Miracle-dz/UniFAR-FTUs huggingface dataset:
Evolution of Line-Force Multiplier Parameters in Radiation Driven Winds of Massive Stars

We present two self-consistent procedures that couple the hydrodynamics with calculations of the line-force in the frame of radiation wind theory. These procedures give us the line-force parameters, the velocity field, and the mass-loss rate. The first one is based on the so-called m-CAK theory. A full set of line-force parameters for $T_\text{eff}\ge 32,000$ K and surface gravities higher than 3.4 dex for two different metallicities are presented, along with their corresponding wind parameters. We find that the dependence of line-force parameters on effective temperature is enhanced by the dependence on $\log g$. For the case of homogeneous winds (without clumping) comparison of self-consistent mass-loss rates shows a good agreement with empirical values. We also consider self-consistent wind solutions that are used as input in FASTWIND to calculate synthetic spectra. By comparison with the observed spectra for three stars with clumped winds, we found that varying the clumping factor the synthetic spectra rapidly converge into the neighbourhood region of the solution. Therefore, this self-consistent m-CAK procedure significantly reduces the number of free parameters needed to obtain a synthetic spectrum. The second procedure (called Lambert-procedure) provides a self-consistent solution beyond m-CAK theory, and line-acceleration is calculated by the full NLTE radiative transfer code CMFGEN. Both the mass-loss rate and the clumping factor are set as free parameters, hence their values are obtained by spectral fitting after the respective self-consistent hydrodynamics is calculated. Since performing the Lambert-procedure requires significant computational power, the analysis is made only for the star z-Puppis. The promising results gives a positive balance about the future applications for the self-consistent solutions presented on this thesis.
Poster Abstract: Towards Scalable and Trustworthy Decentralized Collaborative Intrusion Detection System for IoT

An Intrusion Detection System (IDS) aims to alert users of incoming attacks by deploying a detector that monitors network traffic continuously. As an effort to increase detection capabilities, a set of independent IDS detectors typically work collaboratively to build intelligence of holistic network representation, which is referred to as Collaborative Intrusion Detection System (CIDS). However, developing an effective CIDS, particularly for the IoT ecosystem raises several challenges. Recent trends and advances in blockchain technology, which provides assurance in distributed trust and secure immutable storage, may contribute towards the design of effective CIDS. In this poster abstract, we present our ongoing work on a decentralized CIDS for IoT, which is based on blockchain technology. We propose an architecture that provides accountable trust establishment, which promotes incentives and penalties, and scalable intrusion information storage by exchanging bloom filters. We are currently implementing a proof-of-concept of our modular architecture in a local test-bed and evaluate its effectiveness in detecting common attacks in IoT networks and the associated overhead.
Modified Poisson-Nernst-Planck Model with Coulomb and Hard-sphere Correlations

We develop a modified Poisson-Nernst-Planck model which includes both the long-range Coulomb and short-range hard-sphere correlations in its free energy functional such that the model can accurately describe the ion transport in complex environment and under a nanoscale confinement. The Coulomb correlation {including the dielectric polarization} is treated by solving a generalized Debye-H\"uckel equation which is a Green's function equation with the correlation energy of a test ion described by the self Green's function. The hard-sphere correlation is modeled through the modified fundamental measure theory. The resulted model is available for problems beyond the mean-field theory such as problems with variable dielectric media, multivalent ions, and strong surface charge density. We solve the generalized Debye-H\"uckel equation by the Wentzel-Kramers-Brillouin approximation, and study the electrolytes between two parallel dielectric surfaces. In comparison to other modified models, the new model is shown more accurate in agreement with particle-based simulations and capture the physical properties of ionic structures near interfaces.
STANNIS: Low-Power Acceleration of Deep Neural Network Training Using Computational Storage

This paper proposes a framework for distributed, in-storage training of neural networks on clusters of computational storage devices. Such devices not only contain hardware accelerators but also eliminate data movement between the host and storage, resulting in both improved performance and power savings. More importantly, this in-storage processing style of training ensures that private data never leaves the storage while fully controlling the sharing of public data. Experimental results show up to 2.7x speedup and 69% reduction in energy consumption and no significant loss in accuracy.
Kalman meets Bellman: Improving Policy Evaluation through Value Tracking

Policy evaluation is a key process in Reinforcement Learning (RL). It assesses a given policy by estimating the corresponding value function. When using parameterized value functions, common approaches minimize the sum of squared Bellman temporal-difference errors and receive a point-estimate for the parameters. Kalman-based and Gaussian-processes based frameworks were suggested to evaluate the policy by treating the value as a random variable. These frameworks can learn uncertainties over the value parameters and exploit them for policy exploration. When adopting these frameworks to solve deep RL tasks, several limitations are revealed: excessive computations in each optimization step, difficulty with handling batches of samples which slows training and the effect of memory in stochastic environments which prevents off-policy learning. In this work, we discuss these limitations and propose to overcome them by an alternative general framework, based on the extended Kalman filter. We devise an optimization method, called Kalman Optimization for Value Approximation (KOVA) that can be incorporated as a policy evaluation component in policy optimization algorithms. KOVA minimizes a regularized objective function that concerns both parameter and noisy return uncertainties. We analyze the properties of KOVA and present its performance on deep RL control tasks.
Joint Frame Design and Resource Allocation for Ultra-Reliable and Low-Latency Vehicular Networks

The rapid development of the fifth generation mobile communication systems accelerates the implementation of vehicle-to-everything communications. Compared with the other types of vehicular communications, vehicle-to-vehicle (V2V) communications mainly focus on the exchange of driving safety information with neighboring vehicles, which requires ultra-reliable and low-latency communications (URLLCs). However, the frame size is significantly shortened in V2V URLLCs because of the rigorous latency requirements, and thus the overhead is no longer negligible compared with the payload information from the perspective of size. In this paper, we investigate the frame design and resource allocation for an urban V2V URLLC system in which the uplink cellular resources are reused at the underlay mode. Specifically, we first analyze the lower bounds of performance for V2V pairs and cellular users based on the regular pilot scheme and superimposed pilot scheme. Then, we propose a frame design algorithm and a semi-persistent scheduling algorithm to achieve the optimal frame design and resource allocation with the reasonable complexity. Finally, our simulation results show that the proposed frame design and resource allocation scheme can greatly satisfy the URLLC requirements of V2V pairs and guarantee the communication quality of cellular users.
Estimating processes in adapted Wasserstein distance

A number of researchers have independently introduced topologies on the set of laws of stochastic processes that extend the usual weak topology. Depending on the respective scientific background this was motivated by applications and connections to various areas (e.g. Plug-Pichler - stochastic programming, Hellwig - game theory, Aldous - stability of optimal stopping, Hoover-Keisler - model theory). Remarkably, all these seemingly independent approaches define the same adapted weak topology in finite discrete time. Our first main result is to construct an adapted variant of the empirical measure that consistently estimates the laws of stochastic processes in full generality. A natural compatible metric for the weak adapted topology is the given by an adapted refinement of the Wasserstein distance, as established in the seminal works of Pflug-Pichler. Specifically, the adapted Wasserstein distance allows to control the error in stochastic optimization problems, pricing and hedging problems, optimal stopping problems, etc. in a Lipschitz fashion. The second main result of this article yields quantitative bounds for the convergence of the adapted empirical measure with respect to adapted Wasserstein distance. Surprisingly, we obtain virtually the same optimal rates and concentration results that are known for the classical empirical measure wrt. Wasserstein distance.
Gradient $\ell_1$ Regularization for Quantization Robustness

We analyze the effect of quantizing weights and activations of neural networks on their loss and derive a simple regularization scheme that improves robustness against post-training quantization. By training quantization-ready networks, our approach enables storing a single set of weights that can be quantized on-demand to different bit-widths as energy and memory requirements of the application change. Unlike quantization-aware training using the straight-through estimator that only targets a specific bit-width and requires access to training data and pipeline, our regularization-based method paves the way for "on the fly'' post-training quantization to various bit-widths. We show that by modeling quantization as a $\ell_\infty$-bounded perturbation, the first-order term in the loss expansion can be regularized using the $\ell_1$-norm of gradients. We experimentally validate the effectiveness of our regularization scheme on different architectures on CIFAR-10 and ImageNet datasets.
Denotational semantics as a foundation for cost recurrence extraction for functional languages

Abstract A standard informal method for analyzing the asymptotic complexity of a program is to extract a recurrence that describes its cost in terms of the size of its input and then to compute a closed-form upper bound on that recurrence. We give a formal account of that method for functional programs in a higher order language with $\mathtt{let}$-polymorphism. The method consists of two phases. In the first phase, a monadic translation is performed to extract a cost-annotated version of the original program. In the second phase, the extracted program is interpreted in a model. The key feature of this second phase is that different models describe different notions of size. This plays out in several ways. For example, when analyzing functions that take arguments of inductive types, different notions of size may be appropriate depending on the analysis. When analyzing polymorphic functions, our approach shows that one can formally describe the notion of size of an argument in terms of the data that is common to the notions of size for each type instance of the domain type. We give several examples of different models that formally justify various informal cost analyses to show the applicability of our approach.
Coordination without communication: optimal regret in two players multi-armed bandits

We consider two agents playing simultaneously the same stochastic three-armed bandit problem. The two agents are cooperating but they cannot communicate. We propose a strategy with no collisions at all between the players (with very high probability), and with near-optimal regret $O(\sqrt{T \log(T)})$. We also argue that the extra logarithmic term $\sqrt{\log(T)}$ should be necessary by proving a lower bound for a full information variant of the problem.
On Higher-Order Cryptography (Long Version)

Type-two constructions abound in cryptography: adversaries for encryption and authentication schemes, if active, are modeled as algorithms having access to oracles, i.e. as second-order algorithms. But how about making cryptographic schemes themselves higher-order? This paper gives an answer to this question, by first describing why higher-order cryptography is interesting as an object of study, then showing how the concept of probabilistic polynomial time algorithm can be generalized so as to encompass algorithms of order strictly higher than two, and finally proving some positive and negative results about the existence of higher-order cryptographic primitives, namely authentication schemes and pseudorandom functions.
5G Simulation-Based Experimentation Framework for Vertical Performance Assessment

5G is being designed as a common platform where multiple vertical applications will be able to co-exist and grow in a seamless manner. The diversity of the vertical requirements as well as the particular features of the 5G network itself, make it a real challenge to be able to assess or predict applications performance for those verticals. In this paper we motivate the fact that because of the very nature of verticals and 5G infrastructure, measurements alone will not be sufficient for application performance monitoring and prediction. We then propose a comprehensive framework to integrate those measurements in a simulation setting, presenting the key features and current roadblocks for an end-to-end wide-spread implementation.
Compact Merkle Multiproofs

The compact Merkle multiproof is a new and significantly more memory-efficient way to generate and verify sparse Merkle multiproofs. A standard sparse Merkle multiproof requires to store an index for every non-leaf hash in the multiproof. The compact Merkle multiproof on the other hand requires only $k$ leaf indices, where $k$ is the number of elements used for creating a multiproof. This significantly reduces the size of multirpoofs, especially for larger Merke trees.
Deep Attention Spatio-Temporal Point Processes

We present a novel attention-based sequential model for mutually dependent spatio-temporal discrete event data, which is a versatile framework for capturing the non-homogeneous influence of events. We go beyond the assumption that the influence of the historical event (causing an upper-ward or downward jump in the intensity function) will fade monotonically over time, which is a key assumption made by many widely-used point process models, including those based on Recurrent Neural Networks (RNNs). We borrow the idea from the attention model based on a probabilistic score function, which leads to a flexible representation of the intensity function and is highly interpretable. We demonstrate the superior performance of our approach compared to the state-of-the-art for both synthetic and real data.
GACEM: Generalized Autoregressive Cross Entropy Method for Multi-Modal Black Box Constraint Satisfaction

In this work we present a new method of black-box optimization and constraint satisfaction. Existing algorithms that have attempted to solve this problem are unable to consider multiple modes, and are not able to adapt to changes in environment dynamics. To address these issues, we developed a modified Cross-Entropy Method (CEM) that uses a masked auto-regressive neural network for modeling uniform distributions over the solution space. We train the model using maximum entropy policy gradient methods from Reinforcement Learning. Our algorithm is able to express complicated solution spaces, thus allowing it to track a variety of different solution regions. We empirically compare our algorithm with variations of CEM, including one with a Gaussian prior with fixed variance, and demonstrate better performance in terms of: number of diverse solutions, better mode discovery in multi-modal problems, and better sample efficiency in certain cases.
Are You Sure You're Sure? - Effects of Visual Representation on the Cliff Effect in Statistical Inference

Common reporting styles of statistical results, such as confidence intervals (CI), are prone to dichotomous interpretations especially on null hypothesis testing frameworks, for example by claiming significant differences between drug treatment and placebo groups due to the non-overlapping CIs of the mean effects, while disregarding the magnitudes and absolute difference in the effect sizes. Techniques relying on the visual estimation of the strength of evidence have been recommended to limit such dichotomous interpretations but their effectiveness has been challenged. We ran two experiments to compare several representation alternatives of confidence intervals, and used Bayesian multilevel models to estimate the effects of the representation styles on differences in subjective confidence of the results and preferences in visualization styles. Our results suggest that adding visual information to classic CI representation can decrease the sudden drop around $p$-value 0.05 compared to classic CIs and textual representation of CI with $p$-values. All data analysis and scripts are available at this https URL.
Hardware-Encoding Grid States in a Nonreciprocal Superconducting Circuit

We present a circuit design composed of a non-reciprocal device and Josephson junctions whose ground space is doubly degenerate and the ground states are approximate codewords of the Gottesman-Kitaev-Preskill (GKP) code. We determine the low-energy dynamics of the circuit by working out the equivalence of this system to the problem of a single electron confined in a two-dimensional plane and under the effect of strong magnetic field and of a periodic potential. We find that the circuit is naturally protected against the common noise channels in superconducting circuits, such as charge and flux noise, implying that it can be used for passive quantum error correction. We also propose realistic design parameters for an experimental realization and we describe possible protocols to perform logical one- and two-qubit gates, state preparation and readout.
Deep Gaussian Markov random fields

Gaussian Markov random fields (GMRFs) are probabilistic graphical models widely used in spatial statistics and related fields to model dependencies over spatial structures. We establish a formal connection between GMRFs and convolutional neural networks (CNNs). Common GMRFs are special cases of a generative model where the inverse mapping from data to latent variables is given by a 1-layer linear CNN. This connection allows us to generalize GMRFs to multi-layer CNN architectures, effectively increasing the order of the corresponding GMRF in a way which has favorable computational scaling. We describe how well-established tools, such as autodiff and variational inference, can be used for simple and efficient inference and learning of the deep GMRF. We demonstrate the flexibility of the proposed model and show that it outperforms the state-of-the-art on a dataset of satellite temperatures, in terms of prediction and predictive uncertainty.
Quantitative Evaluation of Time‐Dependent Multidimensional Projection Techniques

Dimensionality reduction methods are an essential tool for multidimensional data analysis, and many interesting processes can be studied as time‐dependent multivariate datasets. There are, however, few studies and proposals that leverage on the concise power of expression of projections in the context of dynamic/temporal data. In this paper, we aim at providing an approach to assess projection techniques for dynamic data and understand the relationship between visual quality and stability. Our approach relies on an experimental setup that consists of existing techniques designed for time‐dependent data and new variations of static methods. To support the evaluation of these techniques, we provide a collection of datasets that has a wide variety of traits that encode dynamic patterns, as well as a set of spatial and temporal stability metrics that assess the quality of the layouts. We present an evaluation of 9 methods, 10 datasets, and 12 quality metrics, and elect the best‐suited methods for projecting time‐dependent multivariate data, exploring the design choices and characteristics of each method. Additional results can be found in the online benchmark repository. We designed our evaluation pipeline and benchmark specifically to be a live resource, open to all researchers who can further add their favorite datasets and techniques at any point in the future.
Artificial graphene in a strong magnetic field: Bulk current distribution and quantum phase transitions

We present calculations of the equilibrium current density and Chern numbers for a two-dimensional electron gas in a sinusoidal periodic potential with infinite strip geometry and a perpendicular magnetic field. We consider a triangular lattice of antidots with large ($a=120$ nm) lattice spacing. Such a system is known as artificial graphene (AG). To compute the current density we numerically diagonalize the AG Hamiltonian over a set of Landau level basis states; this takes into account coupling between different Landau levels. Our calculations show that, at magnetic fields typical for quantum Hall measurements, extended streams of current are present in the bulk of the sample when the chemical potential lies within a bulk band gap. We investigate the scaling of these streams with potential strength. Knowledge of the AG energy levels allows us to compute the Chern number associated with each energy gap. We demonstrate that in tuning the height of the potential modulation the Chern number can undergo a transition between two different values.
Regular resynchronizability of origin transducers is undecidable

We study the relation of containment up to unknown regular resynchronization between two-way non-deterministic transducers. We show that it constitutes a preorder, and that the corresponding equivalence relation is properly intermediate between origin equivalence and classical equivalence. We give a syntactical characterization for containment of two transducers up to resynchronization, and use it to show that this containment relation is undecidable already for one-way non-deterministic transducers, and for simple classes of resynchronizations. This answers the open problem stated in recent works, asking whether this relation is decidable for two-way non-deterministic transducers.
Fundamental Limits of Wireless Caching Under Mixed Cacheable and Uncacheable Traffic

We consider cache-aided wireless communication scenarios where each user requests both a file from an a-priori generated cacheable library (referred to as ‘content’), and an uncacheable ‘non-content’ message generated at the start of the communication session. This scenario is easily found in real-world wireless networks, where the two types of traffic coexist and share limited radio resources. We focus our investigation on single-transmitter wireless networks with cache-aided receivers, where the wireless channel is modelled by a degraded Gaussian broadcast channel (GBC). For this setting, we study the (normalized) delay-rate trade-off, which characterizes the content delivery time and non-content communication rates that can be achieved simultaneously. We propose a scheme based on the separation principle, which isolates the coded caching problem from the physical layer transmission problem, and prove its information-theoretic order optimality up to a multiplicative factor of 2.01. A key insight emerging from our scheme is that substantial amounts of non-content traffic can be communicated while maintaining the minimum content delivery time, achieved in the absence of non-content messages; compliments of ‘topological holes’ arising from asymmetries in wireless channel gains.