Miracle-dz/UniFAR-FTUs huggingface dataset:
Feature Selection on Thermal-stress Dataset

Physical symptoms caused by high stress commonly happen in our daily lives, leading to the importance of stress recognition systems. This study aims to improve stress classification by selecting appropriate features from Thermal-stress data, ANUstressDB. We explored three different feature selection techniques: correlation analysis, magnitude measure, and genetic algorithm. Support Vector Machine (SVM) and Artificial Neural Network (ANN) models were involved in measuring these three algorithms. Our result indicates that the genetic algorithm combined with ANNs can improve the prediction accuracy by 19.1% compared to the baseline. Moreover, the magnitude measure performed best among the three feature selection algorithms regarding the balance of computation time and performance. These findings are likely to improve the accuracy of current stress recognition systems.
Short-Term Traffic State Prediction Based on the Critical Road Selection Optimization in Transportation Networks

Short-term traffic prediction under corrupted or missing data for large-scale transportation networks has become an important and challenging topic in recent decades. Since the critical roads have predictive power on their adjacent roads, this paper proposes a novel hybrid short-term traffic state prediction method based on critical road selection optimization. First, the utility function of the quality of service (QoS) for the critical roads in a large-scale road network is proposed based on the coverage and the data score. Then, the critical road selection optimization model in the transportation networks is presented by selecting an appropriate set of critical roads with the maximum proportion of the total calculation resources to maximize the utility value of the QoS. Also, an innovative critical road selection method is introduced, which is considering the topological structure and the mobility of the urban road network. Subsequently, the traffic speed of the critical roads is regarded as the input of the convolutional long short-term memory neural network to predict the future traffic states of the entire network. Experiment results on the Beijing traffic network indicate that the proposed method outperforms prevailing DL approaches in the case of considering critical road sections.
Multivariate, Heteroscedastic Empirical Bayes via Nonparametric Maximum Likelihood

Multivariate, heteroscedastic errors complicate statistical inference in many large-scale denoising problems. Empirical Bayes is attractive in such settings, but standard parametric approaches rest on assumptions about the form of the prior distribution which can be hard to justify and which introduce unnecessary tuning parameters. We extend the nonparametric maximum likelihood estimator (NPMLE) for Gaussian location mixture densities to allow for multivariate, heteroscedastic errors. NPMLEs estimate an arbitrary prior by solving an infinite-dimensional, convex optimization problem; we show that this convex optimization problem can be tractably approximated by a finite-dimensional version. The empirical Bayes posterior means based on an NPMLE have low regret, meaning they closely target the oracle posterior means one would compute with the true prior in hand. We prove an oracle inequality implying that the empirical Bayes estimator performs at nearly the optimal level (up to logarithmic factors) for denoising without prior knowledge. We provide finite-sample bounds on the average Hellinger accuracy of an NPMLE for estimating the marginal densities of the observations. We also demonstrate the adaptive and nearly-optimal properties of NPMLEs for deconvolution. We apply our method to two denoising problems in astronomy, constructing a fully data-driven color-magnitude diagram of 1.4 million stars in the Milky Way and investigating the distribution of 19 chemical abundance ratios for 27 thousand stars in the red clump. We also apply our method to hierarchical linear models, illustrating the advantages of nonparametric shrinkage of regression coefficients on an education data set and on a microarray data set.
Renovo: A Sensor-Based Therapeutic System for Brachial Monoplegia

Introduction: Patients with Brachial Monoplegia, paralysis of upper limb following various neurological disorders, require therapeutic interventions with proper assessment for rehabilitation. State-of-the-art assessment protocols, the majority of which are qualitative, often cause biased assessment. Very few therapeutic systems aim to assist the physiotherapists in this regard. Methods: We designed a sensor-based therapeutic system for assisting the physiotherapists with real-time visualization of performance metrics and a reliable quantitative assessment. 5 healthy subjects (Mean=24.4±2.4 years, 80% Male), 16 patients with Brachial Monoplegia (Mean=39.56±16.4 years, 76.92% Male), and 5 physiotherapists volunteered with informed consent. Each patient was evaluated by both the system and the physiotherapists in 3 sessions. Results: Insignificant difference between the mean (t(15)=1.39, p=. 184) and the variance (F(1, 15)=1.05, p=. 460) of system evaluation (Mean=6.19, 95% CI, [4.52, 7.86]) and that of the physiotherapists (Mean=6.38, 95% CI, [4.75, 8.01]) having a strong, positive correlation, r=. 9885 was observed. The system showed good reliability from the Cronbach’s Alpha test with α=. 8499. Conclusions: To ensure effective rehabilitation, proper assessment is obligatory. Sensor-based measurement of performance metrics ensures reliability, reducing the chances of human error. Real-time visualization informs about patients’ progress. Automated quantitative assessment reduces the possibility of bias, allowing optimization of rehabilitation scheme.
LuMaMi28: Real-Time Millimeter-Wave Massive MIMO Systems with Antenna Selection

This paper presents LuMaMi28, a real-time 28 GHz massive multiple-input multiple-output (MIMO) testbed. In this testbed, the base station has 16 transceiver chains with a fully-digital beamforming architecture (with different pre-coding algorithms) and simultaneously supports multiple user equipments (UEs) with spatial multiplexing. The UEs are equipped with a beam-switchable antenna array for real-time antenna selection where the one with the highest channel magnitude, out of four pre-defined beams, is selected. For the beam-switchable antenna array, we consider two kinds of UE antennas, with different beam-width and different peak-gain. Based on this testbed, we provide measurement results for millimeter-wave (mmWave) massive MIMO performance in different real-life scenarios with static and mobile UEs. We explore the potential benefit of the mmWave massive MIMO systems with antenna selection based on measured channel data, and discuss the performance results through real-time measurements.
New records and modelling the impacts of climate change on the black-tailed marmosets

Climate change represents an unprecedented threat to global biodiversity and, for many species, gaps in our knowledge of their biology remain acute. Gaps in baseline knowledge, such as confirmed identifications (Linnean shortfalls) and adequate collections (Wallacean shortfalls), need to be minimized with new studies, since this is often critical for effective conservation. Despite the increase in scientific research on primates in the southwest of the Brazilian Amazon, little is known about the species Mico nigriceps (Ferrari & Lopes, 1992) Primates, Platirryni. In the current study, we sought to reduce the extent of the Wallacean shortfall for M. nigriceps, understand whether climate change represents a threat to the distribution of the species, and identify priority areas for its conservation. Accordingly, we provide 121 new records in 14 locations, obtained directly from the field, and five from the literature. Using this, we carried out ecological niche modeling, to better understand how environmental suitability might limit the area occupied by the species. We then projected a distribution for 2070 with the SSP2-4.5 (more optimistic) and SSP5-8.5 (more pessimistic) scenarios. Our data confirmed the geographic distribution of the species as being restricted to headwaters of the Ji-Paraná/Machado river, but with a 400 km extension to the south. Under the modeled climate change scenarios, the area suitable for the species declines by 21% under the most optimistic, and by 27% in the pessimistic, scenario across the projected 50-year period. Although we have expanded the area of known occurrence for this species, we point out that climate change threatens the stability of this newly-discovered population strongly, and that this danger is intensified by deforestation, fire and hunting. We recommend that further studies be carried out to confirm the presence of the species in adjacent areas, those indicated by generated models as being potential environmentally suitable. In addition, we recommend intensifying forest restoration in currently pastured areas, and protection of the areas forming the current and future habitat of this species through such measures as protected area creation.
On the Space of Coefficients of a Feedforward Neural Network

We establish the conditions for ‘equivalent neural networks’ - neural networks with different weights, biases, and threshold functions which result in the same associated function. We prove that given a neural network $\mathcal{N}$ with piecewise linear activation, the space of coefficients describing all equivalent neural networks is given by a semialgebraic set. This result is obtained by studying different representations of a given piecewise linear function using the Tarski-Seidenberg theorem. Given a neural architecture and an initial neural network $\mathcal{N}_{0}$, specified by coefficients, we give an algorithm to compute inequalities defining the corresponding semiaglebraic sets. These algorithms are based on the rules of max-plus algebra.
Learning Local-Global Contextual Adaptation for Fully End-to-End Bottom-Up Human Pose Estimation

This paper presents a method of learning LO cal-G l O bal C ontextual A daptation for fully end-to-end and fast bottom-up human P ose estimation, dubbed as LOGO-CAP . It is built on the conceptually simple center-offset formulation that lacks inaccuracy for pose estimation. When revisiting the bottom-up human pose estimation with the thought of “thinking, fast and slow” by D. Kahneman, we introduce a “slow keypointer” to remedy the lack of sufﬁcient accuracy of the “fast keypointer”. In learning the “slow keypointer”, the proposed LOGO-CAP lifts the initial “fast” keypoints by offset predictions to keypoint expansion maps (KEMs) to counter their uncertainty in two modules. Firstly, the local KEMs (e.g. 11 × 11 ) are extracted from a low-dimensional feature map. A proposed convolutional message passing module learns to “re-focus” the local KEMs to the keypoint attraction maps (KAMs) by accounting for the structured output prediction nature of human pose estimation, which is directly supervised by the object keypoint similarity (OKS) loss in training. Secondly, the global KEMs are extracted, with a sufﬁ-ciently large region-of-interest (e.g., 97 × 97 ), from the keypoint heatmaps that are computed by a direct map-to-map regression. Then, a local-global contextual adaptation module is proposed to convolve the global KEMs using the learned KAMs as the kernels. This convolution can be understood as the learnable offsets guided deformable and dynamic convolution in a pose-sensitive way. The proposed method is end-to-end trainable with near real-time inference speed, obtaining state-of-the-art performance on the COCO keypoint benchmark for bottom-up human pose estimation. With the COCO trained model, our LOGO-CAP also outperforms prior arts by a large margin on the challenging OCHuman dataset.
FaceCook: Face Generation Based on Linear Scaling Factors

With the excellent disentanglement properties of state-of-the-art generative models, image editing has been the dominant approach to control the attributes of synthesised face images. However, these edited results often suffer from artifacts or incorrect feature rendering, especially when there is a large discrepancy between the image to be edited and the desired feature set. Therefore, we propose a new approach to mapping the latent vectors of the generative model to the scaling factors through solving a set of multivariate linear equations. The coefficients of the equations are the eigenvectors of the weight parameters of the pre-trained model, which form the basis of a hyper coordinate system. The qualitative and quantitative results both show that the proposed method outperforms the baseline in terms of image diversity. In addition, the method is much more time-efficient because you can obtain synthesised images with desirable features directly from the latent vectors, rather than the former process of editing randomly generated images requiring many processing steps.
Identification of direct transcriptional targets of NFATC2 that promote β-cell proliferation.

The transcription factor NFATC2 induces β-cell proliferation in mouse and human islets. However, the genomic targets that mediate these effects have not been identified. We expressed active forms of Nfatc2 and Nfatc1 in human islets. By integrating changes in gene expression with genomic binding sites for NFATC2, we identified ~2,200 transcriptional targets of NFATC2. Genes induced by NFATC2 were enriched for transcripts that regulate the cell cycle, and for DNA motifs associated with the transcription factor FOXP. Islets from an endocrine-specific Foxp1, Foxp2, and Foxp4 triple-knockout mouse are less responsive to NFATC2-induced β-cell proliferation, suggesting the FOXP family works to regulate β-cell proliferation in concert with NFATC2. NFATC2 induced β-cell proliferation in both mouse and human islets, whereas NFATC1 did so only in human islets. Exploiting this species difference, we identified ~250 direct transcriptional targets of NFAT in human islets. This gene set enriches for cell cycle-associated transcripts, and includes Nr4a1. Deletion of Nr4a1 reduced the capacity of NFATC2 to induce β-cell proliferation, suggesting that much of the effect of NFATC2 occurs through its induction of Nr4a1. Integration of non-coding RNA expression, chromatin accessibility, and NFATC2 binding sites enabled us to identify NFATC2-dependent enhancer loci that mediate β-cell proliferation.
Autophagy is required during high MUC2 mucin biosynthesis in colonic goblet cells to contend metabolic stress.

Goblet cells are specialized for the production and secretion of MUC2 glycoproteins that forms a thick layer covering the mucosal epithelium as a protective barrier against noxious substances and invading microbes. High MUC2 mucin biosynthesis induces endoplasmic reticulum (ER) stress and apoptosis in goblet cells during inflammatory and infectious diseases. Autophagy is an intracellular degradation process required for maintenance of intestinal homeostasis. In this study, we hypothesized that autophagy was triggered during high MUC2 mucin biosynthesis from colonic goblet cells to cope with metabolic stress. To interrogate this, we analyzed the autophagy process in high MUC2-producing human HT29-H and a clone HT29-L silenced for MUC2 expression by lentivirus-mediated shRNA, and WT and CRISPR/Cas9 MUC2 KO LS174T cells. Autophagy was constitutively increased in high MUC2 producing cells characterized by elevated pULK1S555 expression and increased numbers of autophagosomes as compared to MUC2 silenced or gene edited cells. Similarly, colonoids from Muc2+/+ but not Muc2-/-littermates differentiated into goblet cells showed increased autophagy. IL-22 treatment corrected misfolded MUC2 protein and alleviated the autophagy process in LS174T cells. This study highlights that autophagy plays an essential role in goblet cells to survive during high mucin biosynthesis by regulating cellular homeostasis.
Thioredoxin reductase from Bacillus cereus exhibits distinct reduction and NADPH‐binding properties

Low‐molecular‐weight (low Mr) thioredoxin reductases (TrxRs) are homodimeric NADPH‐dependent dithiol flavoenzymes that reduce thioredoxins (Trxs) or Trx‐like proteins involved in the activation networks of enzymes, such as the bacterial class Ib ribonucleotide reductase (RNR). During the last few decades, TrxR‐like ferredoxin/flavodoxin NADP+ oxidoreductases (FNRs) have been discovered and characterized in several types of bacteria, including those not encoding the canonical plant‐type FNR. In Bacillus cereus, a TrxR‐like FNR has been shown to reduce the flavodoxin‐like protein NrdI in the activation of class Ib RNR. However, some species only encode TrxR and lack the homologous TrxR‐like FNR. Due to the structural similarity between TrxRs and TrxR‐like FNRs, as well as variations in their occurrence in different microorganisms, we hypothesized that low Mr TrxR may be able to replace TrxR‐like FNR in, for example, the reduction of NrdI. In this study, characterization of TrxR from B. cereus has revealed a weak FNR activity toward NrdI reduction. Additionally, the crystal structure shows that only one out of two binding sites of the B. cereus TrxR homodimer is occupied with NADPH, indicating a possible asymmetric co‐substrate binding in TrxR.
SARS-CoV-2 infected cells trigger an acute antiviral response mediated by Plasmacytoid dendritic cells in mild but not severe COVID-19 patients

Type I and III interferons (IFN-I/{lambda}) are key antiviral mediators against SARS-CoV-2 infection. Here, we demonstrate that the plasmacytoid dendritic cells (pDCs) are predominant IFN-I/{lambda} source by sensing SARS-CoV-2-infected cells. We show that sensing of viral RNA by pDCs requires sustained cell adhesion with infected cells. In turn, the pDCs restrict viral spread by a local IFN-I/{lambda} response directed toward SARS-CoV-2-infected cells. This specialized function enables pDCs to efficiently turn-off viral replication, likely by a concentrated flux of antiviral effectors at the contact site with infected cells. Therefore, we propose that pDC activation is essential to locally control SARS-CoV-2-infection. By exploring the pDC response in patients, we further demonstrate that pDC responsiveness correlates with the severity of the disease and in particular that it is impaired in severe COVID-19 patients. Thus, the ability of pDCs to respond to SARS-CoV-2-infected cells could be a key to understand severe cases of COVID-19.
YAHPO Gym - Design Criteria and a new Multifidelity Benchmark for Hyperparameter Optimization

When developing and analyzing new hyperparameter optimization (HPO) meth-ods, it is vital to empirically evaluate and compare them on well-curated benchmark suites. In this work, we list desirable properties and requirements for such benchmarks and propose a new set of challenging and relevant multiﬁdelity HPO benchmark problems motivated by these requirements. For this, we revisit the concept of surrogate-based benchmarks and empirically compare them to more widely-used tabular benchmarks, showing that the latter ones may induce bias in performance estimation and ranking of HPO methods. We present a new surrogate-based benchmark suite for multiﬁdelity HPO methods consisting of 9 benchmark collections that constitute over 700 multiﬁdelity HPO problems in total. All our benchmarks also allow for querying of multiple optimization targets, enabling the benchmarking of multi-objective HPO. We examine and compare our benchmark suite with respect to the deﬁned requirements and show that our benchmarks provide viable additions to existing suites.
Evaluation of the long-term effect of polyhexamethylene guanidine phosphate in a rat lung model using conventional chest computed tomography with histopathologic analysis

There have been no studies on the effects of polyhexamethylene guanidine phosphate (PHMG) after a long period of exposure in the rodent model. We aimed to evaluate long-term lung damage after PHMG exposure using conventional chest computed tomography (CT) and histopathologic analysis in a rat model. A PHMG solution was intratracheally administrated to 24 male rats. At 8, 26, and 52 weeks after PHMG instillation, conventional chest CT was performed in all rats and both lungs were extracted for histopathologic evaluation. At 52 weeks after PHMG instillation, four carcinomas had developed in three of the eight rats (37.5%). Bronchiolo-alveolar hyperplasia and adenoma were found in rats at 8, 26, and 52 weeks post-instillation. The number of bronchiolo-alveolar hyperplasia significantly increased over time (P-value for trend< 0.001). The severity of lung fibrosis and fibrosis scores significantly increased over time (P-values for trend = 0.002 and 0.023, respectively). Conventional chest CT analysis showed that bronchiectasis and linear density scores suggestive of fibrosis significantly increased over time (P-value for trend < 0.001). Our study revealed that one instillation of PHMG in a rat model resulted in lung carcinomas and progressive and irreversible fibrosis one year later based on conventional chest CT and histopathologic analysis. PHMG may be a lung carcinogen in the rat model.
Universal quantum computation and quantum error correction using discrete holonomies

Holonomic quantum computation exploits a quantum state’s non-trivial, matrix-valued geometric phase (holonomy) to perform fault-tolerant computation. Holonomies arising from systems where the Hamiltonian traces a continuous path through parameter space have been well-researched. Discrete holonomies, on the other hand, where the state jumps from point to point in state space, have had little prior investigation. Using a sequence of incomplete projective measurements of the spin operator, we build an explicit approach to universal quantum computation. We show that quantum error correction codes integrate naturally in our scheme, providing a model for measurement-based quantum computation that combines the passive error resilience of holonomic quantum computation and active error correction techniques. In the limit of dense measurements we recover known continuous-path holonomies.
Confidence surfaces for the mean of locally stationary functional time series

The problem of constructing a simultaneous confidence surface for the 2-dimensional mean function of a non-stationary functional time series is challenging as these bands can not be built on classical limit theory for the maximum absolute deviation between an estimate and the time dependent regression function. In this paper we propose new bootstrap methodology to construct such a region. Our approach is based on a Gaussian approximation for the maximum norm of sparse high-dimensional vectors approximating the maximum absolute deviation. The elimination of the zero entries produces (besides the time dependence) additionally dependencies such that ”classical” multiplier bootstrap is not applicable. To solve this issue we develop a novel multiplier bootstrap, where blocks of the coordinates of the vectors are multiplied with random variables, which mimic the specific structure between the vectors appearing in the Gaussian approximation. We prove the validity of our approach by asymptotic theory, demonstrate good finite sample properties by means of a simulation study and illustrate its applicability analyzing a data example. keywords: locally stationary time series, functional data, confidence surface, Gaussian approximation, multiplier bootstrap.