Miracle-dz/UniFAR-FTUs huggingface dataset:
On Generalized Galois Cyclic Orbit Flag Codes

Flag codes that are orbits of a cyclic subgroup of the general linear group acting on flags of a vector space over a finite field, are called cyclic orbit flag codes. In this paper, we present a new contribution to the study of such codes, by focusing this time on the generating flag. More precisely, we examine those ones whose generating flag has at least one subfield among its subspaces. In this situation, two important families arise: the already known Galois flag codes, in case we have just fields, or the generalized Galois flag codes in other case. We investigate the parameters and properties of the latter ones and explore the relationship with their underlying Galois flag code.
Multi-Channel Bayesian Persuasion

The celebrated Bayesian persuasion model considers strategic communication between an informed agent (the sender) and uninformed decision makers (the receivers). The current rapidly-growing literature mostly assumes a dichotomy: either the sender is powerful enough to communicate separately with each receiver (a.k.a. private persuasion), or she cannot communicate separately at all (a.k.a. public persuasion). We study a model that smoothly interpolates between the two, by considering a natural multi-channel communication structure in which each receiver observes a subset of the sender's communication channels. This captures, e.g., receivers on a network, where information spillover is almost inevitable. We completely characterize when one communication structure is better for the sender than another, in the sense of yielding higher optimal expected utility universally over all prior distributions and utility functions. The characterization is based on a simple pairwise relation among receivers - one receiver information-dominates another if he observes at least the same channels. We prove that a communication structure $M_1$ is (weakly) better than $M_2$ if and only if every information-dominating pair of receivers in $M_1$ is also such in $M_2$. We also provide an additive FPTAS for the optimal sender's signaling scheme when the number of states is constant and the graph of information-dominating pairs is a directed forest. Finally, we prove that finding an optimal signaling scheme under multi-channel persuasion is, generally, computationally harder than under both public and private persuasion.
How Much Do Language Models Copy From Their Training Data? Evaluating Linguistic Novelty in Text Generation Using RAVEN

Current language models can generate high-quality text. Are they simply copying text they have seen before, or have they learned generalizable linguistic abstractions? To tease apart these possibilities, we introduce RAVEN, a suite of analyses for assessing the novelty of generated text, focusing on sequential structure (n-grams) and syntactic structure. We apply these analyses to four neural language models trained on English (an LSTM, a Transformer, Transformer-XL, and GPT-2). For local structure—e.g., individual dependencies—text generated with a standard sampling scheme is substantially less novel than our baseline of human-generated text from each model’s test set. For larger-scale structure—e.g., overall sentence structure—model-generated text is as novel or even more novel than the human-generated baseline, but models still sometimes copy substantially, in some cases duplicating passages over 1,000 words long from the training set. We also perform extensive manual analysis, finding evidence that GPT-2 uses both compositional and analogical generalization mechanisms and showing that GPT-2’s novel text is usually well-formed morphologically and syntactically but has reasonably frequent semantic issues (e.g., being self-contradictory).
Adversarial attacks on voter model dynamics in complex networks

This paper investigates adversarial attacks conducted to distort voter model dynamics in complex networks. Specifically, a simple adversarial attack method is proposed to hold the state of opinions of an individual closer to the target state in the voter model dynamics. This indicates that even when one opinion is the majority the vote outcome can be inverted (i.e., the outcome can lean toward the other opinion) by adding extremely small (hard-to-detect) perturbations strategically generated in social networks. Adversarial attacks are relatively more effective in complex (large and dense) networks. These results indicate that opinion dynamics can be unknowingly distorted.
On the nature of the single eclipse per 80d orbit of the H-rich luminous WN star WR22

WR22 = HD 92740 is a bright (V = 6.4 <06), intrinsically luminous, double-line WN7h + O9III-V binary exhibiting one sharp 8% deep eclipse near periastron in its elliptical (e = 0.6) 80-day orbit, when the WR-star passes in front of the O star, with no secondary eclipse. We apply two models (L96, A13) to probe the optical space-based light curves from BRITE-Constellation, including three separate, complete eclipses, that show increased (o-c) scatter compared to the rest of the observations outside the eclipses, likely due to O-star light encountering WR wind-clumps. L96 is a simple atmospheric-eclipse model, often applied to close WR+O binaries, where the O-star is considered a point-source. A13 considers a finite-disk O-star and allows for atmospheric, photospheric and reflection components to the eclipse, permitting a better characterization of its shape through a more physically realistic description of the structures for both stars in WR22. Nevertheless, A13 is still susceptible to uncertainties in the luminosity of the O-star before unique values for the orbital inclination and WR mass-loss rate can be estimated. We present solutions for the two extremes of the O-star, O9V and O9III. As photometry alone cannot allow us to discriminate between these, we compared our results to the spectral models found in the literature and determined the correct solution to be O9V. Our best-fit A13 Model 1 gives 8 = 83.5 ± 0.4◦, with ¤ "WR = (1.86 ± 0.2) × 10−5 ¤ "⊙/HA. The flux ratio in the red BRITE band in this model is O/ WR = 0.064 ± 0.002.
LiDAR Cluster First and Camera Inference Later: A New Perspective Towards Autonomous Driving

Object detection in state-of-the-art Autonomous Vehicles (AV) framework relies heavily on deep neural networks. Typically, these networks perform object detection uniformly on the entire camera LiDAR frames. However, this uniformity jeopardizes the safety of the AV by giving the same priority to all objects in the scenes regardless of their risk of collision to the AV. In this paper, we present a new end-to-end pipeline for AV that introduces the concept of LiDAR cluster first and camera inference later to detect and classify objects. The benefits of our proposed framework are twofold. First, our pipeline prioritizes detecting objects that pose a higher risk of collision to the AV, giving more time for the AV to react to unsafe conditions. Second, it also provides, on average, faster inference speeds compared to popular deep neural network pipelines. We design our framework using the real-world datasets, the Waymo Open Dataset, solving challenges arising from the limitations of LiDAR sensors and object detection algorithms. We show that our novel object detection pipeline prioritizes the detection of higher risk objects while simultaneously achieving comparable accuracy and a 25% higher average speed compared to camera inference only.
A tail of eternal inflation

Non-trivial inflaton self-interactions can yield calculable signatures of primordial non-Gaussianity that are measurable in cosmic surveys. We calculate the non-Gaussian corrections to Stochastic Inflation within the framework of Soft de Sitter Effective Theory, from which we derive the associated probability distribution for the scalar fluctuations. As a consequence of this new result, we show that the phase transition to slow-roll eternal inflation is often incalculable in these models. Instead, this transition is sensitive to the non-Gaussian tail of the distribution of scalar fluctuations, which probes physics inside the horizon, potentially beyond the cutoff scale of the Effective Field Theory of Inflation. We delineate the parameter space consistent with current observations and weak coupling at horizon crossing in which the large fluctuations relevant for eternal inflation can only be determined by appealing to a UV completion. We also argue that this breakdown of the perturbative description is required for the de Sitter entropy to reflect the number of de Sitter microstates.
Reinforcement Learning on Human Decision Models for Uniquely Collaborative AI Teammates

In 2021 the Johns Hopkins University Applied Physics Laboratory held an internal challenge to develop artificially intelligent (AI) agents that could excel at the collaborative card game Hanabi. Agents were evaluated on their ability to play with human players whom the agents had never previously encountered. This study details the development of the agent that won the challenge by achieving a human-play average score of 16.5, outperforming the current state-of-the-art for human-bot Hanabi scores. The winning agent's development consisted of observing and accurately modeling the author's decision making in Hanabi, then training with a behavioral clone of the author. Notably, the agent discovered a human-complementary play style by first mimicking human decision making, then exploring variations to the human-like strategy that led to higher simulated human-bot scores. This work examines in detail the design and implementation of this human compatible Hanabi teammate, as well as the existence and implications of human-complementary strategies and how they may be explored for more successful applications of AI in human machine teams.
Reduction of inappropriate prescriptions in older adults through the support of Asynchronous Geriatric Counseling Online (AGAlink): Implemented in primary care

Background/Aim Medication prescription is a fundamental component in the care of the elderly. Several characteristics of aging and geriatric medicine affect prescriptions for these people and make the selection of drug therapy a difficult and complex process. The objective of this study is to develop a geriatric portal for asynchronous online counseling (AGAlink) for use by physicians specializing in family medicine to reduce medication problems among older adult patients in the first level of care. Method A qualitative study was carried out in the first level of care at the Mexican Institute of Social Security (IMSS), 31 family doctors were interviewed to identify attitudes, preferences about the use of the AGAlink geriatric portal, as well as their recommendations for the implementation of this tool in their daily practice. For the analysis of the data obtained, a qualitative thematic content analysis was used. Results 90% of the physicians used the geriatric portal outside office hours without the need for the patient to be present. The perception of the physician towards the use of the AGAlink geriatric portal was favorable, provided relevant information and had several positive effects on the process of care for medical prescription. The barriers identified to accept the change in medication were not having the proposed therapeutic option, lack of any laboratory analysis, continuing to consider their experience for the prescription of the medication. Conclusions The AGAlink geriatric portal was a tool that was well received by physicians who expressed a positive attitude, considered an investment of a short time that allowed them to update and learn about strategies to reduce the prescription problems presented among the elderly population. However, the main barrier was the use of technology, especially in the doctors with more seniority in the service.
miR-155, miR-21, and let-7a Expressions in MCF-10A and MCF-7 Cell Lines after Low to High Dose Irradiation

Objective Ionizing radiation is a tremendous risk factor for cancer development. MicroRNAs (miRNAs) are regulators that utilize cell pathways, which are implicated in human cancer prognosis. In addition, miRNAs respond to anti-cancer therapy and proliferation after irradiation. However, the changes in miRNA expression profiles in response to irradiation have not been comprehensively analysed. The present study was designed to assess potential changes that occur in miRNA expression following irradiation. Materials and Methods In this experimental study, we used quantitative real-time polymerase chain reaction (qRT- PCR) to measure the expressions of miR-155, miR-21, and let-7a in MCF-10A (normal breast cells) and MCF-7 (breast cancer cells) six hours after the cells were exposed to five different irradiation doses (50, 100, 400, 2000, and 4000 mGY). Results After irradiation from the low to high doses, we observed an upsurge in miR-155 (more than 100%) expression and reduction in let-7a (more than 87%) expression. However, there was an increase and a reduction in miR-21 expression (more than 100%). Conclusion Irradiation can play an important role in cancer development in normal breast cells (MCF-10A) at low dose irradiation. However, the results showed little difference at high doses of radiation.
Reliable equations of state of viscous strong and electroweak matter

For the first time, a reliable estimation for the equations of state (EoS), bulk viscosity, and relaxation time, at temperatures ranging from a few MeV up to TeV or energy density up to 10 GeV/fm. This genuine study covers both strong and electroweak epochs of the early Universe. Non–perturbation (up, down, strange, charm, and bottom quark flavor) and perturbative calculations (up, down, strange, charm, bottom, and top quark flavors), are phenomenologically combined, at vanishing baryon–chemical potential. In these results, calculations from Polyakov linear–sigma model (PLSM) of the vacuum and thermal condensations of the gluons and the quarks (up, down, strange, and charm flavors) are also integrated. Furthermore, additional degrees of freedom (photons, neutrinos, charged leptons, electroweak particles, and scalar Higgs boson) are found significant along the entire range of temperatures. As never done before, the present study brings the standard model of elementary particles closer to the standard model for cosmology.
Chemical characteristics and cancer risk assessment of smokeless tobacco used in Tunisia (neffa)

Introduction neffa, a form of air-dried smokeless tobacco used in North Africa, is spuriously perceived as a lower risk alternative to smoking. The objective of this study was to provide information on some harmful constituents of neffa and to use them for cancer risk assessment. Methods a high-performance liquid chromatography method coupled with fluorescence detector was used to determine polycyclic aromatic hydrocarbons (PAHs) in one sample of neffa. An atomic absorption spectrometry was performed to determine the concentrations of lead and cadmium in three samples of neffa. The levels of toxicants found in neffa were used to assess for lifetime cancer risk as advocated by the US Environment Protection Agency. Results the determination of PAHs in neffa allowed the identification of phenanthrene and anthracene. However, the higher molecular weight PAHs such as Benzo(a)Pyrene B(a)P were not detected. The concentrations of cadmium and lead varied between 1.3 to 2. 8µg/g and 1.7 to 4. 6µg/g respectively. Cancer risk for cadmium and lead varied between 4.2E-03 to 9.3E-03 and 2.5E-06 to 6.4E-06 respectively. Cancer risk for Cd exceeded the range of 10E-04 to 10E-06 of an acceptable risk. Conclusion neffa is not a healthy alternative for overcoming smoking addiction. It contains mineral and organic pulmonary toxicants. This study could serve as a scientific basis to inform consumers about the products´ toxicity and help them to quit smokeless tobacco (SLT) use.
Features selection in NBA outcome prediction through Deep Learning

This manuscript is focused on features' definition for the outcome prediction of matches of NBA basketball championship. It is shown how models based on one a single feature (Elo rating or the relative victory frequency) have a quality of fit better than models using box-score predictors (e.g. the Four Factors). Features have been ex ante calculated for a dataset containing data of 16 NBA regular seasons, paying particular attention to home court factor. Models have been produced via Deep Learning, using cross validation.
Optimal driving strategy for traction energy saving on DC suburban railways

Energy saving on electrified railways has been studied for many years and the technical solution is usually provided by a combination of driving strategy (e.g. coasting), regenerative braking and energy storage systems. An alternative approach is for the driver (or automatic train operation system if fitted) to manage energy consumption more efficiently. A formal method for optimising traction energy consumption during a single-train journey by trading-off reductions in energy against increases in running time has been demonstrated. The balance between saving energy and running faster has been investigated by designing a fitness function with variable weightings. Energy savings were found, both qualitatively and quantitatively, to be affected by acceleration and braking rates, and, by running a series of simulations in parallel with a genetic algorithm search method, the fitness function was used to identify optimal train trajectories. The influence of the fitness function representation on the search results was also explored.
Edge-preserving Domain Adaptation for semantic segmentation of Medical Images

Domain Adaptation is a technique to address the lack of massive amounts of labeled data in unseen environments. Unsupervised domain adaptation is proposed to adapt a model to new modalities using solely labeled source data and unlabeled target domain data. Though many image-spaces domain adaptation methods have been proposed to capture pixel-level domain-shift, such techniques may fail to maintain high-level semantic information for the segmentation task. For the case of biomedical images, fine details such as blood vessels can be lost during the image transformation operations between domains. In this work, we propose a model that adapts between domains using cycle-consistent loss while maintaining edge details of the original images by enforcing an edge-based loss during the adaptation process. We demonstrate the effectiveness of our algorithm by comparing it to other approaches on two eye fundus vessels segmentation datasets. We achieve 1.1 to 9.2 increment in DICE score compared to the SOTA and ~5.2 increments compared to a vanilla CycleGAN implementation.
Matroids on Eight Elements with the Half-Plane Property and Related Concepts

We classify all matroids with at most 8 elements that have the half-plane property, and we provide a list of some matroids on 9 elements that have, and that do not have the half-plane property. Furthermore, we prove that several classes of matroids and polynomials that are motivated by the theory of semidefinite programming are closed under taking minors and under passing to faces of the Newton polytope.
BigFoot: Exploiting and Mitigating Leakage in Encrypted Write-Ahead Logs

Modern databases and data-warehousing systems separate query processing and durable storage. Storage systems have idiosyncratic bugs and security vulnerabilities, thus attacks that compromise only storage are a realistic threat. In this paper, we show that encryption alone is not sufficient to protect databases from compromised storage. Using MongoDB WiredTiger as a concrete example, we demonstrate that sizes of encrypted writes to a durable write-ahead log can reveal sensitive information about the inputs and activities of MongoDB applications. We then design, implement, and evaluate BigFoot, a WAL modification that mitigates size leakage.
CSI Clustering with Variational Autoencoding

The model order of a wireless channel plays an important role for a variety of applications in communications engineering, e.g., it represents the number of resolvable incident wave-fronts with non-negligible power incident from a transmitter to a receiver. Areas such as direction of arrival estimation leverage the model order to analyze the multipath components of channel state information. In this work, we propose to use a variational autoencoder to group unlabeled channel state information with respect to the model order in the variational autoencoder latent space in an unsupervised manner. We validate our approach with simulated 3GPP channel data. Our results suggest that, in order to learn an appropriate clustering, it is crucial to use a more flexible likelihood model for the variational autoencoder decoder than it is usually the case in standard applications.
Research on design and simulation of a FM/cw lidar system

The FM/cw lidar has the advantages of narrow receive bandwidth, non-range-ambiguity, enhanced ability of anti-jamming etc. A summary of FM ranging theory is introduced. The FM/cw lidar system is designed with the incoherent detection approach, which contained the transmitter, receiver and signal processing system, the system complexity and realization difficulty are reduced significantly. The transmitter is consisted of chirp signal generate module, laser modulate system and laser transmit subsystem. Receiver is consisted of receive optical system, photo electricity detector, amplifier and mixing. Signal process system is consisted of lowpass, IF amplifier, signal collecting and processing system. The main affect factors are analyzed, the modulate depth and mixing efficiency effect to the detection capability are simulated, which are the special to the FM/cw lidar based on the designed FM/cw lidar system. The simulation results indicate that the detection capability can be improved efficiently by enhance the modulate depth and mixing efficiency. The research results establish the theory and realization basic to the research on the FM/cw lidar.
Locally Learned Synaptic Dropout for Complete Bayesian Inference

The Bayesian brain hypothesis postulates that the brain accurately operates on statistical distributions according to Bayes' theorem. The random failure of presynaptic vesicles to release neurotransmitters may allow the brain to sample from posterior distributions of network parameters, interpreted as epistemic uncertainty. It has not been shown previously how random failures might allow networks to sample from observed distributions, also known as aleatoric or residual uncertainty. Sampling from both distributions enables probabilistic inference, efficient search, and creative or generative problem solving. We demonstrate that under a population-code based interpretation of neural activity, both types of distribution can be represented and sampled with synaptic failure alone. We first define a biologically constrained neural network and sampling scheme based on synaptic failure and lateral inhibition. Within this framework, we derive drop-out based epistemic uncertainty, then prove an analytic mapping from synaptic efficacy to release probability that allows networks to sample from arbitrary, learned distributions represented by a receiving layer. Second, our result leads to a local learning rule by which synapses adapt their release probabilities. Our result demonstrates complete Bayesian inference, related to the variational learning method of dropout, in a biologically constrained network using only locally-learned synaptic failure rates.