Miracle-dz/UniFAR-FTUs huggingface dataset:
Experimental Demonstration of Multimode Microresonator Sensing by Machine Learning

A multimode microcavity sensor based on a self-interference microring resonator is demonstrated experimentally. The proposed multimode sensing method is implemented by recording wide band transmission spectra that consist of multiple resonant modes. It is different from previous dissipative sensing scheme, which aims at measuring the transmission depth changes of a single resonant mode in a microcavity. Here, by combining the dissipative sensing mechanism and the machine learning algorithm, the multimode sensing information extracted from a broadband spectrum can be efficiently fused to estimate the target parameter. The multimode sensing method is immune to laser frequency noises and robust against system imperfection, thus our work presents a great step towards practical applications of microcavity sensors outside the research laboratory. The voltage applied across the microheater on the chip was adjusted to bring its influence on transmittance through the thermo-optic effects. As a proof-of-principle experiment, the voltage was detected by the multimode sensing approach. The experimental results demonstrate that the limit of detection of the multimode sensing by the general regression neural network is reduced to 6.7% of that of single-mode sensing within a large measuring range.
An Online Multilingual Hate speech Recognition System

The exponential increase in the use of the Internet and social media over the last two decades has changed human interaction. This has led to many positive outcomes, but at the same time it has brought risks and harms. While the volume of harmful content online, such as hate speech, is not manageable by humans, interest in the academic community to investigate automated means for hate speech detection has increased. In this study, we analyse six publicly available datasets by combining them into a single homogeneous dataset and classify them into three classes, abusive, hateful or neither. We create a baseline model and we improve model performance scores using various optimisation techniques. After attaining a competitive performance score, we create a tool which identifies and scores a page with effective metric in near-real time and uses the same as feedback to re-train our model. We prove the competitive performance of our multilingual model on two langauges, English and Hindi, leading to comparable or superior performance to most monolingual models.
Tracking the evolution of crisis processes and mental health on social media during the COVID-19 pandemic

ABSTRACT The COVID-19 pandemic has affected all aspects of society, bringing health hazards and posing challenges to public order, governments, and mental health. This study examines the stages of crisis response and recovery as a sociological problem by operationalising a well-known model of crisis stages in terms of a psycho-linguistic analysis. Based on an extensive collection of Twitter data spanning from March to August 2020 in Argentina, we present a thematic study on the differences in language used in social media posts and look at indicators that reveal the distinctive stages of a crisis and the country response thereof. The analysis was combined with a study of the temporal prevalence of mental health related conversations and emotions. This approach can provide insights for public health policy design to monitor and eventually intervene during the different stages of a crisis, thus improving the adverse mental health effects on the population.
H i intensity mapping with MeerKAT: primary beam effects on foreground cleaning

Upcoming and future neutral hydrogen Intensity Mapping surveys offer a great opportunity to constrain cosmology in the post-reionization Universe, provided a good accuracy is achieved in the separation between the strong foregrounds and the cosmological signal. Cleaning methods are often applied under the assumption of a simplistic Gaussian primary beam. In this work, we test the cleaning in the presence of a realistic primary beam model with a non-trivial frequency dependence. We focus on the Square Kilometre Array precursor MeerKAT telescope and simulate a single-dish wide-area survey. We consider the main foreground components, including an accurate full-sky point source catalogue. We find that the coupling between beam sidelobes and the foreground structure can complicate the cleaning. However, when the beam frequency dependence is smooth, we show that the cleaning is only problematic if the far sidelobes are unexpectedly large. Even in that case, a proper reconstruction is possible if the strongest point sources are removed and the cleaning is more aggressive. We then consider a non-trivial frequency dependence: a sinusoidal type feature in the beamwidth that is present in the MeerKAT beam and is expected in most dishes, including SKA1-MID. Such a feature, coupling with the foreground emission, biases the reconstruction of the signal across frequency, potentially impacting the cosmological analysis. We show that this effect is constrained to a narrow region in k∥ space and can be reduced if the maps are carefully re-smoothed to a common lower resolution.
Revisiting Binary Code Similarity Analysis Using Interpretable Feature Engineering and Lessons Learned

Binary code similarity analysis (BCSA) is widely used for diverse security applications, including plagiarism detection, software license violation detection, and vulnerability discovery. Despite the surging research interest in BCSA, it is significantly challenging to perform new research in this field for several reasons. First, most existing approaches focus only on the end results, namely, increasing the success rate of BCSA, by adopting uninterpretable machine learning. Moreover, they utilize their own benchmark, sharing neither the source code nor the entire dataset. Finally, researchers often use different terminologies or even use the same technique without citing the previous literature properly, which makes it difficult to reproduce or extend previous work. To address these problems, we take a step back from the mainstream and contemplate fundamental research questions for BCSA. Why does a certain technique or a certain feature show better results than the others? Specifically, we conduct the first systematic study on the basic features used in BCSA by leveraging interpretable feature engineering on a large-scale benchmark. Our study reveals various useful insights on BCSA. For example, we show that a simple interpretable model with a few basic features can achieve a comparable result to that of recent deep learning-based approaches. Furthermore, we show that the way we compile binaries or the correctness of underlying binary analysis tools can significantly affect the performance of BCSA. Lastly, we make all our source code and benchmark public and suggest future directions in this field to help further research.
Deep Network Perceptual Losses for Speech Denoising

Contemporary speech enhancement predominantly relies on audio transforms that are trained to reconstruct a clean speech waveform. Here we investigate whether deep feature representations learned for audio classification tasks can be used to improve denoising. We first trained deep neural networks to classify either spoken words or environmental sounds from audio. We then trained an audio transform to map noisy speech to an audio waveform that minimized 'perceptual' losses derived from the recognition network. When the transform was trained to minimize the difference in the deep feature representations between the output audio and the corresponding clean audio, it removed noise substantially better than baseline methods trained to reconstruct clean waveforms. The learned deep features were essential for this improvement, as features from untrained networks with random weights did not provide the same benefit. The results suggest the use of deep features as perceptual metrics to guide speech enhancement.
Conjecturing-Based Discovery of Patterns in Data

We propose the use of a conjecturing machine that suggests feature relationships in the form of bounds involving nonlinear terms for numerical features and Boolean expressions for categorical features. The proposed Conjecturing framework recovers known nonlinear and Boolean relationships among features from data. In both settings, true underlying relationships are revealed. We then compare the method to a previously proposed framework for symbolic regression on the ability to recover equations that are satisfied among features in a data set. The framework is then applied to patient-level data regarding COVID-19 outcomes to suggest possible risk factors that are confirmed in the medical literature. Discovering patterns in data is a first step toward establishing causal relationships, which can be the basis for effective decision making. Data Ethics & Reproducibility Note: Code and data to reproduce results are available here: https://github.com/jpbrooks/conjecturing . COVID-19 synthetic patient data were obtained as part of the Veterans Health Administration (VHA) Innovation Ecosystem and precisionFDA COVID-19 Risk Factor Modeling Challenge and are used here with permission from the Food and Drug Administration (FDA). The e-companion is available at https://doi.org/10.1287/ijds.2021. 0043 . History: Olivia Sheng served as the senior editor for this article.
A Bayesian Account of Measures of Interpretability in Human-AI Interaction

Existing approaches for the design of interpretable agent behavior consider different measures of interpretability in isolation. In this paper we posit that, in the design and deployment of human-aware agents in the real world, notions of interpretability are just some among many considerations; and the techniques developed in isolation lack two key properties to be useful when considered together: they need to be able to 1) deal with their mutually competing properties; and 2) an open world where the human is not just there to interpret behavior in one specific form. To this end, we consider three well-known instances of interpretable behavior studied in existing literature -- namely, explicability, legibility, and predictability -- and propose a revised model where all these behaviors can be meaningfully modeled together. We will highlight interesting consequences of this unified model and motivate, through results of a user study, why this revision is necessary.
Koopman System Approximation Based Optimal Control of Multiple Robots - Part I: Concepts and Formulations

This paper presents a study of the Koopman operator theory and its application to optimal control of a multi-robot system. The Koopman operator, while operating on a set of observation functions of the state vector of a nonlinear system, produces a set of dynamic equations which, through a dynamic transformation, form a new dynamic system. As an operator, it has a rich spectrum of mathematical properties, and as a tool for dynamic system analysis and control design, it has a unique collection of practical meanings. The Koopman system technique is then applied to the development of a linear or bilinear model approximation of nonlinear utility functions for optimal control of a system of multiple (mobile) robots, by selecting the utility functions as the Koopman system state variables and expressing the set of Koopman variables as the state variables of a linear or bilinear system whose parameters are determined through optimization. An iterative (online) algorithm is developed for adaptively estimating the parameters of the approximation model of the robot system with nonlinear utility functions. Finally, the control problems based on a linear or bilinear model for the nonlinear utility functions are formulated for optimal control of the multi-robot system, by transforming the nonlinear programming problem to a linear programming problem.
Using Ensemble Analysis to study the effects of Network Topology on Performance in Urban Road Networks.

Studies of the effect of network structure on performance have, thus far, been restricted to examining ensembles of synthetic networks generated by canonical models from the Network Science literature, which do not plausibly represent real road networks. Furthermore disparate ranges of parameter settings for demand and supply structure are often used, making it difficult to compare one study with another. To address these deficiencies, this paper proposes an experimental framework for the design of numerical investigations to study how network performance varies with respect to the structural properties of urban road networks. The key step within this experimental framework is the generation of a spectrum of ensembles of synthetic networks, which vary with respect to a single aspect of network structure and which adhere to plausibility constraints that ensure relevance to real urban road networks. This paper then demonstrates the application of this experimental framework to an investigation of how two performance indicators; the average link Volume-to-Capacity ratio and the Price of Anarchy, vary with respect to the density of travel demand, and the size, density and connectivity of network infrastructure. In so doing, several key challenges of such investigations are identified, with respect to the computational burden of the experiments and the need for empirical data on some important aspects of network structure such as the distribution of capacity.
A Trace-restricted Kronecker-Factored Approximation to Natural Gradient

Second-order optimization methods have the ability to accelerate convergence by modifying the gradient through the curvature matrix. There have been many attempts to use second-order optimization methods for training deep neural networks. In this work, inspired by diagonal approximations and factored approximations such as Kronecker-factored Approximate Curvature (KFAC), we propose a new approximation to the Fisher information matrix (FIM) called Trace-restricted Kronecker-factored Approximate Curvature (TKFAC), which can hold the certain trace relationship between the exact and the approximate FIM. In TKFAC, we decompose each block of the approximate FIM as a Kronecker product of two smaller matrices and scaled by a coefficient related to trace. We theoretically analyze TKFAC's approximation error and give an upper bound of it. We also propose a new damping technique for TKFAC on convolutional neural networks to maintain the superiority of second-order optimization methods during training. Experiments show that our method has better performance compared with several state-of-the-art algorithms on some deep network architectures.
Multi-Intelligent-Reflecting-Surfaces-Aided Secure Precise Wireless Transmission in 3D Scenario

In this paper, intelligent-reflecting-surface(IRS)-aided secure precise wireless transmission (SPWT) schemes are proposed in the three dimension (3D) wireless communication scenario. Unavailable direct path channels from transmitter to receivers are considered when the direct pathes are obstructed by obstacles. Then, multiple IRSs are utilized to achieve SPWT through the reflection path among transmitter, IRS and receivers in order to enhance the communication performance and energy efficiency simultaneously. First, a maximum-signal-to-interference-and-noise ratio (MSINR) scheme is proposed in a single user scenario. Then, the multi-user scenario is considered where the illegitimate users are regarded as eavesdroppers. A maximum-secrecy-rate (MSR) scheme and a maximum-signal-to-leakage-and-noise ratio (MSLNR) are proposed, respectively. The former has a better performance in secrecy rate (SR), however it has a high complexity. The latter has a lower complexity than MSR scheme with the SR performance loss. Simulation results show that both single-user scheme and multi-user scheme can achieve SPWT which transmits confidential message precisely to location of desired users. Moreover, MSLNR scheme has a lower complexity than the MSR scheme, while the SR performance is close to that of the MSR scheme.
The Effect of Anti-Vascular Endothelial Growth Factor on Retinal Microvascular Changes in Diabetic Macular Edema Using Swept-Source Optical Coherence Tomography Angiography

Purpose This study evaluates the effect of anti-vascular endothelial growth factor (anti-VEGF) therapy on microaneurysm changes and foveal avascular zone (FAZ) using optical coherence tomography angiography (OCTA) in patients with diabetic macular edema (DME). Design Prospective observational study. Materials and Methods This study involved patients with DME undergoing anti-VEGF treatment (aflibercept, ranibizumab, and bevacizumab). Macula OCTA images were obtained before (visit 0) and 1 month after (visit 1) anti-VEGF injection. Microaneurysm counts were performed, and the FAZ was evaluated in the superficial capillary plexus (SCP) and deep capillary plexus (DCP). The differences in microaneurysms, FAZ, and clinical correlations were analyzed. Results A total of 152 eyes were analyzed. The patients’ mean age was 59 years. Bevacizumab was used in 69.7%, ranibizumab in 15.1%, and aflibercept in 15.1%. There was a significant reduction in the FAZ in the SCP and DCP between visits 0 and 1. All anti-VEGF medications reduced the number of microaneurysms (p<0.01). After treatment, changes in the FAZ in SCP and DCP corresponded with changes in visual acuity (p<0.01). Conclusion Microaneurysms as detected by OCTA might serve as a biomarker for a clinical response to anti-VEGF treatment in the short term. The FAZ might also predict visual acuity improvement after anti-VEGF injection. Trial Registration Thai Clinical Trials Registry (TCTR20161010005).
PM2.5 induces autophagy-mediated cell apoptosis via PI3K/AKT/mTOR signaling pathway in mice bronchial epithelium cells

Air pollution can highly impact the respiratory system in healthy individuals. Studies have indicated that particles with an aerodynamic diameter of ≤2.5 µm (PM2.5) can be considered to be harmful for lung alveoli and bronchial epithelium cells. PM2.5 can be directly inhaled and can deeply penetrate into the lung alveoli, causing lung dysfunction. However, the toxicological mechanism mediated by PM2.5 for respiratory disease has still not been clearly determined. The purpose of the current study was to investigate the effects of PM2.5 on mouse bronchial epithelium cells (MBECs) and explored the possible mechanism mediated by PM2.5 in MBECs. The results of the current study indicated that PM2.5 markedly decreased lung function, including total lung capacity, residual volume, vital capacity and airway resistance in experimental mice. The results demonstrated that PM2.5 markedly induced inflammatory responses, oxidative injury and MBEC apoptosis. PM2.5 increased interleukin (IL)-1β and IL-6 expression, and reactive oxygen species production in MBECs. Furthermore, PM2.5 specifically induced PI3K, AKT and mTOR expression in MBECs. Disruption of PI3K/AKT/mTOR signaling was also indicated to effectively inhibit apoptosis of MBECs. In conclusion, the results of the current study systematically demonstrated the role of apoptosis-mediated MBEC apoptosis in PM2.5-treated mice, and provides a potential strategy for preclinical intervention in patients with PM2.5-induced lung diseases.
Short-term efficacy after switching from adefovir dipivoxil and tenofovir disoproxil fumarate therapy to tenofovir alaferamide for chronic hepatitis B

The aim of the present study was to evaluate the effects of switching to tenofovir alafenamide (TAF) in patients who had received a nucleos(t)ide analog (NA) for the treatment of chronic hepatitis B (CHB). The data from 33 Japanese patients with CHB who received TAF therapy after using NA [adefovir dipivoxil (ADV) and/or tenofovir disoproxil fumarate (TDF)] were retrospectively analyzed. Specifically, the biochemical and virological markers from the start of the TAF treatment to 6 months later were assessed. Comparative evaluation was performed by dividing patients into two groups: Long-term (n=19) and short-term administration groups (n=14), with a cutoff administration duration of 10 years. In all 33 patients, the levels of serum hepatitis B surface antigen (HBsAg; 1,126±1,724 to 1,001±1,591 IU/ml; P<0.0001), serum alkaline phosphatase (ALP) (320±126 to 283±124 U/l; P=0.028), serum bone specific alkaline phosphatase (19.7±9.0 to 17.7±8.0 µg/l; P=0.0006) and urine β2-microglobulin-creatinine ratio (U-BMG/Cr; 5,224±17,471 to 3,547±14,652 µg/g·Cre; P=0.002) significantly decreased from baseline after 6 months. Serum HBsAg, serum ALP and U-BMG/Cr showed a significant reduction in both groups. In conclusion, switching from ADV or TDF to TAF resulted in a decrease in serum HBsAg and improvement in serum ALP and U-BMG/Cr after 6 months of treatment in patients regardless of history of treatment with NA.
Comparison of Clinical Outcomes Between Intracapsular Implantation and Intrascleral Fixation Using the Same Model of Intraocular Lens

Purpose To compare the clinical outcomes of intrascleral intraocular lens (IOL) fixation surgery with those of intracapsular IOL implantation in conventional cataract surgery. Patients and Methods Twenty-one eyes of 21 consecutive patients who underwent intrascleral IOL fixation (SF group) and 21 eyes of 21 patients who underwent IOL intracapsular implantation during cataract surgery (IN group) were retrospectively enrolled. For both groups, the same model of IOL was used in all cases. For all cases in the SF group, Yamane’s double-needle technique was performed. Results The mean corrected visual acuity (logMAR) after surgery was significantly better in the IN than in the SF group (−0.063 ± 0.12 vs 0.05 ± 0.14; p = 0.0083). The mean anterior chamber depth after surgery was significantly smaller in the IN than in the SF group (4.65 ± 0.23 mm vs 4.98 ± 0.61 mm; p = 0.0231). The amounts of tilt and decentration were also significantly smaller in the IN group (5.21°± 1.47° and 0.22 ± 0.13 mm, respectively, vs 8.8° ± 3.9° and 0.52 ± 0.35 mm, respectively; p = 0.0003 and p = 0.0007). The mean absolute refractive prediction error was significantly smaller in the IN than in the SF group (0.22 ± 0.17 D vs 0.86 ± 0.59 D; p = 0.0002). Conclusion The intrascleral IOL fixation surgery proved to be highly effective. However, its clinical outcomes were slightly inferior to those of IOL intracapsular implantation, and further improvement of this surgical technique may be needed.
Selectivity of Leguminous Trees by Water Buffaloes in Semi-intensive Systems

Water buffaloes (Bubalus bubalis) manifest different levels of selectivity for different pastures and forages. Knowledge of feed selectivity is important to facilitate the design of efficient production systems that take into account optimal animal welfare. In this study, the selectivity of nine 18-month old female water buffaloes for Leucaena leucocephala, Albizia lebbeck, Gliricidia sepium, and Moringa oleifera was evaluated. After 12 h of grazing Megathyrsus maximum, the animals were housed in individual shelters and 1.2 kg of leaves from each of the four tree species were offered to the animals simultaneously. The selectivity, measured as the intake of dry matter (DM), was highest for A. lebbeck (mean = 0.34 kgDM, SD = 0.05 kg), followed by L. leucocephala (mean = 0.30 kgDM, SD = 0.03 kg), M. oleifera (mean = 0.11 kgDM, SD = 0.05 kg), and G. sepium (mean = 0.10 kgDM, SD = 0.02 kg) (P < 0.01). The crude protein intake was highest for A. lebbeck and L. leucocephala. Notably, the less selected leaves were those of G. sepium and M. oleifera. This study suggests that the inclusion of A. lebbeck and L. leucocephala in silvopastoral systems may increase both the consumption and well-being of water buffaloes in the tropics.