Miracle-dz/UniFAR-FTUs huggingface dataset:
Predictive Modeling for Environmental Protection: Hazardous Waste Management

The improper treatment and disposal of hazardous waste can have disastrous effects on the environment and human health. The Resource Conservation and Recovery Act (RCRA) governs hazardous waste management in the United States. To enforce its regulations, the New York State Department of Environmental Conservation (NYSDEC) inspects facilities that handle hazardous materials. However, due to resource constraints, not all facilities can be inspected each year. We worked with NYSDEC to build predictive models that use reporting, monitoring, and enforcement data to prioritize inspection resources. The past selection of inspection sites results in non-random missing labels in both training and testing. We initially built models that ignore this selection bias. Next we modeled the selection process and incorporated it into two kinds of models: first, we reinforced that process; second, we used reweighting to try to correct its bias. Each of these three approaches produces qualitatively different predictions, though we estimate through cross-validation that each of them can significantly increase the proportion of future inspections finding violations. We propose a novel field trial design for NYSDEC to test, compare, and select from among these models.
Positive End-Expiratory Pressure During Helmet Ventilation in Critically Ill Bronchiolitis: A Multicenter Randomized Clinical Trial

Emanuele Rossetti MD, PhD1*, Zaccaria Ricci, MD2, Daniele Bonacina, MD3, Cristina Giugni MD2 , Ezio Bonanomi MD3, Marina Franci MN1, Marta Ciofi degli Atti MD4, Manuela L’Erario MD2, Daniela Perrotta MD1, Corrado Cecchetti MD, PhD1, Linda Appierto MD1, Luigi Dei Giudici MD1, Alessandro Germani MD1, Francesco Polisca MD1, Francesca Tortora MD1, Ivano Farinelli MD1, Fabrizio Chiusolo MD1, Paola Serio MD2, Roberto Bianchi MD1, Sergio G. Picardo MD1 on behalf of the SARNePI (Italian Society for Pediatric and Neonatal Anaesthesia and Intensive Care).
Adjustable Platform for Exploring Soft Robotic Gripper Design

The growing importance of STEM careers and the imbalance of the demographics of people who pursue them have resulted in a myriad of outreach activities and platforms to expose students to key concepts in fun and interactive ways. Robotics is one of the primary themes of these outreach activities due to the combination of disciplines (e.g., mechanical and electrical engineering, computer science). Specifically, soft robotics, an emerging field, has become a popular topic as it is outside the expectations and experiences of most students, making it more universally accessible. In this work, we present a tendon-driven, soft-robotic gripper platform with multiple adjustable design features in order to emphasize the iterative nature of design. Students can choose the number of fingers, their distance apart, and their length and number of joints. After designing and molding the fingers themselves, threading the tendon and installing them on the base, they have the opportunity to test and iterate their design. Eleven high school girls and their teachers participated in a pilot activity, filling out a survey on the design of the platform. The surveys indicated the students found the activity a fun, interesting, and valuable learning experience, one they would recommend to their friends.
Persistent Debt and Business Cycles in an Economy with Production Heterogeneity

We study an economy with a time-varying distribution of production to examine the role of debt in amplifying and propagating recessions. In our model, entrepreneurs use risky, long-term debt to finance capital. Liquid assets serve as collateral and transaction costs make debt illiquid. Debt payments increase the volatility of earnings relative to output, deterring entrepreneurs with insufficient collateral from financing efficient levels of capital. This results in a misallocation of resources. In a large recession, productive entrepreneurs with high levels of debt deleverage, amplifying the downturn. The model economy exhibits asymmetries over the business cycle. Recessions involve a rapid deterioration of economic activity while expansions are more gradual. When a recession coincides with a rise in leverage resulting from a fall in assets, fewer producers operate at efficient levels. When aggregate business leverage is ten percentage points above average, the half-life of the recovery doubles.
ACS/WFC CTE photometric correction: improved model for bright point sources

We present an updated model to correct aperture photometry of point sources for CTE losses. The model is similar to the CTE photometric correction currently available but with a few major improvements that allow more accurate results for bright stars. The standard CTE photometric correction uses a linear dependence on source brightness, background level and time. Here, we present the improved model using a curvilinear dependence on point source brightness. The overall statistical accuracy of the new model is as good as, or better than the previous one. In this ISR we present the data analysis and describe the data science modeling.
MoiréTag: Angular Measurement and Tracking with a Passive Marker

Stable, low-cost, and precise visual measurement of directional information has many applications in domains such as virtual and augmented reality, visual odometry, or industrial computer vision. Conventional approaches like checkerboard patterns require careful pre-calibration, and can therefore not be operated in snapshot mode. Other optical methods like autocollimators offer very high precision but require controlled environments and are hard to take outside the lab. Non-optical methods like IMUs are low cost and widely available, but suffer from high drift errors. To overcome these challenges, we propose a novel snapshot method for angular measurement and tracking with Moiré patterns that are generated by binary structures printed on both sides of a glass plate. The Moiré effect amplifies minute angular shifts and translates them into spatial phase shifts that can be readily measured with a camera, effectively implementing an optical Vernier scale. We further extend this principle from a simple phase shift to a chirp model, which allows for full 6D tracking as well as estimation of camera intrinsics like the field of view. Simulation and experimental results show that the proposed non-contact object tracking framework is computationally efficient and the average angular accuracy of 0.17° outperforms the state-of-the-arts.
EFFECT-DNN: Energy-efficient Edge Framework for Real-time DNN Inference

Real-time visual computing applications running Deep Neural Networks (DNN) are becoming popular for mission-critical use cases such as, disaster response, tactical scenarios, and medical triage that require establishing ad-hoc edge environments. However, strict latency deadlines of such applications require real-time processing of pre-trained DNN layers (i.e., DNN inference) involving image/video data which is highly challenging to achieve under such resource- constrained edge environments. In this paper, we address the trade-off between end-to-end latency of DNN inference and IoT devices’ energy consumption by proposing ‘EFFECT-DNN’, an energy efficient edge computing framework. The EFFECT-DNN framework aims to strike such balance by employing a collaborative DNN partitioning and task offloading strategy. Such strategy also involves resource allocation from IoT devices and edge servers to satisfy DNN inference deadline requirement even when the network bandwidth is on the lower end, which is often the case for critical use cases. The underlying optimization is formulated as a dynamic Mixed-Integer Nonlinear Programming (MINLP) problem is decoupled and solved by convex optimization and a game-like heuristic algorithm. We evaluate the performance of EFFECT-DNN framework on a hardware testbed and using extensive simulations with real-world DNN s. The results demonstrate that the proposed framework can ensure DNN inference deadline satisfaction with significant (~ 20-30%) device energy savings.
Super Glue Production by Dermatophytes: Review

Dermatophytosis can be caused on by the invasion and infection of keratinized tissues in people and animals via a group of filamentous fungus known as dermatophytes. About a quarter of the world’s population is affected by it which is one of the most prevalent superficial fungal diseases. Some of these fungi have the capacity to develop complex 3-D biofilm structures, or “biofilm,” which are distinguished by the creation of extracellular polymeric molecules and a heightened drug resistance. The assessment of biofilm now relies on a variety of different methods, which frequently results in various evaluations of the microbial strains’ capacity to create biofilms. It has only recently been discovered the architecture and growth features of dermatophytic biofilms (Trichophyton spp., Microsporum spp.). Additionally, the structural complexity and lack of research on filamentous fungal biofilms make therapy challenging. Therefore, there is a demand for newer antifungals or methods for treating resistant dermatophytosis to offer an efficient, original, and safe substitute to current treatments. Therefore, this review highlighted on the significance, characterization and evaluation of biofilm that produced from dermatophytes.
Process Modeling of Aerosol‐Cloud Interaction in Summertime Precipitating Shallow Cumulus Over the Western North Atlantic

Process modeling of Aerosol‐cloud interaction (ACI) is essential to bridging gaps between observational analysis and climate modeling of aerosol effects in the Earth system and eventually reducing climate projection uncertainties. In this study, we examine ACI in summertime precipitating shallow cumuli observed during the Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE). Aerosols and precipitating shallow cumuli were extensively observed with in‐situ and remote‐sensing instruments during two research flight cases on 02 June and 07 June, respectively, during the ACTIVATE summer 2021 deployment phase. We perform observational analysis and large‐eddy simulation (LES) of aerosol effect on precipitating cumulus in these two cases. Given the measured aerosol size distributions and meteorological conditions, LES is able to reproduce the observed cloud properties by aircraft such as liquid water content (LWC), cloud droplet number concentration (Nc) and effective radius reff. However, it produces smaller liquid water path (LWP) and larger Nc compared to the satellite retrievals. Both 02 and 07 June cases are over warm waters of the Gulf Stream and have a cloud top height over 3 km, but the 07 June case is more polluted and has larger LWC. We find that the Na‐induced LWP adjustment is dominated by precipitation feedback for the 2 June precipitating case and there is no clear entrainment feedback in both cases. An increase of cloud fraction due to a decrease of aerosol number concentration is also shown in the simulations for the 02 June case.
xcell: a cell library characterizer for combinational and state-holding gates

—We present an open-source cell library characterizer suitable for characterizing combinational logic as well as general state-holding gates that are used in asynchronous logic as well as clocked circuit families like pre-charged logic. The output of the characterizer uses the Synopsys . lib format, and has been used as the input to an existing asynchronous static timing and power analysis tool for multiple technologies and multiple cell libraries.
Invited Paper: On the Cost-Optimal Parallel Solution of the Majority Problem

The majority problem can be stated as follows: there is a collection of n bank cards that belong to different bank accounts. The only way to “read” these cards is through one or more two-input “equivalence testers”. Each tester accepts two cards and outputs “yes” if two input cards correspond to the same bank account or “no” if they do not. The objective of the majority problem is to design a solution that uses the minimum number of tests to determine if more than n/2 cards belong to the same bank account and then find these cards. We first review two sequential solutions, including the Boyer-Moore optimal linear solution (i.e., with a time complexity of O(n)). Then, we introduce an optimal elimination solution together with a special structure to support a cost-optimal parallel solution that solves the problem in O(logn) using O(n/ logn) processors (i.e., testers). That is, this solution matches the optimal sequential solution with O(n) operations.
Memory-aware Optimization for Sequences of Sparse Matrix-Vector Multiplications

This paper presents a novel approach to optimize multiple invocations of a sparse matrix-vector multiplication (SpMV) kernel performed on the same sparse matrix A and dense vector x, like Ax, A2x, ⋯, Akx, and their linear combinations such as Ax + A2x. Such computations are frequently used in scientific applications for solving linear equations and in multi-grid methods. Existing SpMV optimization techniques typically focus on a single SpMV invocation and do not consider opportunities for optimization across a sequence of SpMV operations (SSpMV), leaving much room for performance improvement. Our work aims to bridge this performance gap. It achieve this by partitioning the sparse matrix into submatrices and devising a new computation pipeline that reduces memory access to the sparse matrix and exploits the data locality of the dense vector of SpMV. Additionally, we demonstrate how our approach can be integrated with parallelization schemes to further improve performance. We evaluate our approach on four distinct multi-core systems, including three ARM and one Intel platform. Experimental results show that our techniques improve the standard implementation and the highly-optimized Intel math kernel library (MKL) by a large margin.
Mid-air Gesture Recognition by Ultra-Wide Band Radar Echoes

Microwave radar sensors in human-computer interaction promote several advantages over wearable and image-based sensors, such as privacy preservation, high reliability regardless of the ambient and lighting conditions, and larger field of view. However, the raw signals produced by such radars are high-dimension and very complex to process and interpret for gesture recognition. For these reasons, machine learning techniques have been mainly used for gesture recognition, but require a significant amount of gesture templates for training and calibration that are specific for each radar. To address these challenges in the context of mid-air gesture interaction, we introduce a data processing pipeline for hand gesture recognition adopting a model-based approach that combines full-wave electromagnetic modelling and inversion. Thanks to this model, gesture recognition is reduced to handling two dimensions: the hand-radar distance and the relative dielectric permittivity, which depends on the hand only (e.g., size, surface, electric properties, orientation). We are developing a software environment that accommodates the significant stages of our pipeline towards final gesture recognition. We already tested it on a dataset of 16 gesture classes with 5 templates per class recorded with the Walabot, a lightweight, off-the-shelf array radar. We are now studying how user-defined radar gestures resulting from gesture elicitation studies could be properly recognized or not by our gesture recognition engine.
Radial Energy Distributions in LiF by Alpha Particle Irradiation Using Monte Carlo Simulation

Monte Carlo calculations of radial energy distributions in LiF irradiated with protons and alpha particles have been performed coupling ion and electron transport according to the scheme proposed by Martin and Ghoniem. Electron interactions are followed in detail as they slow down and transfer their energy along the material. Results for 1, 2 and 3 MeV protons, 1 and 4 MeV alphas are presented. Their comparison with data in tissue-equivalent gas, Monte Carlo calculations performed in water vapour, and analytical calculations in LiF show that important differences arise from the contribution of dielectric energy loss interactions.
On the role of hydrogen bonding and charge ordering in the dynamics of cluster formation in associated liquids

Charge order is an important structuring feature in disordered liquids [1-4]. Since molecular interactions in liquids are essentially governed by neutral Lennard-Jones (LJ) interactions and Coulomb interactions, the quality of the disorder induced by these two types of interactions. Fig.SI1 illustrates this difference. The left panel shows typical binary LJ correlation functions gab(r ) between atoms a and b, as well as the corresponding structure factors Sab(k) (inset), defined as
A Technique for Autocalibration of Cloud Lidar

Abstract In this paper a technique for autocalibration of a cloud lidar is demonstrated. It is shown that the lidar extinction-to-backscatter ratio derived from integrated backscatter for stratocumulus is, in the absence of drizzle, constrained to a theoretical value of 18.8 ± 0.8 sr at a wavelength of 905 nm. The lidar can be calibrated by scaling the backscatter signal so that the observed lidar ratio matches the theoretical value when suitable conditions of stratocumulus are available. For a beam divergence of 1–1.5 mrad, multiple scattering introduces an uncertainty of about 10% into the calibration and for a narrow-beam ground-based lidar, with negligible multiple scattering, calibration may be possible to better than 5%. Some examples of the mean lidar ratio of supercooled liquid water layers and ice clouds inferred using this technique are also shown.
Improving Inference in Latent Variable Models

In various forms of data, we often want to separate certain features in them; for example, in face image data, we want to separate gender, hair, eyes, pose, etc. When we perform discriminative modelling, we are unable to capture or characterize the different features in a meaningful manner. However, generative modelling allows us to capture these features directly by introducing latent variable models, which are a proxy for the actual features. The variational autoencoder is such a generative tool that is extensively used for unsupervised representation learning [1].
This paper is included in the Proceedings of the 32nd USENIX Security Symposium.

Machine learning (ML) models have shown promise in classifying raw executable files (binaries) as malicious or benign with high accuracy. This has led to the increasing influence of ML-based classification methods in academic and real-world malware detection, a critical tool in cybersecurity. However, previous work provoked caution by creating variants of malicious binaries, referred to as adversarial examples , that are transformed in a functionality-preserving way to evade detection. In this work, we investigate the effectiveness of using adversarial training methods to create malware-classification models that are more robust to some state-of-the-art attacks. To train our most robust models, we significantly increase the efficiency and scale of creating adversarial examples to make adversarial training practical, which has not been done before in raw-binary malware detectors. We then analyze the effects of varying the length of adversarial training, as well as analyze the effects of training with various types of attacks. We find that data augmentation does not deter state-of-the-art attacks, but that using a generic gradient-guided method, used in other discrete domains, does improve robustness. We also show that in most cases, models can be made more robust to malware-domain attacks by adversarially training them with lower-effort versions of the same attack. In the best case, we reduce one state-of-the-art attack’s success rate from 90% to 5%. We also find that training with some types of attacks can increase robustness to other types of attacks. Finally, we discuss insights gained from our results, and how they can be used to more effectively train robust malware detectors.
Ceratonia siliqua pods (Carob) methanol extract alleviates doxorubicin – induced nephrotoxicity via antioxidant, anti-in ammatory and anti-apoptotic pathways

Attia H. Atta (  attaattia52@cu.edu.eg ) Cairo University https://orcid.org/0000-0003-1449-0293 Shimaa A. Atta Theodor Bilharz Research Institute Marwa Khattab Cairo University Faculty of Veterinary Medicine Tamer H. Abd El-Aziz National Research Centre Samar M. Mouneir Cairo University Faculty of Veterinary Medicine Marwa Ibrahim Cairo University Faculty of Veterinary Medicine Soad M. Nasr National Research Centre Shimaa Ramadan Cairo University Faculty of Veterinary Medicine
Research on classroom behavior analysis and optimization strategies based on convolutional neural network in smart learning environment

: Realizing the analysis and prediction of attention and learning emotion recognition in teacher-student adaptive interaction in a smart teaching environment is a major development need in the fields of intelligent guidance, teacher classroom evaluation, and classroom stress analysis. However, classroom education and online education big data are characterized by cross-modality, multi-source heterogeneity, content redundancy and structural confusion, and the secrecy of teacher-student relationship between different types of courses is high, which brings great challenges to the analysis and prediction of attention and learning emotion recognition in teacher-student adaptive interaction. This paper is based on the research idea of "sentiment analysis-data modeling-group discovery-behavior prediction", and innovatively uses the core algorithm of convolutional neural network to overcome the key problems of attention and learning emotion recognition and prediction in teacher-student adaptive interaction in the intelligent teaching environment. This paper presents an innovative approach to the analysis and prediction of attention and learning emotions in teacher-student adaptive interaction in a smart teaching environment. The method proposed in this paper achieves 96.88% accuracy in practical application at the expense of computation time. This study is expected to form a key method for the analysis of teacher-learner adaptive interaction states driven by multimodal data in a smart teaching environment, which provides optimized strategies and theoretical support for personalized smart teaching, and has high theoretical significance and application value.
Modelling subreddit interactions by activity overlap

Machine learning methods often require using an appropriate graph representation to discover useful knowledge in large social networks. Here, a method is proposed of inducing a Reddit graph model composed of subreddit nodes, and edges representing a measure of user-activity overlap. In the proposed process a bipartite user-subreddit network is generated and a novel projection is applied to induce a subreddit-subreddit network. An approximate version of the Bipartite Configuration Model (WABiCM) is described and used to extract the backbone of the projected networks. This is done separately for comments and submissions and the constructed networks are analyzed
SYNTHESIS OF 2-CHLORO-N-(3-HYDROXYPHENYL) ACETAMIDE AND 2- CHLORO-N-(4-HYDROXYPHENYL) ACETAMIDE AND STUDY OF THEIR ANTIMICROBIAL ACTIVITY

In this work, the results of chloroacetylation of mand p-aminophenols in the presence of acetic acid, acetonitrile and tetrahydrofuran as a solvent were presented. Methods of purification of the obtained substances were described. The structure of synthesized substances is established by methods of IR, PMR, mass spectroscopy. The antibacterial and antifungal activities of these compounds were investigated. The results of the screening revealed that the compounds showed the appreciable antibacterial activity against Grampositive and Gram-negative bacteria, whereas compounds did not exhibit any potential antifungal activity.