Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhanced quantum sensing with room-temperature solid-state masers

Quantum sensing with solid-state electron spin systems finds broad applications in diverse areas ranging from material and biomedical sciences to fundamental physics. Exploiting collective behavior of noninteracting spins holds the promise of pushing the detection limit to even lower levels, while to date, those levels are scarcely reached because of the broadened linewidth and inefficient readout of solid-state spin ensembles. Here, we experimentally demonstrate that such drawbacks can be overcome by a reborn maser technology at room temperature in the solid state. Owing to maser action, we observe a fourfold reduction in the electron paramagnetic resonance linewidth of an inhomogeneously broadened molecular spin ensemble, which is narrower than the same measured from single spins at cryogenic temperatures. The maser-based readout applied to near zero-field magnetometry showcases the measurement signal-to-noise ratio of 133 for single shots. This technique would be an important addition to the toolbox for boosting the sensitivity of solid-state ensemble spin sensors.
SparseDet: Improving Sparsely Annotated Object Detection with Pseudo-positive Mining

Training with sparse annotations is known to reduce the performance of object detectors. Previous methods have focused on proxies for missing ground truth annotations in the form of pseudo-labels for unlabeled boxes. We observe that existing methods suffer at higher levels of sparsity in the data due to noisy pseudo-labels. To prevent this, we propose an end-to-end system that learns to separate the proposals into labeled and unlabeled regions using Pseudo-positive mining. While the labeled regions are processed as usual, self-supervised learning is used to process the unlabeled regions thereby preventing the negative effects of noisy pseudo-labels. This novel approach has multiple advantages such as improved robustness to higher sparsity when compared to existing methods. We conduct exhaustive experiments on five splits on the PASCAL-VOC and COCO datasets achieving state-of-the-art performance. We also unify various splits used across literature for this task and present a standardized benchmark. On average, we improve by 2.6, 3.9 and 9.6 mAP over previous state-of-the-art methods on three splits of increasing sparsity on COCO. Our project is publicly available at cs.umd. edu/~sakshams/SparseDet.
CLOVER Robot: A Minimally Actuated Jumping Robotic Platform for Space Exploration

Robots have been critical instruments to exploration of extreme environments by providing access to environments beyond human limitations. Jumping robot concepts are attractive solutions to negotiate complex and cluttered terrain. However, among the engineering challenges that need to be addressed to enable sustained operation of jumping robot concepts in extreme environments, the reduction of mechanical failure modes is one of the most fundamental. This study sets out to develop a jumping robot design, with a focus on a minimal actuation to support reduced mechanism maintenance and thus limit the number of mechanical failure modes. We present the synthesis of a Sarrus-style linkage to constrain the system to a single translational degree of freedom thus removing the need for synchronising gears, which exhibit high failure rates in dusty environments. We have restricted the present research to vertical solid jumps to assess the performance of the fundamental main-drive linkage. A laboratory demonstrator assists the transfer of theoretical concepts and approaches to practical implementation. The laboratory demonstrator performs jumps with 63% potential-to-kinetic energy conversion efficiency, with a theoretical maximum of 73%. Satisfactory operation opens up design optimisation and directional jump capability towards the development of a jumping robotic platform for extreme environments exploration.
A Cubic Matrix Action for the Standard Model and Beyond

We propose a new framework for matrix theories that are equivalent to field theories on a toroidal spacetime. The correspondence is accomplished via infinite Toeplitz matrices whose entries match the field degrees of freedom on an energy-momentum lattice, thereby replacing the background geometry with matrix indices. These matrix theories can then be embedded into the purely cubic action of a single matrix and combined into a common universality class. We reconstruct the Standard Model action in this framework and discuss its extensions within the same class.
Novel Algorithms for Computing Correlation Functions of Nuclei

Factor Tree ←→ E = 5 T (1) 122T (2) 232 + T (1) 123T (2) 232 − T (1) 123T (2) 323 = 5 T (1) 122T (2) 232 + T (1) 123 ( T (2) 232 − T (2) 323 ) Figure 3: Abstract Factor Tree example (left) with corresponding tensor expression E (right). T (1) i jk , T (2) i jk are rank-3 tensors. C, we can expect a high degree of both term-wise and factor-wise ‘compression’ in multiplicity accumulation and factorisation. To target expressions of this type we construct factor trees, in which the sum of all root-to-leaf paths through the tree, weighted by the leaf coefficients, is equal to the original tensor expression. A natural way to represent an abstract factor tree that corresponds to a tensor expression E as in Figure 3 on a computer might be to represent each node as a structure and to form links between the nodes using pointers. This would be highly inefficient in two senses: first, it doesn’t exploit the predictable access pattern (depth-first traversal) that evaluation of the factor trees entails and would as a result be significantly memory access bound; and second, it would have a large memory footprint, taken up almost entirely by pointers of parent → child relationships. A representation that is much more efficient on both counts is given by a linearised factor tree. As shown in Figure 4, a linearised factor tree is represented by three arrays: factors, children, and coefficients. The factors array is given by the depth-first traversal of the vertices of the abstract factor tree. The children array is given by the number of children of the vertices in depth-first traversal order. The coefficients array is the list of leaf coefficients in depth-first traversal order. To measure the performance improvement associated with using factor trees, Figure 5 compares wall-clock time for correlator computation (excluding propagator and hadron block computation) using the helium-3 I/II operators as in Eq. (2). Figure 5 demonstrates that factor trees offer between one and two orders of magnitude improvement over unoptimised correlator computations for light nuclei using Hadron Blocks. An up-front computational cost for constructing the factor tree not included in Figure 5 is required and proportional to the unoptimised correlator computational cost. The extra calculation, however, is only required before the first configuration is analysed and hence the cost may be amortised over the full set of configurations. Although the factor tree method shows promising speed-ups for small nuclei, it should be noted that the memory used will become prohibitive for large nuclei2. 2Constructing factor trees directly to hard-disk could extend the method to larger nuclei.
An ultrasonography-based approach for optical diagnostics and phototherapy treatment strategies

Present Address * Optics Group, School of Physics and Astronomy, Kelvin Building, University of Glagsow, Glasgow G12 8QQ Currently, diagnostic medicine uses a multitude of tools ranging from ionising radiation to histology analysis. With advances in piezoelectric crystal technology, high-frequency ultrasound imaging has developed to achieve comparatively high resolution without the drawbacks of ionising radiation. This research proposes a lowcost, non-invasive and real-time protocol for informing photo-therapy procedures using ultrasound imaging. We combine currently available ultrasound procedures with Monte Carlo methods for assessing light transport and photo-energy deposition in the tissue. The measurements from high-resolution ultrasound scans is used as input for optical simulations. Consequently, this provides a pipeline that will inform medical practitioners for better therapy strategy planning. While validating known inferences of light transport through biological tissue, our results highlight the range of information such as temporal monitoring and energy deposition at varying depths. This process also retains the flexibility of testing various wavelengths for individual-specific geometries and anatomy.
Application of Reinforcement Learning and Deep Learning in Multiple-Input and Multiple-Output (MIMO) Systems

The current wireless communication infrastructure has to face exponential development in mobile traffic size, which demands high data rate, reliability, and low latency. MIMO systems and their variants (i.e., Multi-User MIMO and Massive MIMO) are the most promising 5G wireless communication systems technology due to their high system throughput and data rate. However, the most significant challenges in MIMO communication are substantial problems in exploiting the multiple-antenna and computational complexity. The recent success of RL and DL introduces novel and powerful tools that mitigate issues in MIMO communication systems. This article focuses on RL and DL techniques for MIMO systems by presenting a comprehensive review on the integration between the two areas. We first briefly provide the necessary background to RL, DL, and MIMO. Second, potential RL and DL applications for different MIMO issues, such as detection, classification, and compression; channel estimation; positioning, sensing, and localization; CSI acquisition and feedback, security, and robustness; mmWave communication and resource allocation, are presented.
Chaos and Order in Event-Triggered Control

Event-triggered control (ETC) is claimed to provide significant reductions in sampling frequency when compared to periodic sampling, but little is formally known about its generated traffic. This work shows that ETC can exhibit very complex, even chaotic traffic, especially when the triggering condition is aggressive in reducing communications. First, by looking at the map dictating the evolution of states sampled, we characterize limit traffic patterns by observing invariant lines and planes through the origin, as well as their attractivity. Then, we present abstraction-based methods to compute limit metrics, such as limit average and limit inferior intersample time of periodic ETC (PETC), with considerations to the robustness of such metrics, as well as measuring the emergence of chaos. The methodology and tools allow us to find ETC examples that provably outperform periodic sampling in terms of average IST. In particular for PETC, we prove that this requires aperiodic or chaotic traffic.
The Functional Role of Extracellular Matrix Proteins in Cancer

Simple Summary Extracellular matrix is a three-dimensional network of macromolecules that provide structural and biochemical support to surrounding cells. Extracellular matrix plays a critical role in the development and progression of cancer. The extracellular matrix of the tumor is very different from the matrix of the normal tissue. Mainly fibroblasts produce and regulate matrix remodeling, but in cancer, the tumor matrix also originates from cancer cells. We describe the mechanisms of how the protein composition and structure of the extracellular matrix changes during cancer progression and how abnormal matrix deregulates the behavior of stromal cells and influences cancer progression. Abstract The extracellular matrix (ECM) is highly dynamic as it is constantly deposited, remodeled and degraded to maintain tissue homeostasis. ECM is a major structural component of the tumor microenvironment, and cancer development and progression require its extensive reorganization. Cancerized ECM is biochemically different in its composition and is stiffer compared to normal ECM. The abnormal ECM affects cancer progression by directly promoting cell proliferation, survival, migration and differentiation. The restructured extracellular matrix and its degradation fragments (matrikines) also modulate the signaling cascades mediated by the interaction with cell-surface receptors, deregulate the stromal cell behavior and lead to emergence of an oncogenic microenvironment. Here, we summarize the current state of understanding how the composition and structure of ECM changes during cancer progression. We also describe the functional role of key proteins, especially tenascin C and fibronectin, and signaling molecules involved in the formation of the tumor microenvironment, as well as the signaling pathways that they activate in cancer cells.
A Novel Role of Pipecolic Acid Biosynthetic Pathway in Drought Tolerance through the Antioxidant System in Tomato

With global warming and water shortage, drought stress is provoking an increasing impact on plant growth, development, and crop productivity worldwide. Pipecolic acid (Pip) is an emerging lysine catabolite in plants, acting as a critical element in disease resistance with a related signal pathway of phytohormone salicylic acid (SA). While SA plays a vital role in various abiotic stresses, the role of Pip in plant response to abiotic stresses, especially drought, remains largely unknown. To address this issue, Pip biosynthetic gene Slald1 mutants and hydroxylated modification gene Slfmo1 mutants were generated using CRISPR-Cas9 gene-editing approaches. Drought resistance dramatically increased in Slald1 mutants compared with wild-type, which was associated with increased CO2 assimilation, photosystems activities, antioxidant enzymes activities, ascorbate and glutathione content, and reduced reactive oxygen species accumulation, lipid peroxidation and protein oxidation. On the contrary, Slfmo1 mutants were more sensitive to drought, showing damaged photosystems and impaired antioxidant systems, which were significantly alleviated by exogenous ascorbate. Our results demonstrate that Pip biosynthesis and hydroxylated modification pathways play a critical role in drought tolerance through the antioxidant system in tomato. This knowledge can be helpful to breed improved crop cultivars that are better equipped with drought resistance.
A comprehensive study on abnormalities associated with red blood cells in Saudi adult patients

Objectives: The present work aimed to study red blood cells (RBCs) parameters data of Saudi patients to cover hematological patterns and evaluate abnormality prevalence. Methods: RBCs parameters data of adult Saudi patients who attended Prince Nasser bin Saad Al-Sudairy Hospital in Alghat province between January 1, 2019 and December 31 2019 were collected and studied. Results: The study included 1663 patients; 814 (49%) males and 849 (51%) females. The proportion of patients with anemia as defined by low levels of hemoglobin (Hb) was similar in males (20%) and females (21%) with a clear drop in the percentage of anemia among females aged 51–65 years. Data from Alghat province showed bigger proportion of patients with macrocytic anemia (high mean cell volume [MCV] = 35% in males and 25% in females) compared with microcytic anemia (low MCV = 7% in males and 19% in females). Microcytic hypochromic anemia was found to be common in women (18%) compared with men (6%), with the highest prevalence being detected in females (31%) at age 46–50. Conclusion: This is the first study to shed light on RBCs data from Alghat province, and provide insights into anemia in adults. Anemia was seen in 20–21% of the patients, with macrocytic anemia being the most prevalent type of anemia in the studied population.
Proposal for an IIoT Device Solution According to Industry 4.0 Concept

Today, Industrial Internet of Things (IIoT) devices are very often used to collect manufacturing process data. The integration of industrial data is increasingly being promoted by the Open Platform Communications United Architecture (OPC UA). However, available IIoT devices are limited by the features they provide; therefore, we decided to design an IIoT device taking advantage of the benefits arising from OPC UA. The design procedure was based on the creation of sequences of steps resulting in a workflow that was transformed into a finite state machine (FSM) model. The FSM model was transformed into an OPC UA object, which was implemented in the proposed IIoT. The OPC UA object makes it possible to monitor events and provide important information based on a client’s criteria. The result was the design and implementation of an IIoT device that provides improved monitoring and data acquisition, enabling improved control of the manufacturing process.
Time-Domain Joint Training Strategies of Speech Enhancement and Intent Classification Neural Models

Robustness against background noise and reverberation is essential for many real-world speech-based applications. One way to achieve this robustness is to employ a speech enhancement front-end that, independently of the back-end, removes the environmental perturbations from the target speech signal. However, although the enhancement front-end typically increases the speech quality from an intelligibility perspective, it tends to introduce distortions which deteriorate the performance of subsequent processing modules. In this paper, we investigate strategies for jointly training neural models for both speech enhancement and the back-end, which optimize a combined loss function. In this way, the enhancement front-end is guided by the back-end to provide more effective enhancement. Differently from typical state-of-the-art approaches employing on spectral features or neural embeddings, we operate in the time domain, processing raw waveforms in both components. As application scenario we consider intent classification in noisy environments. In particular, the front-end speech enhancement module is based on Wave-U-Net while the intent classifier is implemented as a temporal convolutional network. Exhaustive experiments are reported on versions of the Fluent Speech Commands corpus contaminated with noises from the Microsoft Scalable Noisy Speech Dataset, shedding light and providing insight about the most promising training approaches.
Independent effect of gestational weight gain and prepregnancy obesity on pregnancy outcomes among Saudi women: A sub-cohort analysis from Riyadh mother and baby cohort study (RAHMA)

Background Gestational weight gain (GWG) and prepregnancy obesity are garnering more attention as determining factors of pregnancy outcomes when it comes to the wellbeing of both the mother and her baby. This study was conducted to describe the pattern of GWG among participants of Riyadh Mother and Baby Multicenter Cohort Study (RAHMA) and to investigate the detrimental effects of excessive GWG and prepregnancy obesity on pregnancy outcomes. Methods RAHMA is a multicentre cohort study conducted in three hospitals in Riyadh, Saudi Arabia. Participants were categorized according to the Institute of Medicine into inadequate, adequate, and excessive GWG, and stratified by body mass index (BMI) into under/normal weight, overweight, and obese. To examine the independent effect of maternal prepregnancy obesity and GWG, a multivariate regression model was used and adjusted odds ratio (AOR) and 95% Confidence Interval (CI) for each outcome were calculated. Results A total of 7029 participants were included in this study; 31.8% had adequate GWG, 25.9% had excessive GWG and 42.3% had inadequate GWG, while 29.7% had normal BMI, 33.3% were overweight, 34.8% were obese, and 2.2% were underweight. Excessive GWG was independently associated with increased risk of hypertensive events, (AOR = 1.77, 95% CI 1.20–2.63). Obesity was associated with higher risk of gestational diabetes (AOR 2.11, 95% CI 1.76–2.53), hypertensive events (AOR 2.06, 95% CI 1.48–3.01), and delivery by emergency caesarean section (AOR = 1.63, 95% CI 1.35–1.97). Infants of obese women had increased odds of macrosomia (AOR 3.11, 95% CI 1.94–4.99) and lower odds of low birth weight (AOR = 0.68, 95% CI 0.53–0.88). Conclusion In comparison to excessive GWG, which increases the risk of hypertensive events during pregnancy, prepregnancy obesity is associated with more adverse outcomes including GDM, hypertensive events in pregnancy and emergency CS.
GDNF neurotrophic factor signalling determines the fate of dermal fibroblasts in wound‐induced hair neogenesis and skin regeneration

We propose that GDNF, a glial cell line‐derived neurotrophic factor, can promote hair follicle neogenesis and skin regeneration after wounding by directing the fate of dermal fibroblasts. Our hypothesis is largely based on detailed GDNF and receptor analysis during skin regenerative stages, as well as the induction of GDNF receptors after wounding between the pro‐regenerative spiny mouse (genus Acomys) and its less‐regenerative descendant, the house mouse (Mus musculus). To characterize the GDNF‐target cells, we will conduct a series of lineage‐tracing experiments in conjunction with single‐cell RNA and assay for transposase‐accessible chromatin sequencing experiments. The heterogenetic dynamics of skin regeneration have yet to be fully defined, and this research will help to advance the fields of regenerative medicine and biology. Finally, we believe that stimulating the GDNF signalling pathway in fibroblasts from less‐regenerative animals, such as humans, will promote skin regeneration, morphogenesis and scarless wound healing.
From azoospermia to macrozoospermia, a phenotypic continuum due to mutations in the ZMYND15 gene

Thanks to tremendous advances in sequencing technologies and in particular to whole exome sequencing (WES), many genes have now been linked to severe sperm defects. A precise genetic diagnosis is obtained for a minority of patients and only for the most severe defects like azoospermia or macrozoospermia which is very often due to defects in the aurora kinase C (AURKC gene. Here, we studied a subject with a severe oligozoospermia and a phenotypic diagnosis of macrozoospermia. AURKC analysis did not reveal any deleterious variant. WES was then initiated which permitted to identify a homozygous loss of function variant in the zinc finger MYND-type containing 15 (ZMYND15 gene. ZMYND15 has been described to serve as a switch for haploid gene expression, and mice devoid of ZMYND15 were shown to be sterile due to nonobstructive azoospermia (NOA). In man, ZMYND15 has been associated with NOA and severe oligozoospermia. We confirm here that the presence of a bi-allelic ZMYND15 variant induces a severe oligozoospermia. In addition, we show that severe oligozoospermia can be associated macrozoospermia, and that a phenotypic misdiagnosis is possible, potentially delaying the genetic diagnosis. In conclusion, genetic defects in ZMYND15 can induce complete NOA or severe oligozoospermia associated with a very severe teratozoospermia. In our experience, severe oligozoospermia is often associated with severe teratozoospermia and can sometimes be misinterpreted as macrozoospermia or globozoospermia. In these instances, specific AURKC or dpy-19 like 2 (DPY19L2) diagnosis is usually negative and we recommend the direct use of a pan-genomic techniques such as WES.