Miracle-dz/UniFAR-FTUs huggingface dataset:
Channel Estimation for Large Intelligent Surface Aided MISO Communications: From LMMSE to Deep Learning Solutions

We consider multi-antenna wireless systems aided by large intelligent surfaces (LIS). LIS presents a new physical layer technology for improving coverage and energy efficiency by intelligently controlling the propagation environment. In practice however, achieving the anticipated gains of LIS requires accurate channel estimation. Recent attempts to solve this problem have considered the least-squares (LS) approach, which is simple but also sub-optimal. The optimal channel estimator, based on the minimum mean-squared-error (MMSE) criterion, is challenging to obtain and is non-linear due to the non-Gaussianity of the effective channel seen at the receiver. Here we present approaches to approximate the optimal MMSE channel estimator. As a first approach, we analytically develop the best linear estimator, the LMMSE, together with a corresponding majorization-minimization based algorithm designed to optimize the LIS phase shift matrix during the training phase. This estimator is shown to yield improved accuracy over the LS approach by exploiting second-order statistical properties of the wireless channel and the noise. To further improve performance and better approximate the globally-optimal MMSE channel estimator, we propose data-driven non-linear solutions based on deep learning. Specifically, by posing the MMSE channel estimation problem as an image denoising problem, we propose two convolutional neural network (CNN) based methods to perform the denoising and approximate the optimal MMSE channel estimation solution. Our numerical results show that these CNN-based estimators give superior performance compared with linear estimation approaches. They also have low computational complexity requirements, thereby motivating their potential use in future LIS-aided wireless communication systems.
Compositional study of trans-Neptunian objects at {\lambda} > 2.2 {\mu}m

Using data from the Infrared Array Camera on the Spitzer Space Telescope, we present photometric observations of a sample of 100 trans-Neptunian objects (TNOs) beyond 2.2 {\mu}m. These observations, collected with two broad-band filters centered at 3.6 and 4.5 {\mu}m, were done in order to study the surface composition of TNOs, which are too faint to obtain spectroscopic measurements. With this aim, we have developed a method for the identification of different materials that are found on the surfaces of TNOs. In our sample, we detected objects with colors that are consistent with the presence of small amounts of water and were able to distinguish between surfaces that are predominately composed of complex organics and amorphous silicates. We found that 86% of our sample have characteristics that are consistent with a certain amount of water ice, and the most common composition (73% of the objects) is a mixture of water ice, amorphous silicates, and complex organics. 23% of our sample may include other ices such as carbon monoxide, carbon dioxide, methane or methanol. Additionally, only small objects seem to have surfaces dominated by silicates. This method is a unique tool for the identification of complex organics and to obtain the surface composition of extremely faint objects. Also, this method will be beneficial when using the James Webb Space Telescope for differentiating groups within the trans-Neptunian population.
Language Models not just for Pre-training: Fast Online Neural Noisy Channel Modeling

Pre-training models on vast quantities of unlabeled data has emerged as an effective approach to improving accuracy on many NLP tasks. On the other hand, traditional machine translation has a long history of leveraging unlabeled data through noisy channel modeling. The same idea has recently been shown to achieve strong improvements for neural machine translation. Unfortunately, na ̈ıve noisy channel modeling with modern sequence to sequence models is up to an order of magnitude slower than alternatives. We address this issue by introducing efficient approximations to make inference with the noisy channel approach as fast as strong ensembles while increasing accuracy. We also show that the noisy channel approach can outperform strong pre-training results by achieving a new state of the art on WMT Romanian-English translation.
Long-Acting Injectable GLP-1 Receptor Agonists for the Treatment of Adults with Type 2 Diabetes: Perspectives from Clinical Practice

Abstract Randomized controlled trials (RCTs) have consistently shown glycemic and extra-glycemic benefits of long-acting injectable glucagon-like-peptide-1 receptor agonists (GLP-1RAs, liraglutide, albiglutide, exenatide once-weekly, dulaglutide, and semaglutide) in terms of reduction in the rates of cardiovascular events and mortality among patients with type 2 diabetes. Recently, the analyses of large datasets collecting routinely-accumulated data from clinical practice (ie, real-world studies, RWS) have provided new opportunities to complement the information obtained from RCTs. In this narrative review, we addressed clinically relevant questions that might be answered by well-conducted RWS: are subjects treated with GLP-1RAs in the “real-world” similar to those included in RCTs? Is the performance of GLP-1RA observed in the RWS (effectiveness) similar to that described in RCTs (efficacy)? Is the effectiveness similar in population of patients generally under-represented in RCTs? Are the cardiovascular benefits of GLP-1RAs confirmed in RWS? We also describe a few comparisons currently un-explored by specific RCTs, such as direct comparison between different administration strategies (eg, fixed- versus flexible-combination with basal-insulin) or between GLP-1RAs versus dipeptidyl-peptidase-4 inhibitor (DDP4i) or versus sodium/glucose cotransporter-2 inhibitors (SGLT-2i) on hard cardio-renal outcomes. Altogether, RWS provide highly informative information on treatment with GLP-1RAs. On the one side, RWS showed different clinical characteristics between subjects enrolled in RCTs versus those attending real-world clinics and receiving a GLP-1RA. On the other hand, RWS showed that GLP-1RA effectiveness is overall consistent in subgroups of patients less represented in RCTs. In addition, RWS allowed the identification of modifiable factors (eg, titration or adherence) that might guide physicians towards better GLP-1RAs use. Finally, multiple RWS reported better cardio-renal outcomes with GLP-1RAs than with DPP-4i, while initial findings from RWS described a weaker cardiovascular protection compared to SGLT-2i. Therefore, there is the need for further RWS and RCTs comparing these different classes of glucose lowering medications.
Common trends in recruitment dynamics of north‐east Atlantic fish stocks and their links to environment, ecology and management

Recruitment dynamics are challenging to assess or predict because of the many underlying drivers that vary in their relevance over time and space. Stock size, demographic and trait composition, condition and distribution of spawning fish and the spatio‐temporal dynamics of trophic and environmental interactions all influence recruitment processes. Exploring common patterns among stocks and linking them to potential drivers may therefore provide insights into key mechanisms of recruitment dynamics. Here, we analysed stock‐recruitment data of 64 stocks from the north‐east Atlantic Ocean for common trends in variation and synchrony among stocks using correlation, cluster and dynamic factor analyses. We tested common trends in recruitment success for relationships with large‐scale environmental processes as well as stock state indicators, and we explored links between recruitment success and demographic, environmental and ecological variables for a subset of individual stocks. The results revealed few statistically significant correlations between stocks but showed that underlying common trends in recruitment success are linked to environmental indices and management indicators. Statistical analyses confirmed previously suggested relationships of environmental–ecological factors such as the subpolar gyre and Norwegian coastal current with specific stocks, and indicated a large relevance of spawning stock biomass and demographics, as well as predation, whereas other suggested relationships were not supported by the data. Our study shows that despite persistent challenges in determining drivers of recruitment due to poor data quality and unclear mechanisms, combining different data analysis techniques can improve our understanding of recruitment dynamics in fish stocks.
Causal Inference Methods for Combining Randomized Trials and Observational Studies: A Review

With increasing data availability, treatment causal effects can be evaluated across different dataset, both randomized trials and observational studies. Randomized trials isolate the effect of the treatment from that of unwanted (confounding) co-occuring effects. But they may be applied to limited populations, and thus lack external validity. On the opposite large observational samples are often more representative of the target population but can conflate confounding effects with the treatment of interest. In this paper, we review the growing literature on methods for causal inference on combined randomized trial and observational studies, striving for the best of both worlds. We first discuss identification and estimation methods that improve generalizability of randomized controlled trials (RCTs) using the representativeness of observational data. Classical estimators include weighting, difference between conditional outcome models, and double robust estimators. We then discuss methods that combining RCTs and observational data to improve the (conditional) average treatment effect estimation, handling possible unmeasured confounding in the observational data. We also connect and contrast works developed in both the potential outcomes framework and the structural causal models framework. Finally, we compare the main methods using a simulation study and real world data to analyse the effect of tranexamic acid on the mortality rate in major trauma patients. Code to implement many of the methods is provided.
Excellent Thermoelectric and Piezoelectric Properties of Differently Stacked Layers of Two-Dimensional Transition Metal Dinitride HfN2

Two-dimensional (2D) transition metal dinitride (HfN2) has been studied for their optoelectronic, piezoelectric, and thermoelectric properties. Both monolayer and bilayer of HfN2 were studied using density functional theory (DFT) and Boltzmann transport equation (BTE). The bilayer of HfN2 with different stacking layers (AA and AB) showed different electronic properties. The optical property of the material suggests that it is a very good absorber in the ultraviolet (UV) region thus, can be used as a UV-photodetector and as an absorber layer in photovoltaic devices. The piezoelectric properties of the material also showed promising behavior as the piezoelectric stress and strain tensors have highest value of 8.97*(10)^(-10) C/m and 12.59 pm/V respectively for the bilayer. The piezoelectric tensors have highest value for AB stacked bilayer. The ZT value of 0.8 at 900 K is also highest for bilayer AB stacked HfN2. These high values of piezoelectric and thermoelectric parameters of the material suggest that the material would be an excellent choice as thermoelectric energy harvesting devices as well as mechanical stress sensor or actuator.
Exogenous bone marrow derived-putative endothelial progenitor cells attenuate ischemia reperfusion-induced vascular injury and renal fibrosis in mice dependent on pericytes

Rationale: Capillaries are composed of endothelial cells and the surrounding mural cells, pericytes. Microvascular repair after injury involves not only the proliferation of endothelial cells but also pericyte-based vessel stabilization. Exogenous bone marrow derived-putative endothelial progenitor cells (b-pEPCs) have the potential for vascular repair; however, their effect on vascular structure stabilization and pericyte-related pathobiological outcomes in the injured kidney has not been fully examined. Methods: We applied ischemia-reperfusion (IR) to induce renal vascular injury and renal fibrosis in mice. Platelet-derived growth factor receptor β (PDGFR-β)-DTR-positive mice were generated to deplete pericytes, and exogenous b-pEPCs and the PDGFR-β ligand, PDGF chain B (PDGF-BB), were employed to explore the relationship among b-pEPCs, pericytes, vascular repair, and early renal fibrosis. Results: Administration of b-pEPCs reduced IR-induced pericyte-endothelial detachment, pericyte proliferation, and myofibroblast transition via a paracrine mode, which preserved not only vascular stabilization but also ameliorated IR-initiated renal fibrosis. PDGF-BB upregulated the expression of PDGFR-β, exacerbated vascular abnormality, and pericyte-myofibroblast transition, which were ameliorated by b-pEPCs administration. The exogenous b-pEPCs and their culture medium (CM) induced vascular injury protection, and renal fibrosis was blocked by selective deletion of pericytes. Conclusion: Exogenous b-pEPCs directly protect against IR-induced vascular injury and prevent renal fibrosis by inhibiting the activation of PDGFR-β-positive pericytes.
FRDet: Balanced and Lightweight Object Detector based on Fire-Residual Modules for Embedded Processor of Autonomous Driving

For deployment on an embedded processor for autonomous driving, the object detection network should satisfy all of the accuracy, real-time inference, and light model size requirements. Conventional deep CNN-based detectors aim for high accuracy, making their model size heavy for an embedded system with limited memory space. In contrast, lightweight object detectors are greatly compressed but at a significant sacrifice of accuracy. Therefore, we propose FRDet, a lightweight one-stage object detector that is balanced to satisfy all the constraints of accuracy, model size, and real-time processing on an embedded GPU processor for autonomous driving applications. Our network aims to maximize the compression of the model while achieving or surpassing YOLOv3 level of accuracy. This paper proposes the Fire-Residual (FR) module to design a lightweight network with low accuracy loss by adapting fire modules with residual skip connections. In addition, the Gaussian uncertainty modeling of the bounding box is applied to further enhance the localization accuracy. Experiments on the KITTI dataset showed that FRDet reduced the memory size by 50.8% but achieved higher accuracy by 1.12% mAP compared to YOLOv3. Moreover, the real-time detection speed reached 31.3 FPS on an embedded GPU board(NVIDIA Xavier). The proposed network achieved higher compression with comparable accuracy compared to other deep CNN object detectors while showing improved accuracy than the lightweight detector baselines. Therefore, the proposed FRDet is a well-balanced and efficient object detector for practical application in autonomous driving that can satisfies all the criteria of accuracy, real-time inference, and light model size.
A Randomized, Double-Blind, Placebo-Controlled, Multi-Centered Clinical Study to Evaluate the Efficacy and Safety of Artemisia annua L. Extract for Improvement of Liver Function

Non-alcoholic fatty liver disease (NAFLD) has the potential to develop into hepatic steatosis and progress to terminal liver diseases such as cirrhosis and hepatocellular carcinoma. This human clinical study was aimed to demonstrate that SPB-201 (powdered-water extract of Artemisia annua) can improve liver function in subjects with non-alcoholic liver dysfunction at mild to moderate levels. A decrease of 271% in aspartate aminotransferase (AST) level and a significant decrease of 334% in alanine aminotransferase (ALT) level was observed in the test group as compared to the control group at the 4 weeks follow-up. In addition, after 8 weeks, decreases of 199% in AST level and 216% in ALT level were reported in the test group as compared to the control group. These results confirmed that SPB-201 intake significantly enhanced liver function and health. Moreover, the Multidimensional Fatigue Scale score of the test group decreased but that of the control group increased, implicating that SPB-201 also eliminated overall fatigue. No significant adverse events were observed among all subjects during the study. Taken together, our clinical study confirmed the excellent efficacy and safety of SPB-201 in liver function improvement, showing the possibility of SPB-201 as a functional food to restore liver dysfunction and treat liver diseases.
Runaway Electron Generation by Decelerating Streamers in Inhomogeneous Atmosphere

The dynamics of positive and negative streamers is numerically simulated in atmospheric pressure air. It is shown that positive and negative streamers behave in radically different ways when decelerating and stopping. When the head potential drops, the negative streamer transits to the mode in which the propagation is due to the forward electron drift. In this case, the radius of the ionization wave front increases, whereas the electric field at the streamer head decreases further and the streamer stops. The only advancement mechanism for a positive streamer with decreasing head potential is a decrease in the effective radius of the ionization wave, leading to a local increase in the electric field. This mechanism compensates for the decrease in the efficiency of gas photoionization at small head diameters. The local electric field at the streamer head can exceed the runaway threshold when the head potential decreases to ~1.2 kV in atmospheric pressure air. In this case, pulsed generation of a beam of runaway electrons directed into the channel of a stopping positive streamer can occur. The energy of the formed pulsed electron beam can vary from 700 V (when increasing the photoionization rate by a factor of 10 with respect to the value in atmospheric pressure air) to 2.6 kV (in air with an absorbing additive that reduces the photoionization rate by a factor of 1000). It is possible that this behavior of decelerating positive streamers can explain the observed bursts of X-ray radiation during the streamer propagation.
Prevention of infection in primary THA and TKA

Rates of peri-prosthetic joint infection (PJI) in primary total hip and total knee arthroplasty range between 0.3% and 1.9%, and up to 10% in revision cases. Significant morbidity is associated with this devastating complication, the economic burden on our healthcare system is considerable, and the personal cost to the affected patient is immeasurable. The risk of surgical site infection (SSI) and PJI is related to surgical factors and patient factors such as age, body mass index (BMI), co-morbidities, and lifestyle. Reducing the risk of SSI in primary hip and knee arthroplasty requires a multi-faceted strategy including pre-operative patient bacterial decolonization, screening and avoidance of anaemia, peri-operative patient warming, skin antisepsis, povidone-iodine wound lavage, and anti-bacterial coated sutures. This article also considers newer concepts such as the influence of bearing surfaces on infection risk, as well as current controversies such as the potential effects of blood transfusion, laminar flow, and protective hoods and suits, on infection risk. Cite this article: EFORT Open Rev 2020;5:604-613. DOI: 10.1302/2058-5241.5. 200004
Effect of Light-Dark Cycle Misalignment on the Hypothalamic-Pituitary-Gonadal Axis, Testicular Oxidative Stress, and Expression of Clock Genes in Adult Male Rats

This study investigated the influence of circadian misalignment on the male reproductive system. Adult Sprague-Dawley male rats were exposed to prolonged light (20 h light : 4 h dark) or prolonged darkness (4 h light : 20 h dark) for 12 consecutive weeks. The somatic index of seminal vesicles and prostates increased due to prolonged light exposure. Sperm count and motility were enhanced solely by prolonged light exposure, whereas the percentage of sperm abnormalities was reduced by both prolonged light and darkness. The serum levels of reproductive hormones (follicle-stimulating hormone, luteinizing hormone, testosterone, and prolactin) were elevated, and the estradiol level was reduced by long-term light and dark exposure. Testicular total antioxidant capacity and antioxidant enzyme activities were improved, and lipid peroxidation was inhibited following chronic exposure to light or dark. Chronic light exposure increased, but chronic darkness decreased, testicular nitric oxide production. The mRNA expression of the hypothalamic and testicular clock genes including PER1-2, CRY1-2, BMAL1, CLOCK, and Rev-Erbα was altered by circadian disruption. Prolonged light exposure decreased the levels of thyroid hormones and suppressed the mRNA expression of adiponectin receptors 1 and 2. The immunohistochemical expression of proliferating cell nuclear antigen was decreased only by chronic darkness. The present study thus provides new insights into the physiological changes associated with long-term exposure to light or darkness, in which the expression levels of various clock gene mRNAs are modulated, reproductive hormones are increased, and the antioxidant enzyme system is ameliorated as mechanisms of adaptation to chronic circadian disruption.
Optimal multiple change-point detection for high-dimensional data

This manuscript makes two contributions to the field of change-point detection. In a general change-point setting, we provide a generic algorithm for aggregating local homogeneity tests into an estimator of change-points in a time series. Interestingly, we establish that the error rates of the collection of test directly translate into detection properties of the change-point estimator. This generic scheme is then applied to the problem of possibly sparse multivariate mean change-point detection setting. When the noise is Gaussian, we derive minimax optimal rates that are adaptive to the unknown sparsity and to the distance between change-points. For sub-Gaussian noise, we introduce a variant that is optimal in almost all sparsity regimes.
Unsupervised Contrastive Learning of Sound Event Representations

Self-supervised representation learning can mitigate the limitations in recognition tasks with few manually labeled data but abundant unlabeled data—a common scenario in sound event research. In this work, we explore unsupervised contrastive learning as a way to learn sound event representations. To this end, we propose to use the pretext task of contrasting differently augmented views of sound events. The views are computed primarily via mixing of training examples with unrelated backgrounds, followed by other data augmentations. We analyze the main components of our method via ablation experiments. We evaluate the learned representations using linear evaluation, and in two in-domain downstream sound event classification tasks, namely, using limited manually labeled data, and using noisy labeled data. Our results suggest that unsupervised contrastive pre-training can mitigate the impact of data scarcity and increase robustness against noisy labels.