Miracle-dz/UniFAR-FTUs huggingface dataset:
Planning for Dexterous Ungrasping: Secure Ungrasping through Dexterous Manipulation

This paper presents a robotic manipulation technique for dexterous ungrasping. It refers to the capability of securely transferring a grasped object from the gripper to the robots environment, i.e. the inverse of grasping or picking, through dexterous manipulation. The game of Go offers an example: consider how the player would typically place an initially pinch-grasped stone onto the board through the dexterous interaction between the fingers, the stone, and the board. Likewise, dexterous ungrasping addresses the necessity of changing the objects configuration relative to the gripper or the environment in order to securely keep hold of the object. In particular, we present a planning framework for determining a feasible minimum-cost motion path that completes dexterous ungrasping. Digit asymmetry in a gripper, i.e. difference in digit lengths, is discovered as the key to feasible and secure ungrasping. A set of experiments show the effectiveness of dexterous ungrasping in practical placement tasks.
Segmentation Fault: A Cheap Defense Against Adversarial Machine Learning

Recently published attacks against deep neural networks (DNNs) have stressed the importance of methodologies and tools to assess the security risks of using this technology in critical systems. Efficient techniques for detecting adversarial machine learning helps establishing trust and boost the adoption of deep learning in sensitive and security systems.In this paper, we propose a new technique for defending deep neural network classifiers, and convolutional ones in particular. Our defense is cheap in the sense that it requires less computation power despite a small cost to pay in terms of detection accuracy. The work refers to a recently published technique called ML-LOO. We replace the costly pixel by pixel leave-one-out approach of ML-LOO by adopting coarse-grained leave-one-out. We evaluate and compare the efficiency of different segmentation algorithms for this task. Our results show that a large gain in efficiency is possible, even though penalized by a marginal decrease in detection accuracy.
Seed dressing with mefenpyr-diethyl as a safener for mesosulfuron-methyl application in wheat: The evaluation and mechanisms

Mesosulfuron-methyl is always applied by foliar spraying in combination with the safener mefenpyr-diethyl to avoid phytotoxicity on wheat (Triticum aestivum L.) cultivars. However, it was observed that the tolerance of Tausch’s goatgrass (Aegilops tauschii Coss.) to mesosulfuron-methyl significantly increased in the presence of mefenpyr-diethyl by performing bioassay. This confirmed phenomenon may lead to overuse of mesosulfuron-methyl and weed resistance evolution in field conditions. Therefore, we tested the effect of wheat seed dressing with mefenpyr-diethyl as a possible alternative and disclosed the underlying mechanisms by herbicide dissipation study, enzymatic analysis and transcriptome profiling. The results suggest that increase of ALS activity, enhancement of metabolic processes, and other stress responses are crucial for the regulation of herbicide detoxification induced by mefenpyr-diethyl. Additionally, transcription factors such as AP2/ERF-ERF, bHLH, NAC, and MYB, and protein kinase such as RLK-Pelle_DLSV might play vital regulatory roles. The current study has important implications for mesosulfuron-methyl application in wheat field to control Tausch’s goatgrass and provides a comprehensive understanding of the protective effect of mefenpyr-diethyl.
AAWS-Net: Anatomy-aware weakly-supervised learning network for breast mass segmentation

Accurate segmentation of breast masses is an essential step in computer aided diagnosis of breast cancer. The scarcity of annotated training data greatly hinders the model’s generalization ability, especially for the deep learning based methods. However, high-quality image-level annotations are time-consuming and cumbersome in medical image analysis scenarios. In addition, a large amount of weak annotations is under-utilized which comprise common anatomy features. To this end, inspired by teacher-student networks, we propose an Anatomy-Aware Weakly-Supervised learning Network (AAWS-Net) for extracting useful information from mammograms with weak annotations for efficient and accurate breast mass segmentation. Specifically, we adopt a weakly-supervised learning strategy in the Teacher to extract anatomy structure from mammograms with weak annotations by reconstructing the original image. Besides, knowledge distillation is used to suggest morphological differences between benign and malignant masses. Moreover, the prior knowledge learned from the Teacher is introduced to the Student in an end-to-end way, which improves the ability of the student network to locate and segment masses. Experiments on CBIS-DDSM have shown that our method yields promising performance compared with state-of-the-art alternative models for breast mass segmentation in terms of segmentation accuracy and IoU.
Kinetic, thermodynamic parameters and in vitro digestion of tannase from Aspergillus tamarii URM 7115

ABSTRACT Tannase is an enzyme used in various industries and produced by a large number of microorganisms. The aim of this study was to evaluate tannase production to determine the biochemical, kinetic, and thermodynamic properties and to simulate tannase in vitro digestion. The tannase-producing fungal strain was isolated from “jamun” leaves and identified as Aspergillus tamarii. Temperature at 26°C for 67 h was the best combination for maximum tannase activity (6.35-fold; initial activity in Plackett–Burman design—15.53 U/mL and average final activity in Doehlert design—98.68 U/mL). The crude extract of tannase was optimally active at 40°C, pH 5.5 and 6.5. Moreover, tannase was stimulated by Na+, Ca2+, Mg2+, and Mn2+. The half-life at 40°C lasted 247.55 min. The free energy of Gibbs, enthalpy, and entropy, at 40°C, was 81.47, 16.85, and −0.21 kJ/mol · K, respectively. After total digestion, 123.95% of the original activity was retained. Results suggested that tannase from A. tamarii URM 7115 is an enzyme of interest for industrial applications, such as gallic acid production, additive for feed industry, and for beverage manufacturing, due to its catalytic and thermodynamic properties.
Is Artificial Intelligence Making Audit Firms More Efficient?

How does artificial intelligence (AI) impact audit quality and efficiency? We explore this question by leveraging a unique dataset of more than 310,000 detailed individual resumes for the 36 largest audit firms to identify audit firms’ employment of AI workers. We provide a first look into the AI workforce within the auditing sector. AI workers tend to be male, relatively young, and hold mostly but not exclusively technical degrees. Importantly, AI is a centralized function within the firm, with workers concentrating in a handful of teams and geographic locations. Our results show that investing in AI helps improve audit quality, reduces fees, and ultimately displaces human auditors, although the effect on labor takes several years to materialize. Specifically, a one-standard-deviation change in recent AI investments is associated with a 5.0% reduction in the likelihood of an audit restatement, a 0.009 drop in log per client fees, and a reduction in the number of accounting employees that reaches 3.6% after three years and 7.1% after four years.
SNAI2 is induced by transforming growth factor-β1, but is not essential for epithelial-mesenchymal transition in human keratinocyte HaCaT cells

Epithelial-mesenchymal transition (EMT) is a cellular process in which epithelial cells lose their epithelial traits and shift to the mesenchymal phenotype, and is associated with various biological events, such as embryogenesis, wound healing and cancer progression. The transcriptional program that promotes phenotype switching is dynamically controlled by transcription factors during EMT, including Snail (SNAI1), twist family bHLH transcription factor (TWIST) and zinc finger E-box binding homeobox 1 (ZEB1). The present study aimed to investigate the molecular mechanisms underlying EMT in squamous epithelial cells. Western blot analysis and immunocytochemical staining identified Slug (SNAI2) as a transcription factor that is induced during transforming growth factor (TGF)-β1-mediated EMT in the human keratinocyte cell line HaCaT. The effect of SNAI2 overexpression and knockdown on the phenotypic characteristics of HaCaT cells was evaluated. Filamentous actin staining and western blot analysis revealed that the overexpression of SNAI2 did not induce the observed EMT-related phenotypic changes. In addition, SNAI2 knockdown demonstrated almost no impact on the EMT phenotypes induced by TGF-β1. Notably, DNA microarray analysis followed by comprehensive bioinformatics analysis revealed that the differentially expressed genes upregulated by TGF-β1 were significantly enriched in cell adhesion and extracellular matrix binding, whereas the genes downregulated in response to TGF-β1 were significantly enriched in the cell cycle. No enriched gene ontology term and biological pathways were identified in the differentially expressed gene sets of SNAI2-overexpressing cells. In addition, the candidates for master transcription factors regulating the TGF-β1-induced EMT were identified using transcription factor enrichment analysis. In conclusion, the results of study demonstrated that SNAI2 does not play an essential role in the EMT of HaCaT cells and identified candidate transcription factors that may be involved in EMT-related gene expression induced by TGF-β1. These findings may enhance the understanding of molecular events in EMT and contribute to the development of a novel therapeutic approach against EMT in cancers and wound healing.
An FEA surrogate model with Boundary Oriented Graph Embedding approach

In this work, we present a Boundary Oriented Graph Embedding (BOGE) approach for the Graph Neural Network (GNN) to serve as a general surrogate model for regressing physical fields and solving boundary value problems. Providing shortcuts for both boundary elements and local neighbor elements, the BOGE approach can embed structured mesh elements into the graph and performs an efficient regression on large-scale triangular-mesh-based FEA results, which cannot be realized by other machine-learning-based surrogate methods. Focusing on the cantilever beam problem, our BOGE approach cannot only fit the distribution of stress fields but also regresses the topological optimization results, which show its potential of realizing abstract decision-making design process. The BOGE approach with 3-layer DeepGCN model \textcolor{blue}{achieves the regression with MSE of 0.011706 (2.41\% MAPE) for stress field prediction and 0.002735 MSE (with 1.58\% elements having error larger than 0.01) for topological optimization. } The overall concept of the BOGE approach paves the way for a general and efficient deep-learning-based FEA simulator that will benefit both industry and design-related areas.
Receiver phase alignment using fitted SVD derived sensitivities from routine prescans

Magnetic resonance imaging radio frequency arrays are composed of multiple receive coils that have their signals combined to form an image. Combination requires an estimate of the radio frequency coil sensitivities to align signal phases and prevent destructive interference. At lower fields this can be accomplished using a uniform physical reference coil. However, at higher fields, uniform volume coils are lacking and, when available, suffer from regions of low receive sensitivity that result in poor sensitivity estimation and combination. Several approaches exist that do not require a physical reference coil but require manual intervention, specific prescans, or must be completed post-acquisition. This makes these methods impractical for large multi-volume datasets such as those collected for novel types of functional MRI or quantitative susceptibility mapping, where magnitude and phase are important. This pilot study proposes a fitted SVD method which utilizes existing combination methods to create a phase sensitive combination method targeted at large multi-volume datasets. This method uses any multi-image prescan to calculate the relative receive sensitivities using voxel-wise singular value decomposition. These relative sensitivities are fitted to the solid harmonics using an iterative least squares fitting algorithm. Fits of the relative sensitivities are used to align the phases of the receive coils and improve combination in subsequent acquisitions during the imaging session. This method is compared against existing approaches in the human brain at 7 Tesla by examining the combined data for the presence of singularities and changes in phase signal-to-noise ratio. Two additional applications of the method are also explored, using the fitted SVD method in an asymmetrical coil and in a case with subject motion. The fitted SVD method produces singularity-free images and recovers between 95–100% of the phase signal-to-noise ratio depending on the prescan data resolution. Using solid harmonic fitting to interpolate singular value decomposition derived receive sensitivities from existing prescans allows the fitted SVD method to be used on all acquisitions within a session without increasing exam duration. Our fitted SVD method is able to combine imaging datasets accurately without supervision during online reconstruction.
A review of mobile robot motion planning methods: from classical motion planning workflows to reinforcement learning-based architectures

Motion planning is critical to realize the autonomous operation of mobile robots. As the complexity and randomness of robot application scenarios increase, the planning capability of the classical hierarchical motion planners is challenged. With the development of machine learning, deep reinforcement learning (DRL)-based motion planner has gradually become a research hotspot due to its several advantageous features. DRL-based motion planner is model-free and does not rely on the prior structured map. Most importantly, DRL-based motion planner achieves the unification of the global planner and the local planner. In this paper, we provide a systematic review of various motion planning methods. First, we summarize the representative and state-of-the-art works for each submodule of the classical motion planning architecture and analyze their performance features. Subsequently, we concentrate on summarizing RL-based motion planning approaches, including motion planners combined with RL improvements, map-free RL-based motion planners, and multi-robot cooperative planning methods. Last but not least, we analyze the urgent challenges faced by these mainstream RL-based motion planners in detail, review some state-of-the-art works for these issues, and propose suggestions for future research.
Discretized Integrated Gradients for Explaining Language Models

As a prominent attribution-based explanation algorithm, Integrated Gradients (IG) is widely adopted due to its desirable explanation axioms and the ease of gradient computation. It measures feature importance by averaging the model’s output gradient interpolated along a straight-line path in the input data space. However, such straight-line interpolated points are not representative of text data due to the inherent discreteness of the word embedding space. This questions the faithfulness of the gradients computed at the interpolated points and consequently, the quality of the generated explanations. Here we propose Discretized Integrated Gradients (DIG), which allows effective attribution along non-linear interpolation paths. We develop two interpolation strategies for the discrete word embedding space that generates interpolation points that lie close to actual words in the embedding space, yielding more faithful gradient computation. We demonstrate the effectiveness of DIG over IG through experimental and human evaluations on multiple sentiment classification datasets. We provide the source code of DIG to encourage reproducible research.
Advanced Laser Technology for Quantum Communications (Tutorial Review)

Quantum communications is the art of exchanging and manipulating information beyond the capabilities of the conventional technologies using the laws of quantum mechanics. With applications ranging from quantum computing to cryptographic systems with information‐theoretic security, there is strong incentive to introduce quantum communications into many areas of the society. However, an important challenge is to develop viable technologies meeting the stringent requirements of low noise and high coherence for quantum state encoding, of high bit rate and low power for the integration with classical communication networks, and of scalable and low‐cost production for a practical wide‐deployment. This tutorial presents recent advances in laser modulation technologies that have enabled the development of efficient and versatile light sources for quantum communications, with a particular focus on quantum key distribution (QKD). Such approaches have been successfully used to demonstrate several QKD protocols with state‐of‐the‐art performance. The applications and experimental results are reviewed and interpreted in the light of a complete theoretical background, allowing the reader to model and simulate such sources.
The all-sky PLATO input catalogue

Context. The ESA PLAnetary Transits and Oscillations of stars (PLATO) mission will search for terrestrial planets in the habitable zone of solar-type stars. Because of telemetry limitations, PLATO targets need to be pre-selected. Aims. In this paper, we present an all sky catalogue that will be fundamental to selecting the best PLATO fields and the most promising target stars, deriving their basic parameters, analysing the instrumental performances, and then planing and optimising follow-up observations. This catalogue also represents a valuable resource for the general definition of stellar samples optimised for the search of transiting planets. Methods. We used Gaia Data Release 2 (DR2) astrometry and photometry and 3D maps of the local interstellar medium to isolate FGK (V≤13) and M (V≤16) dwarfs and subgiant stars. Results. We present the first public release of the all-sky PLATO Input Catalogue (asPIC1.1) containing a total of 2 675 539 stars including 2 378 177 FGK dwarfs and subgiants and 297 362 M dwarfs. The median distance in our sample is 428 pc for FGK stars and 146 pc for M dwarfs, respectively. We derived the reddening of our targets and developed an algorithm to estimate stellar fundamental parameters (Teff , radius, mass) from astrometric and photometric measurements. Conclusions. We show that the overall (internal+external) uncertainties on the stellar parameter determined in the present study are ∼230 K (4%) for the effective temperatures, ∼0.1 R (9%) for the stellar radii, and ∼0.1 M (11%) for the stellar mass. We release a special target list containing all known planet hosts cross-matched with our catalogue.
A New Approach to Multilinear Dynamical Systems and Control

The current paper presents a new approach to multilinear dynamical systems analysis and control. The approach is based upon recent developments in tensor decompositions and a newly defined algebra of circulants. In particular, it is shown that under the right tensor multiplication operator, a third order tensor can be written as a product of third order tensors that is analogous to a traditional matrix eigenvalue decomposition where the"eigenvectors"become eigenmatrices and the"eigenvalues"become eigen-tuples. This new development allows for a proper tensor eigenvalue decomposition to be defined and has natural extension to linear systems theory through a \textit{tensor-exponential}. Through this framework we extend many of traditional techniques used in linear system theory to their multilinear counterpart.
Heterogeneous Graph Neural Network With Multi-View Representation Learning

In recent years, graph neural networks (GNNs)-based methods have been widely adopted for heterogeneous graph (HG) embedding, due to their power in effectively encoding rich information from a HG into the low-dimensional node embeddings. However, previous works usually easily fail to fully leverage the inherent heterogeneity and rich semantics contained in the complex local structures of HGs. On the one hand, most of the existing methods either inadequately model the local structure under specific semantics, or neglect the heterogeneity when aggregating information from the local structure. On the other hand, representations from multiple semantics are not comprehensively integrated to obtain node embeddings with versatility. To address the problem, we propose a Heterogeneous Graph Neural Network for HG embedding within a Multi-View representation learning framework (named MV-HetGNN), which consists of a view-specific ego graph encoder and auto multi-view fusion layer. MV-HetGNN thoroughly learns complex heterogeneity and semantics in the local structure to generate comprehensive and versatile node representations for HGs. Extensive experiments on three real-world HG datasets demonstrate the significant superiority of our proposed MV-HetGNN compared to the state-of-the-art baselines in various downstream tasks, e.g., node classification, node clustering, and link prediction.
Prevalence of antibodies against seasonal influenza A and B viruses among older adults in rural Thailand: A cross-sectional study

Assessing the seroprevalence of the high-risk individuals against the influenza virus is essential to evaluate the progress of vaccine implementation programs and establish influenza virus interventions. Herein, we identified the pre-existing cross-protection of the circulating seasonal influenza viruses among the older-aged population. A cross-sectional study was performed base on the 176 residual sera samples collected from older adults aged 60 to 95 years without a history of vaccination in rural Thailand in 2015. Sera antibody titers against influenza A and B viruses circulating between 2016 and 2019 were determined by hemagglutination inhibition assay. These findings indicated the low titers of pre-existing antibodies to circulating influenza subtypes and showed age-independent antibody titers among the old adults. Moderate seropositive rates (HAI ≥ 1:40) were observed in influenza A viruses (65.9%A(H3N2), 50.0% for A(H1N1) pdm09), and found comparatively lower rates in influenza B viruses (14% B/Yam2, 21% B/Yam3 and 25% B/Vic). Only 5% of individuals possessed broadly protective antibodies against both seasonal influenza A and B virus in this region. Our findings highlighted the low pre-existing antibodies to circulating influenza strains in the following season observed in older adults. The serological study will help inform policy-makers for health care planning and guide control measures concerning vaccination programs.