Miracle-dz/UniFAR-FTUs huggingface dataset:
Toxicity Effects of Lipid Nanoparticles Containing Artemisia Absinthium L Essential Oil on BRC-03 Breast Cancer Cell Line

Background & Objective: According to research, breast cancer is the second most common cancer among women. Nanomaterials of various materials significantly increase the solubility, stability and effective drug delivery, and in recent years research on the effectiveness of essential oils and plant extracts in inhibiting the growth of cancer cells is expanding. Artemisia belongs to the Asteraceae family. In many studies, the effectiveness of the essential oil and extract of this plant in inhibiting the growth of cancer cells has been reported. Materials & Methods: In this experiment, nanoparticles containing Artemisia absinthium L essential oil (thistle) were first synthesized by homogenizer & sonication method and then their physicochemical properties were determined, such as particle size, particle size distribution, zeta potential, percentage of loading efficiency. Then, the anti-apoptotic effect of lipid nanoparticles containing Artemisia essential oil in breast cancer cells (BRC-03) was evaluated. Results: The results of cellular effect of lipid nanoparticles containing essential oil of Artemisia absinthium L (thistle) on breast cancer cells showed that increasing the concentration of lipid nanoparticles containing essential oil of this plant reduced the survival rate of cancer cells. Breast (BRC-03) was purified compared to essential oil (p <0.05). Conclusion: Nano-essential oil of this plant is effective in reducing the IC50 of the drug and in increasing the cytotoxicity of the essential oil of the plant. Lipid nanoparticles containing essential oil of Artemisia absinthium L (thistle) activated the apoptotic pathway in breast cancer cells.
EFFECT OF HIGH-INTENSITY STRENGTH EXERCISE ON COGNITIVE PERFORMANCE EFECTO DE UN EJERCICIO DE FUERZA DE ALTA INTENSIDAD SOBRE EL RENDIMIENTO COGNIVITO

OBJECTIVES The first aim of this study was to elucidate the effects of a single high-intensity strength exercise until failure on behavioral (i.e., attention) and physiological (i.e., salivary cortisol) responses. The second goal was to evaluate the effect of the performance of the cognitive tasks on cortisol levels. METHODS Fourteen physically active subjects completed a physical stress exercise consisting of six sets of squat repetitions to failure. Salivary cortisol and cognitive functions were evaluated in counterbalanced order prior to and following exercise-induced Rev.int.med.cienc. act. fís. deporte vol. 21 número 84 ISSN: 1577-0354 654 stress. RESULTS The results showed lower cortisol levels before the exercise and higher cortisol values before the cognitive task (p < 0.05). CONCLUSION Exerciseinduced stress had a detrimental effect on attention. Furthermore, the effects of stress on cognitive performance seemed to depend on the time elapsed between the cessation of the exercise and the evaluation of these but not the type of highintensity exercise performed.
Challenges of Offshore LNG Transfer

Developing maritime gas fields in deep water by Floating Liquefied Natural Gas (FLNG) concepts poses demanding technical challenges. So far, no systems are in operation but projects in the design or construction phase are characterized by a floating terminal barge that produces, liquefies and stores natural gas at the offshore location. Frequently operating shuttle tankers are moored either alongside (side-by-side) or at the stern of the terminal (tandem) to receive the cryogenic liquefied cargo. During the offloading procedure, which takes 18 to 24 hours in changing environmental conditions, the transfer system has to tolerate the occurring relative motions between the terminal and the tanker. Gradually changing filling levels and free surface effects inside the tanks significantly influence the seakeeping behavior of the LNG carrier. Methods and research results published so far encompass experimental and numerical analyses of individual aspects of the complex hydrodynamic problem related to offshore LNG transfer. Well known work includes the determination of pressure peaks on tank walls caused by violent sloshing or exemplary reproductions of coupling effects between resonant internal fluid motions and wave-induced vessel motions. However, available results are mostly based on idealized conditions (two-dimensional setups, model testing with fresh water instead of LNG) where relevant hydrodynamic effects are observed to some extend but their consequences on the extrapolation of data to full scale operations is not fully comprehended. Due to these restrictions, most of the results obtained by current standard approaches are defective or at least incomplete. In this thesis, the first validated holistic numerical method, which captures all hydrodynamic aspects that are relevant during offloading operations is presented. By in-depth studies on the basis of this approach, the background of the occurring phenomena can be fully comprehended, which allows accurate extrapolation of results from model scale to full scale. Combining the introduced method and the gained background knowledge is a critical prerequisite for the conduction of trustworthy feasibility studies and the determination of operational ranges for FLNG projects. The selected linear potential theory based procedure is capable to excellently reproduce seakeeping characteristics as well as internal fluid motions. The entire cal-
Parallel Computation of Ad-hoc Multi-Dimensional Aggregates

The θ-constrained multidimensional aggregation operator (θ-MDA) is an effective and flexible operator for the computation of ad-hoc complex aggregation queries. θ-MDA separates the specification of groupings (base table B) for which aggregates are reported from the specification of the associated aggregation groups over which the aggregates are computed (detail table R). In order to evaluate θ-MDA queries several algorithms have been proposed, among them TCMDA and TCMDA. For the evaluation, the base table B has to be updated for every entry of detail table R. This leads to an immense number of incremental updates on the base table B for range aggregates. TCMDA reduces the number of incremental updates by reducing range aggregates to point aggregates followed by a post-processing step. However, both algorithms do not take advantage of the current evolution of modern computer architectures, where the clocking frequency stays constant and the number of computing cores increases. In this thesis, we parallelize the TCMDA and the TCMDA algorithm in order to take advantage of the evolution of multi-core architectures. In order to distribute the workload, we propose a horizontal partitioning of the base table B among the available processors. The incremental updates on these partitions can be computed by the available processors in isolation, where the horizontal partitioning strategy distributes the workload effectively. In addition, the reduction of the detail table R using point aggregates in TCMDA is pushed to the database management system (DBMS). Both algorithms were implemented using the C programming language on top of an Oracle database. The dataset used in order to evaluate the performance of the algorithms was generated using the dbgen tool of the TPC-H benchmark framework. The empirical evaluation shows the effectiveness of the optimizations. The parallel algorithms using the reduction of the detail table R to SQL outperform the current state of the art algorithms in order of magnitudes in all tested data settings, where the main part of the computation time resides at the DBMS to construct the separate intermediate result tables using a GROUP BY statement.
IN Midazolam versus Dexmedetomidine Sedation with Regards to Dental Fear and Behavioral Characteristics: A Randomized Clinical Trial

Katayoun Salem , DDS; Hossein Khoshrang , MD; Elham Esmaeeli , DDS; Mona Vatankhah , DDS; 1 Dept. of Pediatric Dentistry, Faculty of Dentistry, Tehran Medical Sciences, Azad University Tehran, Iran. 2 Dept. of Pediatric Dentistry, Guilan Dental school, Guilan University of Medical Sciences, Rasht, Iran. 3 Dept. of Anesthesia, Guilan Medical School, Guilan University of Medical Sciences, Rasht, Iran. 4 Dentist, Guilan Dental School, Guilan University of Medical Sciences, Rasht, Iran. 5 Pediatric Dentist, Dept. of Pediatric Dentistry, Faculty of Dentistry, Tehran Medical Sciences, Azad University Tehran, Iran.
Does over-education raise productivity and wages equally? The moderating role of workers’ origin and immigrants’ background

ABSTRACT We provide first evidence of the impact of over-education, among natives and immigrants, on firm-level productivity and wages. Our results show that the over-education wage premium is higher for natives than for immigrants. However, since the differential in productivity gains associated with over-education outweighs the corresponding wage premium differential, we conclude that over-educated native workers are in fact underpaid to a greater extent than their over-educated immigrant counterparts. This conclusion is refined by sensitivity analyses, when testing the role of immigrants’ background (e.g. region of birth, immigrant generation).
REASONS FOR PARTICIPATING IN CHARITY SPORT EVENTS MOTIVOS PARA LA PARTICIPACIÓN EN EVENTOS

The aim of this paper is to research a) participants' motives for joining races and walks organized for causes, and b) to what extent their decisions to take part are influenced by the way the cause is run and its accountability. A 31-question survey with answers given on the Likert scale (from 1 to 7) was shared online, drawing a response from 150 runners. The results demonstrate that the most significant motives for taking part in these events relate to personal benefit: Rev.int.med.cienc. act. fís. deporte vol. 21 número 84 ISSN: 1577-0354 730 enjoyment, physical and mental well-being and desire to socialize. Motives of an altruistic nature scored lowest: making a difference and a feeling of obligation. The results also suggested participants give significantly more importance to the information about the cause provided before the event than they do to accountability.
The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models

In this paper, we consider the problem of sparsifying BERT models, which are a key building block for natural language processing, in order to reduce their storage and computational cost. We introduce the Optimal BERT Surgeon (oBERT), an efficient and accurate pruning method based on approximate second-order information, which we show to yield state-of-the-art results in both stages of language tasks: pre-training and fine-tuning. Specifically, oBERT extends existing work on second-order pruning by allowing for pruning weight blocks, and is the first such method that is applicable at BERT scale. Second, we investigate compounding compression approaches to obtain highly compressed but accurate models for deployment on edge devices. These models significantly push boundaries of the current state-of-the-art sparse BERT models with respect to all metrics: model size, inference speed and task accuracy. For example, relative to the dense BERT-base, we obtain 10x model size compression with < 1% accuracy drop, 10x CPU-inference speedup with < 2% accuracy drop, and 29x CPU-inference speedup with < 7.5% accuracy drop. Our code, fully integrated with Transformers and SparseML, is available at https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT.
Summarizing a virtual robot's past actions in natural language

We propose and demonstrate the task of giving natural language summaries of the actions of a robotic agent in a virtual environment. We explain why such a task is important, what makes it difficult, and discuss how it might be addressed. To encourage others to work on this, we show how a popular existing dataset that matches robot actions with natural language descriptions designed for an instruction following task can be repurposed to serve as a training ground for robot action summarization work. We propose and test several methods of learning to generate such summaries, starting from either egocentric video frames of the robot taking actions or intermediate text representations of the actions used by an automatic planner. We provide quantitative and qualitative evaluations of our results, which can serve as a baseline for future work.
The authors' reply Sir:

Sir: A n objective of the work discussed in the above letter was to test the proposal by Dr. Berman and his colleagues that the perception of spatial brightness is related to the S/P ratio – they have promoted the expression P*(S/P) as a correlate for brightness perception. The results of our study suggested that this expression does not work. In their letter, Drs. Berman and Clear highlight what they believe to be errors in our statistical analyses and misunderstandings in our application of trichromacy. In recent years, following Dr. Berman’s suggestions, we have carried out new experiments to test his assertions about the visual field (for example: flat neutral surface, neutral interior space) [Fotios and Cheal, in press] and evaluation mode (for example: rapid-sequential, side-by-side) [Houser, Fotios and Royer, 2009; Fotios and Cheal, 2010]. In both cases, we did not find statistically significant effects. This demonstrates our willingness to consider and test alternative ideas even though this is done at the expense of a delay to our focus, which is to understand the spectral determinants of brightness and color perceptions in natural viewing conditions. This work is important because light sources with spectra more closely aligned with human vision have the potential to reduce energy use while simultaneously enhancing color and brightness perceptions.
Earthquakes on the Once-Punctured Torus

We study earthquake deformations on Teichm\"uller space associated with simple closed curves of the once-punctured torus. We describe two methods to get an explicit form of the earthquake deformation for any simple closed curve. The first method is rooted in linear recurrence relations, the second in hyperbolic geometry. The two methods align, providing both an algebraic and geometric interpretation of the earthquake deformations. We convert the expressions to other coordinate systems for Teichm\"uller space to examine earthquake deformations further. Two families of curves are used as examples. Examining the limiting behaviour of each gives insight into earthquakes about measured geodesic laminations, of which simple closed curves are a special case.
Generating Practical Adversarial Network Traffic Flows Using NIDSGAN

Network intrusion detection systems (NIDS) are an essential defense for computer networks and the hosts within them. Machine learning (ML) nowadays predominantly serves as the basis for NIDS decision making, where models are tuned to reduce false alarms, increase detection rates, and detect known and unknown attacks. At the same time, ML models have been found to be vulnerable to adversarial examples that undermine the downstream task. In this work, we ask the practical question of whether real-world ML-based NIDS can be circumvented by crafted adversarial flows, and if so, how can they be created. We develop the generative adversarial network (GAN)-based attack algorithm NIDSGAN and evaluate its effectiveness against realistic ML-based NIDS. Two main challenges arise for generating adversarial network traffic flows: (1) the network features must obey the constraints of the domain (i.e., represent realistic network behavior), and (2) the adversary must learn the decision behavior of the target NIDS without knowing its model internals (e.g., architecture and meta-parameters) and training data. Despite these challenges, the NIDSGAN algorithm generates highly realistic adversarial traffic flows that evade ML-based NIDS. We evaluate our attack algorithm against two state-of-the-art DNN-based NIDS in whitebox, blackbox, and restricted-blackbox threat models and achieve success rates which are on average 99%, 85%, and 70%, respectively. We also show that our attack algorithm can evade NIDS based on classical ML models including logistic regression, SVM, decision trees and KNNs, with a success rate of 70% on average. Our results demonstrate that deploying ML-based NIDS without careful defensive strategies against adversarial flows may (and arguably likely will) lead to future compromises.
STRENGTH AND BEHAVIOUR OF EXTERIOR BEAM COLUMN JOINTS WITH DIAGONAL CROSS BRACING BARS

The present work aims to study the seismic performance of exterior beam column joint with non-conventional reinforcement detailing. Four joint sub assemblages were tested under reverse cyclic loading applied at beam end. The specimens were sorted into two groups based on the joint reinforcement detailing. The first group (Group A) comprises of two joint assemblages having joint detailing as per construction code of practice in India (IS 456:2000) with two axial load cases. The second group (Group B) comprises of two specimens having additional cross bracing reinforcements for the joints detailed as per IS 456:2000 with similar axial load cases that in first group. The experimental investigations are validated with the analytical studies carried out by finite element models using ANSYS. The experimental results and analytical study indicate that additional cross bracing reinforcements improves the seismic performance.
Building Embedded Systems Like It's 1996

Embedded devices are ubiquitous. However, preliminary evidence shows that attack mitigations protecting our desktops/servers/phones are missing in embedded devices, posing a significant threat to embedded security. To this end, this paper presents an in-depth study on the adoption of common attack mitigations on embedded devices. Precisely, it measures the presence of standard mitigations against memory corruptions in over 10k Linux-based firmware of deployed embedded devices. The study reveals that embedded devices largely omit both user-space and kernel-level attack mitigations. The adoption rates on embedded devices are multiple times lower than their desktop counterparts. An equally important observation is that the situation is not improving over time. Without changing the current practices, the attack mitigations will remain missing, which may become a bigger threat in the upcoming IoT era. Throughout follow-up analyses, we further inferred a set of factors possibly contributing to the absence of attack mitigations. The exemplary ones include massive reuse of non-protected software, lateness in upgrading outdated kernels, and restrictions imposed by automated building tools. We envision these will turn into insights towards improving the adoption of attack mitigations on embedded devices in the future.
Rethinking Stability for Attribution-based Explanations

As attribution-based explanation methods are increasingly used to establish model trustworthiness in high-stakes situations, it is critical to ensure that these explanations are stable, e.g., robust to infinitesimal perturbations to an input. However, previous works have shown that state-of-the-art explanation methods generate unstable explanations. Here, we introduce metrics to quantify the stability of an explanation and show that several popular explanation methods are unstable. In particular, we propose new Relative Stability metrics that measure the change in output explanation with respect to change in input, model representation, or output of the underlying predictor. Finally, our experimental evaluation with three real-world datasets demonstrates interesting insights for seven explanation methods and different stability metrics.
Promising Technologies and R&D Directions for the Future Muon Collider Detectors

Among the post-LHC generation of particle accelerators, the muon collider represents a unique machine with capability to provide very high energy leptonic collisions and to open the path to a vast and mostly unexplored physics programme. However, on the experimental side, such great physics potential is accompanied by unprecedented technological challenges, due to the fact that muons are unstable particles. Their decay products interact with the machine elements and produce an intense flux of background particles that eventually reach the detector and may degrade its performance. In this paper, we present technologies that have a potential to match the challenging specifications of a muon collider detector and outline a path forward for the future R&D efforts.
InsetGAN for Full-Body Image Generation

While GANs can produce photo-realistic images in ideal conditions for certain domains, the generation of full-body human images remains difficult due to the diversity of identities, hairstyles, clothing, and the variance in pose. In-stead of modeling this complex domain with a single GAN, we propose a novel method to combine multiple pretrained GANs, where one GAN generates a global canvas (e.g., human body) and a set of specialized GANs, or insets, focus on different parts (e.g., faces, shoes) that can be seamlessly inserted onto the global canvas. We model the problem as jointly exploring the respective latent spaces such that the generated images can be combined, by inserting the parts from the specialized generators onto the global canvas, without introducing seams. We demonstrate the setup by combining a full body GAN with a dedicated high-quality face GAN to produce plausible-looking humans. We evalu-ate our results with quantitative metrics and user studies.
Sparsity and Heterogeneous Dropout for Continual Learning in the Null Space of Neural Activations

Continual/lifelong learning from a non-stationary input data stream is a cornerstone of intelligence. Despite their phenomenal performance in a wide variety of applications, deep neural networks are prone to forgetting their previously learned information upon learning new ones. This phenomenon is called"catastrophic forgetting"and is deeply rooted in the stability-plasticity dilemma. Overcoming catastrophic forgetting in deep neural networks has become an active field of research in recent years. In particular, gradient projection-based methods have recently shown exceptional performance at overcoming catastrophic forgetting. This paper proposes two biologically-inspired mechanisms based on sparsity and heterogeneous dropout that significantly increase a continual learner's performance over a long sequence of tasks. Our proposed approach builds on the Gradient Projection Memory (GPM) framework. We leverage k-winner activations in each layer of a neural network to enforce layer-wise sparse activations for each task, together with a between-task heterogeneous dropout that encourages the network to use non-overlapping activation patterns between different tasks. In addition, we introduce two new benchmarks for continual learning under distributional shift, namely Continual Swiss Roll and ImageNet SuperDog-40. Lastly, we provide an in-depth analysis of our proposed method and demonstrate a significant performance boost on various benchmark continual learning problems.