Miracle-dz/UniFAR-FTUs huggingface dataset:
The Intention of Waterpipe Tobacco Smoking by Women in the COVID-19 Pandemic and its Contributing Factors: a Nonparametric Path Analysis

Background People with waterpipe tobacco smoking (WTS) seem to be more at risk for the serious complications of coronavirus disease 2019 (COVID-19). This study aimed at assessing the behavioral intention (BI) of WTS by women in the COVID-19 pandemic and its contributing factors. Methods This cross-sectional descriptive-correlational study was conducted in 2020 (ie, during the COVID-19 pandemic). Participants were 300 women randomly selected through multistage sampling from comprehensive healthcare centers in Khorramabad, Iran. Data collection instrument was a 42-item questionnaire with 4 main subscales, namely knowledge, attitude, differential association, and BI. Data were collected through both online and phone-based methods and were analyzed using non-parametirc path analysis. Results The prevalence of WTS among women was 13% (95% CI, 11.06-14.94) and the mean scores of attitude, differential association, and behavioral intention among participants with WTS were significantly higher than participants without WTS (P < 0.001). Moreover, 46.12% (95% CI, 38.12-54.08) of participants with WTS reported intention to quit WTS due to the COVID-19 pandemic and 43.6% (95% CI, 35.66-51.54) of women with WTS and 16.5% (95% CI, 14.20-18.80) of women without WTS believed in the protective effects of WTS against COVID-19. The path analysis model showed that the BI of WTS had a significant inverse relationship with knowledge and a significant direct relationship with attitude and differential association. Conclusion This study suggests the need for quality educational and counseling interventions for the general public to correct popular misconceptions about the protective effects of WTS against COVID-19.
FREE MAGNETIC ENERGY AND FLARE PRODUCTIVITY OF ACTIVE REGIONS

In this study, the photospheric vector magnetograms, obtained with the Spectro-Polarimeter of the Solar Optical Telescope on board Hinode, are used as the boundary conditions to extrapolate the three-dimensional nonlinear force-free (NLFF) coronal magnetic fields. The observed non-force-free photospheric magnetic fields are preprocessed toward the nearly force-free chromospheric magnetic fields. The performance of the preprocessing procedure is evaluated by comparing with chromospheric magnetic fields obtained by the Vector SpectroMagnetograph instrument located on the Synoptic Optical Long-term Investigations of the Sun Tower. Then, the weighted optimization method is applied to the preprocessed boundary data to extrapolate the NLFF fields with which we are able to estimate the free magnetic energy stored in the active regions. The magnitude scaling correlation between the free magnetic energy and the soft X-ray flare index (FI) of active regions is then studied. The latter quantifies the impending flare production of active regions over the subsequent 1, 2, and 3 day time windows. Based on 75 samples, we find a positive correlation between the free energy and the FI. We also study the temporal variation of free magnetic energy for three active regions, of which two are flare-active and one is flare-quiet during the observation over a period of several days. While the magnitude of free magnetic energy unambiguously differentiates between the flare-active and the flare-quiet regions, the temporal variation of free magnetic energy does not exhibit a clear and consistent pre-flare pattern. This may indicate that the trigger mechanism of flares is as important as the energy storage in active regions.
Solutions with clustered bubbles and a boundary layer of an elliptic problem

We study positive solutions of the equation $e^2 \Delta u - u + u^\frac{n+2}{n-2} = 0$ where $e >0$ is small, with Neumann boundary condition in a unit ball $B\subset\mathbb R^3$. We prove the existence of solutions with multiple interior bubbles near the center and a boundary layer. The method may also be used to the case $n=4$, $5$ and get the analogous results.
Using Anomaly Detection to Detect Poisoning Attacks in Federated Learning Applications

Adversarial attacks such as poisoning attacks have attracted the attention of many machine learning researchers. Traditionally, poisoning attacks attempt to inject adversarial training data in order to manipulate the trained model. In federated learning (FL), data poisoning attacks can be generalized to model poisoning attacks, which cannot be detected by simpler methods due to the lack of access to local training data by the detector. State-of-the-art poisoning attack detection methods for FL have various weaknesses, e.g., the number of attackers has to be known or not high enough, working with i.i. d. data only, and high computational complexity. To overcome above weaknesses, we propose a novel framework for detecting poisoning attacks in FL, which employs a reference model based on a public dataset and an auditor model to detect malicious updates. We implemented a detector based on the proposed framework and using a one-class support vector machine (OC-SVM), which reaches the lowest possible computational complexity O(K) where K is the number of clients. We evaluated our detector's performance against state-of-the-art (SOTA) poisoning attacks for two typical applications of FL: electrocardiograph (ECG) classification and human activity recognition (HAR). Our experimental results validated the performance of our detector over other SOTA detection methods.
Constraining gravitational wave amplitude birefringence with GWTC-3

The propagation of gravitational waves can reveal fundamental features of the structure of spacetime. For instance, differences in the propagation of gravitational-wave polarizations would be a smoking gun for parity violations in the gravitational sector, as expected from birefringent theories like Chern-Simons gravity. Here we look for evidence of amplitude birefringence in the third catalog of detections by the Laser Interferometer Gravitational Wave Observatory and Virgo through the use of birefringent templates inspired by dynamical Chern-Simons gravity. From $71$ binary-black-hole signals, we obtain the most precise constraints on gravitational-wave amplitude birefringence yet, measuring a birefringent attenuation of $\kappa = -0.019^{+0.038}_{-0.029} \, \mathrm{Gpc}^{-1}$ at $100 \, \mathrm{Hz}$ with $90\%$ credibility, equivalent to a parity-violation energy scale of $M_{\rm PV} \gtrsim 6.8 \times 10^{-21}\, {\rm GeV}$.
iEdit: Localised Text-guided Image Editing with Weak Supervision

Diffusion models (DMs) can generate realistic images with text guidance using large-scale datasets. However, they demonstrate limited controllability in the output space of the generated images. We propose a novel learning method for text-guided image editing, namely \texttt{iEdit}, that generates images conditioned on a source image and a textual edit prompt. As a fully-annotated dataset with target images does not exist, previous approaches perform subject-specific fine-tuning at test time or adopt contrastive learning without a target image, leading to issues on preserving the fidelity of the source image. We propose to automatically construct a dataset derived from LAION-5B, containing pseudo-target images with their descriptive edit prompts given input image-caption pairs. This dataset gives us the flexibility of introducing a weakly-supervised loss function to generate the pseudo-target image from the latent noise of the source image conditioned on the edit prompt. To encourage localised editing and preserve or modify spatial structures in the image, we propose a loss function that uses segmentation masks to guide the editing during training and optionally at inference. Our model is trained on the constructed dataset with 200K samples and constrained GPU resources. It shows favourable results against its counterparts in terms of image fidelity, CLIP alignment score and qualitatively for editing both generated and real images.
V2X-Seq: A Large-Scale Sequential Dataset for Vehicle-Infrastructure Cooperative Perception and Forecasting

Utilizing infrastructure and vehicle-side information to track and forecast the behaviors of surrounding traffic participants can significantly improve decision-making and safety in autonomous driving. However, the lack of real-world sequential datasets limits research in this area. To address this issue, we introduce V2X-Seq, the first large-scale sequential V2X dataset, which includes data frames, trajectories, vector maps, and traffic lights captured from natural scenery. V2X-Seq comprises two parts: the sequential perception dataset, which includes more than 15,000 frames captured from 95 scenarios, and the trajectory forecasting dataset, which contains about 80,000 infrastructure-view scenarios, 80,000 vehicle-view scenarios, and 50,000 cooperative-view scenarios captured from 28 intersections' areas, covering 672 hours of data. Based on V2X-Seq, we introduce three new tasks for vehicle-infrastructure cooperative (VIC) autonomous driving: VIC3D Tracking, Online-VIC Forecasting, and Offline-VIC Forecasting. We also provide benchmarks for the introduced tasks. Find data, code, and more up-to-date information at https://github.com/AIR-THU/DAIR-V2X-Seq.
The Frtb-Ima Computational Challenge for Equity Autocallables

When the Orthogonal Chebyshev Sliding Technique was introduced it was applied to a portfolio of swaps and swaptions within the context of the FRTB-IMA capital calculation. The computational cost associated to the computation of the ES values - an essential component of the capital caluclation under FRTB-IMA - was reduced by more than $90\%$ while passing PLA tests. This paper extends the use of the Orthogonal Chebyshev Sliding Technique to portfolios of equity autocallables defined over a range of spot underlyings. Results are very positive as computational reductions are of about $90\%$ with passing PLA metrics. Since equity autocallables are a commonly traded exotic trade type, with significant FRTB-IMA computational costs, the extension presented in this paper constitutes an imporant step forward in tackling the computational challenges associated to an efficient FRTB-IMA implementation.
Long-time asymptotics for the integrable nonlocal Lakshmanan-Porsezian-Daniel equation with decaying initial value problem

In this work, we study the Cauchy problem of integrable nonlocal Lakshmanan-Porsezian-Daniel equation with rapid attenuation of initial data. The basis Riemann-Hilbert problem of integrable nonlocal Lakshmanan-Porsezian-Daniel equation is constructed from Lax pair. Using Deift-Zhou nonlinear steepest descent method, the explicit long-time asymptotic formula of integrable nonlocal Lakshmanan-Porsezian-Daniel equation is derived. For the integrable nonlocal Lakshmanan-Porsezian-Daniel equation, the asymptotic behavior is different from the local model, due to they have different symmetry for the scattering matrix. Besides, since the increase of real stationary phase points also makes the asymptotic behavior have more complex error term which has nine possibilities in our analysis.
Assessment of Reinforcement Learning Algorithms for Nuclear Power Plant Fuel Optimization

The nuclear fuel loading pattern optimization problem belongs to the class of large-scale combinatorial optimization. It is also characterized by multiple objectives and constraints, which makes it impossible to solve explicitly. Stochastic optimization methodologies including Genetic Algorithms and Simulated Annealing are used by different nuclear utilities and vendors, but hand-designed solutions continue to be the prevalent method in the industry. To improve the state-of-the-art, Deep Reinforcement Learning (RL), in particular, Proximal Policy Optimization is leveraged. This work presents a first-of-a-kind approach to utilize deep RL to solve the loading pattern problem and could be leveraged for any engineering design optimization. This paper is also to our knowledge the first to propose a study of the behavior of several hyper-parameters that influence the RL algorithm. The algorithm is highly dependent on multiple factors such as the shape of the objective function derived for the core design that behaves as a fudge factor that affects the stability of the learning. But also, an exploration/exploitation trade-off that manifests through different parameters such as the number of loading patterns seen by the agents per episode, the number of samples collected before a policy update nsteps, and an entropy factor ent_coef that increases the randomness of the policy during training. We found that RL must be applied similarly to a Gaussian Process in which the acquisition function is replaced by a parametrized policy. Then, once an initial set of hyper-parameters is found, reducing nsteps and ent_coef until no more learning is observed will result in the highest sample efficiency robustly and stably. This resulted in an economic benefit of 535,000- 642,000 $/year/plant.
Stealth Shaper: Reflectivity Optimization as Surface Stylization

We present a technique to optimize the reflectivity of a surface while preserving its overall shape. The naïve optimization of the mesh vertices using the gradients of reflectivity simulations results in undesirable distortion. In contrast, our robust formulation optimizes the surface normal as an independent variable that bridges the reflectivity term with differential rendering, and the regularization term with as-rigid-as-possible elastic energy. We further adaptively subdivide the input mesh to improve the convergence. Consequently, our method can minimize the retroreflectivity of a wide range of input shapes, resulting in sharply creased shapes ubiquitous among stealth aircraft and Sci-Fi vehicles. Furthermore, by changing the reward for the direction of the outgoing light directions, our method can be applied to other reflectivity design tasks, such as the optimization of architectural walls to concentrate light in a specific region. We have tested the proposed method using light-transport simulations and real-world 3D-printed objects.
Evaluating Twitter's Algorithmic Amplification of Low-Trust Content: An Observational Study

Artificial intelligence powered recommender systems play a crucial role in determining the content that users are exposed to on social media platforms. However, the behavioural patterns of these systems are often opaque and non-replicable, complicating the evaluation of their impact on the dissemination and consumption of disinformation and misinformation. To begin addressing this evidence gap, this study presents a measurement approach that uses observed digital traces to infer the current status of algorithmic amplification of low-trust content on Twitter. Using an original dataset of 2.7 million posts on COVID-19 and climate change published on the platform in a 14-day period in January 2023, this study identifies tweets sharing information from domains rated as low-trust by IffyNews (n=74,467), and for each tweet author, compares the impressions of tweets containing low-trust information against an impressions benchmark generated from the author's timeline. To minimise the influence of factors traditionally understood to influence impressions, the data is grouped by tweet author, and the sample is stratified twice, controlling for the effect of tweet-level sentiment and engagement. Through this approach, it is possible to assess whether, for any given user, Twitter's recommender system amplifies the impressions - and as a consequence, the visibility - of potentially misleading information regardless of critical confounding factors. This analysis provides observational evidence on whether the Twitter algorithm favours the visibility of low-trust content, with results indicating that tweets containing low-trust URL domains perform significantly better than tweets that do not across all sample stratifications. This suggests that in its current form, Twitter's recommender system may be facilitating the diffusion of false content, even when originating from notoriously low-trust sources.
Evaluating plate discipline in Major League Baseball with Bayesian Additive Regression Trees

Abstract We introduce a three-step framework to determine at which pitches Major League batters should swing. Unlike traditional plate discipline metrics, which implicitly assume that all batters should always swing at (resp. take) pitches inside (resp. outside) the strike zone, our approach explicitly accounts not only for the players and umpires involved in the pitch but also in-game contextual information like the number of outs, the count, baserunners, and score. We first fit flexible Bayesian nonparametric models to estimate (i) the probability that the pitch is called a strike if the batter takes the pitch; (ii) the probability that the batter makes contact if he swings; and (iii) the number of runs the batting team is expected to score following each pitch outcome (e.g. swing and miss, take a called strike, etc.). We then combine these intermediate estimates to determine whether swinging increases the batting team’s run expectancy. Our approach enables natural uncertainty propagation so that we can not only determine the optimal swing/take decision but also quantify our confidence in that decision. We illustrate our framework using a case study of pitches faced by Mike Trout in 2019.
Best-Effort Adaptation

We study a problem of best-effort adaptation motivated by several applications and considerations, which consists of determining an accurate predictor for a target domain, for which a moderate amount of labeled samples are available, while leveraging information from another domain for which substantially more labeled samples are at one's disposal. We present a new and general discrepancy-based theoretical analysis of sample reweighting methods, including bounds holding uniformly over the weights. We show how these bounds can guide the design of learning algorithms that we discuss in detail. We further show that our learning guarantees and algorithms provide improved solutions for standard domain adaptation problems, for which few labeled data or none are available from the target domain. We finally report the results of a series of experiments demonstrating the effectiveness of our best-effort adaptation and domain adaptation algorithms, as well as comparisons with several baselines. We also discuss how our analysis can benefit the design of principled solutions for fine-tuning.
Post-training Model Quantization Using GANs for Synthetic Data Generation

Quantization is a widely adopted technique for deep neural networks to reduce the memory and computational resources required. However, when quantized, most models would need a suitable calibration process to keep their performance intact, which requires data from the target domain, such as a fraction of the dataset used in model training and model validation (i.e. calibration dataset). In this study, we investigate the use of synthetic data as a substitute for the calibration with real data for the quantization method. We propose a data generation method based on Generative Adversarial Networks that are trained prior to the model quantization step. We compare the performance of models quantized using data generated by StyleGAN2-ADA and our pre-trained DiStyleGAN, with quantization using real data and an alternative data generation method based on fractal images. Overall, the results of our experiments demonstrate the potential of leveraging synthetic data for calibration during the quantization process. In our experiments, the percentage of accuracy degradation of the selected models was less than 0.6%, with our best performance achieved on MobileNetV2 (0.05%). The code is available at: https://github.com/ThanosM97/gsoc2022-openvino
Learning Optimal "Pigovian Tax" in Sequential Social Dilemmas

In multi-agent reinforcement learning, each agent acts to maximize its individual accumulated rewards. Nevertheless, individual accumulated rewards could not fully reflect how others perceive them, resulting in selfish behaviors that undermine global performance. The externality theory, defined as ``the activities of one economic actor affect the activities of another in ways that are not reflected in market transactions,'' is applicable to analyze the social dilemmas in MARL. One of its most profound non-market solutions, ``Pigovian Tax'', which internalizes externalities by taxing those who create negative externalities and subsidizing those who create positive externalities, could aid in developing a mechanism to resolve MARL's social dilemmas. The purpose of this paper is to apply externality theory to analyze social dilemmas in MARL. To internalize the externalities in MARL, the \textbf{L}earning \textbf{O}ptimal \textbf{P}igovian \textbf{T}ax method (LOPT), is proposed, where an additional agent is introduced to learn the tax/allowance allocation policy so as to approximate the optimal ``Pigovian Tax'' which accurately reflects the externalities for all agents. Furthermore, a reward shaping mechanism based on the approximated optimal ``Pigovian Tax'' is applied to reduce the social cost of each agent and tries to alleviate the social dilemmas. Compared with existing state-of-the-art methods, the proposed LOPT leads to higher collective social welfare in both the Escape Room and the Cleanup environments, which shows the superiority of our method in solving social dilemmas.
A Glimpse in ChatGPT Capabilities and its impact for AI research

Large language models (LLMs) have recently become a popular topic in the field of Artificial Intelligence (AI) research, with companies such as Google, Amazon, Facebook, Amazon, Tesla, and Apple (GAFA) investing heavily in their development. These models are trained on massive amounts of data and can be used for a wide range of tasks, including language translation, text generation, and question answering. However, the computational resources required to train and run these models are substantial, and the cost of hardware and electricity can be prohibitive for research labs that do not have the funding and resources of the GAFA. In this paper, we will examine the impact of LLMs on AI research. The pace at which such models are generated as well as the range of domains covered is an indication of the trend which not only the public but also the scientific community is currently experiencing. We give some examples on how to use such models in research by focusing on GPT3.5/ChatGPT3.4 and ChatGPT4 at the current state and show that such a range of capabilities in a single system is a strong sign of approaching general intelligence. Innovations integrating such models will also expand along the maturation of such AI systems and exhibit unforeseeable applications that will have important impacts on several aspects of our societies.