1 was tightly associated with the tumor size (OR=1.82 [1.16–2.84], P=0.009), tumor tissue differentiation (OR=1.71 [1.24–2.37], P=0.001), depth of invasion (OR=3.12 [2.55–3.83], P<0.001), lymphatic metastasis (OR=2.99 [2.02–4.43], P<0.001), distant metastasis (OR=4.66 [1.13–19.24], P=0.034), TNM stage (OR=4.28 [2.76–6.63], P<0.001). In addition, MTA1 played the negative effects in 1- (RR=2.48 [1.45–4.25], P=0.001), 3- (RR=1.66 [1.30–2.11], P<0.001) and 5-year (RR=1.73 [1.37–2.20], P<0.001). Study in subgroup, grouped by language and tumor type, we reached similar conclusions. Further validation by IHC yielded similar conclusions. Tumor size (P=0.008), lymph node metastasis (P=0.007) and distant metastasis (P=0.023) significantly accompanied with higher expression of MAT1 in GC cases. Besides, the expression level of MTA1 was statistically significantly correlated with OS in GC cases (HR=2.061 [1.066–3.986], P=0.032), which suggested that MTA1 might be an independent prognostic marker for GC. Finally, we verified the correlation between the expression level of MTA1 and prognosis of GC in 80 GC samples. Conclusions MTA1 is tightly associated with metastasis-related factors and may constitute a promising prognostic factor of GIC.
Context Is Key: Delineating the Unique Functions of IFNα and IFNβ in Disease

Type I interferons (IFNs) are critical effector cytokines of the immune system and were originally known for their important role in protecting against viral infections; however, they have more recently been shown to play protective or detrimental roles in many disease states. Type I IFNs consist of IFNα, IFNβ, IFNϵ, IFNκ, IFNω, and a few others, and they all signal through a shared receptor to exert a wide range of biological activities, including antiviral, antiproliferative, proapoptotic, and immunomodulatory effects. Though the individual type I IFN subtypes possess overlapping functions, there is growing appreciation that they also have unique properties. In this review, we summarize some of the mechanisms underlying differential expression of and signaling by type I IFNs, and we discuss examples of differential functions of IFNα and IFNβ in models of infectious disease, cancer, and autoimmunity.
Establishment of CRISPR/Cas9 Genome Editing in Witloof (Cichorium intybus var. foliosum)

Cichorium intybus var. foliosum (witloof) is an economically important crop with a high nutritional value thanks to many specialized metabolites, such as polyphenols and terpenoids. However, witloof plants are rich in sesquiterpene lactones (SL) which are important for plant defense but also impart a bitter taste, thus limiting industrial applications. Inactivating specific genes in the SL biosynthesis pathway could lead to changes in the SL metabolite content and result in altered bitterness. In this study, a CRISPR/Cas9 genome editing workflow was implemented for witloof, starting with polyethylene glycol (PEG) mediated protoplast transfection for CRISPR/Cas9 vector delivery, followed by whole plant regeneration and mutation analysis. Protoplast transfection efficiencies ranged from 20 to 26 %. A CRISPR/Cas9 vector targeting the first exon of the phytoene desaturase (CiPDS) gene was transfected into witloof protoplasts and resulted in the knockout of CiPDS, giving rise to an albino phenotype in 23% of the regenerated plants. Further implementing our protocol, the SL biosynthesis pathway genes germacrene A synthase (GAS), germacrene A oxidase (GAO), and costunolide synthase (COS) were targeted in independent experiments. Highly multiplex (HiPlex) amplicon sequencing of the genomic target loci revealed plant mutation frequencies of 27.3, 42.7, and 98.3% in regenerated plants transfected with a CRISPR/Cas9 vector targeting CiGAS, CiGAO, and CiCOS, respectively. We observed different mutation spectra across the loci, ranging from consistently the same +1 nucleotide insertion in CiCOS across independent mutated lines, to a complex set of 20 mutation types in CiGAO across independent mutated lines. These results demonstrate a straightforward workflow for genome editing based on transfection and regeneration of witloof protoplasts and subsequent HiPlex amplicon sequencing. Our CRISPR/Cas9 workflow can enable gene functional research and faster incorporation of novel traits in elite witloof lines in the future, thus facilitating the development of novel industrial applications for witloof.
The Association Between Adolescent Residential Mobility and Adult Social Anxiety, BDNF and Amygdala-Orbitofrontal Functional Connectivity in Young Adults With Higher Education

Background: Large-scale epidemiological studies demonstrate that house moves during adolescence lead to an increase in anxiety and stress-sensitivity that persists into adulthood. As such, it might be expected that moves during adolescence have strong negative and long-lasting effects on the brain. We hypothesized that moves during adolescence impair fear circuit maturation, as measured by the connectivity between amygdala and orbitofrontal cortex, and expression of brain-derived neurotrophic factor (BDNF). Methods: We examined young adults with middle and high economic status recruited from the community using clinical interviews, self-report questionnaires, functional magnetic resonance imaging during an emotional faces task and during a 10 min rest phase, and serum BDNF serum concentration. Results: Out of 234 young adults, 164 did not move between ages 10 and 16 (i.e., moves with change of school), 50 moved once, and 20 moved twice or more than twice. We found relationships between adolescent moving frequency and social avoidance (pcorr = 0.012), right amygdala-orbitofrontal cortex connectivity (pcorr = 0.016) and low serum BDNF concentrations in young adulthood (pcorr = 0.012). Perceived social status of the mother partly mitigated the effects of moving on social avoidance and BDNF in adulthood. Conclusions: This study confirms previous reports on the negative and persistent effects of residential mobility during adolescence on mental health. It suggests that these effects are mediated by impairments in fear circuit maturation. Finally, it encourages research into protecting factors of moving during adolescents such as the perceived social status of the mother.
On the Regioselectivity of Imidoyl Radical Cyclisations

The previously reported tandem cyclisation of N-aryl α-(2-cyanophenyl)sulfanyl imidoyl radicals affords one quinoxaline derivative arising from exclusive 1,6-cyclisation of the final iminyl radical onto the N-aryl ring. When the imidoyl radicals are generated by addition of photolytically generated (2-cyanophenyl)sulfanyl radicals to isocyanides, the reaction also gives small amounts of a by-product that is formed by an analogous route and whose X-ray crystallographic data are reported here. The formation of this product entails a rare ortho-selective photo-Fries rearrangement of the starting disulfide, followed by addition to the isocyanide and regioselective 1,5-cyclisation of the resulting imidoyl onto only one of the two available radical acceptors, i.e. the cyano group and the sulfide moiety. Semiempirical MNDO-d calculations were performed in order to throw some light on the factors affecting these competitive cyclisations.
Trends of Bead Counting-Based Technologies Toward the Detection of Disease-Related Biomarkers

Nowadays, the biomolecular assay platforms built-up based on bead counting technologies have emerged to be powerful tools for the sensitive and high-throughput detection of disease biomarkers. In this mini-review, we classified the bead counting technologies into statistical counting platforms and digital counting platforms. The design principles, the readout strategies, as well as the pros and cons of these platforms are introduced in detail. Finally, we point out that the digital bead counting technologies will lead the future trend for the absolute quantification of critical biomarkers, and the integration of new signal amplification approaches and routine optical/clinical instruments may provide new opportunities in building-up easily accessible digital assay platforms.