Miracle-dz/UniFAR-FTUs huggingface dataset:
Integrated Optical Device for Frequency Conversion Across the Full Telecom 
C
-Band Spectrum

High-density communication through optical fiber is made possible by Wavelength Division Multiplexing, which is the simultaneous transmission of many discrete signals at different optical frequencies. Vast quantities of data may be transmitted without interference using this scheme but flexible routing of these signals requires an electronic middle step, carrying a cost in latency. We present a technique for frequency conversion across the entire WDM spectrum with a single device, which removes this latency cost. Using an optical waveguide in lithium niobate and two infrared pump beams, we show how to maximize conversion efficiency between arbitrary frequencies by analyzing the role of dispersion in cascaded nonlinear processes. The technique is presented generally and may be applied to any suitable nonlinear material or platform, and to classical or quantum optical signals.
The diversity of the circumgalactic medium around z = 0 Milky Way-mass galaxies from the Auriga simulations

Galaxies are surrounded by massive gas reservoirs (i.e. the circumgalactic medium; CGM) which play a key role in their evolution. The properties of the CGM, which are dependent on a variety of internal and environmental factors, are often inferred from absorption line surveys which rely on a limited number of single lines-of-sight. In this work we present an analysis of 28 galaxy haloes selected from the Auriga project, a cosmological magneto-hydrodynamical zoom-in simulation suite of isolated Milky Way-mass galaxies, to understand the impact of CGM diversity on observational studies. Although the Auriga haloes are selected to populate a narrow range in halo mass, our work demonstrates that the CGM of L⋆ galaxies is extremely diverse: column densities of commonly observed species span ∼3 − 4 dex and their covering fractions range from ${\sim } 5$ to $90{{\ \rm per\ cent}}$. Despite this diversity, we identify the following correlations: 1) the covering fractions (CF) of hydrogen and metals of the Auriga haloes positively correlate with stellar mass, 2) the CF of H i, C iv, and Si ii anticorrelate with active galactic nucleus luminosity due to ionization effects, and 3) the CF of H i, C iv, and Si ii positively correlate with galaxy disc fraction due to outflows populating the CGM with cool and dense gas. The Auriga sample demonstrates striking diversity within the CGM of L⋆ galaxies, which poses a challenge for observations reconstructing CGM characteristics from limited samples, and also indicates that long-term merger assembly history and recent star formation are not the dominant sculptors of the CGM.
Assessing Transferability From Simulation to Reality for Reinforcement Learning

Learning robot control policies from physics simulations is of great interest to the robotics community as it may render the learning process faster, cheaper, and safer by alleviating the need for expensive real-world experiments. However, the direct transfer of learned behavior from simulation to reality is a major challenge. Optimizing a policy on a slightly faulty simulator can easily lead to the maximization of the ‘Simulation Optimization Bias’ (SOB). In this case, the optimizer exploits modeling errors of the simulator such that the resulting behavior can potentially damage the robot. We tackle this challenge by applying domain randomization, i.e., randomizing the parameters of the physics simulations during learning. We propose an algorithm called Simulation-based Policy Optimization with Transferability Assessment (SPOTA) which uses an estimator of the SOB to formulate a stopping criterion for training. The introduced estimator quantifies the over-fitting to the set of domains experienced while training. Our experimental results on two different second order nonlinear systems show that the new simulation-based policy search algorithm is able to learn a control policy exclusively from a randomized simulator, which can be applied directly to real systems without any additional training.
The Evolution of Electron Dispersion in the Series of Rare-Earth Tritelluride Compounds Obtained from Their Charge-Density-Wave Properties and Susceptibility Calculations

We calculated the electron susceptibility of rare-earth tritelluride compounds RTe3 as a function of temperature, wave vector, and electron-dispersion parameters. Comparison of the results obtained with the available experimental data on the transition temperature and on the wave vector of a charge-density wave in these compounds allowed us to predict the values and evolution of electron-dispersion parameters with the variation of the atomic number of rare-earth elements (R).
Identifying Algorithm Names in Code Comments

For recent machine-learning-based tasks like API sequence generation, comment generation, and document generation, large amount of data is needed. When software developers implement algorithms in code, we find that they often mention algorithm names in code comments. Code annotated with such algorithm names can be valuable data sources. In this paper, we propose an automatic method of algorithm name identification. The key idea is extracting important N-gram words containing the word `algorithm' in the last. We also consider part of speech patterns to derive rules for appropriate algorithm name identification. The result of our rule evaluation produced high precision and recall values (more than 0.70). We apply our rules to extract algorithm names in a large amount of comments from active FLOSS projects written in seven programming languages, C, C++, Java, JavaScript, Python, PHP, and Ruby, and report commonly mentioned algorithm names in code comments.
On Designing Machine Learning Models for Malicious Network Traffic Classification

Machine learning (ML) started to become widely deployed in cyber security settings for shortening the detection cycle of cyber attacks. To date, most ML-based systems are either proprietary or make specific choices of feature representations and machine learning models. The success of these techniques is difficult to assess as public benchmark datasets are currently unavailable. In this paper, we provide concrete guidelines and recommendations for using supervised ML in cyber security. As a case study, we consider the problem of botnet detection from network traffic data. Among our findings we highlight that: (1) feature representations should take into consideration attack characteristics; (2) ensemble models are well-suited to handle class imbalance; (3) the granularity of ground truth plays an important role in the success of these methods.
Robust Humanoid Locomotion Using Trajectory Optimization and Sample-Efficient Learning*

Trajectory optimization (TO) is one of the most powerful tools for generating feasible motions for humanoid robots. However, including uncertainties and stochasticity in the TO problem to generate robust motions can easily lead to intractable problems. Furthermore, since the models used in TO have always some level of abstraction, it can be hard to find a realistic set of uncertainties in the model space. In this paper we leverage a sample-efficient learning technique (Bayesian optimization) to robustify TO for humanoid locomotion. The main idea is to use data from full-body simulations to make the TO stage robust by tuning the cost weights. To this end, we split the TO problem into two phases. The first phase solves a convex optimization problem for generating center of mass (CoM) trajectories based on simplified linear dynamics. The second stage employs iterative Linear-Quadratic Gaussian (iLQG) as a whole-body controller to generate full body control inputs. Then we use Bayesian optimization to find the cost weights to use in the first stage that yields robust performance in the simulation/experiment, in the presence of different disturbance/uncertainties. The results show that the proposed approach is able to generate robust motions for different sets of disturbances and uncertainties.
A Little Charity Guarantees Almost Envy-Freeness

Fair division of indivisible goods is a very well-studied problem. The goal of this problem is to distribute $m$ goods to $n$ agents in a "fair" manner, where every agent has a valuation for each subset of goods. We assume general valuations. Envy-freeness is the most extensively studied notion of fairness. However, envy-free allocations do not always exist when goods are indivisible. The notion of fairness we consider here is "envy-freeness up to any good" (EFX) where no agent envies another agent after the removal of any single good from the other agent's bundle. It is not known if such an allocation always exists even when $n=3$. We show there is always a partition of the set of goods into $n+1$ subsets $(X_1,\ldots,X_n,P)$ where for $i \in [n]$, $X_i$ is the bundle allocated to agent $i$ and the set $P$ is unallocated (or donated to charity) such that we have$\colon$ 1) envy-freeness up to any good, 2) no agent values $P$ higher than her own bundle, and 3) fewer than $n$ goods go to charity, i.e., $|P| < n$ (typically $m \gg n$). Our proof is constructive. When agents have additive valuations and $\lvert P \rvert$ is large (i.e., when $|P|$ is close to $n$), our allocation also has a good maximin share (MMS) guarantee. Moreover, a minor variant of our algorithm also shows the existence of an allocation which is $4/7$ groupwise maximin share (GMMS): this is a notion of fairness stronger than MMS. This improves upon the current best bound of $1/2$ known for an approximate GMMS allocation.
Model Development Process

Predictive modeling has an increasing number of applications in various fields. High demand for predictive models drives creation of tools that automate and support work of data scientist on the model development. To better understand what can be automated we need first a description of the model life-cycle. In this paper we propose a generic Model Development Process (MDP). This process is inspired by Rational Unified Process (RUP) which was designed for software development. There are other approached to process description, like CRISP DM or ASUM DM, in this paper we discuss similarities and differences between these methodologies. We believe that the proposed open standard for model development will facilitate creation of tools for automation of model training, testing and maintaining.
5. Spatial Distribution and Structure of Remotely Sensed Surface Water Content Estimated by a Thermal Inertia Approach

A major problem recurring in soil and hydrological sciences is the representation of flow and transport processes in the presence of large spatial and temporal variability of soil hydraulic properties. Their measurement is normally time consuming and expensive and it is usually considered imprac- tical to perform sufficiently dense in situ measurements. Measurement techniques primarily designed for field, plot or local scale monitoring, are often impractical for larger scales such as watersheds. However, remote sensing from air- or space-borne platforms offers the possibility to address this problem by providing large spatial coverage and temporal continuity. A cru- cial variable that can actually be monitored by remote sensors is the water content in a thin soil layer, usually up to a depth of 5 cm below the surface. However, difficulties arise in the estimation of the vertical and horizontal distribution of the water content within the soil profile, which are closely connected to soil hydraulic properties and their spatial distribution. A promis- ing approach for estimating soil water content profiles is the integration of remote sensing of surface water content and hydrological modeling. A major goal of the scientific group is to develop a practical and robust procedure for estimating water contents throughout the soil profile from surface water contents to be deduced from thermal inertia data, which in turn have to be estimated by multi-spectral remote sensing data. The procedure is largely based on the integration of the remote sensing information into a hydrological model to be used in a stochastic simulation framework, in the perspective of predicting the crucial vadose zone processes at large scale. As a first step, in this work we will show some preliminary results from aircraft image analysis and their validation by field campaign data. The data extracted from the airborne sensors provided the opportunity to retrieve land surface temperatures with a very high spatial resolution. The distribution of surface moisture, as deduced by the thermal inertia estimates, was compared to the surface water content maps measured in situ by TDR-based probes.
Near-infrared spectroscopy using digital phase-sensitive detection

ABSTRACT Scanning near-infrared (NIR) spectrometers commonly employ analog lock-in amplifiers for demodulation that require complex circuitry and are sensitive to electromagnetic interference. Here, a new sampling system based on digital phase-sensitive detection is reported that used a quadrature sampling algorithm to establish the sampling frequency as an integral multiple of four times the optical signal frequency. A complex programmable logic device was used to control the analog-to-digital converter. A digital signal processor was used to receive data, complete the quadrature calculations, and transfer the results to a computer. Consequently, the circuitry was simplified with reduced electromagnetic interference. Spectra were obtained using instrumentation based on analog and digital sensitive detection that showed the latter provided more accurate results. The optimum signal-to-noise ratio of 66.34 dB was higher than the value obtained by the analog system.
Tails of Triangular Flows

Triangular maps are a construct in probability theory that allows the transformation of any source density to any target density. We consider flow based models that learn these triangular transformations which we call triangular flows and study the properties of these triangular flows with the goal of capturing heavy tailed target this http URL one dimension, we prove that the density quantile functions of the source and target density can characterize properties of the increasing push-forward transformation and show that no Lipschitz continuous increasing map can transform a light-tailed source to a heavy-tailed target density. We further precisely relate the asymptotic behavior of these density quantile functions with the existence of certain function moments of distributions. These results allow us to give a precise asymptotic rate at which an increasing transformation must grow to capture the tail properties of a target given the source distribution. In the multivariate case, we show that any increasing triangular map transforming a light-tailed source density to a heavy-tailed target density must have all eigenvalues of the Jacobian to be unbounded. Our analysis suggests the importance of source distribution in capturing heavy-tailed distributions and we discuss the implications for flow based models.
Retention of rising oil droplets in density stratification

Following the Deepwater Horizon oil well leak in 2010, approximately 40 percent of the spilled oil remained trapped deep beneath the ocean surface. While it has been demonstrated that these trapped intrusion layers, composed of small oil droplets, are caused by interactions with the oceanic density gradient, the exact dynamics underlying the residence time of the oil in these layers is not well understood. In this study, we present results from experiments on the retention of single oil droplets rising through a two-layer density stratification. We track the motion of droplets as they rise through the water column, and delineate two timescales: an entrainment time, and a retention time. The entrainment time is a measure of the time that an oil droplet spends below its upper-layer terminal velocity and relates to the time over which the droplet entrains denser fluid. The retention time is a measure of the time that the droplet is delayed from reaching an upper threshold far from the density transition. The retention time relates to the entrainment time, as well as to the magnitude of the drop's slowdown. These two timescales are found to strongly depend on the Froude number of the system. We find that both timescales are only significantly large for Fr $\lesssim1$, indicating that trapping dynamics arise from a balance between drop inertia and buoyancy. We discuss the implications of these findings on the scale of oceanic oil spills, and propose that the size of the bulk plume of oil droplets may be the relevant length scale for trapping in that scenario.
Adaptive elliptical aperture photometry: A software package for high-cadence ground-based photometry

Context. Modern space telescopes are currently providing high-precision light curves for a large fraction of the sky, such that many new variable stars are being discovered. However, some stars have periodic variability with periods on the order of minutes and require high-cadence photometry to probe the physical mechanisms responsible. A cadence of less than a minute is often required to remove Nyquist ambiguities and confirm rapid variability, which forces observers to obtain high-cadence ground-based photometry. Aims. We aim to provide a modern software package to reduce ground-based photometric time series data and deliver optimised (differential) light curves. To produce high-quality light curves, which maximise the amplitude signal-to-noise ratio of short-period variability in a Fourier spectrum, we require adaptive elliptical aperture photometry as this represents a significant advantage compared to aperture photometry using circular apertures of fixed radii. Methods. The methodology of our code and its advantages are demonstrated using high-cadence ground-based photometry from the South African Astronomical Observatory (SAAO) of a confirmed rapidly oscillating Ap (roAp) star. Furthermore, we employed our software package to search for rapid oscillations in three candidate roAp stars. Results. We demonstrate that our pipeline represents a significant improvement in the quality of light curves, and we make it available to the community for use with different instruments and observatories. We search for and demonstrate the lack of high-frequency roAp pulsations to a limit of ∼1 mmag using B data in the three Ap stars HD 158596, HD 166542, and HD 181810. Conclusions. We demonstrate the significant improvement in the extraction of short-period variability caused by high-frequency pulsation modes, and discuss the implication of null detections in three Ap stars.
Matrix model generating function for quantum weighted Hurwitz numbers

The KP -function of hypergeometric type serving as generating function for quantum weighted Hurwitz numbers is used to compute the Baker function and the corresponding adapted basis elements, expressed as absolutely convergent Laurent series in the spectral parameter. These are equivalently expressed as Mellin–Barnes integrals, analogously to Meijer G-functions, but with an infinite product of -functions as integral kernel. A matrix model representation is derived for the -function evaluated at trace invariants of an externally coupled matrix.
A review on deep learning techniques for 3D sensed data classification

Over the past decade deep learning has driven progress in 2D image understanding. Despite these advancements, techniques for automatic 3D sensed data understanding, such as point clouds, is comparatively immature. However, with a range of important applications from indoor robotics navigation to national scale remote sensing there is a high demand for algorithms that can learn to automatically understand and classify 3D sensed data. In this paper we review the current state-of-the-art deep learning architectures for processing unstructured Euclidean data. We begin by addressing the background concepts and traditional methodologies. We review the current main approaches, including RGB-D, multi-view, volumetric and fully end-to-end architecture designs. Datasets for each category are documented and explained. Finally, we give a detailed discussion about the future of deep learning for 3D sensed data, using literature to justify the areas where future research would be most valuable.
Generalized Rank Minimization based Group Sparse Coding for Low-level Image Restoration via Dictionary Learning

Recently, low-rank matrix recovery theory has been emerging as a significant progress for various image processing problems. Meanwhile, the group sparse coding (GSC) theory has led to great successes in image restoration with group contains low-rank property. In this paper, we introduce a novel GSC framework using generalized rank minimization for image restoration tasks via an effective adaptive dictionary learning scheme. For a more accurate approximation of the rank of group matrix, we proposed a generalized rank minimization model with a generalized and flexible weighted scheme and the generalized nonconvex nonsmooth relaxation function. Then an efficient generalized iteratively reweighted singular-value function thresholding (GIR-SFT) algorithm is proposed to handle the resulting minimization problem of GSC. Our proposed model is connected to image restoration (IR) problems via an alternating direction method of multipliers (ADMM) strategy. Extensive experiments on typical IR problems of image compressive sensing (CS) reconstruction, inpainting, deblurring and impulsive noise removal demonstrate that our proposed GSC framework can enhance the image restoration quality compared with many state-of-the-art methods.
A Comparative Study of Synchronous Clocking Schemes for VLSI Based Systems

Recently a novel clock distribution scheme called Branch-and-Combine(BaC) has been proposed. The scheme guarantees constant skew bound irrespective of the size of the clocked network. It utilizes simple nodes to process clock signals such that clock paths are adaptively selected to guarantee constant skew bound. The paper uses a VLSI model to compare the properties of the new scheme to those of the well established H-Tree approach. The H-Tree is a binary tree of simple buffers which is laid out such that leaves are at equal distances from the root. Our study considers clocking 2-D processor meshes of arbitrary sizes. We evaluate and compare the relevant parameters of both schemes in a VLSI layout context. We utilize parameters such as clock skew, link costs, node costs and area efficiency as the basis for comparison. We show that for each BaC network, there is a certain threshold size after which it outperforms the corresponding tree network in terms of skew. We also show that except for node costs, BaC networks outperform the H-Tree, especially when the size of the clocked network is large. As an extension we show that BaC clocking does not suffer from potential pulse disappearance, no matter how large the network is.