Miracle-dz/UniFAR-FTUs huggingface dataset:
SRAR-RISs: Simultaneous Reflecting and Refracting Reconfigurable Intelligent Surfaces

In this letter, simultaneous reflecting and refracting reconfigurable intelligent surfaces (SRAR-RISs) are studied. Compared with RISs that only operate in the reflection mode, the coverage area of SRAR-RISs is extended via simultaneous reflection and refraction. We separately present a hardware model and channel models for the near-field region and the far-field region. The proposed channel models give closed-form expressions of the channel gains in terms of physical parameters of the SRAR-RIS and the geometrical information of the receivers. The differences between the far-field and the near-field channel models are revealed by analytical results and simulations.
Improving Few-Shot Learning with Auxiliary Self-Supervised Pretext Tasks

Recent work on few-shot learning \cite{tian2020rethinking} showed that quality of learned representations plays an important role in few-shot classification performance. On the other hand, the goal of self-supervised learning is to recover useful semantic information of the data without the use of class labels. In this work, we exploit the complementarity of both paradigms via a multi-task framework where we leverage recent self-supervised methods as auxiliary tasks. We found that combining multiple tasks is often beneficial, and that solving them simultaneously can be done efficiently. Our results suggest that self-supervised auxiliary tasks are effective data-dependent regularizers for representation learning. Our code is available at: \url{https://github.com/nathanielsimard/improving-fs-ssl}.
2-generated axial algebras of Monster type

In this paper we provide the basic setup for a project, initiated by Felix Rehren in [16], aiming at classifying all 2-generated primitive axial agebras of Monster type (α, β). We first revise Rehren’s construction of an initial object in the cathegory of primitive n-generated axial algebras giving a formal one, filling some gaps and, though correcting some inaccuracies, confirm Rehren’s results. Then we focus on 2-generated algebras which naturally part into three cases: the two critical cases α = 2β and α = 4β, and the generic case (i.e. all the rest). About these cases, which will be dealt in detail in subsequent papers, we give bounds on the dimensions (the generic case already treated by Rehen) and classify all 2-generated primitive axial algebras of Monster type (α, β) over Q(α, β) for α and β algebraically independent indeterminates over Q. Finally we restrict to the 2-generated Majorana algebras (i.e. when α = 1 4 and β = 1 32 ), showing that these fall precisely into the nine isomorphism types of the Norton-Sakuma algebras.
Memory-Efficient Semi-Supervised Continual Learning: The World is its Own Replay Buffer

Rehearsal is a critical component for class-incremental continual learning, yet it requires a substantial memory budget. Our work investigates whether we can significantly reduce this memory budget by leveraging unlabeled data from an agent's environment in a realistic and challenging continual learning paradigm. Specifically, we explore and formalize a novel semi-supervised continual learning (SSCL) setting, where labeled data is scarce yet non-i.i. d. unlabeled data from the agent's environment is plentiful. Importantly, data distributions in the SSCL setting are realistic and therefore reflect object class correlations between, and among, the labeled and unlabeled data distributions. We show that a strategy built on pseudo-labeling, consistency regularization, Out-of- Distribution (OoD) detection, and knowledge distillation reduces forgetting in this setting. Our approach, DistillMatch, increases performance over the state-of-the-art by no less than 8.7% average task accuracy and up to 54.5% average task accuracy in SSCL CIFAR-100 experiments. Moreover, we demonstrate that DistillMatch can save up to 0.23 stored images per processed unlabeled image compared to the next best method which only saves 0.08. Our results suggest that focusing on realistic correlated distributions is a significantly new perspective, which accentuates the importance of leveraging the world's structure as a continual learning strategy. Our code is available at https://github.com/GT-RIPL/DistillMatch-SSCL
AdderNet and its Minimalist Hardware Design for Energy-Efficient Artificial Intelligence

Convolutional neural networks (CNN) have been widely used for boosting the performance of many machine intelligence tasks. However, the CNN models are usually computationally intensive and energy consuming, since they are often designed with numerous multiply-operations and considerable parameters for the accuracy reason. Thus, it is difficult to directly apply them in the resource-constrained environments such as 'Internet of Things' (IoT) devices and smart phones. To reduce the computational complexity and energy burden, here we present a novel minimalist hardware architecture using adder convolutional neural network (AdderNet), in which the original convolution is replaced by adder kernel using only additions. To maximally excavate the potential energy consumption, we explore the low-bit quantization algorithm for AdderNet with shared-scaling-factor method, and we design both specific and general-purpose hardware accelerators for AdderNet. Experimental results show that the adder kernel with int8/int16 quantization also exhibits high performance, meanwhile consuming much less resources (theoretically ~81% off). In addition, we deploy the quantized AdderNet on FPGA (Field Programmable Gate Array) platform. The whole AdderNet can practically achieve 16% enhancement in speed, 67.6%-71.4% decrease in logic resource utilization and 47.85%-77.9% decrease in power consumption compared to CNN under the same circuit architecture. With a comprehensive comparison on the performance, power consumption, hardware resource consumption and network generalization capability, we conclude the AdderNet is able to surpass all the other competitors including the classical CNN, novel memristor-network, XNOR-Net and the shift-kernel based network, indicating its great potential in future high performance and energy-efficient artificial intelligence applications.
Extraction of Artemisinin, an Active Antimalarial Phytopharmaceutical from Dried Leaves of Artemisia annua L., Using Microwaves and a Validated HPTLC-Visible Method for Its Quantitative Determination

A simple, rapid, precise, and accurate high-performance thin-layer chromatographic method coupled with visible densitometric detection of artemisinin is developed and validated. Samples of the dried Artemisia annua leaves were extracted via microwaves using different solvents. This method shows the advantage of shorter extraction time of artemisinin from leaves under the influence of electromagnetic radiations. Results obtained from microwave-assisted extraction (MAE) were compared with hot soxhlet extraction. Chromatographic separation of artemisinin from plant extract was performed over silica gel 60 F254 HPTLC plate using n-hexane : ethyl acetate as mobile phase in the ratio of 75 : 25, v/v. The plate was developed at room temperature 25 ± 2.0°C. Artemisinin separation over thin-layer plate was visualized after postchromatographic derivatization with anisaldehyde-sulphuric acid reagent. HPTLC plate was scanned in a CAMAG’s TLC scanner 3 at 540 nm. Artemisinin responses were found to be linear over a range of 400–2800 ng spot−1 with a correlation coefficient 0.99754. Limits of detection and quantification were 40 and 80 ng spot−1, respectively. The HPTLC method was validated in terms of system suitability, precision, accuracy, sensitivity (LOD and LOQ), and robustness. Additionally, calculation of plate efficiency and flow constant were included as components of validation. Extracts prepared from different parts of the plant (leaves, branches, main stem, and roots) were analyzed for artemisinin content, in which, artemisinin content was found higher in the leaf extract with respect to branches and main stem extracts; however, no artemisinin was detected in root extract. The developed HPTLC-visible method of artemisinin determination will be very useful for pharmaceutical industries, which are involved in monitoring of artemisinin content during different growth stages (in vitro and in vivo) of A. annua for qualitative and quantitative assessment of final produce prior to commercial-scale processing for assessment of cost-benefit ratio.
Reconstructing missing seismic data using Deep Learning

In current seismic acquisition practice, there is an increasing drive for sparsely (in space) acquired data, often in irregular geometry. These surveys can trade off subsurface information for efficiency/cost - creating a problem of "missing seismic data" that can greatly hinder subsequent processing and interpretation. Reconstruction of regularly sampled dense data from highly sparse, irregular data can therefore aid in processing and interpretation of these far sparser, more efficient seismic surveys. Here, two methods are compared to solve the reconstruction problem in both space-time and wavenumber-frequency domain. This requires an operator that maps sparse to dense data: the operator is generally unknown, being the inverse of a known data sampling operator. As such, here the deterministic inversion is efficiently solved by least squares optimisation using a numerically efficient Python-based linear operator representation. An alternative approach is probabilistic and uses deep learning. Here, two deep learning architectures are benchmarked against each other and the deterministic approach; a Recurrent Inference Machine (RIM), which is designed specifically to solve inverse problems given known forward operators, and the well-known U-Net. The trained deep learning networks are capable of successfully mapping sparse to dense seismic data for a range of different datasets and decimation percentages, thereby significantly reducing spatial aliasing in the wavenumber-frequency domain. The deterministic inversion on the contrary, could not reconstruct the missing data and thus did not reduce the undesired spatial aliasing. Results show that the application of Deep Learning for seismic reconstruction is promising, but the treatment of large-volume, multi-component seismic datasets will require dedicated learning architectures not yet realisable with existing tools.
Prevalence and Symptom Characteristics of Irritable Bowel Syndrome Among Bronchial Asthma Patients in Pakistan

Introduction: Irritable bowel syndrome (IBS) is a common gastrointestinal disorder that leads to a variety of symptoms including abdominal discomfort and change in stool frequency and consistency. Asthma is a common disease of the airway. Some studies have suggested that a relationship between IBS and asthma exist, while others have contradicted the claim. This study aims to determine the prevalence of IBS in asthmatic patients and compare their symptoms with symptoms of IBS patients in non-asthmatic patients. Methodology: In this case-control study, 100 known and documented asthmatic patients were included as cases, and 100 non-asthmatic healthy patients were included as controls from July to August 2019. These patients were given a questionnaire based on ROME II criteria for the diagnosis of IBS. Prevalence and symptoms of IBS were compared between cases and controls. A probability level, P < 0.05 was considered significant. Result: IBS was found in 41 out of 100 asthma patients (41%) and 18 out of 100 controls (18%) with a P-value of 0.0005 and was more common in females in both asthmatic (63.41%) and non-asthmatic patients (66.66%). Symptoms such as abdominal pain/distress (63.41% vs. 11.11%, P-value: 0.0013) and bloating (82.92% vs. 33.33%, P-value: 0.0005) were significantly higher in asthmatic patient with IBS compared to non-asthmatic patient with IBS. Conclusion: Prevalence of IBS among asthma patients was significantly higher as compared to non-asthmatics. Routine screening of asthma patients and further studies to understand the pathogenesis underlying association between IBS and asthma should be conducted to detect and manage such patients effectively.
Current Knowledge of Hypertrophic Cardiomyopathy Among Health Care Providers in Sweden

Introduction Hypertrophic cardiomyopathy (HCM) is a common disorder with various manifestations, including sudden cardiac death. Patients with suspected or confirmed HCM may be encountered throughout the healthcare system, especially in internal medicine and cardiology. Thus, thorough knowledge of HCM is essential among healthcare providers. Methods A web-based questionnaire was developed to assess the cross-sectional evaluation of HCM knowledge. It covered aspects such as epidemiology and diagnosis, treatment, lifestyle, risk stratification of sudden cardiac death, and implantable cardioverter-defibrillator knowledge. Results In total, 123 subjects completed the survey. The mean age was 38.5 ±10.7 years and two-thirds (n=82) were females; 43.1% were physicians (non-specialist 24.4%, cardiologists 8.9%, specialist, other than cardiology 9.8%); and the remaining were nurses (nurses within cardiology 37.4%, nurses outside cardiology 19.5%). Almost all subjects had heard about the disease (95.9%) and the vast majority (77.2%) had taken part in the management of a patient with HCM. The total mean score was 15.9 ±3.9 credits and the 25th, 50th, and 75th percentiles were 14, 15, and 18 credits, respectively. The predefined arbitrary pass score of ≥60% was reached by 61.8%, and 20.3% were considered to pass with distinction. Physicians scored higher than nurses (70.7 ±17.0% vs 58.1 ±11.8; p<0.001). Within each professional category, there was a similar score with regard to gender. Conclusions There is a considerable lack of knowledge of HCM among healthcare professionals working within the field of internal medicine/cardiology. This insufficient knowledge may contribute to less implementation of evidence-based medicine and current guidelines, although further studies are needed to confirm this.
Understanding Barriers to Implementing and Managing Therapeutic Diets for People Living with Chronic Kidney Disease in Remote Indigenous Communities

ABSTRACT Indigenous peoples in Canada, and globally, experience a disproportionate burden of chronic kidney disease (CKD) and end-stage renal disease (ESRD) ESRD patients in remote Indigenous communities might experience significant challenges in adhering to dietary guidelines. Much research has documented the poor quality, high cost, and limited availability of healthy foods in remote, Indigenous communities. Food quality and availability are poor in remote communities, indicating that persons with ESRD and CKD might have limited ability to adhere to dietary guidelines. This article reports on research designed to understand food-access barriers in remote First Nations for persons living with stage 4 and 5 CKD/ESRD. The study involved semi-structured interviews with 38 patients in remote communities. It concludes with some reflections on the significance of this issue in the context of dietetic practice.
miR-296-5p Inhibits the Secretion of Pulmonary Surfactants in Pulmonary Epithelial Cells via the Downregulation of Wnt7b/β-Catenin Signaling

Neonatal respiratory distress syndrome (NRDS) is a common disease that occurs in premature infants. However, the mechanisms underlying the disease remain unclear. microRNAs (miRNAs) have been indicated to play a crucial role in the development of NRDS. In this study, we aimed to explore the regulatory mechanisms of miR-296-5p in NRDS. The expression levels of miR-296-5p in preterm infants with NRDS were determined using quantitative reverse-transcription polymerase chain reaction (RT-qPCR). A549 cells were transfected with lentiviral vectors encoding miR-296-5p, and the transfection efficiency was determined using RT-qPCR. Flow cytometry and CCK8 assay were performed to measure apoptosis and proliferation of A549 cells, respectively. The protein levels of pulmonary surfactant SP-A (SFTPA1), SP-B, Wnt7b, and β-catenin were measured using western blotting. We demonstrated an upregulation of miR-296-5p in NRDS. The miR-296-5p was successfully overexpressed in A549 cells via lentivirus transfection, and the upregulation of miR-296-5p inhibited cell proliferation and secretion of SP-A and SP-B and also induced downregulation of the Wnt7b/β-catenin in vitro. Therefore, miR-296-5p inhibits cell proliferation and secretion of pulmonary surfactants in A549 cells via downregulation of Wnt7b/β-catenin signaling.
Tocilizumab for the Treatment of COVID-19 Among Hospitalized Patients: A Matched Retrospective Cohort Analysis

Abstract Background There is currently no single treatment that mitigates all harms caused by severe acute respiratory syndrome coronavirus 2 infection. Tocilizumab, an interleukin-6 antagonist, may have a role as an adjunctive immune-modulating therapy. Methods This was an observational retrospective study of hospitalized adult patients with confirmed coronavirus disease 2019 (COVID-19). The intervention group comprised patients who received tocilizumab; the comparator arm was drawn from patients who did not receive tocilizumab. The primary outcome was all-cause mortality censored at 28 days; secondary outcomes were all-cause mortality at discharge, time to clinical improvement, and rates of secondary infections. Marginal structural Cox models via inverse probability treatment weights were applied to estimate the effect of tocilizumab. A time-dependent propensity score–matching method was used to generate a 1:1 match for tocilizumab recipients; infectious diseases experts then manually reviewed these matched charts to identify secondary infections. Results This analysis included 90 tocilizumab recipients and 1669 controls. Under the marginal structural Cox model, tocilizumab was associated with a 62% reduced hazard of death (adjusted hazard ratio [aHR], 0.38; 95% CI, 0.21 to 0.70) and no change in time to clinical improvement (aHR, 1.13; 95% CI, 0.68 to 1.87). The 1:1 matched data set also showed a lower mortality rate (27.8% vs 34.4%) and reduced hazards of death (aHR, 0.47; 95% CI, 0.25 to 0.88). Elevated inflammatory markers were associated with reduced hazards of death among tocilizumab recipients compared with controls. Secondary infection rates were similar between the 2 groups. Conclusions Tocilizumab may provide benefit in a subgroup of patients hospitalized with COVID-19 who have elevated biomarkers of hyperinflammation, without increasing the risk of secondary infection.
Effectiveness and Safety of Posterior Vaginal Repair with Single-Incision, Ultralightweight, Monofilament Propylene Mesh: First Evidence from a Case Series with Short-Term Results

Objective The use of transvaginal mesh is controversial, and over time, multiple surgical methods for the treatment of posterior vaginal prolapse (PVP) have been proposed including different surgical approaches and techniques. To date, no clear conclusion has been reached about the use of mesh for reinforcing transvaginal posterior repair. The aim of this study was to evaluate the feasibility, safety, and effectiveness of a novel, ultralightweight mesh for the treatment of PVP. Methods We performed a single-center, prospective observational study on consecutive patients referred for primary or recurrent, symptomatic stage II PVP (according to the international Pelvic Organ Prolapse Quantification System) from April 2017 to September 2018. In all patients, transvaginal posterior repair was augmented with a single-incision, isoelastic polypropylene mesh. Data about the postoperative outcomes were collected until December 2019. Results A total number of 15 patients were included. The median follow-up after surgery was 18 months (IQR = 14). Surgery was completed in all cases without complications. Regarding the anatomical outcomes (as measured according to POP-q classification), a significant improvement was observed in terms of Bp, D, and C (p < 0.05). The functional outcomes were significantly ameliorated after surgery, with a reduction of bulge symptom, stypsis, incomplete evacuation, and excessive staining (p < 0.05). The quality of life was significantly improved in the majority of patients (p < 0.05). Median patients' satisfaction rate was 100% (IQR = 22.5%). Neither early nor late postoperative complications occurred. Conclusions Single-incision, ultralightweight polypropylene meshes were safe and highly effective in the treatment of PVP. As our study has some limitations, further large, controlled studies are needed.
Angiotensin (1-7) Antagonist Diminished the Anti-Tumor Effect of Olmesartan in Tumor Cell Lines Grown In-vitro and In-vivo

Olmesartan is a selective angiotensin II type 1 receptor (AT1R) antagonist. It achieves blood pressure reduction in a dose-dependent manner through arterial vasodilation and reduced sodium retention. Secondly, olmesartan exhibits anti-angiogenic activity through inhibition of Insulin growth factor, vascular endothelial growth factor and their receptors and this effect was mediated through the Ang (1-7). The current study was to investigate the anti-tumor effect of olmesartan; first, the cytotoxic activity of olmesartan and/or Ang (1-7) antagonist on MCF-7 cell line using 3-(4, 5-dimethylthiazole-2-yl)-2, 5-diphenyltetrazolium bromide (MTT) assay was explored. Then EAC solid tumor grown in vivo was employed to determine the impact of concurrent administration of an Ang (1-7) agonist or antagonist on the anti-tumor effect of olmesartan. In addition, the impact of concurrent administration of olmesartan on the cytotoxic activity of sorafenib in MCF-7 cell line or its anti-tumor effect in EAC solid tumor grown in vivo was investigated. It was observed that the cell viability was reduced by approximately 40% after sorafenib (250 µg/ml) treatment. On the other hand, olmesartan did not show any cytotoxic effect except when higher concentrations were used. IC 50 value for sorafenib in MCF-7 was 250.9 µg/ml, while the IC 50 value for olmesartan was 674.8 µg/ml. Ang (1-7) antagonist increased the IC 50 value of olmesartan from 674.8 µg/ml to 722 µg/ml. The high cytotoxic concentration of olmesartan in combination with sorafenib failed to enhance the cytotoxicity more than the sorafenib itself. Sorafenib (30 mg/kg/day), olmesartan (3, 10 or 30 mg/kg/day) or their combination significantly (P<0.05) reduced tumor volume and the relative tumor volume compared to EAC-Control group. Similarly, concurrent administration of the Ang (1-7) agonist with olmesartan (30 mg/kg) significantly (P<0.05) reduced tumor volume and the relative tumor volume compared to EAC-control group or olmesartan (30 mg/kg) group. Moreover, the administration of Ang (1-7) antagonist with olmesartan reduced the anti-tumor effect of olmesartan. In conclusion, olmesartan (30 mg/kg) posses anti-tumor activity. This anti-tumor activity did not depend on the direct cytotoxic activity but might be attributed to antiangiogenic activity as proven in a previous work from our lab. The anti-tumor effect of olmesartan was, at least in part, mediated through the Ang (1-7) receptor. In addition, the present results showed that olmesartan (30 mg/kg) potentiated the anti-tumor effect of sorafenib.