Miracle-dz/UniFAR-FTUs huggingface dataset:
Spreading out and staying sharp – creating diverse rotation curves via baryonic and self-interaction effects

Galactic rotation curves are a fundamental constraint for any cosmological model. We use controlled N-body simulations of galaxies to study the gravitational effect of baryons in a scenario with collisionless cold dark matter (CDM) versus one with a self-interacting dark matter (SIDM) component. In particular, we examine the inner profiles of the rotation curves in the velocity range Vmax = 30-250 km/s, whose diversity has been found to be greater than predicted by the Lambda-CDM scenario. We find that the scatter in the observed rotation curves exceeds that predicted by dark matter only mass-concentration relations in either the CDM nor SIDM models. Allowing for realistic baryonic content and spatial distributions, however, helps create a large variety of rotation curve shapes; which is in better agreement with observations in the case of self-interactions due to the characteristic cored profiles being more accommodating to the slowly rising rotation curves than CDM. We find individual fits to model two of the most remarkable outliers of similar Vmax, UGC 5721 and IC 2574, the former a cusp-like rotation curve and the latter a seemingly 8 kpc cored profile. This diversity in SIDM arises as permutations of overly concentrated halos with compact baryonic distributions versus underdense halos with extended baryonic disks. The SIDM solution is promising and its feasibility ultimately depends on the sampling of the halo mass-concentration relation and its interplay with the baryonic profiles, emphasising the need for a better understanding of the frequency of extreme outliers present in current observational samples.
Flow-insensitive static analysis for detecting integer anomalies in programs

This paper describes a static analysis algorithm to detect potential integer anomalies in software. Integer anamolies take place when arithmetic operations on integer values yield new values that cannot be represented in the range for the integer type. Two common integer anomalies are integer overflow and integer underflow. Unexpected behavior could occur in a program's execution if an attempt is made to represent a value outside the range of the integer type. Such anomalies in integers representing buffer sizes can lead to critical buffer overruns that compromise the security of a system. In this paper, we present a flow-insensitive intra-procedural static analysis algorithm that detects integer overflow and underflow anomalies in integer variables that represent memory allocation size, buffer access size or loop terminating condition. We ran the analysis on approximately 55 MLOC from some future Microsoft products. We successfully uncovered and fixed over 2000 such anomalies with an overall noise rate of as low as 6.76 percent.
The VOICE survey: VST Optical imaging of the CDFS and ES1 fields

We present the VST Optical Imaging of the CDFS and ES1 Fields (VOICE) Survey, a VST INAF Guaranteed Time program designed to provide optical coverage of two 4 deg$^2$ cosmic windows in the Southern hemisphere. VOICE provides the first, multi-band deep optical imaging of these sky regions, thus complementing and enhancing the rich legacy of longer-wavelength surveys with VISTA, Spitzer, Herschel and ATCA available in these areas and paving the way for upcoming observations with facilities such as the LSST, MeerKAT and the SKA. VOICE exploits VST's OmegaCAM optical imaging capabilities and completes the reduction of WFI data available within the ES1 fields as part of the ESO-Spitzer Imaging Extragalactic Survey (ESIS) program providing $ugri$ and $uBVR$ coverage of 4 and 4 deg$^2$ areas within the CDFS and ES1 field respectively. We present the survey's science rationale and observing strategy, the data reduction and multi-wavelength data fusion pipeline. Survey data products and their future updates will be released at http://www.mattiavaccari. net/voice/ and on CDS/VizieR.
Electronic transport and the localization length in the quantum Hall effect

We report on recent experimental results from transport measurements with large Hall bars made of high mobility GaAs/AlGaAs heterostructures. Thermally activated conductivities and hopping transport were investigated in the integer quantum Hall regime. The predominant transport processes in two dimensions are discussed. The implications of transport regime on prefactor universality and on the relation between $\rho_{xx}$ and $\rho_{xy}$ are studied. Particularly in the Landau level tails, strictly linear dependence $\delta\rho_{xy}(\rho_{xx})$ was found, with pronounced asymmetries with respect to the plateau centre. At low temperatures, Ohmic (temperature dependent) as well as non-Ohmic (current dependent) transport were investigated and analysed on the basis of variable-range hopping theory. The non-Ohmic regime could successfully be described by an effective electron temperature model. The results from either the Ohmic transport or from a comparison of Ohmic and non-Ohmic data allowed to determine the localization length $\xi$ in two different ways. The observed divergence of $\xi(\nu)$ with the filling factor $\nu$ approaching a Landau level centre, is in qualitative agreement with scaling theories of electron localization. The absolute values of $\xi$ far from the $\rho_{xx}$ peaks are compared with theoretical predictions. On one hand, discrepancies between the $\xi$ results obtained from the two experimental methods are attributed to an inhomogeneous electric field distribution. Extrapolation yields an effective width of dominant potential drop of about $100 mu$m. On the other hand, our analysis suggests a divergence of the dielectric function $\epsilon_{r} \propto \xi^{\beta}$ with $\beta \simeq 1$.
The 2HWC HAWC Observatory Gamma-Ray Catalog

We present the first catalog of TeV gamma-ray sources realized with data from the newly completed High Altitude Water Cherenkov Observatory (HAWC). It is the most sensitive wide field-of-view TeV telescope currently in operation, with a one-year survey sensitivity of ∼5%–10% of the flux of the Crab Nebula. With an instantaneous field of view >1.5 sr and >90% duty cycle, it continuously surveys and monitors the sky for gamma-ray energies between hundreds of GeV and tens of TeV. HAWC is located in Mexico, at a latitude of 19° N, and was completed in 2015 March. Here, we present the 2HWC catalog, which is the result of the first source search performed with the complete HAWC detector. Realized with 507 days of data, it represents the most sensitive TeV survey to date for such a large fraction of the sky. A total of 39 sources were detected, with an expected number of false detections of 0.5 due to background fluctuation. Out of these sources, 19 are new sources that are not associated with previously known TeV sources (association criteria: <0.°5 away). The source list, including the position measurement, spectrum measurement, and uncertainties, is reported, then each source is briefly discussed. Of the 2HWC associated sources, 10 are reported in TeVCat as PWN or SNR: 2 as blazars and the remaining eight as unidentified.
Social Networks are Divulging Your Identity behind Crypto Addresses

Cryptocurrencies, such as Bitcoin and Ethereum, are becoming increasingly prevalent mainly due to their anonymity, decentralization, transparency, and security. However, the completely public ledger makes the trace and analysis of each account possible as long as the identity behind the public address is revealed. Theoretically, social networks could make that happen when addresses are posted on social network platforms using accounts containing personal information. To verify such a possibility, we have collected public data from two major platforms, i.e. Twitter and Reddit, aiming to find potential privacy leakage behind the ETH public address. In the end, an easy-to-use retrieval application is also built for a better illustration.
Features for the 0-1 knapsack problem based on inclusionwise maximal solutions

Decades of research on the 0-1 knapsack problem led to very efficient algorithms that are able to quickly solve large problem instances to optimality. This prompted researchers to also investigate whether relatively small problem instances exist that are hard for existing solvers and investigate which features characterize their hardness. Previously the authors proposed a new class of hard 0-1 knapsack problem instances and demonstrated that the properties of so-called inclusionwise maximal solutions (IMSs) can be important hardness indicators for this class. In the current paper, we formulate several new computationally challenging problems related to the IMSs of arbitrary 0-1 knapsack problem instances. Based on generalizations of previous work and new structural results about IMSs, we formulate polynomial and pseudopolynomial time algorithms for solving these problems. From this we derive a set of 14 computationally expensive features, which we calculate for two large datasets on a supercomputer in approximately 540 CPU-hours. We show that the proposed features contain important information related to the empirical hardness of a problem instance that was missing in earlier features from the literature by training machine learning models that can accurately predict the empirical hardness of a wide variety of 0-1 knapsack problem instances. Using the instance space analysis methodology, we also show that hard 0-1 knapsack problem instances are clustered together around a relatively dense region of the instance space and several features behave differently in the easy and hard parts of the instance space.
Cas A and the Crab Were Not Stellar Binaries At Death

The majority of massive stars are in binaries, which implies that many core collapse supernovae (ccSNe) should be binaries at the time of the explosion. Here we show that the three most recent, local (visual) SNe (the Crab, Cas A and SN 1987A) were not binaries, with limits on the initial mass ratios of q = M2/M1 <∼ 0.1. No quantitative limits have previously been set for Cas A and the Crab, while for SN 1987A we merely updated existing limits in view of new estimates of the dust content. The lack of stellar companions to these three ccSNe implies a 90% confidence upper limit on the q > ∼ 0.1 binary fraction at death of fb < 44%. In a passively evolving binary model (meaning no binary interactions), with a flat mass ratio distribution and a Salpeter IMF, the resulting 90% confidence upper limit on the initial binary fraction of F < 63% is in considerable tension with observed massive binary statistics. Allowing a significant fraction fM ≃ 25% of stellar binaries to merge reduces the tension, with F < 63(1 − fM ) % ≃ 81%, but allowing for the significant fraction in higher order systems (triples, etc.) reintroduces the tension. That Cas A was not a stellar binary at death also shows that a massive binary companion is not necessary for producing a Type IIb SNe. Much larger surveys for binary companions to Galactic SNe will become feasible with the release of the full Gaia proper motion and parallax catalogs, providing a powerful probe of the statistics of such binaries and their role in massive star evolution, neutron star velocity distributions and runaway stars.
Linear isometries on Weighted Coordinates Poset Block Space

Given [ n ] = { 1 , 2 ,. ,n } , a poset order (cid:22) on [ n ], a label map π : [ n ] → N deﬁned by π ( i ) = k i with P ni =1 π ( i ) = N , and a weight function w on F q , let F Nq be the vector space of N -tuples over the ﬁeld F q equipped with ( P,w,π )-metric where F Nq is the direct sum of spaces F k 1 q , F k 2 q ,. , F k n q . In this paper, we determine the groups of linear isometries of ( P,w,π )-metric spaces in terms of a semi-direct product, which turns out to be similar to the case of poset (block) metric spaces. In particular, we re-obtain the group of linear isometries of the ( P,w )-mertic spaces and ( P,π )-mertic spaces.
Optimization approach to entanglement distillation

We put forward a method for optimized distillation of partly entangled pairs of qubits into a smaller number of more entangled pairs by recurrent local unitary operations and projections. Optimized distillation is achieved by minimization of a cost function with up to 30 real parameters, which is chosen to be sensitive to the fidelity and the projection probability at each step. We show that in many cases this approach can significantly improve the distillation efficiency in comparison to the present methods.
Analytical and Numerical Approaches to Squeezing Flow and Heat Transfer between Two Parallel Disks with Velocity Slip and Temperature Jump

Analytic and numerical techniques are presented to analyze the influence of temperature and wall slip conditions on the unsteady flow and heat transfer via viscous fluid squeezed between two parallel disks in the presence of an applied magnetic field. The governing partial differential equations for momentum and heat transfer are reduced to a system of coupled nonlinear ordinary differential equations using similarity transformations. The homotopy analysis method (HAM) is then utilized to find explicit series solution of the resulting problem. The convergence of the obtained solution is carefully analyzed. To check the reliability of the method the same problem is also solved by using the shooting method and an excellent agreement is observed between the two sets of results. Influence of various parameters of practical importance on the velocity and temperature profiles is studied and portrayed graphically. Values of skin friction coefficient and local Nusselt number are tabulated by assigning different values to various emerging parameters.
Dynamic Pricing with Volume Discounts in Online Settings

According to the main international reports, more pervasive industrial and business-process automation, thanks to machine learning and advanced analytic tools, will unlock more than 14 trillion USD worldwide annually by 2030. In the specific case of pricing problems, which constitute the class of problems we investigate in this paper, the estimated unlocked value will be about 0.5 trillion USD per year. In particular, this paper focuses on pricing in e-commerce when the objective function is profit maximization and only transaction data are available. This setting is one of the most common in real-world applications. Our work aims to find a pricing strategy that allows defining optimal prices at different volume thresholds to serve different classes of users. Furthermore, we face the major challenge, common in real-world settings, of dealing with limited data available. We design a two-phase online learning algorithm, namely PVD-B, capable of exploiting the data incrementally in an online fashion. The algorithm first estimates the demand curve and retrieves the optimal average price, and subsequently it offers discounts to differentiate the prices for each volume threshold. We ran a real-world 4-month-long A/B testing experiment in collaboration with an Italian e-commerce company, in which our algorithm PVD-B - corresponding to A configuration - has been compared with human pricing specialists - corresponding to B configuration. At the end of the experiment, our algorithm produced a total turnover of about 300 KEuros, outperforming the B configuration performance by about 55%. The Italian company we collaborated with decided to adopt our algorithm for more than 1,200 products since January 2022.
Design, Optimization, and Reverse Engineering of High-Power Laser Systems

Chirped pulse ampliﬁcation (CPA) followed by nonlinear optical (NLO) systems constitute the backbone of myriad advancements in semiconduc-tor and additive manufacturing, communication networks, biology and medicine, defense and national security, and a host of other sectors over the past decades. However, accurately and eﬃciently modeling CPA-and NLO-based laser systems is challenging because of the multitude of coupled linear and nonlinear processes and high variability in modeling frameworks. The lack of fully-integrated software CPA+NLO modeling severely hampers further advances to tailor existing or materialize new CPA+NLO systems and curtails their full potential in emerging inverse design approaches through data-driven machine learning methods. Here, we present a modular start-to-end software model, allowing for the inclu-sion of an array of ampliﬁer designs and nonlinear optics techniques, which renders time- and frequency-resolved electromagnetic ﬁelds along-side essential physical characteristics on energy, ﬂuence, and spectral distribution. To demonstrate its robustness and real-world applicability– speciﬁcally, reverse engineering, system optimization, and inverse design– we present a case study on the LCLS-II photo-injector laser, representa-tive of a high-power and spectro-temporally complex CPA+NLO system.
Taylor dispersion in arbitrarily shaped axisymmetric channels

Abstract Advective dispersion of solutes in long thin axisymmetric channels is important to the analysis and design of a wide range of devices, including chemical separation systems and microfluidic chips. Despite extensive analysis of Taylor dispersion in various scenarios, most studies focus on long-term dispersion behaviour and cannot capture the transient evolution of the solute zone across the spatial variations in the channel. In the current study, we analyse the Taylor–Aris dispersion for arbitrarily shaped axisymmetric channels. We derive an expression for solute dynamics in terms of two coupled ordinary differential equations, which allow prediction of the time evolution of the mean location and axial (standard deviation) width of the solute zone as a function of the channel geometry. We compare and benchmark our predictions with Brownian dynamics simulations for a variety of cases, including linearly expanding/converging channels and periodic channels. We also present an analytical description of the physical regimes of transient positive versus negative axial growth of solute width. Finally, to further demonstrate the utility of the analysis, we demonstrate a method to engineer channel geometries to achieve desired solute width distributions over space and time. We apply the latter analysis to generate a geometry that results in a constant axial width and a second geometry that results in a sinusoidal axial variance in space.
Incremental Approximate Maximum Flow in m1/2+o(1) update time

We show an $(1+\epsilon)$-approximation algorithm for maintaining maximum $s$-$t$ flow under $m$ edge insertions in $m^{1/2+o(1)} \epsilon^{-1/2}$ amortized update time for directed, unweighted graphs. This constitutes the first sublinear dynamic maximum flow algorithm in general sparse graphs with arbitrarily good approximation guarantee.
Learning Adaptive Evolutionary Computation for Solving Multi-Objective Optimization Problems

Multi-objective evolutionary algorithms (MOEAs) are widely used to solve multi-objective optimization problems. The algorithms rely on setting appropriate parameters to find good solutions. However, this parameter tuning could be very computationally expensive in solving non-trial (combinatorial) optimization problems. This paper proposes a framework that integrates MOEAs with adaptive parameter control using Deep Reinforcement Learning (DRL). The DRL policy is trained to adaptively set the values that dictate the intensity and probability of mutation for solutions during optimization. We test the proposed approach with a simple benchmark problem and a real-world, complex warehouse design and control problem. The experimental results demonstrate the advantages of our method in terms of solution quality and computation time to reach good solutions. In addition, we show the learned policy is transferable, i.e., the policy trained on a simple benchmark problem can be directly applied to solve the complex warehouse optimization problem, effectively, without the need for retraining.
AligNeRF: High-Fidelity Neural Radiance Fields via Alignment-Aware Training

Neural Radiance Fields (NeRFs) are a powerful representation for modeling a 3D scene as a continuous function. Though NeRF is able to render complex 3D scenes with view-dependent effects, few efforts have been devoted to exploring its limits in a high-resolution setting. Specifically, existing NeRF-based methods face several limitations when reconstructing high-resolution real scenes, including a very large number of parameters, misaligned input data, and overly smooth details. In this work, we conduct the first pilot study on training NeRF with high-resolution data and propose the corresponding solutions: 1) marrying the multilayer perceptron (MLP) with convolutional layers which can encode more neighborhood information while reducing the total number of parameters; 2) a novel training strategy to address misalignment caused by moving objects or small camera calibration errors; and 3) a high-frequency aware loss. Our approach is nearly free without introducing obvious training/testing costs, while experiments on different datasets demonstrate that it can recover more high-frequency details compared with the current state-of-the-art NeRF models. Project page: https://yifanjiang19.github. io/alignerf.
van der Waals coupling in atomically doped carbon nanotubes

We have investigated atom-nanotube van der Waals (vdW) coupling in atomically doped carbon nanotubes (CNs). Our approach is based on the perturbation theory for degenerated atomic levels, thus accounting for both weak and strong atom-vacuum-field coupling. The vdW energy is described by an integral equation represented in terms of the local photonic density of states (DOS). By solving it numerically, we demonstrate the inapplicability of standard weak-coupling-based vdW interaction models in a close vicinity of the CN surface where the local photonic DOS effectively increases, giving rise to an atom-field coupling enhancement. An inside encapsulation of atoms into the CN has been shown to be energetically more favorable than their outside adsorption by the CN surface. If the atom is fixed outside the CN, the modulus of the vdW energy increases with the CN radius provided that the weak atom-field coupling regime is realized (i.e., far enough from the CN). For inside atomic position, the modulus of the vdW energy decreases with the CN radius, representing a general effect of the effective interaction area reduction with lowering the CN curvature.
TAX-Pose: Task-Specific Cross-Pose Estimation for Robot Manipulation

How do we imbue robots with the ability to efficiently manipulate unseen objects and transfer relevant skills based on demonstrations? End-to-end learning methods often fail to generalize to novel objects or unseen configurations. Instead, we focus on the task-specific pose relationship between relevant parts of interacting objects. We conjecture that this relationship is a generalizable notion of a manipulation task that can transfer to new objects in the same category; examples include the relationship between the pose of a pan relative to an oven or the pose of a mug relative to a mug rack. We call this task-specific pose relationship"cross-pose"and provide a mathematical definition of this concept. We propose a vision-based system that learns to estimate the cross-pose between two objects for a given manipulation task using learned cross-object correspondences. The estimated cross-pose is then used to guide a downstream motion planner to manipulate the objects into the desired pose relationship (placing a pan into the oven or the mug onto the mug rack). We demonstrate our method's capability to generalize to unseen objects, in some cases after training on only 10 demonstrations in the real world. Results show that our system achieves state-of-the-art performance in both simulated and real-world experiments across a number of tasks. Supplementary information and videos can be found at https://sites.google. com/view/tax-pose/home.