Miracle-dz/UniFAR-FTUs huggingface dataset:
Cosmological beam plasma instabilities

Blazars are the main source of extragalactic very high energy gamma-rays. These gamma rays annihilate on the extragalactic background light, producing electron-positron pair beams with TeV energies. The pair beams are very dilute, with beam-IGM density ratio of α ∼ 10−15, ultra-relativistic, γ ∼ 10, and energetically subdominant (γα ∼ 10−9). Such pair beams suffer from prevailing cosmological scale, linear beam-plasma instabilities. The associated instability growth rates suggest that at least initially these overwhelmingly dominate inverse Compton cooling, currently the only alternative mechanism by which the pair beams lose energy. Therefore, the full non-linear evolution of the instabilities is key to determining the mechanism by which these pair-beams lose their energy. Kinetic numerical simulations are the only method by which we can currently study the full nonlinear evolution of the blazar-induced beam-plasma instabilities. However, the extreme parameters of the pair beams make direct simulations via existing particle-in-cell (PIC) codes infeasible. To address this, we developed a new Spatially Higher-order Accurate, Relativistic PIC algorithm (SHARP). A one dimensional implementation of the SHARP algorithm (SHARP-1D) is given in detail. We show explicitly that SHARP-1D can overcome a number of the limitations of existing PIC algorithms. Using SHARP-1D, we demonstrate a number of points that are important to correctly simulate the full evolution of beam-plasma instabilities. We show that convergence for PIC algorithms requires increasing both spatial resolution and the number of particles per cell concurrently. For a beam-plasma system, we show that the spectral resolution is another important resolution criteria and under-resolved simulations can lead to erroneous physical conclusions. We quantify the required box sizes to faithfully resolve the spectral support of the instabilities. When the background plasmas contain structure, we show that a significant fraction of beam energy (similar to that in uniform plasma simulations; ∼ 20%) is, still, lost during the linear evolution of the electrostatic unstable modes. Compared to uniform plasma growth rates, we find lower growth rates, however, the non-uniform
AoT - Attack on Things: A security analysis of IoT firmware updates

IoT devices implement firmware update mechanisms to fix security issues and deploy new features. These mechanisms are often triggered and mediated by mobile companion apps running on the users’ smartphones. While it is crucial to update devices, these mechanisms may cause critical security flaws if they are not implemented correctly. Given their relevance, in this paper, we perform a systematic security analysis of the firmware update mechanisms adopted by IoT devices via their companion apps. First, we define a threat model for IoT firmware updates, and we categorize the different potential security issues affecting them. Then, we analyze 23 popular IoT devices (and corresponding companion apps) to identify vulnerable devices and the SDKs that such devices use to implement the update functionality. Our analysis reveals that 6 popular SDKs present dangerous security flaws. Additionally, we fingerprint each vulnerable SDK and we leverage our fingerprints to perform a large-scale analysis of companion apps from the Google Play Store. Our results show that 61 popular devices and 1,356 apps rely on vulnerable SDKs, thus, they potentially adopt an insecure firmware update mechanism.
Taintmini: Detecting Flow of Sensitive Data in Mini-Programs with Static Taint Analysis

Mini-programs, which are programs running inside mobile super apps such as WeChat, often have access to privacy-sensitive information, such as location data and phone numbers, through APUs provided by the super apps. This access poses a risk of privacy sensitive data leaks, either accidentally from carelessly programmed mini-programs or intentionally from malicious ones. To address this concern, it is crucial to track the flow of sensitive data in mini-programs for either human analysis or automated tools. Although existing taint analysis techniques have been widely studied, they face unique challenges in tracking sensitive data flows in mini-programs, such as cross-language, cross-page, and cross-mini-program data flows. This paper presents a novel framework, Taintmini, which addresses these challenges by using a novel universal data flow graph approach that captures data flows within and across mini-programs. We have evaluated Taintminiwith 238,866 mini-programs and detect 27,184 that contain sensitive data flows. We have also applied Taintminito detect privacy leakage colluding mini-programs and identify 455 such programs from them that clearly violate privacy policy.
The Effect of Charcoal Based Chewing Gums in Reducing the Concentration of Unwanted Substances and Compounds: Dyes, Microbes and Viruses in Saliva

Adding various substances to the chewing gums has led to the formation of new applications for them, which include drug delivery and oral and dental hygiene. In this research, the physicochemical and rheological properties of charcoal chewing gum were studied using chemical analysis and sensorial methods. The results of this study show that the addition of activated charcoal in the studied chewing gums has a Longer-lasting flavor and more saliva secretion. Longer-lasting flavor in charcoal chewing gum increases the acceptance and willingness to keep charcoal based chewing gums for panelists. In line with the results of sensory evaluation, histometric analysis of charcoal and non-charcoal based chewing gums shows the same texture profile against pressure on both types of chewing gum, with the difference that the maximum pressure tolerance threshold in charcoal based chewing gums is higher than in chewing gums without charcoal. In general, it seems that increasing the susceptibility along with increasing saliva secretion and increasing the pH in general create a suitable substrate for more activated charcoal in chewing gum and therefore the efficiency of charcoal chewing gum to absorb unwanted substances such as dyes, microbes and viruses in saliva. Therefore, it is expected that the use of charcoal gum by reducing the concentration of microbes and viruses in saliva can have a great impact on oral health, dental protection, general health, and reduce the prevalence of infectious and viral diseases.
Securing Whole Applications with SGX

Intel Software Guards Extension (SGX) is a CPU-based mechanism for creating a Trusted Execution Environment (TEE), called an enclave, for user-level application code. The enclave is a hardware-isolated runtime environment whose memory is isolated from other hardware and software on the system, including the Operating System (OS) and hypervisors. This isolation mechanism comes at the cost of severely restricting the application’s programming model, since standard OS abstractions, such as Input/Output (I/O) operations, are not safely available. This makes it necessary to adapt applications to run in a standard enclave context. Using SGX to run unmodified applications requires a secure runtime environment that provides the necessary C Standard Library (libc) interface that interacts securely with the untrusted host OS services. The most promising approaches to implementing such an environment are library OS or container-based.
Targeted Privacy Attacks by Fingerprinting Mobile Apps in LTE Radio Layer

We investigate the feasibility of targeted privacy attacks using only information available in physical channels of LTE mobile networks and propose three privacy attacks to demonstrate this feasibility: mobile-app fingerprinting attack, history attack, and correlation attack. These attacks can reveal the geolocation of targeted mobile devices, the victim's app usage patterns, and even the relationship between two users within the same LTE network cell. An attacker also may launch these attacks stealthily by capturing radio signals transmitted over the air, using only a passive sniffer as equipment. To ensure the impact of these attacks on mobile users' privacy, we perform evaluations in both laboratory and real-world settings, demonstrating their practicality and dependability. Furthermore, we argue that these attacks can target not only 4G/LTE but also the evolving 5G standards.
Structural and Magnetic Properties of Mechanochemically Prepared Li Ferrite Nanoparticles

In this work, lithium ferrite (Li0.5Fe2.5O4) nanoparticles were prepared via mechanochemical processing and subsequent heat treatment at a relatively low (600 °C) calcining temperature. The raw materials used were high purity Fe2O3 and Li2CO3 that were milled for between 2 and 20 h. The milled powders were then calcined at temperatures of 500 and 600 °C for 5 h in air. XRD results show that optimum conditions to obtain single phase lithium ferrite nanoparticles with a mean crystallite size of about 23 nm, using Scherrer’s formula, are 10 h milling and calcination at 600 °C. Saturation magnetization and coercivity of the single phase Li ferrite nanoparticles are 44.6 emu/g and 100 Oe respectively, which are both smaller than those of the bulk Li ferrite. The Curie temperature of the single sample was determined by a Faraday balance, which is 578 °C and smaller than that of bulk Li ferrite.
Poster: Packing-aware Pruning for Efficient Private Inference based on Homomorphic Encryption

—Due to the extensive application of machine learning in a wide range of fields and the necessity of privacy, privacy-preserving machine learning (PPML) solutions have recently gained significant traction. One group of approaches relies on Homomorphic Encryption (HE), which enables us to perform computation over encrypted data. However, even with state-of-the-art schemes, HE operations are still significantly slow compared to their plaintext counterparts and require considerable memory. Therefore, here we propose a framework that automatically makes an already trained model HE-friendly using a learning-based method. Then, it performs a packing-aware pruning method that prunes the model’s parameters in arbitrary block shape in correspondence with the ciphertext packing method while maintaining the model’s performance. This allows for dropping a significant number of HE operations and reducing latency and memory consumption of the private inference (PI). We evaluate our method on the LeNet model trained on MNIST; our results demonstrate that even at a 98% pruning ratio, the model performs almost the same as the baseline model. At the same time, the number of required HE operations is reduced by a factor of 15 which implies 9.63 and 4.04 factors of reduction in the latency and the required memory of PI.
Bootcamp Method for Training General Purpose AI Agents

— General purpose agents have long been an ultimate goal of AI research. One promising approach to this goal is to first train an agent to use a variety of skills, called a skillnet agent, and then allow the agent to learn how to choose the appropriate skill instead of having to choose the appropriate low-level action. We propose a method for training skillnet agents called Bootcamp that helps agents efficiently learn basic skills in an environment. We found that Bootcamp agents outperform skillnet agents trained randomly on various tasks defined in the ViZDoom simulated environment. We also found that skillnet agents outperform more conventional reinforcement-based learning approaches such as DQNs in ViZDoom.
Design and Analysis of 140-Degree Miter Bend for High Power Electron Cyclotron Heating Transmission Lines

Abstract The ITER Electron Cyclotron Heating (ECH) system Transmission Lines (TL) require highly polished copper mirrors on miter bends (both 90° and 140°) to direct microwaves from their origin to the tokamak. This will result in substantial heat dissipation on the miter bends and mirrors and will require water cooling in order to achieve long pulse operation. Analysis and optimization of the cooling design for the 140° miter bend assembly used ANSYS® Multiphysics™ software to develop and verify the fluid, thermal, and structural behavior of the mirror and miter bend assembly. Simulation model choices included a thermo-mechanical model of the mirror-only, a thermo-mechanical model of the miter bend assembly, and a thermo-mechanical model of the mirror with coolant. These analyses revealed an optimal solution that uses a major-axis cooling channel configuration for the 140° miter bend to meet the design criteria (e.g. structural stresses, mirror deflection, vacuum seal, coolant temperatures and pressures).
First record of Phoxinus csikii Hankó, 1922 (Actinopterygii, Cypriniformes) in France

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. First record of Phoxinus csikii Hankó, 1922 (Actinopterygii, Cypriniformes) in France Gaël P.J. Denys, Sébastien Manné
Research on the Application of Artiﬁcial Intelligence Based Technology in Chinese Book Procurement in Universities—Take Shaoyang University Library as an Example

To reduce the risk of zero borrowing of purchased books, the live use of library space, and the optimization of the library’s shelving procedures, given the limited funds and space in university libraries, this article uses algorithms such as Light Gradient Boosting Machine (LightGBM), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) for classiﬁcation and regression The analysis was conducted and the results were cross-checked 10 times. By comparing the experimental results, the LightGBM method has an accuracy of 73.17% and a RMSEvalueof4.154,andthemodelcanbeusedasabasisforeffectivepurchasing decisions.
A sensor complements the steric gate when DNA polymerase ε discriminates ribonucleotides

The cellular imbalance between high concentrations of ribonucleotides (NTPs) and low concentrations of deoxyribonucleotides (dNTPs), is challenging for DNA polymerases when building DNA from dNTPs. It is currently believed that DNA polymerases discriminate against NTPs through a steric gate model involving a clash between a tyrosine and the 2’-hydroxyl of the ribonucleotide in the polymerase active site in B-family DNA polymerases. With the help of crystal structures of a B-family polymerase with a UTP or CTP in the active site, molecular dynamics simulations, biochemical assays and yeast genetics, we have identified a mechanism by which the finger domain of the polymerase sense NTPs in the polymerase active site. In contrast to the previously proposed polar filter, our experiments suggest that the amino acid residue in the finger domain senses ribonucleotides by steric hindrance. Furthermore, our results demonstrate that the steric gate in the palm domain and the sensor in the finger domain are both important when discriminating NTPs. Structural comparisons reveal that the sensor residue is conserved among B-family polymerases and we hypothesize that a sensor in the finger domain should be considered in all types of DNA polymerases.
Poster: A Self-auditing Protocol for Decentralized Cloud Storage via Trusted Hardware Components

—Ensuring integrity of the data outsourced to a decentralized cloud storage system is a critical but challenging problem. To provide this guarantee, current decentralized cloud storage systems rely on blockchain and smart contracts to establish a trusted entity which can audit the storage peers. This would result in a significant overhead as each smart contract is run on all the miners of the blockchain. By leveraging trusted hardware components equipped with the storage peer, this work has designed a unique self-auditing protocol which can ensure data integrity in the decentralized cloud without relying on the blockchain and smart contracts.
Navigating Classic Atari Games with Deep Learning

Games for the Atari 2600 console provide great environments for testing reinforcement learning algorithms. In reinforcement learning algorithms, an agent typically learns about its environment via the delivery of periodic rewards. Deep Q-Learning, a variant of Q-Learning, utilizes neural networks which train a Q-function to predict the highest future reward given an input state and action. Deep Q-learning has shown great results in training agents to play Atari 2600 games like Space Invaders and Breakout. However, Deep Q-Learning has historically struggled with learning how to play games with greater emphasis on exploration and delayed rewards, like Ms. PacMan. In this project, we train a neural network that learns control policies for Ms. PacMan. We experiment with various methods to boost Q-agent performance. Through our novel training method, Frame-Diff, we are able to not only optimize, but beat, known benchmarks for reinforcement learning agents for Ms. PacMan by up to 50%.
The Rough Wall High Reynolds Number Turbulent Boundary Layer Surface Pressure Spectrum

There have been very few studies investigating the rough wall pressure spectra under fully rough flows, which are relevant to many common engineering applications operating within this regime. This investigation uses the Virginia Tech Stability Wind Tunnel to perform experiments on a series of high Reynolds number zero pressure gradient turbulent boundary layers formed over rough walls in an effort to better understand and characterize the behavior of the rough wall pressure spectrum. The boundary layers were fully rough, and the boundary layer thickness remained sufficiently larger than the height of the roughness elements. Two rough surfaces were tested. One consisted of an array of 1mm ordered hemispherical elements spaced 5.5-mm apart, and the other contained 3-mm hemispherical elements randomly spaced, but with the same element density as 1/3 of the 1-mm ordered roughness. The wall pressure spectrum and its scaling were then studied in detail, and it was found that the rough wall turbulent pressure spectrum at vehicle relevant conditions is defined by three scaling regions. One of which is a newly discovered high frequency scaling defined by viscosity, but controlled by the friction velocity adjusted to exclude the pressure drag on the roughness elements. Based on these three scaling regions an empirical model describing the wall pressure spectra for hydraulically smooth, traditionally rough, and fully rough flows was explored. Two point wall pressure fluctuations were also analyzed for each surface condition, and it was found that the roughness inhibits the convective velocities within the inner portions of the boundary layer.
Your spouse needs professional help: Determining the Contextual Appropriateness of Messages through Modeling Social Relationships

Understanding interpersonal communication requires, in part, understanding the social context and norms in which a message is said. However, current methods for identifying offensive content in such communication largely operate independent of context, with only a few approaches considering community norms or prior conversation as context. Here, we introduce a new approach to identifying inappropriate communication by explicitly modeling the social relationship between the individuals. We introduce a new dataset of contextually-situated judgments of appropriateness and show that large language models can readily incorporate relationship information to accurately identify appropriateness in a given context. Using data from online conversations and movie dialogues, we provide insight into how the relationships themselves function as implicit norms and quantify the degree to which context-sensitivity is needed in different conversation settings. Further, we also demonstrate that contextual-appropriateness judgments are predictive of other social factors expressed in language such as condescension and politeness.
Training Models to Generate, Recognize, and Reframe Unhelpful Thoughts

Many cognitive approaches to well-being, such as recognizing and reframing unhelpful thoughts, have received considerable empirical support over the past decades, yet still lack truly widespread adoption in self-help format. A barrier to that adoption is a lack of adequately specific and diverse dedicated practice material. This work examines whether current language models can be leveraged to both produce a virtually unlimited quantity of practice material illustrating standard unhelpful thought patterns matching specific given contexts, and generate suitable positive reframing proposals. We propose PATTERNREFRAME, a novel dataset of about 10k examples of thoughts containing unhelpful thought patterns conditioned on a given persona, accompanied by about 27k positive reframes. By using this dataset to train and/or evaluate current models, we show that existing models can already be powerful tools to help generate an abundance of tailored practice material and hypotheses, with no or minimal additional model training required.
The Emotional Dilemma: Influence of a Human-like Robot on Trust and Cooperation

Increasing anthropomorphic robot behavioral design could affect trust and cooperation positively. However, studies have shown contradicting results and suggest a task-dependent relationship between robots that display emotions and trust. Therefore, this study analyzes the effect of robots that display human-like emotions on trust, cooperation, and participants’ emotions. In the between-group study, participants play the coin entrustment game with an emotional and a non-emotional robot. The results showthat the robot that displays emotions induces more anxiety than the neutral robot. Accordingly, the participants trust the emotional robot less and are less likely to cooperate. Furthermore, the perceived intelligence of a robot increases trust, while a desire to outcompete the robot can reduce trust and cooperation. Thus, the design of robots expressing emotions should be task dependent to avoid adverse effects that reduce trust and cooperation.
CityTrack: Improving City-Scale Multi-Camera Multi-Target Tracking by Location-Aware Tracking and Box-Grained Matching

Multi-Camera Multi-Target Tracking (MCMT) is a computer vision technique that involves tracking multiple targets simultaneously across multiple cameras. MCMT in urban traffic visual analysis faces great challenges due to the complex and dynamic nature of urban traffic scenes, where multiple cameras with different views and perspectives are often used to cover a large city-scale area. Targets in urban traffic scenes often undergo occlusion, illumination changes, and perspective changes, making it difficult to associate targets across different cameras accurately. To overcome these challenges, we propose a novel systematic MCMT framework, called CityTrack. Specifically, we present a Location-Aware SCMT tracker which integrates various advanced techniques to improve its effectiveness in the MCMT task and propose a novel Box-Grained Matching (BGM) method for the ICA module to solve the aforementioned problems. We evaluated our approach on the public test set of the CityFlowV2 dataset and achieved an IDF1 of 84.91%, ranking 1st in the 2022 AI CITY CHALLENGE. Our experimental results demonstrate the effectiveness of our approach in overcoming the challenges posed by urban traffic scenes.