Miracle-dz/UniFAR-FTUs huggingface dataset:
AI-based Radio Resource Management and Trajectory Design for PD-NOMA Communication in IRS-UAV Assisted Networks

In this paper, we consider that the unmanned aerial vehicles (UAVs) with attached intelligent reflecting surfaces (IRSs) play the role of flying reflectors that reflect the signal of users to the destination, and utilize the power-domain non-orthogonal multiple access (PD-NOMA) scheme in the uplink. We investigate the benefits of the UAV-IRS on the internet of things (IoT) networks that improve the freshness of collected data of the IoT devices via optimizing power, sub-carrier, and trajectory variables, as well as, the phase shift matrix elements. We consider minimizing the average age-of-information (AAoI) of users subject to the maximum transmit power limitations, PD-NOMA-related restriction, and the constraints related to UAV's movement. The optimization problem consists of discrete and continuous variables. Hence, we divide the resource allocation problem into two sub-problems and use two different reinforcement learning (RL) based algorithms to solve them, namely the double deep Qnetwork (DDQN) and a proximal policy optimization (PPO). Our numerical results illustrate the performance gains that can be achieved for IRS enabled UAV communication systems. Moreover, we compare our deep RL (DRL) based algorithm with matching algorithm and random trajectory, showing the combination of DDQN and PPO algorithm proposed in this paper performs 10% and 15% better than matching algorithm and random-trajectory algorithm, respectively.
Action Recognition using Transfer Learning and Majority Voting for CSGO

Presently online video games have become a progressively favorite source of recreation and Counter Strike: Global Offensive (CS: GO) is one of the top-listed online first-person shooting games. Numerous competitive games are arranged every year by Esports. Nonetheless, (i) No study has been conducted on video analysis and action recognition of CS: GO game-play which can play a substantial role in the gaming industry for prediction model (ii) No work has been done on the real-time application on the actions and results of a CS: GO match (iii) Game data of a match is usually available in the HLTV as a CSV formatted file however it does not have open access and HLTV tends to prevent users from taking data. This manuscript aims to develop a model for accurate prediction of 4 different actions and compare the performance among the five different transfer learning models with our self-developed deep neural network and identify the best-fitted model and also including major voting later on, which is qualified to provide real time prediction and the result of this model aids to the construction of the automated system of gathering and processing more data alongside solving the issue of collecting data from HLTV.
AGGLIO: Global Optimization for Locally Convex Functions

This paper presents AGGLIO (Accelerated Graduated Generalized LInear-model Optimization), a stage-wise, graduated optimization technique that offers global convergence guarantees for non-convex optimization problems whose objectives offer only local convexity and may fail to be even quasi-convex at a global scale. In particular, this includes learning problems that utilize popular activation functions such as sigmoid, softplus and SiLU that yield non-convex training objectives. AGGLIO can be readily implemented using point as well as mini-batch SGD updates and offers provable convergence to the global optimum in general conditions. In experiments, AGGLIO outperformed several recently proposed optimization techniques for non-convex and locally convex objectives in terms of convergence rate as well as convergent accuracy. AGGLIO relies on a graduation technique for generalized linear models, as well as a novel proof strategy, both of which may be of independent interest.
Epitaxial titanium nitride microwave resonators: Structural, chemical, electrical, and microwave properties

Titanium nitride is an attractive material for a range of superconducting quantum-circuit applications owing to its low microwave losses, high surface inductance, and chemical stability. The physical properties and device performance, nevertheless, depend strongly on the quality of the materials. Here we focus on the highly crystalline and epitaxial titanium nitride thin films deposited on sapphire substrates using magnetron sputtering at an intermediate temperature (300$^{\circ}$C). We perform a set of systematic and comprehensive material characterization to thoroughly understand the structural, chemical, and transport properties. Microwave losses at low temperatures are studied using patterned microwave resonators, where the best internal quality factor in the single-photon regime is measured to be $3.3\times 10^6$, and $>1.0\times 10^7$ in the high-power regime. Adjusted with the material filling factor of the resonators, the microwave loss-tangent here compares well with the previously reported best values for superconducting resonators. This work lays the foundation of using epitaxial titanium nitride for low-loss superconducting quantum circuits.
A Multiwavelength Dynamical State Analysis of ACT-CL J0019.6+0336

In our study, we show a multiwavelength view of ACT-CL J0019.6+0336 (which hosts a radio halo), to investigate the cluster dynamics, morphology, and ICM. We use a combination of XMM-Newton images, Dark Energy Survey (DES) imaging and photometry, SDSS spectroscopic information, and 1.16 GHz MeerKAT data to study the cluster properties. Various X-ray and optical morphology parameters are calculated to investigate the level of disturbance. We find disturbances in two X-ray parameters and the optical density map shows elongated and axisymmetric structures with the main cluster component southeast of the cluster centre and another component northwest of the cluster centre. We also find a BCG offset of ∼950 km/s from the mean velocity of the cluster, and a discrepancy between the SZ mass, X-ray mass, and dynamical mass (MX,500 and MSZ,500 lies >3σ away from Mdyn,500), showing that J0019 is a merging cluster and probably in a post-merging phase.
Observation of a Flat and Extended Surface State in a Topological Semimetal

A flat band structure in momentum space is considered key for the realization of novel phenomena. A topological flat band, also known as a drumhead state, is an ideal platform to drive new exotic topological quantum phases. Using angle-resolved photoemission spectroscopy experiments, we reveal the emergence of a highly localized surface state in a topological semimetal BaAl4 and provide its full energy and momentum space topology. We find that the observed surface state is localized in momentum, inside a square-shaped bulk Dirac nodal loop, and in energy, leading to a flat band and a peak in the density of state. These results imply this class of materials as an experimental realization of drumhead surface states and provide an important reference for future studies of the fundamental physics of correlated quantum effects in topological materials.
Locomotion of medical micro robot with spiral ribs using mucus

An intracorporeal microrobot will not become practical unless an efficient and low invasive driving device is developed. This paper presents a new technology to drive medical microrobots and experimental results which confirm the new principle. Thrust force up to 1 N was measured when gap width was small and sliding speed was high. While it is pointed out that rupture of fluid film may limit the magnitude of thrust force. The experimental study also confirms that as thrust force arises only due to hydrodynamic action and direct contact does not arise, low invasive operation is possible if good lubrication conditions are kept.
Insights Into Incitement: A Computational Perspective on Dangerous Speech on Twitter in India

Dangerous speech on social media platforms can be framed as blatantly inflammatory, or be couched in innuendo. It is also centrally tied to who engages it – it can be driven by openly sectarian social media accounts, or through subtle nudges by influential accounts, allowing for complex means of reinforcing vilification of marginalized groups, an increasingly significant problem in the media environment in the Global South. We identify dangerous speech by influential accounts on Twitter in India around three key events, examining both the language and networks of messaging that condones or actively promotes violence against vulnerable groups. We characterize dangerous speech users by assigning Danger Amplification Belief scores and show that dangerous users are more active on Twitter as compared to other users as well as most influential in the network, in terms of a larger following as well as volume of verified accounts. We find that dangerous users have a more polarized viewership, suggesting that their audience is more susceptible to incitement. Using a mix of network centrality measures and qualitative analysis, we find that most dangerous accounts tend to either be in mass media related occupations or allied with low-ranking, right-leaning politicians, and act as “broadcasters” in the network, where they are best positioned to spearhead the rapid dissemination of dangerous speech across the platform.
Neural BRDFs: Representation and Operations

Bidirectional reflectance distribution functions (BRDFs) are pervasively used in computer graphics to produce realistic physically-based appearance. In recent years, several works explored using neural networks to represent BRDFs, taking advantage of neural networks' high compression rate and their ability to fit highly complex functions. However, once represented, the BRDFs will be fixed and therefore lack flexibility to take part in follow-up operations. In this paper, we present a form of"Neural BRDF algebra", and focus on both representation and operations of BRDFs at the same time. We propose a representation neural network to compress BRDFs into latent vectors, which is able to represent BRDFs accurately. We further propose several operations that can be applied solely in the latent space, such as layering and interpolation. Spatial variation is straightforward to achieve by using textures of latent vectors. Furthermore, our representation can be efficiently evaluated and sampled, providing a competitive solution to more expensive Monte Carlo layering approaches.
Thermal Phase Curves of XO-3b: An Eccentric Hot Jupiter at the Deuterium Burning Limit

We report Spitzer full-orbit phase observations of the eccentric hot Jupiter XO-3b at 3.6 and 4.5 μm. Our new eclipse depth measurements of 1770 ± 180 ppm at 3.6 μm and 1610 ± 70 ppm at 4.5 μm show no evidence of the previously reported dayside temperature inversion. We also empirically derive the mass and radius of XO-3b and its host star using Gaia DR3's parallax measurement and find a planetary mass M p = 11.79 ± 0.98 M Jup and radius R p = 1.295 ± 0.066 R Jup. We compare our Spitzer observations with multiple atmospheric models to constrain the radiative and advective properties of XO-3b. While the decorrelated 4.5 μm observations are pristine, the 3.6 μm phase curve remains polluted with detector systematics due to larger amplitude intrapixel sensitivity variations in this channel. We focus our analysis on the more reliable 4.5 μm phase curve and fit an energy balance model with solid body rotation to estimate the zonal wind speed and the pressure of the bottom of the mixed layer. Our energy balance model fit suggests an eastward equatorial wind speed of 3.13−0.83+0.26 km s−1, an atmospheric mixed layer down to 2.40−0.16+0.92 bars, and a Bond albedo of 0.106−0.106+0.008 . We assume that the wind speed and mixed layer depth are constant throughout the orbit. We compare our observations with 1D planet-averaged model predictions at apoapse and periapse and 3D general circulation model predictions for XO-3b. We also investigate the inflated radius of XO-3b and find that it would require an unusually large amount of internal heating to explain the observed planetary radius.
Cryptography Vulnerabilities on HackerOne

Previous studies have shown that cryptography is hard for developers to use and misusing cryptography leads to severe security vulnerabilities. We studied relevant vulnerability reports on the HackerOne bug bounty platform to understand what types of cryptography vulnerabilities exist in the wild. We extracted eight themes of vulnerabilities from the vulnerability reports and discussed their real-world implications and mitigation strategies. We hope that our findings alert developers, familiarize them with the dire consequences of cryptography misuses, and support them in avoiding such mistakes.
Securing Your Airspace: Detection of Drones Trespassing Protected Areas

Unmanned Aerial Vehicle (UAV) deployment has risen rapidly in recent years. They are now used in a wide range of applications, from critical safety-of-life scenarios like nuclear power plant surveillance to entertainment and hobby applications. While the popularity of drones has grown lately, the associated intentional and unintentional security threats require adequate consideration. Thus, there is an urgent need for real-time accurate detection and classification of drones. This article provides an overview of drone detection approaches, highlighting their benefits and limitations. We analyze detection techniques that employ radars, acoustic and optical sensors, and emitted radio frequency (RF) signals. We compare their performance, accuracy, and cost under different operating conditions. We conclude that multi-sensor detection systems offer more compelling results, but further research is required.
Virtual Reality for Emotion Elicitation – A Review

Emotions are multifaceted phenomena that affect our behaviour, perception, and cognition. Increasing evidence indicates that induction mechanisms play a crucial role in triggering emotions by simulating the sensations required for an experimental design. Over the years, many reviews have evaluated a passive elicitation mechanism where the user is an observer, ignoring the importance of self-relevance in emotional experience. So, in response to the gap in the literature, this study intends to explore the possibility of using Virtual Reality (VR) as an active mechanism for emotion induction. VR can simulate controlled environments with high immersion, presence, and interaction to induce intense emotions. Therefore, researchers can evaluate emotional experiences in a realistic context. For the success and quality of research settings, VR must select the appropriate material to effectively evoke emotions. Therefore, in the present review, we evaluated to what extent VR virtual environments, videos, games, tasks, avatar, images, and 360-degree panoramas can elicit emotions. Further, we present public datasets, discuss challenges and recommendations, and review emotion-sensing interfaces related to VR research. The conclusions reveal the VR's potential to evoke emotions effectively and naturally by generating motivational and empathy mechanisms, which makes it an ecologically valid paradigm to study emotions.
Gibbsian dynamics and the generalized Langevin equation

We study the statistically invariant structures of the nonlinear generalized Langevin equation (GLE) with a power-law memory kernel. For a broad class of memory kernels, including those in the subdiﬀusive regime, we construct solutions of the GLE using a Gibbsian framework, which does not rely on existing Markovian approximations. Moreover, we provide conditions on the decay of the memory to ensure uniqueness of statistically steady states, generalizing previous known results for the GLE under particular kernels as a sum of exponentials.
Algebraic Analysis of Electric Load and Electricity Pricing

This paper presents a perspective of functional analysis to analyze electric load and electricity pricing in the L2 space and its isomorphic vector space, which also yields the general algebraic model of load and pricing associated with time course. It clarifies law of interaction between payment and load associated with time course and necessity of pricing to sufficiently convey information on how load results in supply cost. It describes classic paradigm of modeling in a general algebraic form and formally proves the ineffectiveness of classic integral-based pricing in modeling. It consequently introduces to: describe electricity supply cost by a mapping defined on an isomorphic space of the original space of load constituted by orthonormal basis that quantifies the dynamism of load, named the space of dynamism; a pricing model defined on the space of dynamism that sufficiently conveys the information; a further derived computational model of pricing based on the Fourier series; simple examples to demonstrate use of the proposed pricing and its effectiveness in distinguishing loads and reflecting supply cost associated with time course, which theoretically completes the incentive to reshape loads.
An Act of State: The Execution of Martin Luther King

On the evening of April 4 1968, Martin Luther King was in Memphis supporting a workers' strike. By the end of the day, top-level army snipers were in the position to knock him out if ordered. Two military officers were in place on the roof of a fire station near the Lorraine Motel, to photograph the events. Two black firemen had been ordered not to report to duty that day and a black Memphis Police Department detective on surveillance duty in the fire station was physically removed from his post and taken home. Dr King's room at the motel was changed from a secluded ground-floor room to number 306 on the balcony. Lloyd Jowers, owner of Jim's Grill which backed on to the motel from the other side of the street, had already received $100,000 in cash for his agreement to participate in the assassination. He was to go out into the brush area behind the grill with the shooter and take possession of the gun immediately after the fatal shot was fired. When the dust settled, King had been hit, and a clean-up procedure was immediately set in motion. James Earl Ray was effectively framed, the snipers dispersed, any witness who could not be controlled were killed, and the crime scene was destroyed. William pepper, attorney and friend of Dr King and the King family, became convinced after years of investigation that not only was Ray not the shooter, but that King had been targeted as part of a larger conspiracy to stop the anti-war movement, and to prevent King from gaining momentum in his promising Poor People's campaign. Ten years into his investigation, in 1988, Pepper agreed to represent Ray. While he was never able to successfully appeal the sentence before Ray's death, he was bale to build an air-tight case against the real perpetrators. In 1999, Lloyd Jowers and co-conspirators were brought to trial in a wrongful death civil action suit on behalf of the King family. Seventy witnesses set out the details of a conspiracy in a plot to murder King that involved J. Edgar Hoover and the FBI, Richard Helms and the CIA, the military, the local Memphis police, and organized crime figures from New Orleans and Memphis. The evidence was unimpeachable. The jury took an hour to find for the. But the silence following these shocking revelations was deafening. Like the pattern during all the investigations of the assassination throughout the years, no major media outlet would cover the story. It was effectively buried. Until now, the details, evidence, and personalities of all these nefarious characters have gone unreported. In An Act of State, you finally have the truth before you - how the United States government effectively shut down one of the most galvanising movements for social change by stopping its leader dead in his tracks.
Terahertz Wireless Channels: A Holistic Survey on Measurement, Modeling, and Analysis

Terahertz (0.1-10 THz) communications are envisioned as a key technology for sixth generation (6G) wireless systems. The study of underlying THz wireless propagation channels provides the foundations for the development of reliable THz communication systems and their applications. This article provides a comprehensive overview of the study of THz wireless channels. First, the three most popular THz channel measurement methodologies, namely, frequency-domain channel measurement based on a vector network analyzer (VNA), time-domain channel measurement based on sliding correlation, and time-domain channel measurement based on THz pulses from time-domain spectroscopy (THz-TDS), are introduced and compared. Current channel measurement systems and measurement campaigns are reviewed. Then, existing channel modeling methodologies are categorized into deterministic, stochastic, and hybrid approaches. State-of-the-art THz channel models are analyzed, and the channel simulators that are based on them are introduced. Next, an in-depth review of channel characteristics in the THz band is presented. Finally, open problems and future research directions for research studies on THz wireless channels for 6G are elaborated.
Understanding Layer-wise Contributions in Deep Neural Networks through Spectral Analysis

Spectral analysis is a powerful tool, decomposing any function into simpler parts. In machine learning, Mercer's theorem generalizes this idea, providing for any kernel and input distribution a natural basis of functions of increasing frequency. More recently, several works have extended this analysis to deep neural networks through the framework of Neural Tangent Kernel. In this work, we analyze the layer-wise spectral bias of Deep Neural Networks and relate it to the contributions of different layers in the reduction of generalization error for a given target function. We utilize the properties of Hermite polynomials and Spherical Harmonics to prove that initial layers exhibit a larger bias towards high-frequency functions defined on the unit sphere. We further provide empirical results validating our theory in high dimensional datasets for Deep Neural Networks.
Computing simple mechanisms: Lift-and-round over marginal reduced forms

We study revenue maximization in multi-item multi-bidder auctions under the natural item-independence assumption – a classical problem in Multi-Dimensional Bayesian Mechanism Design. One of the biggest challenges in this area is developing algorithms to compute (approximately) optimal mechanisms that are not brute-force in the size of the bidder type space, which is usually exponential in the number of items in multi-item auctions. Unfortunately, such algorithms were only known for basic settings of our problem when bidders have unit-demand or additive valuations. In this paper, we significantly improve the previous results and design the first algorithm that runs in time polynomial in the number of items and the number of bidders to compute mechanisms that are O(1)-approximations to the optimal revenue when bidders have XOS valuations, resolving an open problem raised by Chawla, Miller and Cai, Zhao. Moreover, the computed mechanism has a simple structure: It is either a posted price mechanism or a two-part tariff mechanism. As a corollary of our result, we show how to compute an approximately optimal and simple mechanism efficiently using only sample access to the bidders’ value distributions. Our algorithm builds on two innovations that allow us to search over the space of mechanisms efficiently: (i) a new type of succinct representation of mechanisms – the marginal reduced forms, and (ii) a novel Lift-and-Round procedure that concavifies the problem.