Miracle-dz/UniFAR-FTUs huggingface dataset:
S3NAS: Fast NPU-aware Neural Architecture Search Methodology

As the application area of convolutional neural networks (CNN) is growing in embedded devices, it becomes popular to use a hardware CNN accelerator, called neural processing unit (NPU), to achieve higher performance per watt than CPUs or GPUs. Recently, automated neural architecture search (NAS) emerges as the default technique to find a state-of-the-art CNN architecture with higher accuracy than manually-designed architectures for image classification. In this paper, we present a fast NPU-aware NAS methodology, called S3NAS, to find a CNN architecture with higher accuracy than the existing ones under a given latency constraint. It consists of three steps: supernet design, Single-Path NAS for fast architecture exploration, and scaling. To widen the search space of the supernet structure that consists of stages, we allow stages to have a different number of blocks and blocks to have parallel layers of different kernel sizes. For a fast neural architecture search, we apply a modified Single-Path NAS technique to the proposed supernet structure. In this step, we assume a shorter latency constraint than the required to reduce the search space and the search time. The last step is to scale up the network maximally within the latency constraint. For accurate latency estimation, an analytical latency estimator is devised, based on a cycle-level NPU simulator that runs an entire CNN considering the memory access overhead accurately. With the proposed methodology, we are able to find a network in 3 hours using TPUv3, which shows 82.72% top-1 accuracy on ImageNet with 11.66 ms latency. Code are released at this https URL
Approximation of stochastic integrals with jumps in weighted bounded mean oscillation spaces

This article investigates discrete-time approximation methods of stochastic integrals driven by semimartingales with jumps. The error process is measured with two weighted bounded mean oscillation norms (which coincide in the case of no jumps) and lead especially to $L_p$-estimates. Besides, this approach also allows a change of the underlying measure which leaves the error estimates unchanged if the change of measure satisfies a reserve Holder inequality. We propose a new approximation scheme and discuss a way to optimise the approximation rate by adapting the discretisation times to the setting, especially to the jump behavior of the considered semimartingale. The research was inspired by Mathematical Finance: We apply the methods in the special case where the semimartingale is an exponential Levy process to mean variance hedging of European type options. To do this, an explicit representation for the hedging strategy is shown under a general condition using Malliavin calculus. The results reveal the interplay between properties of the Levy measure, the regularity of the pay-off function and the approximation rate.
Variational Deep Learning for the Identification and Reconstruction of Chaotic and Stochastic Dynamical Systems from Noisy and Partial Observations

The data-driven recovery of the unknown governing equations of dynamical systems has recently received an increasing interest. However, the identification of the governing equations remains challenging when dealing with noisy and partial observations. Here, we address this challenge and investigate variational deep learning schemes. Within the proposed framework, we jointly learn an inference model to reconstruct the true states of the system from series of noisy and partial data and the governing equations of these states. In doing so, this framework bridges classical data assimilation and state-of-the-art machine learning techniques and we show that it generalizes state-of-the-art methods. Importantly, both the inference model and the governing equations embed stochastic components to account for stochastic variabilities, model errors and reconstruction uncertainties. Various experiments on chaotic and stochastic dynamical systems support the relevance of our scheme w.r. t. state-of-the-art approaches.
Demonstration of Quantum Brachistochrones between Distant States of an Atom

Transforming an initial quantum state into a target state through the fastest possible route---a quantum brachistochrone---is a fundamental challenge for many technologies based on quantum mechanics. Here, we demonstrate fast coherent transport of an atomic wave packet over a distance of 15 times its size---a paradigmatic case of quantum processes where the target state cannot be reached through a local transformation. Our measurements of the transport fidelity reveal the existence of a minimum duration---a quantum speed limit---for the coherent splitting and recombination of matter waves. We obtain physical insight into this limit by relying on a geometric interpretation of quantum state dynamics. These results shed light upon a fundamental limit of quantum state dynamics and are expected to find relevant applications in quantum sensing and quantum computing.
Molecular Gas Properties on Cloud Scales across the Local Star-forming Galaxy Population

Using the PHANGS–ALMA CO(2–1) survey, we characterize molecular gas properties on ∼100 pc scales across 102,778 independent sightlines in 70 nearby galaxies. This yields the best synthetic view of molecular gas properties on cloud scales across the local star-forming galaxy population obtained to date. Consistent with previous studies, we observe a wide range of molecular gas surface densities (3.4 dex), velocity dispersions (1.7 dex), and turbulent pressures (6.5 dex) across the galaxies in our sample. Under simplifying assumptions about subresolution gas structure, the inferred virial parameters suggest that the kinetic energy of the molecular gas typically exceeds its self-gravitational binding energy at ∼100 pc scales by a modest factor (1.3 on average). We find that the cloud-scale surface density, velocity dispersion, and turbulent pressure (1) increase toward the inner parts of galaxies, (2) are exceptionally high in the centers of barred galaxies (where the gas also appears less gravitationally bound), and (3) are moderately higher in spiral arms than in inter-arm regions. The galaxy-wide averages of these gas properties also correlate with the integrated stellar mass, star formation rate, and offset from the star-forming main sequence of the host galaxies. These correlations persist even when we exclude regions with extraordinary gas properties in galaxy centers, which contribute significantly to the inter-galaxy variations. Our results provide key empirical constraints on the physical link between molecular cloud populations and their galactic environment.
Silent Speech Interfaces for Speech Restoration: A Review

This review summarises the status of silent speech interface (SSI) research. SSIs rely on non-acoustic biosignals generated by the human body during speech production to enable communication whenever normal verbal communication is not possible or not desirable. In this review, we focus on the first case and present latest SSI research aimed at providing new alternative and augmentative communication methods for persons with severe speech disorders. SSIs can employ a variety of biosignals to enable silent communication, such as electrophysiological recordings of neural activity, electromyographic (EMG) recordings of vocal tract movements or the direct tracking of articulator movements using imaging techniques. Depending on the disorder, some sensing techniques may be better suited than others to capture speech-related information. For instance, EMG and imaging techniques are well suited for laryngectomised patients, whose vocal tract remains almost intact but are unable to speak after the removal of the vocal folds, but fail for severely paralysed individuals. From the biosignals, SSIs decode the intended message, using automatic speech recognition or speech synthesis algorithms. Despite considerable advances in recent years, most present-day SSIs have only been validated in laboratory settings for healthy users. Thus, as discussed in this paper, a number of challenges remain to be addressed in future research before SSIs can be promoted to real-world applications. If these issues can be addressed successfully, future SSIs will improve the lives of persons with severe speech impairments by restoring their communication capabilities.
Naive Artificial Intelligence

In the cognitive sciences, it is common to distinguish between crystal intelligence, the ability to utilize knowledge acquired through past learning or experience and fluid intelligence, the ability to solve novel problems without relying on prior knowledge. Using this cognitive distinction between the two types of intelligence, extensively-trained deep networks that can play chess or Go exhibit crystal but not fluid intelligence. In humans, fluid intelligence is typically studied and quantified using intelligence tests. Previous studies have shown that deep networks can solve some forms of intelligence tests, but only after extensive training. Here we present a computational model that solves intelligence tests without any prior training. This ability is based on continual inductive reasoning, and is implemented by deep unsupervised latent-prediction networks. Our work demonstrates the potential fluid intelligence of deep networks. Finally, we propose that the computational principles underlying our approach can be used to model fluid intelligence in the cognitive sciences.
X-ray Spectral Evolution of High Energy Peaked Blazars

The synchrotron hump of the high energy peaked blazars generally lies in the 0.1–10 keV range and such sources show extreme flux and spectral variability in X-ray bands. Various spectral studies showed that the X-ray spectra of high energy peaked blazars are curved and better described by the log-parabolic model. The curvature is attributed to the energy dependent statistical acceleration mechanism. In this work, we review the X-ray spectral studies of high energy peaked blazars. It is found that the log-parabolic model well describes the spectra in a wide energy interval around the peak. The log-parabolic model provides the possibility of investigating the correlation between the spectral parameters derived from it. Therefore, we compiled the studies of correlations between the various parameters derived from the log-parabolic model and their implications to describe the variability mechanism of blazars.
The Galactic cosmic ray intensity at the evolving Earth and young exoplanets

<p>Cosmic rays may have contributed to the start of life on Earth. Cosmic rays also influence and contribute to atmospheric electrical circuits, cloud cover and biological mutation rates which are important for the characterisation of exoplanetary systems. The flux of Galactic cosmic rays present at the time when life is thought to have begun on the young Earth or in other young exoplanetary systems is largely determined by the properties of the stellar wind. &#160;</p> <p>The spectrum of Galactic cosmic rays that we observe at Earth is modulated, or suppressed, by the magnetised solar wind and thus differs from the local interstellar spectrum observed by Voyager 1 and 2 outside of the solar system. Upon reaching 1au, Galactic cosmic rays subsequently interact with the Earth&#8217;s magnetosphere and some of their energy is deposited in the upper atmosphere. The properties of the solar wind, such as the magnetic field strength and velocity profile, evolve with time. Generally, young solar-type stars are very magnetically active and are therefore thought to drive stronger stellar winds. &#160;</p> <p>Here I will present our recent results which simulate the propagation of Galactic cosmic rays through the heliosphere to the location of Earth as a function of the Sun's life, from 600 Myr to 6 Gyr, in the Sun&#8217;s future. I will specifically focus on the flux of Galactic cosmic rays present at the time when life is thought to have started on Earth (~1 Gyr). I will show that the intensity of Galactic cosmic rays which reached the young Earth, by interacting with the solar wind, would have been greatly reduced in comparison to the present day intensity. I will also discuss the effect that the Sun being a slow/fast rotator would have had on the flux of cosmic rays reaching Earth at early times in the solar system's life. </p> <p>Despite the importance of Galactic cosmic rays, their chemical signature in the atmospheres&#8217; of young Earth-like exoplanets may not be observable with instruments in the near future. On the other hand, it may instead be possible to detect their chemical signature by observing young warm Jupiters. Thus, I will also discuss the HR 2562b exoplanetary system as a candidate for observing the chemical signature of Galactic cosmic rays in a young exoplanetary atmosphere with upcoming missions such as JWST. </p>
Three years of HARPS-N high-resolution spectroscopy and precise radial velocity data for the Sun

Context. The solar telescope connected to HARPS-N has been observing the Sun since the summer of 2015. Such a high-cadence, long-baseline data set is crucial for understanding spurious radial-velocity signals induced by our Sun and by the instrument. On the instrumental side, this data set allowed us to detect sub- m s−1 systematics that needed to be corrected for. Aims. The goals of this manuscript are to (i) present a new data reduction software for HARPS-N, (ii) demonstrate the improvement brought by this new software during the first three years of the HARPS-N solar data set, and (iii) release all the obtained solar products, from extracted spectra to precise radial velocities. Methods. To correct for the instrumental systematics observed in the data reduced with the current version of the HARPS-N data reduction software (DRS version 3.7), we adapted the newly available ESPRESSO DRS (version 2.2.3) to HARPS-N and developed new optimised recipes for the spectrograph. We then compared the first three years of HARPS-N solar data reduced with the current and new DRS. Results. The most significant improvement brought by the new DRS is a strong decrease in the day-to-day radial-velocity scatter, from 1.27 to 1.07 m s−1; this is thanks to a more robust method to derive wavelength solutions, but also to the use of calibrations closer in time. The newly derived solar radial-velocities are also better correlated with the chromospheric activity level of the Sun in the long term, with a Pearson correlation coefficient of 0.93 compared to 0.77 before, which is expected from our understanding of stellar signals. Finally, we also discuss how HARPS-N spectral ghosts contaminate the measurement of the calcium activity index, and we present an efficient technique to derive an index free of instrumental systematics. Conclusions. This paper presents a new data reduction software for HARPS-N and demonstrates its improvements, mainly in terms of radial-velocity precision, when applied to the first three years of the HARPS-N solar data set. Those newly reduced solar data, representing an unprecedented time series of 34 550 high-resolution spectra and precise radial velocities, are released alongside this paper. Those data are crucial to understand stellar activity signals in solar-type stars further and develop the mitigating techniques that will allow us to detect other Earths.
Robustness of helical hinge states of weak second-order topological insulators

Robustness of helical hinge states of three-dimensional weak second-order topological i sulators (WSOTIs) against disorders is studied. The pure WSOTI is obtained from a weak $\mathbb{Z}_2$ first-order topological insulator through a surface band inversion. Both bulk states and surface states in the WSOTI are gapped, and in-gap valley-momentum locked helical hinge states are topologically protected by the surface valley-Chern number. In the presence of weak disorders, helical hinge states are robust against disorders while the quantized conductance of the states is fragile due to the inter-valley scattering. As disorder increases, the system undergoes a series of quantum phase transitions: from the WSOTI to the weak first-order topological insulator, then to a diffusive metal and finally to an Anderson insulator. Our results thus fully establish the WSOTI phase as a genuine state of matters and open a door for the second-order valleytronics that allows one to control the valley degree of freedom through helical hinge states.
Regression analysis for covariate‐adaptive randomization: A robust and efficient inference perspective

Linear regression is arguably the most fundamental statistical model; however, the validity of its use in randomized clinical trials, despite being common practice, has never been crystal clear, particularly when stratified or covariate‐adaptive randomization is used. In this article, we investigate several of the most intuitive and commonly used regression models for estimating and inferring the treatment effect in randomized clinical trials. By allowing the regression model to be arbitrarily misspecified, we demonstrate that all these regression‐based estimators robustly estimate the treatment effect, albeit with possibly different efficiency. We also propose consistent non‐parametric variance estimators and compare their performances to those of the model‐based variance estimators that are readily available in standard statistical software. Based on the results and taking into account both theoretical efficiency and practical feasibility, we make recommendations for the effective use of regression under various scenarios. For equal allocation, it suffices to use the regression adjustment for the stratum covariates and additional baseline covariates, if available, with the usual ordinary‐least‐squares variance estimator. For unequal allocation, regression with treatment‐by‐covariate interactions should be used, together with our proposed variance estimators. These recommendations apply to simple and stratified randomization, and minimization, among others. We hope this work helps to clarify and promote the usage of regression in randomized clinical trials.
PolSIRD: Modeling Epidemic Spread under Intervention Policies and an Application to the Spread of COVID-19

Epidemic spread in a population is traditionally modeled via compartmentalized models which represent the free evolution of disease in absence of any intervention policies. In addition, these models assume full observability and do not account for under-reporting of cases. We present a mathematical model, namely PolSIRD, which accounts for the under-reporting by introducing an observation mechanism. It also captures the effects of intervention policies on the disease spread parameters by leveraging intervention policy data along with the reported disease cases. Furthermore, we allow our recurrent model to learn the initial hidden state of all compartments end-to-end along with other parameters via gradient-based training. We apply our model to spread of the recent global outbreak of COVID-19 where our model outperforms the current methods employed by the CDC in most of the metrics. We also provide actionable guidance on the lifting of intervention policies via counterfactual simulations from our model.
Super-resolution optical fluctuation imaging—fundamental estimation theory perspective

We provide a rigorous quantitative analysis of super-resolution imaging techniques which exploit temporal fluctuations of luminosity of the sources in order to beat the Rayleigh limit. We define an operationally justified resolution gain figure of merit, that allows us to connect the estimation theory concepts with the ones typically used in the imaging community, and derive fundamental resolution bounds that scale at most as the fourth-root of the mean luminosity of the sources. We fine-tune and benchmark the performance of state-of-the-art methods, focusing on the cumulant based image processing techniques, taking into account the impact of limited photon number and sampling time.
FnF-BFT: Exploring Performance Limits of BFT Protocols

We introduce FnF-BFT, a parallel-leader byzantine fault-tolerant state-machine replication protocol for the partially synchronous model with theoretical performance bounds during synchrony. By allowing all replicas to act as leaders and propose requests independently, FnF-BFT parallelizes the execution of requests. Leader parallelization distributes the load over the entire network -- increasing throughput by overcoming the single-leader bottleneck. We further use historical data to ensure that well-performing replicas are in command. FnF-BFT's communication complexity is linear in the number of replicas during synchrony and thus competitive with state-of-the-art protocols. Finally, with FnF-BFT, we introduce a BFT protocol with performance guarantees in stable network conditions under truly byzantine attacks.
A Generalization of Stiebitz-Type Results on Graph Decomposition

In this paper, we consider the decomposition of multigraphs under minimum degree constraints and give a unified generalization of several results by various researchers. Let $G$ be a multigraph in which no quadrilaterals share edges with triangles and other quadrilaterals and let $\mu_G(v)=\max\{\mu_G(u,v):u\in V(G)\setminus\{v\}\}$, where $\mu_G(u,v)$ is the number of edges joining $u$ and $v$ in $G$. We show that for any two functions $a,b:V(G)\rightarrow\mathbb{N}\setminus\{0,1\}$, if $d_G(v)\ge a(v)+b(v)+2\mu_G(v)-3$ for each $v\in V(G)$, then there is a partition $(X,Y)$ of $V(G)$ such that $d_X(x)\geq a(x)$ for each $x\in X$ and $d_Y(y)\geq b(y)$ for each $y\in Y$. This extends the related results due to Diwan, Liu–Xu and Ma–Yang on simple graphs to the multigraph setting.
Limit groups over coherent right-angled Artin groups

A new class of groups $\mathcal{C}$, containing all coherent RAAGs and all toral relatively hyperbolic groups, is defined. It is shown that, for a group $G$ in the class $\mathcal{C}$, the $\mathbb{Z}[t]$-exponential group $G^{\mathbb{Z}[t]}$ may be constructed as an iterated centraliser extension. Using this fact, it is proved that $G^{\mathbb{Z}[t]}$ is fully residually $G$ (i.e. it has the same universal theory as $G$) and so its finitely generated subgroups are limit groups over $G$. If $\mathbb{G}$ is a coherent RAAG, then the converse also holds - any limit group over $\mathbb{G}$ embeds into $\mathbb{G}^{\mathbb{Z}[t]}$. Moreover, it is proved that limit groups over $\mathbb{G}$ are finitely presented, coherent and CAT$(0)$, so in particular have solvable word and conjugacy problems.
Evidence for and Analysis of Multiple Hidden Coronal Strands in Cross-sectional Emission Profiles: Further Results from NASA’s High-resolution Solar Coronal Imager

Previous work utilizing NASA’s High-resolution Coronal Imager (Hi-C 2.1) 172 Å observations revealed that, even at the increased spatial scales available in the dataset, there may be evidence for coronal structures that are still not fully resolved. In this follow-up study, cross-section slices of coronal strands are taken across the Hi-C 2.1 field of view. Following previous loop-width studies, the background emission is removed to isolate the coronal strands. The resulting intensity variations are reproduced by simultaneously fitting multiple Gaussian profiles using a nonlinear least-squares curve-fitting method. In total, 183 Gaussian profiles are examined for possible structures that are hinted at in the data. The full width at half maximum is determined for each Gaussian, which are then collated and analyzed. The most frequent structural widths are ≈450–575 km with 47% of the strand widths beneath NASA’s Solar Dynamics Observatory Atmospheric Imaging Assembly (AIA) resolving scale (600–1000 km). Only 17% reside beneath an AIA pixel width (435 km) with just 6% of the strands at the Hi-C 2.1 resolving scale (≈220–340 km). These results suggest that non-Gaussian shaped cross-sectional emission profiles observed by Hi-C 2.1 are the result of multiple strands along the integrated line of sight that can be resolved, rather than being the result of even finer sub-resolution elements.