Miracle-dz/UniFAR-FTUs huggingface dataset:
Myrtenal attenuates oxidative stress and inflammation in a rat model of streptozotocin-induced diabetes

Abstract The present study was aimed to investigate the effect of myrtenal on diabetes-associated oxidative stress, lipid peroxidation (LPO), and inflammation using a rat model of streptozotocin (STZ)-induced diabetes. Following the induction of diabetes in male Wistar rats using STZ (40 mg/kg body weight), myrtenal (80 mg/kg body weight) was administered orally to diabetic rats for four weeks and then sacrificed to harvest tissues. We measured the levels of antioxidants, LPO, and proinflammatory cytokines such as tumor necrosis factor-α (TNF-α) and interleukin 6 (IL-6), and the p65 subunit of nuclear factor-kappa B (NF-kB p65). Diabetic rats revealed increased levels of LPO, proinflammatory cytokines, and NF-kB p65, and decreased levels of antioxidants in the liver and pancreas. Supplementation with myrtenal significantly attenuated the diabetes-induced changes in the liver and pancreas of diabetic rats. Our findings suggest that myrtenal may serve as an antioxidant and anti-inflammatory agent against diabetes-associated oxidative stress and inflammation. Highlights Oral administration of myrtenal improved the antioxidant status in the liver and pancreas of diabetic rats. Myrtenal treatment diminished inflammation in the liver and pancreas of diabetic rats. Myrtenal supplementation averts oxidative stress and inflammation in diabetic rats. Myrtenal could serve as a potent antioxidant and anti-inflammatory agent in the management of diabetes.
Blind calibration for compressed sensing: state evolution and an online algorithm

Compressed sensing allows for the acquisition of compressible signals with a small number of measurements. In experimental settings, the sensing process corresponding to the hardware implementation is not always perfectly known and may require a calibration. To this end, blind calibration proposes to perform at the same time the calibration and the compressed sensing. Schülke and collaborators suggested an approach based on approximate message passing for blind calibration (cal-AMP) in (Schülke C et al 2013 Advances in Neural Information Processing Systems 26 1–9 and Schülke C et al 2015 J. Stat. Mech. P11013). Here, their algorithm is extended from the already proposed offline case to the online case, for which the calibration is refined step by step as new measured samples are received. We show that the performance of both the offline and the online algorithms can be theoretically studied via the state evolution formalism. Finally, the efficiency of cal-AMP and the consistency of the theoretical predictions are confirmed through numerical simulations.
Learning Generative Adversarial RePresentations (GAP) under Fairness and Censoring Constraints

We present Generative Adversarial rePresentations (GAP) as a data-driven framework for learning censored and/or fair representations. GAP leverages recent advancements in adversarial learning to allow a data holder to learn universal representations that decouple a set of sensitive attributes from the rest of the dataset. Under GAP, finding the optimal fair/censoring encoder is formulated as a constrained minimax game between a data encoder and an adversary. We show that for appropriately chosen adversarial loss functions, GAP provides censoring guarantees against strong information-theoretic adversaries and enforces demographic parity. We also evaluate the performance of GAP on multi-dimensional Gaussian mixture models and real datasets, and show how a designer can certify that representations learned under an adversary with a fixed architecture perform well against more complex adversaries.
Predicting Alzheimer’s Disease by Hierarchical Graph Convolution from Positron Emission Tomography Imaging

Imaging-based early diagnosis of Alzheimer Disease (AD) has become an effective approach, especially by using nuclear medicine imaging techniques such as Positron Emission Topography (PET). In various literature it has been found that PET images can be better modeled as signals (e.g. uptake of florbetapir) defined on a network (non-Euclidean) structure which is governed by its underlying graph patterns of pathological progression and metabolic connectivity. In order to effectively apply deep learning framework for PET image analysis to overcome its limitation on Euclidean grid, we develop a solution for 3D PET image representation and analysis under a generalized, graph-based CNN architecture (PETNet), which analyzes PET signals defined on a group-wise inferred graph structure. Computations in PETNet are defined in non-Euclidean, graph (network) domain, as it performs feature extraction by convolution operations on spectral-filtered signals on the graph and pooling operations based on hierarchical graph clustering. Effectiveness of the PETNet is evaluated on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, which shows improved performance over both deep learning and other machine learning-based methods.
Orbital-angular-momentum free-space optical communication via the azimuthal phase-shift

Free-space optical communication using the orbital angular momentum (OAM) of light has garnered significant interest lately due to the potentially vast bandwidth intrinsic to the infinite Hilbert space of OAM modes. Unfortunately, OAM light beams suffer from serious distortions due to atmospheric turbulence (AT) that has become a dominant factor limiting the advance of OAM-based free-space communication. Here we propose and demonstrate a free-space communication scheme---using OAM beams and their azimuthal-mode phase-shift for keying (OAM-APSK)---which is resilient to AT-induced distortions. Combined with a digital holographic mode sorting (DHMS) technique, the proposed approach is able to achieve high signal-to-noise ratios and to maintain low modal crosstalk, even for extremely strong turbulence conditions, with magnitudes significantly beyond existing AT mitigation methods. The demonstrated OAM-APSK and DHMS schemes may now open up a great avenue for OAM-based free-space optical communication that could elegantly resolve the long-standing challenge imposed by atmospheric turbulence.
Noisy Guesses

We consider the problem of guessing a random, finite–alphabet, secret n–vector, where the guesses are transmitted via a noisy channel. We provide a single–letter formula for the best achievable exponential growth rate of the ρ–th moment of the number of guesses, as a function of n. This formula exhibits a fairly clear insight concerning the penalty due to the noise. We describe two different randomized schemes that achieve the optimal guessing exponent. One of them is fully universal in the sense of being independent of source (that governs the vector to be guessed), the channel (that corrupts the guesses), and the moment power ρ. Interestingly, it turns out that, in general, the optimal guessing exponent function exhibits a phase transition when it is examined either as a function of the channel parameters, or as a function of ρ: as long as the channel is not too distant (in a certain sense to be defined precisely) from the identity channel (i.e., the clean channel), or equivalently, as long ρ is larger than a certain critical value, ρc, there is no penalty at all in the guessing exponent, compared to the case of noiseless guessing.
Layered native defects in chemically disordered topological insulators.

Topological insulators host on their surface unique conductive electron states, characterized by their almost linear dispersion. Their properties are robust against wide range of perturbations while they respect symmetries, which leads to forming of surface states. There exist possibilities to open the surface gap by breaking the time reversal symmetry, \textit{e.g.} by magnetic doping, which brings new possibilities for applications. In reality, however, several kinds of native defects are present in the material which influence the actual properties of the material. In the present work we focus on the presence of stacking faults, actually twinning planes, in the Bi$_{\mathrm{2}}$Se$_{\mathrm{3}}$ layered structures based on the TB-LMTO-ASA calculations. Their behavior under the presence of magnetic and non-magnetic chemical disorder is studied as well. Finally, the influence of the presence of twinning planes on the surface states in topological insulators is examined. We show the distribution of the twinning planes in the material in an ideal structure same as in dependence on the (non)-magnetic doping, likewise the influence on the surface gap.
An Efficient Sampling Algorithm for Non-smooth Composite Potentials

We consider the problem of sampling from a density of the form $p(x) \propto \exp(-f(x)- g(x))$, where $f: \mathbb{R}^d \rightarrow \mathbb{R}$ is a smooth and strongly convex function and $g: \mathbb{R}^d \rightarrow \mathbb{R}$ is a convex and Lipschitz function. We propose a new algorithm based on the Metropolis-Hastings framework, and prove that it mixes to within TV distance $\varepsilon$ of the target density in at most $O(d \log (d/\varepsilon))$ iterations. This guarantee extends previous results on sampling from distributions with smooth log densities ($g = 0$) to the more general composite non-smooth case, with the same mixing time up to a multiple of the condition number. Our method is based on a novel proximal-based proposal distribution that can be efficiently computed for a large class of non-smooth functions $g$.
LICSTER - A Low-cost ICS Security Testbed for Education and Research

Unnoticed by most people, Industrial Control Systems (ICSs) control entire productions and critical infrastructures such as water distribution, smart grid and automotive manufacturing. Due to the ongoing digitalization, these systems are becoming more and more connected in order to enable remote control and monitoring. However, this shift bears significant risks, namely a larger attack surface, which can be exploited by attackers. In order to make these systems more secure, it takes research, which is, however, difficult to conduct on productive systems, since these often have to operate twenty-four-seven. Testbeds are mostly very expensive or based on simulation with no real-world physical process. In this paper, we introduce LICSTER, an open-source low-cost ICS testbed, which enables researchers and students to get hands-on experience with industrial security for about 500 Euro. We provide all necessary material to quickly start ICS hacking, with the focus on low-cost and open-source for education and research.
Learning scalable and transferable multi-robot/machine sequential assignment planning via graph embedding

Can the success of reinforcement learning methods for simple combinatorial optimization problems be extended to multi-robot sequential assignment planning? In addition to the challenge of achieving near-optimal performance in large problems, transferability to an unseen number of robots and tasks is another key challenge for real-world applications. In this paper, we suggest a method that achieves the first success in both challenges for robot/machine scheduling problems. Our method comprises of three components. First, we show a robot scheduling problem can be expressed as a random probabilistic graphical model (PGM). We develop a mean-field inference method for random PGM and use it for Q-function inference. Second, we show that transferability can be achieved by carefully designing two-step sequential encoding of problem state. Third, we resolve the computational scalability issue of fitted Q-iteration by suggesting a heuristic auction-based Q-iteration fitting method enabled by transferability we achieved. We apply our method to discrete-time, discrete space problems (Multi-Robot Reward Collection (MRRC)) and scalably achieve 97% optimality with transferability. This optimality is maintained under stochastic contexts. By extending our method to continuous time, continuous space formulation, we claim to be the first learning-based method with scalable performance among multi-machine scheduling problems; our method scalability achieves comparable performance to popular metaheuristics in Identical parallel machine scheduling (IPMS) problems.
Data-Driven Model Set Design for Model Averaged Particle Filter

This paper is concerned with sequential state filtering in the presence of nonlinearity, non-Gaussianity and model uncertainty. For this problem, the Bayesian model averaged particle filter (BMAPF) is perhaps one of the most efficient solutions. Major advances of BMAPF have been made, while it still lacks a generic and practical approach to design the model set. This paper fills in this gap by proposing a generic data-driven method for BMAPF model set design. Unlike existent methods, the proposed solution does not require any prior knowledge on the parameter value of the true model; it only assumes that a small number of noisy observations are pre-obtained. The Bayesian optimization (BO) method is adapted to search the model components, each of which is associated with a specific segment of the pre-obtained dataset. The average performance of these model components is guaranteed since each one’s parameter is elaborately tuned via BO to maximize the marginal likelihood. The diversity in the model components is also ensured, as different components match the different segments of the pre-obtained dataset, respectively. Computer simulations are used to demonstrate the effectiveness of the proposed method.
Deep learning for Chemometric and non-translational data

We propose a novel method to train deep convolutional neural networks which learn from multiple data sets of varying input sizes through weight sharing. This is an advantage in chemometrics where individual measurements represent exact chemical compounds and thus signals cannot be translated or resized without disturbing their interpretation. Our approach show superior performance compared to transfer learning when a medium sized and a small data set are trained together. While we observe a small improvement compared to individual training when two medium sized data sets are trained together, in particular through a reduction in the variance.
A Self-Healing Hardware Architecture for Safety-Critical Digital Embedded Devices

Digital Embedded Devices of next-generation safety-critical industrial automation systems require high levels of survivability and resilience against the hardware and software failure. One of the concepts for achieving this requirement is the design of resilient and survivable digital embedded systems. In the last two decades, development of self-healing digital systems based on molecular and cellular biology have received attention for the design of robust digital systems. However, many of these approaches have not been architected from the outset with safety in mind, nor have they been targeted for the applications of automation community where a significant need exists. This paper presents a new self-healing hardware architecture, inspired from the way nature responds, defends and heals: the stem cells in the immune system of living organisms, the life cycle of the living cell, and the pathway from Deoxyribonucleic acid (DNA) to protein. The proposed architecture is integrating cellular-based biological concepts, traditional fault tolerance techniques, and operational schematics for the international standard IEC 61131-3 to facilitate adoption in the automation industry and safety-critical applications. To date, two industrial applications have been mapped on the proposed architecture, which are capable of tolerating a significant number of faults that can stem from harsh environmental changes and external disturbances and we believe the nexus of its concepts can positively impact the next generation of critical systems in the automation industry
Optimal Generation and Transmission Expansion Planning Addressing Short-Term Constraints with Co-optimization of Energy and Reserves

The penetration of variable renewable energy (VRE) in electrical systems has changed the way the expansion planning is treated. This kind of resource has great variability in small amounts of time, which makes it important to represent hourly constraints that require chronology. Besides that, the generation reserves should also be adjusted in order to capture the intermittent effect, and since many countries use rapid thermal plants as part of these reserves, unit commitment and ramp constraint have also become more significant. In this paper, we propose a MILP expansion planning model that can represent hourly time steps while maintaining reasonable computational times, where both investment and operation problems are simultaneously solved. Because the planning horizons are long (decades), the resolution of the entire horizon in a single optimization problem would be computationally infeasible for large real systems, making it necessary, therefore, to apply a horizon decomposition heuristic in smaller sub-horizons, and use the representation of typical days and seasons to reduces the size of the problem.
Correlations between Black Holes and Host Galaxies in the Illustris and IllustrisTNG Simulations

We study black hole–host galaxy correlations, and the relation between the overmassiveness (the distance from the average MBH–σ relation) of supermassive black holes (SMBHs) and the star formation histories of their host galaxies in the Illustris and TNG100 simulations. We find that both simulations are able to produce black hole scaling relations in general agreement with observations at z = 0, but with noticeable discrepancies. Both simulations show an offset from the observations for the MBH–σ relation, and the relation between MBH and the Sérsic index. The relation between MBH and stellar mass M* is tighter than the observations, especially for TNG100. For massive galaxies in both simulations, the hosts of overmassive SMBHs (those above the mean MBH–σ relation) tend to have larger Sérsic indices and lower baryon conversion efficiency, suggesting a multidimensional link between SMBHs and the properties of their hosts. In Illustris, the hosts of overmassive SMBHs have formed earlier and have lower present-day star formation rates, in qualitative agreement with the observations for massive galaxies with σ > 100 km s−1. For low-mass galaxies, such a correlation still holds in Illustris but does not exist in the observed data. For TNG100, the correlation between SMBH overmassiveness and star formation history is much weaker. The hosts of overmassive SMBHs generally have consistently larger star formation rates throughout history. These galaxies have higher stellar mass as well, due to the strong MBH–M* correlation. Our findings show that simulated SMBH scaling relations and correlations are sensitive to features in the modeling of SMBHs.
A review of group-based methods for teaching statistics in higher education

The teaching of statistics in higher education in the UK is still largely lecture based. This is despite recommendations such as those given by the American Statistical Association’s Guidelines for Assessment and Instruction in Statistics Education report that more emphasis should be placed on active learning strategies where students take more responsibility for their own learning. One possible model is that of collaborative learning, where students learn in groups through carefully crafted ‘problems’, which has long been suggested as a strategy for teaching statistics. In this article, we review two specific approaches that fall under the collaborative learning model: problem- and team-based learning. We consider the evidence for changing to this model of teaching in statistics, as well as give practical suggestions on how this could be implemented in typical statistics classes in higher education.
Proximal Policy Optimization for Improved Convergence in IRGAN

IRGAN is an information retrieval (IR) modeling approach that uses a theoretical minimax game between a generative and a discriminative model to iteratively optimize both of them, hence unifying the generative and discriminative approaches. Despite significant performance improvements in several information retrieval tasks, IRGAN training is an unstable process, and the solution varies largely with the random parameter initialization. In this work, we present an improved training objective based on proximal policy optimization objective and Gumbel-Softmax based sampling for the generator. We also propose a modified training algorithm which takes a single gradient update on both the generator as well as discriminator for each iteration step. We present empirical evidence of the improved convergence of the proposed model over the original IRGAN and a comparison on three different IR tasks on benchmark datasets is also discussed, emphasizing the proposed model's superior performance.
Generalized inferential models for meta-analyses based on few studies.

Meta-analysis based on only a few studies remains a challenging problem, as an accurate estimate of the between-study variance is apparently needed, but hard to attain, within this setting. Here we offer a new approach, based on the generalized inferential model framework, whose success lays in marginalizing out the between-study variance, so that an accurate estimate is not essential. We show theoretically that the proposed solution is at least approximately valid, with numerical results suggesting it is, in fact, nearly exact. We also demonstrate that the proposed solution outperforms existing methods across a wide range of scenarios.
Excessive Transverse Coordinates for Orbital Stabilization of (Underactuated) Mechanical Systems

Transverse linearization-based approaches have become among the most prominent methods for orbitally stabilizing feedback design in regards to (periodic) motions of underactuated mechanical systems. Yet, in an n-dimensional state-space, this requires knowledge of a set of (n −1) independent transverse coordinates, which can be nontrivial to find and whose definitions might vary for different motions (trajectories). In this paper, we consider instead a generic set of excessive transverse coordinates which are defined in terms of a particular parameterization of the motion and a projection operator recovering the "position" along the orbit. We present a constructive procedure for obtaining the corresponding transverse linearization, as well as state a sufficient condition for the existence of a feedback controller rendering the desired trajectory (locally) asymptotically orbitally stable. The presented approach is applied to stabilizing oscillations of the underactuated cart-pendulum system about its unstable upright position, in which a novel motion planning approach based on virtual constraints is utilized for trajectory generation.
UltraShare: FPGA-based Dynamic Accelerator Sharing and Allocation

Despite all the available commercial and open-source frameworks to ease deploying FPGAs in accelerating applications, the current schemes fail to support sharing multiple accelerators among various applications. There are three main features that an accelerator sharing scheme requires to support: exploiting dynamic parallelism of multiple accelerators, sharing accelerators among multiple applications, and providing a nonblocking congestion-free environment for multiple applications to call multiple accelerators. In this paper, we developed a scalable fully functional hardware controller, called UltraShare, with a supporting software stack that provides a dynamic accelerator sharing scheme through an accelerators grouping mechanism. UltraShare allows software applications to fully utilize FPGA accelerators in a non-blocking congestion-free environment. Our experimental results for a simple scenario of a combination of three streaming accelerators invocation show an improvement of up to 8x in throughput of the accelerators by removing accelerators idle times.