Miracle-dz/UniFAR-FTUs huggingface dataset:
NASCarD (Nanopore Adaptive Sampling with Carrier DNA): A rapid, PCR-free method for whole genome sequencing of pathogens in clinical samples

Whole-genome sequencing (WGS) represents the main technology for SARS-CoV-2 lineage characterization in diagnostic laboratories worldwide. The rapid, near-full-length sequencing of the viral genome is commonly enabled by high-throughput sequencing of PCR amplicons derived from cDNA molecules. Here, we present a new approach, called NASCarD (Nanopore adaptive sampling with carrier DNA), which allows low amount of nucleic acids to be sequenced while selectively enriching for sequences of interest, hence limiting the production of non-target sequences. Using clinical samples positive for SARS-CoV-2 during the Omicron wave, we demonstrate how the method leads to up to >100x coverage of the full genome sequences of the target organism as compared to standard shotgun metatranscriptomics approach. It provides complete and accurate genome sequence reconstruction within seven hours at a competitive cost. The new approach may have applications beyond SARS-CoV-2 sequencing for other DNA or RNA pathogens in clinical samples.
RELATIONSHIP BETWEEN THE LEVEL OF SPIRITUALITY AND BLOOD PRESSURE CONTROL AMONG ADULT HYPERTENSIVE PATIENTS IN RURAL SOUTHWESTERN NIGERIA

Background: Spirituality has been strongly associated with good blood pressure control as it forms a strong coping mechanism in hypertensive patients. Purpose: This hospital-based cross-sectional study was done to determine the relationship between spirituality and blood pressure control among adult hypertensive patients in rural Southwestern Nigeria with a view to achieving good blood pressure control. Method: The selection was done by systematic random sampling technique. Socio-demographic and clinical information were obtained through semi-structured interviewer-administered questionnaires. The level of spirituality was assessed using the Spiritual Perspective Scale. Data were analysed using the Statistical Package for Social Sciences version 20.0. Statistical significance was set at p [≤] 0.05. Results: The mean age of the respondents was 61.1{+/-}11.1 years. More than half (52.6%) had high level of spirituality and more than two-thirds (67.1%) of respondents had controlled blood pressure. Respondents with high level of spirituality were 4.76 times more likely to have a good blood pressure control {p<0.001, 95%CI (1.05-14.99)}than those with low level of spirituality. Conclusion: Proper understanding and effective utilization of this relationship will assist health professionals and researchers in the appropriate integration of this concept into patients' holistic care with the aim of achieving better blood pressure control among hypertensive patients.
Nobel Begets Nobel

I construct the professor-student network for laureates of and candidates for the Nobel Prize in Economics. I study the effect of proximity to previous Nobelists on winning the Nobel Prize. Conditional on being Nobel-worthy, students and grandstudents of Nobel laureates are significantly less likely to win. Professors and fellow students of Nobel Prize winners, however, are significantly more likely to win.
Chemical kinetic theory of aging

A theory of aging based on the principles of the kinetics of chemical reactions and the rules of natural selection of organisms is proposed. The theory is based on a hypothesis that the biochemical processes in the organism can be described in the terms of chemical reaction kinetics. The evolutionary process of organisms is determined by the goal of continuing life, and the natural selection forced organisms to develop in an optimized way for survival and reproduction, after which any further development of the organisms did not matter for natural selection and therefore was not regulated by it. Accordingly, the ongoing biochemical processes in the organism after giving birth were not stabilised and continued casual, which, in the case of complex systems of biochemical processes, led to imbalances and biochemical processes that did not contribute to health in the organism, resulting in aging. Based on this view, it is necessary to look for the key biochemical processes that regulate the vital activity of the organism. Balancing of these processes in the period after giving birth can artificially lead to the stabilization of the kinetics of biochemical reactions, and consequently, the continuation of life almost indefinitely.
Rearrange Indoor Scenes for Human-Robot Co-Activity

We present an optimization-based framework for rearranging indoor furniture to accommodate human-robot co-activities better. The rearrangement aims to afford sufficient accessible space for robot activities without compromising everyday human activities. To retain human activities, our algorithm preserves the functional relations among furniture by integrating spatial and semantic co-occurrence extracted from SUNCG and ConceptNet, respectively. By defining the robot's accessible space by the amount of open space it can traverse and the number of objects it can reach, we formulate the rearrangement for human-robot co-activity as an optimization problem, solved by adaptive simulated annealing (ASA) and covariance matrix adaptation evolution strategy (CMA-ES). Our experiments on the SUNCG dataset quantitatively show that rearranged scenes provide a robot with 14% more accessible space and 30% more objects to interact with on average. The quality of the rearranged scenes is qualitatively validated by a human study, indicating the efficacy of the proposed strategy.
Towards Generalized Robot Assembly through Compliance-Enabled Contact Formations

Contact can be conceptualized as a set of constraints imposed on two bodies that are interacting with one another in some way. The nature of a contact, whether a point, line, or surface, dictates how these bodies are able to move with respect to one another given a force, and a set of contacts can provide either partial or full constraint on a body's motion. Decades of work have explored how to explicitly estimate the location of a contact and its dynamics, e.g., frictional properties, but investigated methods have been computationally expensive and there often exists significant uncertainty in the final calculation. This has affected further advancements in contact-rich tasks that are seemingly simple to humans, such as generalized peg-in-hole insertions. In this work, instead of explicitly estimating the individual contact dynamics between an object and its hole, we approach this problem by investigating compliance-enabled contact formations. More formally, contact formations are defined according to the constraints imposed on an object's available degrees-of-freedom. Rather than estimating individual contact positions, we abstract out this calculation to an implicit representation, allowing the robot to either acquire, maintain, or release constraints on the object during the insertion process, by monitoring forces enacted on the end effector through time. Using a compliant robot, our method is desirable in that we are able to complete industry-relevant insertion tasks of tolerances <0.25mm without prior knowledge of the exact hole location or its orientation. We showcase our method on more generalized insertion tasks, such as commercially available non-cylindrical objects and open world plug tasks.
Semi-supervised Adversarial Learning for Complementary Item Recommendation

Complementary item recommendations are a ubiquitous feature of modern e-commerce sites. Such recommendations are highly effective when they are based on collaborative signals like co-purchase statistics. In certain online marketplaces, however, e.g., on online auction sites, constantly new items are added to the catalog. In such cases, complementary item recommendations are often based on item side-information due to a lack of interaction data. In this work, we propose a novel approach that can leverage both item side-information and labeled complementary item pairs to generate effective complementary recommendations for cold items, i.e., for items for which no co-purchase statistics yet exist. Given that complementary items typically have to be of a different category than the seed item, we technically maintain a latent space for each item category. Simultaneously, we learn to project distributed item representations into these category spaces to determine suitable recommendations. The main learning process in our architecture utilizes labeled pairs of complementary items. In addition, we adopt ideas from Cycle Generative Adversarial Networks (CycleGAN) to leverage available item information even in case no labeled data exists for a given item and category. Experiments on three e-commerce datasets show that our method is highly effective.
New Research Trends in Electrically Tunable 2D van der Waals Magnetic Materials

The recent discovery of two-dimensional (2D) van der Waals (vdW) magnetic materials has provided new, unprecedented opportunities for both fundamental science and technological applications. Unlike three-dimensional (3D) magnetic systems, the electric manipulation of vdW magnetism (e.g., magnetization state, magnetic anisotropy, magnetic ordering temperature) down to the monolayer limit at ambient conditions enables high efficiency operation and low energy consumption, which has the potential to revolutionize the fields of spintronics, spin-caloritronics, and valleytronics. This article provides an in-depth analysis of the recent progress, emerging opportunities, and technical challenges in the electric manipulation of magnetic functionalities of a wide variety of 2D vdW magnetic systems ranging from metals to semiconductors and heterostructures. The state-of-the-art understanding of the mechanisms behind the electric modulation of magnetism in these 2D vdW magnetic systems will drive future research towards novel applications in spintronics, spin-caloritronics, valleytronics, and quantum computation.
On the optimality of Kalman Filter for Fault Detection

Kalman filter is widely used for residual generation in fault detection. It leads to optimality in fault detection using some performance indices and also leads to statistically sound residual evaluation and threshold setting. This paper shows that these nice features do not necessarily imply an optimal fault detection performance. Based on a performance index related to fault detection rate and false alarm rate, several occasions where Kalman filter should not be used are pointed out; further the residual evaluation and threshold setting are discussed, in which it is pointed out that in stochastic setting an optimal statistical test of Kamlan filter is not related to optimality of commonly used detection performance indicators. The theoretical analysis is verified through Monte Carlo simulations and Tennessee Eastman process (TEP) dataset.
Quantum loop effects on the power spectrum and constraints on primordial black holes

We present a detailed exposition on the prospects of the formation of Primordial Black Holes (PBHs) during Slow Roll (SR) to Ultra Slow Roll (USR) sharp transitions in the framework of single-field inflation. We use an effective field theory (EFT) approach in order to keep the analysis model-independent and applicable to both the canonical and non-canonical cases. We show in detail how renormalizing the power spectrum to one loop order in P(X,ϕ) theories severely limits the prospects for PBH formation in a single-field inflationary framework. We demonstrate that for the allowed range of effective sound speed, 1 < cs < 1.17, the consistency of one-loop corrected power spectrum leaves a small window for black hole masses, M PBH∼𝒪(102–103)gm to have sufficient e-foldings, Δ𝒩Total ∼ 𝒪(54–59) for inflation. We confirm that adding an SR regime after USR before the end of inflation does not significantly alter our conclusions. Our findings for sharp transition strictly rule out the possibility of generating large masses of PBHs from all possible models of single field inflation (canonical and non-canonical). Our results are at least valid for the situation where constraints from the loop effects are computed using either Late-Time (LT) or Adiabatic-Wave function (AF) scheme followed by Power Spectrum (PS) renormalization schemes.
Exphormer: Sparse Transformers for Graphs

Graph transformers have emerged as a promising architecture for a variety of graph learning and representation tasks. Despite their successes, though, it remains challenging to scale graph transformers to large graphs while maintaining accuracy competitive with message-passing networks. In this paper, we introduce Exphormer, a framework for building powerful and scalable graph transformers. Exphormer consists of a sparse attention mechanism based on two mechanisms: virtual global nodes and expander graphs, whose mathematical characteristics, such as spectral expansion, pseduorandomness, and sparsity, yield graph transformers with complexity only linear in the size of the graph, while allowing us to prove desirable theoretical properties of the resulting transformer models. We show that incorporating Exphormer into the recently-proposed GraphGPS framework produces models with competitive empirical results on a wide variety of graph datasets, including state-of-the-art results on three datasets. We also show that Exphormer can scale to datasets on larger graphs than shown in previous graph transformer architectures. Code can be found at \url{https://github.com/hamed1375/Exphormer}.
Multiple Description Video Coding for Real-Time Applications using HEVC

Remote control vehicles require the transmission of large amounts of data, and video is one of the most important sources for the driver. To ensure reliable video transmission, the encoded video stream is transmitted simultaneously over multiple channels. However, this solution incurs a high transmission cost. To address this issue, it is necessary to use more efficient video encoding methods that can make the video stream robust to noise. Moreover it should have a less complexity to adapt to the real time requirement. In this paper, we propose a low-complexity, low-latency 2-channel Multiple Description Coding (MDC) solution with an adaptive Instantaneous Decoder Refresh (IDR) frame period, which is compatible with the HEVC standard with adaptive redundancy adjustment. This method shows a better resistance to high packet loss rates with lower complexity.
Quantum stochastic thermodynamics: A semiclassical theory in phase space

A formalism for quantum many-body systems is proposed through a semiclassical treatment in phase space, allowing us to establish a stochastic thermodynamics incorporating quantum statistics. Specifically, we utilize a stochastic Fokker-Planck equation as the dynamics at the mesoscopic level. Here, the noise term characterizing the fluctuation of the flux density accounts for the finite-$N$ effects of random collisions between the system and the reservoir. Accordingly, the stationary solution is a quasi-equilibrium state in a canonical system. We define stochastic thermodynamic quantities based on the trajectories of the phase-space distribution. The conservation law of energy, $H$ theorem and fluctuation theorems are therefore obtained. Our work sets an alternative formalism of quantum stochastic thermodynamics that is independent of the two-point measurement scheme. The numerous projective measurements of quantum systems are replaced by the sampling of the phase-space distribution, offering hope for experimental verifications in the future.
Uncovering the Handwritten Text in the Margins: End-to-end Handwritten Text Detection and Recognition

The pressing need for digitization of historical documents has led to a strong interest in designing computerised image processing methods for automatic handwritten text recognition. However, not much attention has been paid on studying the handwritten text written in the margins, i.e. marginalia, that also forms an important source of information. Nevertheless, training an accurate and robust recognition system for marginalia calls for data-efficient approaches due to the unavailability of sufficient amounts of annotated multi-writer texts. Therefore, this work presents an end-to-end framework for automatic detection and recognition of handwritten marginalia, and leverages data augmentation and transfer learning to overcome training data scarcity. The detection phase involves investigation of R-CNN and Faster R-CNN networks. The recognition phase includes an attention-based sequence-to-sequence model, with ResNet feature extraction, bidirectional LSTM-based sequence modeling, and attention-based prediction of marginalia. The effectiveness of the proposed framework has been empirically evaluated on the data from early book collections found in the Uppsala University Library in Sweden. Source code and pre-trained models are available at Github.
A dual basis approach to multidimensional scaling: spectral analysis and graph regularity

Classical multidimensional scaling (CMDS) is a technique that aims to embed a set of objects in a Euclidean space given their pairwise Euclidean distance matrix. The main part of CMDS is based on double centering a squared distance matrix and employing a truncated eigendecomposition to recover the point coordinates. A central result in CMDS connects the squared Euclidean matrix to a Gram matrix derived from the set of points. In this paper, we study a dual basis approach to classical multidimensional scaling. We give an explicit formula for the dual basis and fully characterize the spectrum of an essential matrix in the dual basis framework. We make connections to a related problem in metric nearness.
Small-scale structure and dynamics of the lower solar atmosphere

The chromosphere of the quiet Sun is a highly intermittent and dynamic phenomenon. Three-dimensional radiation (magneto-)hydrodynamic simulations exhibit a mesh-like pattern of hot shock fronts and cool expanding post-shock regions in the sub-canopy part of the inter-network. This domain might be called “fluctosphere”. The pattern is produced by propagating shock waves, which are excited at the top of the convection zone and in the photospheric overshoot layer. New high-resolution observations reveal a ubiquitous small-scale pattern of bright structures and dark regions in-between. Although it qualitatively resembles the picture seen in models, more observations – e.g. with the future ALMA – are needed for thorough comparisons with present and future models. Quantitative comparisons demand for synthetic intensity maps and spectra for the three-dimensional (magneto-)hydrodynamic simulations. The necessary radiative transfer calculations, which have to take into account deviations from local thermodynamic equilibrium, are computationally very involved so that no reliable results have been produced so far. Until this task becomes feasible, we have to rely on careful qualitative comparisons of simulations and observations. Here we discuss what effects have to be considered for such a comparison. Nevertheless we are now on the verge of assembling a comprehensive picture of the solar chromosphere in inter-network regions as dynamic interplay of shock waves and structuring and guiding magnetic fields.
Self-Supervised Facial Action Unit Detection with Region and Relation Learning

Facial action unit (AU) detection is a challenging task due to the scarcity of manual annotations. Recent works on AU detection with self-supervised learning have emerged to address this problem, aiming to learn meaningful AU representations from numerous unlabeled data. However, most existing AU detection works with self-supervised learning utilize global facial features only, while AU-related properties such as locality and relevance are not fully explored. In this paper, we propose a novel self-supervised framework for AU detection with the region and relation learning. In particular, AU related attention map is utilized to guide the model to focus more on AU-specific regions to enhance the integrity of AU local features. Meanwhile, an improved Optimal Transport (OT) algorithm is introduced to exploit the correlation characteristics among AUs. In addition, Swin Transformer is exploited to model the long-distance dependencies within each AU region during feature learning. The evaluation results on BP4D and DISFA demonstrate that our proposed method is comparable or even superior to the state-of-the-art self-supervised learning methods and supervised AU detection methods.
On the Soundness of XAI in Prognostics and Health Management (PHM)

The aim of predictive maintenance, within the field of prognostics and health management (PHM), is to identify and anticipate potential issues in the equipment before these become serious. The main challenge to be addressed is to assess the amount of time a piece of equipment will function effectively before it fails, which is known as remaining useful life (RUL). Deep learning (DL) models, such as Deep Convolutional Neural Networks (DCNN) and Long Short-Term Memory (LSTM) networks, have been widely adopted to address the task, with great success. However, it is well known that these kinds of black box models are opaque decision systems, and it may be hard to explain their outputs to stakeholders (experts in the industrial equipment). Due to the large number of parameters that determine the behavior of these complex models, understanding the reasoning behind the predictions is challenging. This paper presents a critical and comparative revision on a number of explainable AI (XAI) methods applied on time series regression models for PM. The aim is to explore XAI methods within time series regression, which have been less studied than those for time series classification. This study addresses three distinct RUL problems using three different datasets, each with its own unique context: gearbox, fast-charging batteries, and turbofan engine. Five XAI methods were reviewed and compared based on a set of nine metrics that quantify desirable properties for any XAI method. One of the metrics introduced in this study is a novel metric. The results show that Grad-CAM is the most robust method, and that the best layer is not the bottom one, as is commonly seen within the context of image processing.
KGNv2: Separating Scale and Pose Prediction for Keypoint-Based 6-DoF Grasp Synthesis on RGB-D Input

We propose an improved keypoint approach for 6-DoF grasp pose synthesis from RGB-D input. Keypoint-based grasp detection from image input demonstrated promising results in a previous study, where the visual information provided by color imagery compensates for noisy or imprecise depth measurements. However, it relies heavily on accurate keypoint prediction in image space. We devise a new grasp generation network that reduces the dependency on precise keypoint estimation. Given an RGB-D input, the network estimates both the grasp pose and the camera-grasp length scale. Re-design of the keypoint output space mitigates the impact of keypoint prediction noise on Perspective-n-Point (PnP) algorithm solutions. Experiments show that the proposed method outperforms the baseline by a large margin, validating its design. Though trained only on simple synthetic objects, our method demonstrates sim-to-real capacity through competitive results in real-world robot experiments.
A robust face recognition method using edge-based features

Face recognition, as one of the most interesting and successful applications of image understanding and machine vision, has grown a lot of attention during the past years. Basically human faces are very similar in structure with minor differences from person to person. In this paper a new robust face recognition method is proposed which exploit edge-based features of faces. The performance of this method is independent from the scenario of face recognition. Difficult scenarios of face recognition are including pose variation, illumination conditions, scale variability, images taken years apart, glasses, moustaches, beards, and low quality image.