Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of a new quantification method of Sarcocystis cruzi through detection of the acetyl-CoA synthetase gene

Sarcocystis cruzi is a member of the genus Sarcocystis, infecting bovine animals such as cattle and bison as intermediate hosts, and canids such as dogs and raccoon dogs as definitive hosts. Acute sarcocystosis of S. cruzi causes occasional symptoms in cattle, including weight loss, reduced milk production, abortions, and death, and similar to other Sarcocystis species can potentially cause food poisoning in humans when raw or undercooked infected cattle meat is consumed. Despite these issues, genetic information on S. cruzi is scarce, and there is no specific quantitative method for the detection and quantification of the parasite in infected cattle. In this study, we aimed to develop a method based on high-throughput sequencing of S. cruzi genome and transcriptome that specifically and quantitatively detects the S. cruzi acetyl-CoA synthetase gene (ScACS). Cardiac muscles were collected from slaughterhouses in Saitama Prefecture to obtain sarcocysts from which DNA and RNA were extracted for the high-throughput sequencing. Using the sequences, we developed a specific quantitative PCR assay which could distinguish S. cruzi ACS from that of Toxoplasma gondii by taking advantage of the differences in their exon/intron organizations and validated the assay with the microscopic counting of the S. cruzi bradyzoites. Thus, this assay will be useful for future studies of S. cruzi pathogenesis in cattle and for the surveillance of infected animals, thereby easing public health concerns.
Inner Annular Gap and Related Topics

Geometrical properties of the annular gap model, efforts of observational tests on it and applications of the model to explain the bi-drifting phenomenon are introduced in this paper. It was shown that in the frame of the annular gap model the observed γ-ray and radio radiation properties of pulsars can be well reproduced. The annular gap model can naturally explain the bi-drifting phenomenon, which is difficult to be understood in classical inner vacuum models. Recently, Wang et al. proposed an independent geometrical method to determine the location for the multi-band radiation regions of PSR B1055–52. They found that the γ-ray emission and radio main pulse emission come from the annular gap, while the radio inter-pulse originates from the core cap region. The results show that the constrained radio and γ-ray emission regions are generally consistent with the predictions of the annular gap model.
The Synthesis of Invariant Systems on the Base of The Vortex Algorithm

Abstract The solution of the invariance problem for arbitrary external perturbations is proposed for the system output control. It is assumed that perturbations are not measured, and acts through another channels than control inputs (so called unmatched perturbation). The nonlinear control algorithms are developed on the base of discontinuous function. The main idea consists in using relay linearization effect with the help of self damping oscillations of the system output. In such operation mode the theoretically infinite linearization coefficient can be realized, and asymptotical invariance of the system output is provided. The simulation results show the efficiency of the designed algorithms.
Clinical Outcomes of Extracranial Carotid Artery-Related Stroke Eligible for Mechanical Reperfusion on Top of Per-Guidelines Thrombolytic Therapy: Analysis from a 6-Month Consecutive Patient Sample in 2 Centers

Background Systemic intravenous thrombolysis and mechanical thrombectomy (MT) are guideline-recommended reperfusion therapies in large-vessel-occlusion ischemic stroke. However, for acute ischemic stroke of extracranial carotid artery origin (AIS-CA) there have been no specific trials, resulting in a data gap. Material/Methods We evaluated referral/treatment pathways, serial imaging, and neurologic 90-day outcomes in consecutive patients, presenting in a real-life series in 2 stroke centers over a period of 6 months, with AIS-CA eligible for emergency mechanical reperfusion (EMR) on top of thrombolysis as per guideline criteria. Results Of 30 EMR-eligible patients (33.3% in-window for thrombolysis and thrombolysed, 73.3% male, age 39-87 years, median Alberta Stroke Program Early Computed Tomography Score (ASPECTS) 10, pre-stroke mRS 0–1 in all, tandem lesions 26.7%), 20 (66.7%) were EMR-referred (60% – endovascular, 6.7% – surgery referrals). Only 40% received EMR, nearly exclusively in stroke centers with carotid artery stenting (CAS) expertise (100% eligible patient acceptance rate, 100% treatment delivery involving CAS±MT with culprit lesion sequestration using micronet-covered stents). The emergency surgery rate was 0%. Baseline clinical and imaging characteristics did not differ between EMR-treated and EMR-untreated patients. Ninety-day neurologic status was profoundly better in EMR-treated patients: mRS 0–2 (91.7% vs 0%; P<0.001); mRS 3–5 (8.3% vs 88.9%; P<0.001), mRS 6 (0% vs 11.1%; P<0.001). Conclusions In a real-life AIS-CA setting, the referral rate of EMR-eligible patients for EMR was low, and the treatment rate was even lower. AIS-CA revascularization was delivered predominantly in stroke thrombectomy-capable cardioangiology centers, resulting in overwhelmingly superior patient outcome. Large vessel occlusion stroke referral and management pathways should involve centers with proximal-protected CAS expertise. AIS-CA, irrespective of any thrombolysis administration, is a hyperacute cerebral emergency and EMR-eligible patients should be immediately referred for mechanical reperfusion.
Rationale and design of IMPACT-women: a randomised controlled trial of the effect of time-restricted eating, healthy eating and reduced sedentary behaviour on metabolic health during chemotherapy for early-stage breast cancer

Abstract Metabolic dysfunction and excess accumulation of adipose tissue are detrimental side effects from breast cancer treatment. Diet and physical activity are important treatments for metabolic abnormalities, yet patient compliance can be challenging during chemotherapy treatment. Time-restricted eating (TRE) is a feasible dietary pattern where eating is restricted to 8 h/d with water-only fasting for the remaining 16 h. The purpose of this study is to evaluate the effect of a multimodal intervention consisting of TRE, healthy eating, and reduced sedentary time during chemotherapy treatment for early-stage (I–III) breast cancer on accumulation of visceral fat (primary outcome), other fat deposition locations, metabolic syndrome and cardiovascular disease risk (secondary outcomes) compared with usual care. The study will be a two-site, two-arm, parallel-group superiority randomised control trial enrolling 130 women scheduled for chemotherapy for early-stage breast cancer. The intervention will be delivered by telephone, including 30–60-minute calls with a registered dietitian who will provide instructions on TRE, education and counselling on healthy eating, and goal setting for reducing sedentary time. The comparison group will receive usual cancer and supportive care including a single group-based nutrition class and healthy eating and physical activity guidelines. MRI, blood draws and assessment of blood pressure will be performed at baseline, after chemotherapy (primary end point), and 2-year follow-up. If our intervention is successful in attenuating the effect of chemotherapy on visceral fat accumulation and cardiometabolic dysfunction, it has the potential to reduce risk of cardiometabolic disease and related mortality among breast cancer survivors.
Testing the acceptability of stillbirth awareness messages in an SMS program for fathers

Abstract Issue addressed To raise expectant fathers' awareness of risk factors for stillbirth. Methods A set of brief text messages was developed addressing recognised risk factors for stillbirth: avoidance of maternal cigarette smoking, maternal going to sleep on side messaging, awareness of the importance of noticing and reporting changes in foetal movement and fathers' involvement in shared decision making for timing of birth. Eight messages were inserted into the SMS4dads pilot program being conducted by NSW Health. Feedback on the messages was requested. Participants rated the quality of the messages on a three‐point Likert scale and provided comments. Results Overall, 2528 messages were sent to 626 fathers' mobile phones, 45% of fathers replied with 666 ratings and 115 comments evaluating the texts. The quantitative ratings indicated substantial overall approval of the messages. Within the coding category “Evaluation of Message Content,” three themes described fathers' reactions and feelings about the smoking, movement, side sleeping and birth timing messages: “important‐good information,” “not appropriate/anxiety provoking” and “not relevant‐obvious. ” Three themes reflecting the attributes of the messages within the “Service Quality” category were “need more information,” “complements public health” and “child voice fit. ” Conclusions Results indicate that the messages are an acceptable way to provide information and suggested actions addressing stillbirth risk factors to fathers‐to‐be. So what? Fathers' awareness of the risk factors for stillbirth can assist mothers to take appropriate actions for a healthy birth. Information on risk factors can be provided to fathers via a father‐focused text messaging service.
Novel energy-preserving splitting integration for Hamiltonian Monte Carlo method

Splitting schemes are numerical integrators for Hamiltonian problems that may ad- vantageously replace the St¨ormer-Verlet method within Hamiltonian Monte Carlo (HMC) methodology. However, HMC performance is very sensitive to the step size parameter; in this paper we propose a new method in the one-parameter family of second-order of splitting procedures that uses a well-ﬁtting parameter that nulliﬁes the expectation of the energy error for univariate and multi- variate Gaussian distributions, taken as a problem-guide for more realistic situations; we also provide a new algorithm that through an adaptive choice of the b parameter and the step-size ensures high sampling performance of HMC. For similar methods introduced in recent literature, by using the proposed step size selection, the splitting integration within HMC method never rejects a sample when applied to univariate and multivariate Gaussian distributions. For more general non Gaussian target distributions the proposed approach exceeds the principal especially when the adaptive choice is used. The eﬀectiveness of the proposed is ﬁrstly tested on some benchmarks examples taken from literature. Then, we conduct experiments by considering as target distribution, the Log-Gaussian Cox process and Bayesian Logistic Regression.
ESG in Corporate Filings: An AI Perspective

Using Artificial Intelligence (AI) techniques, we quantitatively examine the mentions of ESG terms in the U.S. corporate filings with the SEC over the 2019-20 period. We find that in our sample of companies, the management tends to focus ESG discussions in corporate filings along three dimensions: 1) diversity, 2) hazardous materials, and 3) greenhouse gasses. However, overall, the companies and their stakeholders approach ESG from the following three “ESG pillars”: 1) a combination of greenhouse gasses, data security and inclusion, 2) a tradeoff between emissions and product quality, and 3) product labeling. These dimensions can be effective ESG ratings aspects for companies. We further measure the investor appreciation of the corporate ESG communication through a cross-sectional analysis of the ESG discussions on the contemporaneous and subsequent stock returns. Using standard linear regression methodology, we find that the markets are much more responsive to the broad spectrum of ESG pillars, as opposed to the management “party line. ” However, when we account for potential correlation of ESG communication dimensions using AI-based techniques, the statistical superiority of the full communication disappears and only the official corporate message carries through to the investors. We find that the strategic ESG messaging in corporate filings matters and significantly impacts corporations’ forward-looking returns.
Analyzing the State of Computer Science Research with the DBLP Discovery Dataset

The number of scientific publications continues to rise exponentially, especially in Computer Science (CS). However, current solutions to analyze those publications restrict access behind a paywall, offer no features for visual analysis, limit access to their data, only focus on niches or sub-fields, and/or are not flexible and modular enough to be transferred to other datasets. In this thesis, we conduct a scientometric analysis to uncover the implicit patterns hidden in CS metadata and to determine the state of CS research. Specifically, we investigate trends of the quantity, impact, and topics for authors, venues, document types (conferences vs. journals), and fields of study (compared to, e.g., medicine). To achieve this we introduce the CS-Insights system, an interactive web application to analyze CS publications with various dashboards, filters, and visualizations. The data underlying this system is the DBLP Discovery Dataset (D3), which contains metadata from 5 million CS publications. Both D3 and CS-Insights are open-access, and CS-Insights can be easily adapted to other datasets in the future. The most interesting findings of our scientometric analysis include that i) there has been a stark increase in publications, authors, and venues in the last two decades, ii) many authors only recently joined the field, iii) the most cited authors and venues focus on computer vision and pattern recognition, while the most productive prefer engineering-related topics, iv) the preference of researchers to publish in conferences over journals dwindles, v) on average, journal articles receive twice as many citations compared to conference papers, but the contrast is much smaller for the most cited conferences and journals, and vi) journals also get more citations in all other investigated fields of study, while only CS and engineering publish more in conferences than journals.
Exploiting Socially-Aware Tasks for Embodied Social Navigation

Learning how to navigate among humans in an occluded and spatially constrained indoor environment, is a key ability required to embodied agents to be integrated into our society. In this paper, we propose an end-to-end architecture that exploits Socially-Aware Tasks (referred as to Risk and Social Compass) to inject into a reinforcement learning navigation policy the ability to infer common-sense social behaviors. To this end, our tasks exploit the notion of immediate and future dangers of collision. Furthermore, we propose an evaluation protocol speciﬁcally designed for the Social Navigation Task in simulated environments. This is done to capture ﬁne-grained features and characteristics of the policy by analyzing the minimal unit of human-robot spatial interaction, called Encounter. We validate our approach on Gibson4+ and Habitat-Matterport3D datasets.
The Cosmic Neutrino Background on the Surface of the Earth

We argue that the reflection of relic neutrinos from the surface of the Earth results in a significant local $\nu-\bar{\nu}$ asymmetry, far exceeding the expected primordial lepton asymmetry. The net fractional electron neutrino number $\frac{n_{\nu_e}-n_{\bar{\nu}_e}}{n_{\nu_e}}$ is up to $\mathcal{O}(10^5) \sqrt{\frac{m_\nu}{0.1~\text{eV}}}$ larger than that implied by the baryon asymmetry. This enhancement is due to the weak 4-Fermi repulsion of the $\nu_e$ from ordinary matter which slows down the $\nu_e$ near the Earth's surface, and to the resulting evanescent neutrino wave that penetrates below the surface. This repulsion thus creates a net $\nu_e$ overdensity in a shell $\sim 7~\text{meters} \sqrt{\frac{0.1~\text{eV}}{m_\nu}}$ thick around the Earth's surface. Similarly the repulsion between $\bar{\nu}_\mu$ or $\bar{\nu}_\tau$ and ordinary matter creates an overdensity of $\bar{\nu}_{\mu, \tau}$ of similar size. These local enhancements increase the size of $\mathcal{O}(G_F)$ torques of the $C\nu B$ on spin-polarized matter by a factor of order $10^5$. In addition, they create a gradient of the net neutrino density which naturally provides a way out of the forty-year-old ``no-go'' theorems on the vanishing of $\mathcal{O}(G_F)$ forces. The torque resulting from such a gradient force can be $10^8$ times larger than that of earlier proposals. Although the size of these effects is still far from current reach, they may point to new directions for $C\nu B$ detection.
Advanced Audio Aid for Blind People

One of the most important senses in human life is vision, without it one’s life is totally filled with darkness. According to WHO globally millions of people are visually impaired estimated there are 285 million, of whom some millions are blind. Unfortunately, there are around 2.4 million people are blind in our beloved country Pakistan. Human are a crucial part of society and the blind community is a main part of society. The technologies are grown so far to make the life of humans easier more comfortable and more reliable for. However, this disability of the blind community would reduce their chance of using such innovative products. Therefore, the visually impaired community believe that they are burden to other societies and they do not capture in normal activities separates the blind people from society and because of this believe did not participate in the normally tasks of society. The visual impair people mainly face most of the problems in this real-time The aim of this work is to turn the real time world into an audio world by telling blind person about the objects in their way and can read printed text. This will enable blind persons to identify the things and read the text without any external help just by using the object detection and reading system in real time. Objective of this work: i) Object detection ii) Read printed text, using state-of-the-art (SOTA) technology.
The etching of silicon with XeF2 vapor

It is shown that silicon is isotropically etched by exposure to XeF2(gas) at T=300 K. Si etch rates as large as 7000 A/min were observed for P (XeF2) <1.4×10−2 Torr and the etch rate varies linearly with P (XeF2). There was no observable etching of SiO2, Si3N4, or SiC, demonstrating an extremely large selectivity between silicon and its compounds. Therefore, thin masks constructed from silicon compounds can be used for pattern delineation. The implication of these experimental results for understanding mechanisms associated with plasma etching (including RIE) will be discussed.
Design of a computer vision system for a differential spraying operation in precision agriculture using hebbian learning

One objective in precision agriculture is to minimise the volume of herbicides that are applied to the fields through the use of site-specific weed management systems. An automatic computer vision system for the detection and differential spraying of weeds in crop fields is discussed. The system involves an image segmentation approach and a decision maker. The first is designed as a sequence of image processing techniques and the second is a decision maker based on the Hebbian learning paradigm under the self-organising criterion. The combination of both and the use of the second are the main findings. The method is compared favourably with other existing strategies achieving a suitable performance.
FakeOut: Leveraging Out-of-domain Self-supervision for Multi-modal Video Deepfake Detection

Video synthesis methods rapidly improved in recent years, allowing easy creation of synthetic humans. This poses a problem, especially in the era of social media, as synthetic videos of speaking humans can be used to spread misinformation in a convincing manner. Thus, there is a pressing need for accurate and robust deepfake detection methods, that can detect forgery techniques not seen during training. In this work, we explore whether this can be done by leveraging a multi-modal, out-of-domain backbone trained in a self-supervised manner, adapted to the video deepfake domain. We propose FakeOut; a novel approach that relies on multi-modal data throughout both the pre-training phase and the adaption phase. We demonstrate the efficacy and robustness of FakeOut in detecting various types of deepfakes, especially manipulations which were not seen during training. Our method achieves state-of-the-art results in cross-dataset generalization on audio-visual datasets. This study shows that, perhaps surprisingly, training on out-of-domain videos (i.e., not especially featuring speaking humans), can lead to better deepfake detection systems. Code is available on GitHub.
Robust Task-Specific Beamforming with Low-Resolution ADCs for Power-Efficient Hybrid MIMO Receivers

Multiple-input multiple-output (MIMO) systems exploit spatial diversity to facilitate multi-user communications with high spectral efficiency by beamforming. As MIMO systems utilize multiple antennas and radio frequency (RF) chains, they are typically costly to implement and consume high power. A common method to reduce the cost of MIMO receivers is utilizing less RF chains than antennas by employing hybrid analog/digital beamforming (HBF). However, the added analog circuitry involves active components whose consumed power may surpass that saved in RF chain reduction. An additional method to realize power-efficient MIMO systems is to use low-resolution analog-to-digital converters (ADCs), which typically compromises signal recovery accuracy. In this work, we propose a power-efficient hybrid MIMO receiver with low-quantization rate ADCs, by jointly optimizing the analog and digital processing in a hardware-oriented manner using task-specific quantization techniques. To mitigate power consumption on the analog front-end, we utilize efficient analog hardware architecture comprised of sparse low-resolution vector modulators, while accounting for their properties in design to maintain recovery accuracy and mitigate interferers in congested environments. To account for common mismatches induced by non-ideal hardware and inaccurate channel state information, we propose a robust mismatch aware design. Supported by numerical simulations and power analysis, our power-efficient MIMO receiver achieves comparable signal recovery performance to power-hungry fully-digital MIMO receivers using high-resolution ADCs. Furthermore, our receiver outperforms the task-agnostic HBF receivers with low-rate ADCs in recovery accuracy at lower power and successfully copes with hardware mismatches.
TCN-CUTIE: A 1,036-TOp/s/W, 2.72-µJ/Inference, 12.2-mW All-Digital Ternary Accelerator in 22-nm FDX Technology

Tiny machine learning (TinyML) applications impose µJ/inference constraints, with a maximum power consumption of tens of megawatt. It is extremely challenging to meet these requirements at a reasonable accuracy level. This work addresses the challenge with a flexible, fully digital ternary neural network (TNN) accelerator in a reduced instruction set computer-five (RISC-V)-based System-on-Chip (SoC). Besides supporting ternary convolutional neural networks, we introduce extensions to the accelerator design that enable the processing of time-dilated temporal convolutional neural networks (TCNs). The design achieves 5.5-µJ/inference, 12.2 mW, 8,000 inferences/s at 0.5 V for a dynamic vision sensor (DVS)-based TCN and an accuracy of 94.5%, and 2.72-µJ/inference, 12.2 mW, 3,200 inferences/s at 0.5 V for a nontrivial 9-layer, 96 channels-per-layer convolutional network with CIFAR-10 accuracy of 86%. The peak energy efficiency is 1,036 TOp/s/W, outperforming the state-of-the-art silicon-proven TinyML quantized accelerators by 1.67× while achieving competitive accuracy.