Miracle-dz/UniFAR-FTUs huggingface dataset:
Chaotic mixing in three-dimensional porous media

Under steady flow conditions, the topological complexity inherent to all random three-dimensional (3D) porous media imparts complicated flow and transport dynamics. It has been established that this complexity generates persistent chaotic advection via a 3D fluid mechanical analogue of the baker’s map which rapidly accelerates scalar mixing in the presence of molecular diffusion. Hence, pore-scale fluid mixing is governed by the interplay between chaotic advection, molecular diffusion and the broad (power-law) distribution of fluid particle travel times which arise from the non-slip condition at pore walls. To understand and quantify mixing in 3D porous media, we consider these processes in a model 3D open porous network and develop a novel stretching continuous time random walk (CTRW), which provides analytic estimates of pore-scale mixing which compare well with direct numerical simulations. We find that the chaotic advection inherent to 3D porous media imparts scalar mixing which scales exponentially with the longitudinal advection, whereas the topological constraints associated with two-dimensional porous media limit the mixing to scale algebraically. These results decipher the role of wide transit time distributions and complex topologies on porous media mixing dynamics, and provide the building blocks for macroscopic models of dilution and mixing which resolve these mechanisms.
The Full CPT-Even Photon Sector of the Standard Model Extension at Finite Temperature

We study the finite temperature behavior of the CPT-even pure-photon sector of the Standard Model Extension, which is defined by the standard Maxwell Lagrangian plus the term $(k_F)_{\mu\nu\alpha\beta}F^{\mu\nu}F^{\alpha\beta}$. The Hamiltonian analysis is performed, from which the degrees of freedom and constraints of the theory are derived. We have explicitly calculated the partition function for an arbitrary configuration of the $(k_F)_{\mu\nu\alpha\beta}$ coefficients, to second order, and we have used it to obtain the thermodynamic properties of the modified photon sector. We find the correction to the frequency dependence in Planck's radiation law, and we identify that the total energy density is adjusted, relative to the standard scenario, by a global proportionality constant containing the Lorentz-violating contributions. Nevertheless, the equation of state is not affected by these modifications.
On the velocity autocorrelation function of a Brownian particle

Memory effect of Brownian motion in an incompressible fluid is studied. The reasoning is based on the Mori-Zwanzig formalism and a new formulation of the Langevin force as a result of collisions between an effective and the Brownian particles. Thus, the stochastic force autocorrelation function with finite dispersion and the corresponding Brownian particle velocity autocorrelation function are obtained.
Rotation, activity, and lithium abundance in cool binary stars

We have used two robotic telescopes to obtain time-series high-resolution spectroscopy and V I and/or by photometry for a sample of 60 active stars. Orbital solutions are presented for 26 SB2 and 19 SB1 systems with unprecedented phase coverage and accuracy. The total of 6,609 R=55,000 echelle spectra are also used to systematically determine effective temperatures, gravities, metallicities, rotational velocities, lithium abundances and absolute H{\alpha}-core fluxes as a function of time. The photometry is used to infer unspotted brightness, V - I and/or b - y colors, spot-induced brightness amplitudes and precise rotation periods. Our data are complemented by literature data and are used to determine rotation-temperature-activity relations for active binary components. We also relate lithium abundance to rotation and surface temperature. We find that 74% of all known rapidly-rotating active binary stars are synchronized and in circular orbits but 26% are rotating asynchronously of which half have Prot > Porb and e > 0. Because rotational synchronization is predicted to occur before orbital circularization active binaries should undergo an extra spin-down besides tidal dissipation. We suspect this to be due to a magnetically channeled wind with its subsequent braking torque. We find a steep increase of rotation period with decreasing effective temperature for active stars. For inactive, single giants with Prot > 100 d, the relation is much weaker. Our data also indicate a period-activity relation for H{\alpha} of the form RH{\alpha} \propto P - 0.24 for binaries and RH{\alpha} \propto P -0.14 for singles. Lithium abundances in our sample increase with effective temperature. On average, binaries of comparable effective temperature appear to exhibit 0.25 dex less surface lithium than singles. We also find a trend of increased Li abundance with rotational period of form log n(Li) \propto - 0.6 log Prot.
Microwave and radio emission of dusty star-forming galaxies: implication for the cosmic radio background

We use the most up-to-date cosmological evolution models of dusty star-forming galaxies and radio sources to compute the extragalactic number counts and the cosmic background from 408 MHz to 12 THz (or 24 μm). The evolution model of star-forming galaxies reproduces the recent constraints obtained by Spitzer, Herschel, and ground-based submm and mm experiments: number counts, redshift distribution of galaxies, cosmic background intensity and anisotropies. The template spectral energy distributions used in this model are extrapolated to the radio domain adding three emission components: synchrotron, free-free, and spinning dust. To fix the synchrotron intensity, we use the well-known IR/radio flux ratio, q70, and a constant spectral index βS = −3, consistent with measurements made in local galaxies taking account the spinning dust emission. For a constant q70, our model added to the active galactic nucleus (AGN) contribution provides a good fit to the extragalactic number counts from 24 μm to 408 MHz, and to the cosmic background intensity in the far- and mid-IR. The spinning dust emission accounts for up to 20% of the cosmic microwave background produced by star-forming galaxies, but for only less than 10% of the total background when AGN are included. The star-forming galaxies account for 77.5% of the number counts at 1.4 GHz for a flux of 100 μJy. However, the model falls short of reproducing the cosmic radio background measured with the ARCADE2 balloon-borne experiment. Considering the case when q70 decreases strongly with redshift, this still does not explain the ARCADE2 measurements. It also yields to an overestimate of the low-flux number counts in the radio. As a result, we rule out a steep variation of q70 with the redshift at least for z 3.5. Then, adding a population of faint ,
External photon fields in Fermi bright blazars

The external Compton (EC) model is used to study the high energy emission of some blazars, in which the external photon field is considered to dominate inverse Compton radiation. We explore the properties of the external photon field through analyzing the FERMI LAT bright AGN sample within three months of the start of operations in sky-survey mode. In the sample, assuming the high energy radiation of low synchrotron peaked blazars is from the EC process, we find that the external photon parameter U-ext/nu(ext) may not be a constant. Calculating synchrotron and inverse Compton luminosity from the quasi-simultaneous broadband spectral energy distributions, we find that they have an approximately linear relation. This indicates that the ratio of external photon and magnetic energy density is a constant in the comoving frame, implying that the Lorentz factor of the emitting blob depends on the external photon field and magnetic field. The result gives a strong constraint on the dynamic jet model.
Mechanics of elastic structures

Journal of Mechanics of Materials and Structures[PDF] Geotechnical Engineering (Soil Mechanics And Solid Mechanics Mechanical Engineering Purdue UniversitySol Mech course text Feb10 Solid Mechanics at Harvard International Journal of Mechanics and Materials in Design Fracture Mechanics | MechaniCalcApplied Mechanics of Solids (A.F. Bower)
ASPiRe: Adaptive Skill Priors for Reinforcement Learning

We introduce ASPiRe (Adaptive Skill Prior for RL), a new approach that leverages prior experience to accelerate reinforcement learning. Unlike existing methods that learn a single skill prior from a large and diverse dataset, our framework learns a library of different distinction skill priors (i.e., behavior priors) from a collection of specialized datasets, and learns how to combine them to solve a new task. This formulation allows the algorithm to acquire a set of specialized skill priors that are more reusable for downstream tasks; however, it also brings up additional challenges of how to effectively combine these unstructured sets of skill priors to form a new prior for new tasks. Specifically, it requires the agent not only to identify which skill prior(s) to use but also how to combine them (either sequentially or concurrently) to form a new prior. To achieve this goal, ASPiRe includes Adaptive Weight Module (AWM) that learns to infer an adaptive weight assignment between different skill priors and uses them to guide policy learning for downstream tasks via weighted Kullback-Leibler divergences. Our experiments demonstrate that ASPiRe can significantly accelerate the learning of new downstream tasks in the presence of multiple priors and show improvement on competitive baselines.
Spike-based local synaptic plasticity: a survey of computational models and neuromorphic circuits

Understanding how biological neural networks carry out learning using spike-based local plasticity mechanisms can lead to the development of real-time, energy-efficient, and adaptive neuromorphic processing systems. A large number of spike-based learning models have recently been proposed following different approaches. However, it is difficult to assess if these models can be easily implemented in neuromorphic hardware, and to compare their features and ease of implementation. To this end, in this survey, we provide an overview of representative brain-inspired synaptic plasticity models and mixed-signal complementary metal–oxide–semiconductor neuromorphic circuits within a unified framework. We review historical, experimental, and theoretical approaches to modeling synaptic plasticity, and we identify computational primitives that can support low-latency and low-power hardware implementations of spike-based learning rules. We provide a common definition of a locality principle based on pre- and postsynaptic neural signals, which we propose as an important requirement for physical implementations of synaptic plasticity circuits. Based on this principle, we compare the properties of these models within the same framework, and describe a set of mixed-signal electronic circuits that can be used to implement their computing principles, and to build efficient on-chip and online learning in neuromorphic processing systems.
A Lyapunov Approach for the Exponential Stability of a Damped Timoshenko Beam

In this technical note, we consider the stability properties of a viscously damped Timoshenko beam equations with spatially varying parameters. With the help of the port-Hamiltonian framework, we first prove the existence of solutions and show, by the use of an appropriate Lyapunov function, that the system is exponentially stable and has an explicit decay rate. The explicit exponential bound is computed for an illustrative example for which we provide some numerical simulations.
Rethinking skip connection model as a learnable Markov chain

Over past few years afterward the birth of ResNet, skip connection has become the defacto standard for the design of modern architectures due to its widespread adoption, easy optimization and proven performance. Prior work has explained the effectiveness of the skip connection mechanism from different perspectives. In this work, we deep dive into the model's behaviors with skip connections which can be formulated as a learnable Markov chain. An efficient Markov chain is preferred as it always maps the input data to the target domain in a better way. However, while a model is explained as a Markov chain, it is not guaranteed to be optimized following an efficient Markov chain by existing SGD-based optimizers which are prone to get trapped in local optimal points. In order to towards a more efficient Markov chain, we propose a simple routine of penal connection to make any residual-like model become a learnable Markov chain. Aside from that, the penal connection can also be viewed as a particular model regularization and can be easily implemented with one line of code in the most popular deep learning frameworks~\footnote{Source code: \url{https://github.com/densechen/penal-connection}}. The encouraging experimental results in multi-modal translation and image recognition empirically confirm our conjecture of the learnable Markov chain view and demonstrate the superiority of the proposed penal connection.
A Survey: Credit Sentiment Score Prediction

Manual approvals are still used by banks and other NGOs to approve loans. It takes time and is prone to mistakes because it is controlled by a bank employee. Several ﬁelds of machine learning mining technologies have been utilized to enhance various areas of credit rating forecast. A major goal of this research is to look at current sentiment analysis techniques that are being used to generate creditworthiness.
Wave propagation in rotating magnetised plasmas

Wave propagation properties in a medium are fundamentally affected when this medium is moving instead of at rest. In isotropic dielectric media rotation has two noteworthy contributions: one is a mechanically induced circular birefringence, which materialises as a rotation of the polarisation, the other is image rotation, which corresponds to a rotation of the transverse structure of a wave. Here, we review the effect of rotation in a magnetised plasma. We also point out applications to both astrophysical phenomena and laboratory devices. We first show that the mechanical effect of rotation on polarisation is in a magnetised plasma superimposed onto the classical Faraday rotation and that failing to account for this new contribution could lead to errors in the interpretation of polarimetry data. We also demonstrate that image rotation is recovered in plasmas for a number of low-frequency magnetised plasma waves carrying orbital angular momentum and that this phenomenon holds promise for the development of new rotation diagnostic tools in plasmas.
Rethinking and Recomputing the Value of ML Models

In this paper, we argue that the way we have been training and evaluating ML models has largely forgotten the fact that they are applied in an organization or societal context as they provide value to people. We show that with this perspective we fundamentally change how we evaluate, select and deploy ML models - and to some extent even what it means to “learn”. Speciﬁcally, we stress that the notion of value plays a central role in learning and evaluating, and different models may require different learning practices and provide different values based on the application context they are applied. We also show that this concretely impacts how we select and embed models into human workﬂows based on experimental datasets. Nothing of what is presented here is hard: to a large extent is a series of fairly trivial observations with massive practical implications.
Primordial black holes and gravitational waves from nonminimally coupled supergravity inflation

We study the formation of primordial black holes and the generation of gravitational waves in a class of cosmological models that are direct supersymmetric analogs of the observationally favored nonminimally coupled Higgs inflation model. It is known that this type of model naturally includes multiple scalar fields which may be regarded as the inflaton. For the sake of simplicity we focus on the case where the inflaton field space is two dimensional. We analyze the multifield dynamics and find the region of parameters that gives copious production of primordial black holes that may comprise a significant part of the present dark matter abundance. We also compute the spectrum of the gravitational waves and discuss their detectability by means of future ground-based and space-borne gravitational wave observatories.
Hidden in Plain Sight: Exploring Encrypted Channels in Android Apps

As privacy features in Android operating system improve, privacy-invasive apps may gradually shift their focus to non-standard and covert channels for leaking private user/device information. Such leaks also remain largely undetected by state-of-the-art privacy analysis tools, which are very effective in uncovering privacy exposures via regular HTTP and HTTPS channels. In this study, we design and implement, ThirdEye, to significantly extend the visibility of current privacy analysis tools, in terms of the exposures that happen across various non-standard and covert channels, i.e., via any protocol over TCP/UDP (beyond HTTP/S), and using multi-layer custom encryption over HTTP/S and non-HTTP protocols. Besides network exposures, we also consider covert channels via storage media that also leverage custom encryption layers. Using ThirdEye, we analyzed 12,598 top-apps in various categories from Androidrank, and found that 2887/12,598 (22.92%) apps used custom encryption/decryption for network transmission and storing content in shared device storage, and 2465/2887 (85.38%) of those apps sent device information (e.g., advertising ID, list of installed apps) over the network that can fingerprint users. Besides, 299 apps transmitted insecure encrypted content over HTTP/non-HTTP protocols; 22 apps that used authentication tokens over HTTPS, happen to expose them over insecure (albeit custom encrypted) HTTP/non-HTTP channels. We found non-standard and covert channels with multiple levels of obfuscation (e.g., encrypted data over HTTPS, encryption at nested levels), and the use of vulnerable keys and cryptographic algorithms. Our findings can provide valuable insights into the evolving field of non-standard and covert channels, and help spur new countermeasures against such privacy leakage and security issues.
Embedded System Performance Analysis for Implementing a Portable Drowsiness Detection System for Drivers

Drowsiness on the road is a widespread problem with fatal consequences; thus, a multitude of systems and techniques have been proposed. Among existing methods, Ghoddoosian et al. utilized temporal blinking patterns to detect early signs of drowsiness, but their algorithm was tested only on a powerful desktop computer, which is not practical to apply in a moving vehicle setting. In this paper, we propose an efficient platform to run Ghoddoosian’s algorithm, detail the performance tests we ran to determine this platform, and explain our threshold optimization logic. After considering the Jetson Nano and Beelink (Mini PC), we concluded that the Mini PC is most efficient and practical to run our embedded system in a vehicle. To determine this, we ran communication speed tests and evaluated total processing times for inference operations. Based on our experiments, the average total processing time to run the drowsiness detection model was 94.27 ms for the Jetson Nano and 22.73 ms for the Beelink (Mini PC). Considering the portability and power efficiency of each device, along with the processing time results, the Beelink (Mini PC) was determined to be most suitable. Additionally, we propose a threshold optimization algorithm, which determines whether the driver is drowsy, or alert based on the trade-off between the sensitivity and specificity of the drowsiness detection model. Our study will serve as a crucial next step for drowsiness detection research and its application in vehicles. Through our experiments, we have determined a favorable platform that can run drowsiness detection algorithms in real-time and can be used as a foundation to further advance drowsiness detection research. In doing so, we have bridged the gap between an existing embedded system and its actual implementation in vehicles to bring drowsiness technology a step closer to prevalent real-life implementation.
Angular Power Spectrum of B-mode Polarization from Cosmic String Wakes

Cosmic string wakes produce direct B-mode polarization at leading order in cosmological perturbation theory, as worked out in a previous publication \cite{BDH} in the case of a single string wake. Here we compute the angular power spectrum of B-mode polarization from a scaling distribution of string wakes. We find that the averaging which enters in computing the power spectrum renders the signal, which is distinctive in position space maps, too small in amplitude to be detectable with the first generation B-mode surveys. In addition, the spectral shape is similar to that of gravitational lensing, making it additionally difficult to detect the cosmic string signal from the angular power spectrum. Hence, a more promising way to constrain cosmic strings using B-mode polarization is by analyzing position space maps using novel algorithms such as the Canny algorithm.
On Symmetric Pseudo-Boolean Functions: Factorization, Kernels and Applications

A symmetric pseudo-Boolean function is a map from Boolean tuples to real numbers which is invariant under input variable interchange. We prove that any such function can be equivalently expressed as a power series or factorized. The kernel of a pseudo-Boolean function is the set of all inputs that cause the function to vanish identically. Any $n$-variable symmetric pseudo-Boolean function $f(x_1, x_2, \dots, x_n)$ has a kernel corresponding to at least one $n$-affine hyperplane, each hyperplane is given by a constraint $\sum_{l=1}^n x_l = \lambda$ for $\lambda\in \mathbb{C}$ constant. We use these results to analyze symmetric pseudo-Boolean functions appearing in the literature of spin glass energy functions (Ising models), quantum information and tensor networks.
Restricted Strong Convexity of Deep Learning Models with Smooth Activations

We consider the problem of optimization of deep learning models with smooth activation functions. While there exist influential results on the problem from the ``near initialization'' perspective, we shed considerable new light on the problem. In particular, we make two key technical contributions for such models with $L$ layers, $m$ width, and $\sigma_0^2$ initialization variance. First, for suitable $\sigma_0^2$, we establish a $O(\frac{\text{poly}(L)}{\sqrt{m}})$ upper bound on the spectral norm of the Hessian of such models, considerably sharpening prior results. Second, we introduce a new analysis of optimization based on Restricted Strong Convexity (RSC) which holds as long as the squared norm of the average gradient of predictors is $\Omega(\frac{\text{poly}(L)}{\sqrt{m}})$ for the square loss. We also present results for more general losses. The RSC based analysis does not need the ``near initialization"perspective and guarantees geometric convergence for gradient descent (GD). To the best of our knowledge, ours is the first result on establishing geometric convergence of GD based on RSC for deep learning models, thus becoming an alternative sufficient condition for convergence that does not depend on the widely-used Neural Tangent Kernel (NTK). We share preliminary experimental results supporting our theoretical advances.