Miracle-dz/UniFAR-FTUs huggingface dataset:
Non-Fermi liquid phase and linear-in-temperature scattering rate in overdoped two-dimensional Hubbard model

Significance Most metals display an electron-scattering rate 1/τ that follows 1/τ∼T2 at low temperatures, as prescribed by Fermi liquid theory. But there are important exceptions. One of the most prominent examples is the “strange” metal regime in overdoped cuprate supercondcutors, which exhibits a linear T dependence of the scattering rate 1/τ∼T that reaches a putative Planckian limit. Here, using cutting-edge computational approaches, we show that T-linear scattering rate can emerge from the overdoped Hubbard model at low temperatures. Our results agree with cuprate experiments in various aspects but challenge the Planckian limit. Finally, by identifying antiferromagnetic fluctuations as the physical origin of the T-linear scattering rate, we discover the microscopic mechanism of strange metallicity in cuprates.
Model‐free portfolio theory: A rough path approach

Based on a rough path foundation, we develop a model‐free approach to stochastic portfolio theory (SPT). Our approach allows to handle significantly more general portfolios compared to previous model‐free approaches based on Föllmer integration. Without the assumption of any underlying probabilistic model, we prove a pathwise formula for the relative wealth process, which reduces in the special case of functionally generated portfolios to a pathwise version of the so‐called master formula of classical SPT. We show that the appropriately scaled asymptotic growth rate of a far reaching generalization of Cover's universal portfolio based on controlled paths coincides with that of the best retrospectively chosen portfolio within this class. We provide several novel results concerning rough integration, and highlight the advantages of the rough path approach by showing that (nonfunctionally generated) log‐optimal portfolios in an ergodic Itô diffusion setting have the same asymptotic growth rate as Cover's universal portfolio and the best retrospectively chosen one.
A Low-rank Approach to Image Defringing

In this work, we revisit the problem of interference fringe patterns in CCD chips occurring in near-infrared bands due to multiple light reflections within the chip. We briefly discuss the traditional approaches that were developed to remove these patterns from science images, and mention their limitations. We then introduce a new method to globally estimate the fringe patterns in a collection of science images without additional external data, allowing for some variation of the patterns between images. We demonstrate this new method on near-infrared images taken by the CFHT wide-field imager Megacam.
A Fully Integrated 5-mW, 0.8-Gbps Energy-Efficient Chip-to-Chip Data Link for Ultralow-Power IoT End-Nodes in 65-nm CMOS

The increasing complexity of Internet-of-Things (IoT) applications and near-sensor processing algorithms is pushing the computational power of low-power, battery-operated end-node systems. This trend also reveals growing demands for high-speed and energy-efficient inter-chip communications to manage the increasing amount of data coming from off-chip sensors and memories. While traditional microcontroller interfaces such as SPIs cannot cope with tight energy and large bandwidth requirements, low-voltage swing transceivers can tackle this challenge, thanks to their capability to achieve several Gbps of the communication speed at milliwatt power levels. However, recent research on high-speed serial links focused on high-performance systems, with a power consumption significantly larger than the one of low-power IoT end-nodes, or on stand-alone designs not integrated at a system level. This article presents a low-swing transceiver for the energy-efficient and low-power chip-to-chip communication fully integrated within an IoT end-node system-on-chip, fabricated in CMOS 65-nm technology. The transceiver can be easily controlled via a software interface; thus, we can consider realistic scenarios for the data communication, which cannot be assessed in stand-alone prototypes. Chip measurements show that the transceiver achieves $8.46\times $ higher energy efficiency at $15.9\times $ higher performance than a traditional microcontroller interface such as a single-SPI.
Primal and dual conic representable sets: a fresh view on multiparametric analysis ∗

This paper introduces the concepts of the primal and dual conic (linear inequality) representable sets and applies them to explore a novel kind of duality in multiparametric conic linear optimization. Such a kind of duality may be described by the set-valued mappings between the primal and dual conic representable sets, which allows us to generalize as well as treat previous results for mulitparametric analysis in a uniﬁed framework. We then discuss the behaviour of the optimal partition of a conic representable set and in-vestigate the multiparametric analysis of conic linear optimization problems. This leads to the invariant region decomposition of a conic representable set that is more general than the known results in the literatures. We also study the properties of the optimal objective values as a function of that parametric vectors. All results are corroborated by examples having correlation.
Tripartite Wigner’s friend scenario and its test

Wigner’s friend thought experiment is intended to reveal the inherent tension between unitary evolution and measurement collapse. Inspired by the Wigner’s friend experiment, Brukner derives a no-go theorem for observer-independent facts, focusing on two entangled observers in their respective laboratories. We construct an extended Wigner’s friend scenario, including three laboratories, namely, Alice’s laboratory, Bob’s laboratory and Charlie’s laboratory, where Alice, Bob and Charlie are standing outside the laboratories while their friends are placed inside their own laboratories. To realize this scenario, we create a universal quantum circuit, especially the circuit realization of the state evolution and measurement inside each laboratory, simple but novel. We carry out experiments on the IBM quantum computers to test the primary circuits, i.e. state preparation, state evolution and measurement inside each laboratory. Furthermore, we perform the simulation of the quantum circuit via Q# quantum programming on a classical computer.
Relative pressure functions and their equilibrium states

Abstract For a subshift $(X, \sigma _{X})$ and a subadditive sequence ${\mathcal F}=\{\log f_{n}\}_{n=1}^{\infty }$ on X, we study equivalent conditions for the existence of $h\in C(X)$ such that $\lim _{n\rightarrow \infty }(1/{n})\int \log f_{n}\, d\kern-1pt\mu =\int h \,d\kern-1pt\mu $ for every invariant measure $\mu $ on X. For this purpose, we first we study necessary and sufficient conditions for ${\mathcal F}$ to be an asymptotically additive sequence in terms of certain properties for periodic points. For a factor map $\pi : X\rightarrow Y$ , where $(X, \sigma _{X})$ is an irreducible shift of finite type and $(Y, \sigma _{Y})$ is a subshift, applying our results and the results obtained by Cuneo [Additive, almost additive and asymptotically additive potential sequences are equivalent. Comm. Math. Phys. 37 (3) (2020), 2579–2595] on asymptotically additive sequences, we study the existence of h with regard to a subadditive sequence associated to a relative pressure function. This leads to a characterization of the existence of a certain type of continuous compensation function for a factor map between subshifts. As an application, we study the projection $\pi \mu $ of an invariant weak Gibbs measure $\mu $ for a continuous function on an irreducible shift of finite type.
On Faster Convergence of Scaled Sign Gradient Descent

Communication has been seen as a significant bottleneck in industrial applications over large-scale networks. To alleviate the communication burden, sign-based optimization algorithms have gained popularity recently in both industrial and academic communities, which is shown to be closely related to adaptive gradient methods, such as Adam. Along this line, this article investigates faster convergence for a variant of sign-based gradient descent, called scaled <sc>sign</sc>GD, in three cases: First, the objective function is strongly convex; second, the objective function is nonconvex but satisfies the Polyak–Łojasiewicz inequality; third the gradient is stochastic, called scaled <sc>sign</sc>SGD in this case. For the first two cases, it can be shown that the scaled <sc>sign</sc>GD converges at a linear rate. For case third, the algorithm is shown to converge linearly to a neighborhood of the optimal value when a constant learning rate is employed, and the algorithm converges at a rate of <inline-formula><tex-math notation="LaTeX">$O(1/k+1/k^{2}+1/k^{3})$</tex-math></inline-formula> when using a diminishing learning rate, where <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> is the iteration number. The results are also extended to the distributed setting by majority vote in a parameter-server framework. Finally, numerical experiments are performed to corroborate the theoretical findings.
Physically Motivated Fit to Mass Surface Density Profiles Observed in Galaxies

Polytropes have gained renewed interest because they account for several seemingly disconnected observational properties of galaxies. Here we study whether polytropes are also able to explain the stellar mass distribution within galaxies. We develop a code to fit surface density profiles using polytropes projected in the plane of the sky (propols). Sérsic profiles are known to be good proxies for the global shapes of galaxies and we find that, ignoring central cores, propols, and Sérsic profiles are indistinguishable within observational errors (within 5% over five orders of magnitude in surface density). The range of physically meaningful polytropes yields Sérsic indexes between 0.4 and 6. The code has been systematically applied to ∼750 galaxies with carefully measured mass density profiles and including all morphological types and stellar masses ( 7<log[M⋆/M⊙]<12 ). The propol fits are systematically better than Sérsic profiles when log(M⋆/M⊙)≲9 and systematically worse when log(M⋆/M⊙)≳10 . Although with large scatter, the observed polytropic indexes increase with increasing mass and tend to cluster around m = 5. For the most massive galaxies, propols are very good at reproducing their central parts, but they do not handle well cores and outskirts overall. Polytropes are self-gravitating systems in thermal meta-equilibrium as defined by the Tsallis entropy. Thus, the above results are compatible with the principle of maximum Tsallis entropy dictating the internal structure in dwarf galaxies and in the central region of massive galaxies.
Origin of charge density wave in the layered kagome metal 
CsV3Sb5

Using first-principles calculations, we identify the origin of the observed charge density wave (CDW) formation in a layered kagome metal CsV3Sb5. It is revealed that the structural distortion of kagome lattice forming the trimeric and hexameric V atoms is accompanied by the stabilization of quasimolecular states, which gives rise to the opening of CDW gaps for the V-derived multibands lying around the Fermi level. This Jahn-Tellerlike instability having the local lattice distortion and its derived quasimolecular states is a driving force of the CDW order. Specifically, the saddle points of multiple Dirac bands near the Fermi level, located at the M point, are hybridized to disappear along the kz direction, therefore not supporting the widely accepted Peierls-like electronic instability due to Fermi surface nesting. It is further demonstrated that applied hydrostatic pressure significantly reduces the interlayer spacing to destabilize the quasimolecular states, leading to a disappearance of the CDW phase at a pressure of ∼2 GPa. The presently proposed underlying mechanism of the CDW order in CsV3Sb5 can also be applicable to other isostructural kagome lattices such as KV3Sb5 and RbV3Sb5.
On Empirical Risk Minimization with Dependent and Heavy-Tailed Data

In this work, we establish risk bounds for the Empirical Risk Minimization (ERM) with both dependent and heavy-tailed data-generating processes. We do so by extending the seminal works of Mendelson [Men15, Men18] on the analysis of ERM with heavy-tailed but independent and identically distributed observations, to the strictly stationary exponentially $\beta$-mixing case. Our analysis is based on explicitly controlling the multiplier process arising from the interaction between the noise and the function evaluations on inputs. It allows for the interaction to be even polynomially heavy-tailed, which covers a significantly large class of heavy-tailed models beyond what is analyzed in the learning theory literature. We illustrate our results by deriving rates of convergence for the high-dimensional linear regression problem with dependent and heavy-tailed data.
EPick: Multi-Class Attention-based U-shaped Neural Network for Earthquake Detection and Seismic Phase Picking

Earthquake detection and seismic phase picking not only play a crucial role in travel time estimation of body waves(P and S waves) but also in the localisation of the epicenter of the corresponding event. Generally, manual phase picking is a trustworthy and the optimum method to determine the phase arrival time, however, its capacity is restricted by available resources and time. Moreover, noisy seismic data renders an additional critical challenge for fast and accurate phase picking. In this study, a deep learning based model, EPick, is proposed which benefits both from U shaped neural network (also called UNet)and attention mechanism, as a strong alternative for seismic event detection and phase picking. On one hand, the utilization of UNet structure enables addressing different levels of deep features. On the other hand, attention mechanism promotes the decoder in the UNet structure to focus on the efficient exploitation of the low-resolution features learned from the encoder part to achieve precise phase picking. Extensive experimental results demonstrate that EPick achieves better performance over the benchmark method, and show the models robustness when tested on a different seismic dataset.
Dark matter admixed neutron star as a possible compact component in the GW190814 merger event

We put constraints on the secondary component of GW190814 by analyzing the observational data of the event. The relativistic mean-field models are used to calculate the mass-radius profile and tidal deformability of the compact object, considering it as a massive neutron star with the presence of dark matter particles inside it. With the increase of dark matter percentage, the maximum mass, radius, and tidal deformability of the neutron star decreases. We observe that the predicted properties are well consistent with GW190814 observational data, suggesting the possibility of a dark matter admixed neutron star if the underlying nuclear equation of state is sufficiently stiff.
Learning Object-Compositional Neural Radiance Field for Editable Scene Rendering

Implicit neural rendering techniques have shown promising results for novel view synthesis. However, existing methods usually encode the entire scene as a whole, which is generally not aware of the object identity and limits the ability to the high-level editing tasks such as moving or adding furniture. In this paper, we present a novel neural scene rendering system, which learns an object-compositional neural radiance field and produces realistic rendering with editing capability for a clustered and real-world scene. Specifically, we design a novel two-pathway architecture, in which the scene branch encodes the scene geometry and appearance, and the object branch encodes each standalone object conditioned on learnable object activation codes. To survive the training in heavily cluttered scenes, we propose a scene-guided training strategy to solve the 3D space ambiguity in the occluded regions and learn sharp boundaries for each object. Extensive experiments demonstrate that our system not only achieves competitive performance for static scene novel-view synthesis, but also produces realistic rendering for object-level editing.
Crowd-Aware Robot Navigation for Pedestrians with Multiple Collision Avoidance Strategies via Map-based Deep Reinforcement Learning

It is challenging for a mobile robot to navigate through human crowds. Existing approaches usually assume that pedestrians follow a predefined collision avoidance strategy, like social force model (SFM) or optimal reciprocal collision avoidance (ORCA). However, their performances commonly need to be further improved for practical applications, where pedestrians follow multiple different collision avoidance strategies. In this paper, we propose a map-based deep reinforcement learning approach for crowd-aware robot navigation with various pedestrians. We use the sensor map to represent the environmental information around the robot, including its shape and observable appearances of obstacles. We also introduce the pedestrian map that specifies the movements of pedestrians around the robot. By applying both maps as inputs of the neural network, we show that a navigation policy can be trained to better interact with pedestrians following different collision avoidance strategies. We evaluate our approach under multiple scenarios both in the simulator and on an actual robot. The results show that our approach allows the robot to successfully interact with various pedestrians and outperforms compared methods in terms of the success rate.
Non-Markovian Quantum Thermometry

The rapidly developing quantum technologies and thermodynamics have put forward a require-ment to precisely control and measure the temperature of microscopic matter at the quantum level. Many quantum thermometry schemes have been proposed. However, precisely measuring low temperature is still challenging because the obtained sensing errors generally tend to diverge with decreasing temperature. Using a continuous-variable system as a thermometer, we propose non-Markovian quantum thermometry to measure the temperature of a quantum reservoir. A mechanism to make the sensing error δT scale with the temperature T as the Landau bound δT (cid:39) T in the full-temperature regime is discovered. Our analysis reveals that it is the quantum criticality of the total thermometer-reservoir system that causes this enhanced sensitivity. Eﬃciently avoiding the error-divergence problem, our result gives an eﬃcient way to precisely measure the low temperature of quantum systems.
Bayesian Estimation of the Degrees of Freedom Parameter of the Student-$t$ Distribution---A Beneficial Re-parameterization

In this paper, conditional data augmentation (DA) is investigated for the degrees of freedom parameter ν of a Student-t distribution. Based on a restricted version of the expected augmented Fisher information, it is conjectured that the ancillarity DA is progressively more efficient for MCMC estimation than the sufficiency DA as ν increases; with the break even point lying at as low as ν ≈ 4. The claim is examined further and generalized through a large simulation study and a application to U.S. macroeconomic time series. Finally, the ancillarity-sufficiency interweaving strategy is empirically shown to combine the benefits of both DAs. The proposed algorithm may set a new standard for estimating ν as part of any model.
Galaxy-scale Test of General Relativity with Strong Gravitational Lensing

Although general relativity (GR) has been precisely tested at the solar system scale, precise tests at a galactic or cosmological scale are still relatively insufficient. Here, in order to test GR at the galactic scale, we use the newly compiled galaxy-scale strong gravitational lensing (SGL) sample to constrain the parameter γ PPN in the parameterized post-Newtonian (PPN) formalism. We employ the Pantheon sample of Type Ia supernova observations to calibrate the distances in the SGL systems using the Gaussian Process method, which avoids the logical problem caused by assuming a cosmological model within GR to determine the distances in the SGL sample. Furthermore, we consider three typical lens models in this work to investigate the influences of the lens-mass distributions on the fitting results. We find that the choice of lens models has a significant impact on the constraints on the PPN parameter γ PPN. By using a minimum χ 2 comparison and the Bayesian information criterion as evaluation tools to make comparisons for the fitting results of the three lens models, we find that the most reliable lens model gives the result of γPPN=1.065−0.074+0.064 , which is in good agreement with the prediction of γ PPN = 1 by GR. As far as we know, our 6.4% constraint result is the best result so far among recent works using the SGL method.
Assessing the Use Cases of Persistent Memory in High-Performance Scientific Computing

As the High Performance Computing (HPC) world moves towards the Exa-Scale era, huge amounts of data should be analyzed, manipulated and stored. In the traditional stor-age/memory hierarchy, each compute node retains its data objects in its local volatile DRAM. Whenever the DRAM's capacity becomes insufficient for storing this data, the computation should either be distributed between several compute nodes, or some portion of these data objects must be stored in a non-volatile block device such as a hard disk drive (HDD) or an SSD storage device. These standard block devices offer large and relatively cheap non-volatile storage, but their access times are orders-of-magnitude slower than those of DRAM. Optane™ DataCenter Persistent Memory Module (DCPMM) [1], a new technology introduced by Intel, provides non-volatile memory that can be plugged into standard memory bus slots (DDR DIMMs) and therefore be accessed much faster than standard storage devices. In this work, we present and analyze the results of a comprehensive performance assessment of several ways in which DCPMM can 1) replace standard storage devices, and 2) replace or augment DRAM for improving the performance of HPC scientific computations. To achieve this goal, we have configured an HPC system such that DCPMM can service I/O operations of scientific applications, replace standard storage devices and file systems (specifically for diagnostics and checkpoint-restarting), and serve for expanding applications' main memory. We focus on keeping the scientific codes with as few changes as possible, while allowing them to access the NVM transparently as if they access persistent storage. Our results show that DCPMM allows scientific applications to fully utilize nodes' locality by providing them with sufficiently-large main memory. Moreover, it can also be used for providing a high-performance replacement for persistent storage. Thus, the usage of DCPMM has the potential of replacing standard HDD and SSD storage devices in HPC architectures and enabling a more efficient platform for modern supercomputing annlications. The source code used by this work, as well as the benchmarks and other relevant sources, are available at: https://github.com/Scientific-Computing-Lab-NRCN/StoringStorage.
Quantum master equations and steady states for the ultrastrong-coupling limit and the strong-decoherence limit

In the framework of theory of open quantum systems, we derive quantum master equations for the ultrastrong system-bath coupling regime and, more generally, the strong-decoherence regime. In this regime, the strong decoherence is complemented by slow relaxation processes. We use a generalization of the Förster and modified Redfield peturbation theories known in theory of excitation energy transfer. Also, we show that the mean force Gibbs state in the corresponding limits are stationary for the derived master equations.