Miracle-dz/UniFAR-FTUs huggingface dataset:
Non-linear Eulerian hydrodynamics of dark energy: Riemann problem and finite volume schemes

Upcoming large-scale structure surveys can shed new light on the properties of dark energy. In particular, if dark energy is a dynamical component, it must have spatial perturbations. Their behaviour is regulated by the speed of sound parameter, which is currently unconstrained. In this work, we present the numerical methods that will allow to perform cosmological simulations of inhomogeneous dark energy scenarios where the speed of sound is small and non-vanishing. We treat the dark energy component as an effective fluid and build upon established numerical methods for hydrodynamics to construct a numerical solution of the effective continuity and Euler equations. In particular, we develop conservative finite volume schemes that rely on the solution of the Riemann problem, which we provide here in both exact and approximate forms for the case of a dark energy fluid.
Beamforming approaches towards detecting the 21-cm global signal from Cosmic Dawn with radio array telescopes

Abstract The formation of the first stars and galaxies during ‘Cosmic Dawn’ is thought to have imparted a faint signal onto the 21-cm spin temperature from atomic Hydrogen gas in the early Universe. Observationally, an absorption feature should be measurable as a frequency dependence in the sky-averaged (i.e. global) temperature at meter wavelengths. This signal should be separable from the smooth—but orders of magnitude brighter—foregrounds by jointly fitting a log-polynomial and absorption trough to radiometer spectra. A majority of approaches to measure the global 21-cm signal use radiometer systems on dipole-like antennas. Here, we argue that beamforming-based methods may allow radio arrays to measure the global 21-cm signal. We simulate an end-to-end drift-scan observation of the radio sky at 50–100 MHz using a zenith-phased array, and find that the complex sidelobe structure introduces a significant frequency-dependent systematic. However, the $\lambda/D$ evolution of the beam width with frequency does not confound detection. We conclude that a beamformed array with a median sidelobe level ${\sim}-50$ dB may offer an alternative method to measure the global 21-cm signal. This level is achievable by arrays with $O(10^5)$ antennas.
Multi-material topology optimization of adhesive backing layers via J-integral and strain energy minimizations

Strong adhesives often rely on reduced stress concentrations obtained via specific functional grading of material properties. This can be seen in many examples in nature and engineering. Basic design principles have been formulated based on parametric optimization, but a general design tool is still missing. We propose here the use of topology optimization to achieve optimal stiffness distribution in a multi-material adhesive backing layer, reducing stress concentration at selected (crack tip) locations. The method involves the minimization of a linear combination of (i) the J-integral around the crack tip and (ii) the strain energy of the structure. This combination is due to the compromise between numerical stability and accuracy of the method, where (i) alone is numerically unstable and (ii) alone cannot eliminate the crack-tip stress singularity. We analyze three cases in plane strain conditions, namely (1) double-edged crack and (2) center crack, in tension, and (3) edge crack under shear. Each case evidences a different optimal topology with (1-2) providing similar results. The optimal topology allocates stiffness in regions that are far away from the crack tip, and the allocation of softer materials over stiffer ones produces a sophisticated structural hierarchy. To test our solutions, we plot the contact stress distribution across the interface. In all observed cases, we eliminate the stress singularity at the crack tip, albeit generating (mild) stress concentrations in other locations. The optimal topologies result independent of crack size.
seL4 Microkernel for virtualization use-cases: Potential directions towards a standard VMM

Virtualization plays an essential role in providing security to computational systems by isolating execution environments. Many software solutions, called hypervisors, have been proposed to provide virtualization capabilities. However, only a few were designed for being deployed at the edge of the network in devices with fewer computation resources when compared with servers in the Cloud. Among the few lightweight software that can play the hypervisor role, seL4 stands out by providing a small Trusted Computing Base and formally verified components, enhancing its security. Despite today being more than a decade with seL4 microkernel technology, its existing userland and tools are still scarce and not very mature. Over the last few years, the main effort has been to increase the maturity of the kernel itself, and not the tools and applications that can be hosted on top. Therefore, it currently lacks proper support for a full-featured userland Virtual Machine Monitor, and the existing one is quite fragmented. This article discusses the potential directions to a standard VMM by presenting our view of design principles and the feature set needed. This article does not intend to define a standard VMM, we intend to instigate this discussion through the seL4 community.
Roadmap to explore vectorlike quarks decaying to a new scalar or pseudoscalar

The current experimental data allow for a sub-TeV colourless weak-singlet scalar or pseudoscalar. If such a singlet field is present together with TeV-range vectorlike top and bottom partners, there is a possibility that the heavy quarks decay dominantly to the singlet state and a third-generation quark, and the singlet state decays to quark and boson pairs. Such a possibility may arise in various models but it has not been explored experimentally, especially in the context of vectorlike-quark searches. We consider some minimal models, covering the possible weak representations of the top and bottom partners, that can be mapped to many well-motivated ultraviolet-complete theories. We chart out the possible interesting and unexplored signatures of the exotic decay of vectorlike quarks and identify benchmark points representing different signal topologies for the high luminosity LHC. We perform a general scan of the parameter space with the relevant direct search bounds and find that large regions, which do not require any fine-tuning, remain open for the unexplored channels. We also perform a simple projection study in the cleanest channel and indicate how other new but experimentally challenging channels can be used to probe more regions of the parameter space.
Edge Device Deployment of Multi-Tasking Network for Self-Driving Operations

—A safe and robust autonomous driving system relies on accurate perception of the environment for application-oriented scenarios. This paper proposes deployment of the three most crucial tasks (i.e., object detection, drivable area segmentation and lane detection tasks) on embedded system for self-driving operations. To achieve this research objective, multi-tasking network is utilized with a simple encoder-decoder architecture. Comprehensive and extensive comparisons for two models based on different backbone networks are performed. All training experiments are performed on server while Nvidia Jetson Xavier NX is chosen as deployment device.
Very Large Telescope-ISAAC Near-Infrared Spectroscopy of ISO-selected Hubble Deep Field South Galaxies

We report the results of near-infrared Very Large Telescope (VLT)-Infrared Spectrometer and Array Camera (ISAAC) spectroscopy of a sample of 12 galaxies at z = 0.4-1.4, drawn from the ISOCAM survey of the Hubble Deep Field South. We find that the rest-frame R-band spectra of the ISOCAM galaxies resemble those of powerful dust-enshrouded starbursts. Hα emission is detected in 11 out of 12 objects down to a flux limit of 7 × 10-17 ergs cm-2 s-1, corresponding to a luminosity limit of 1041 ergs s-1 at z = 0.6 (for an H0 = 50 and Ω = 0.3 cosmology). From the Hα luminosities in these galaxies we derive estimates of the star formation rate (SFR) in the range 2-50 M☉ yr-1 for stellar masses 1-100 M☉. The raw Hα-based SFRs are an order of magnitude or more lower than SFR (far-infrared) estimates based on ISOCAM LW3 fluxes. If the Hα emission is corrected for extinction, the median offset is reduced to a factor of 3. The sample galaxies are part of a new population of optically faint but infrared-luminous active starburst galaxies, which are characterized by an extremely high rate of evolution with redshift up to z ~ 1.5 and are expected to contribute significantly to the cosmic far-infrared extragalactic background.
Guided Nonlocal Patch Regularization and Efficient Filtering-Based Inversion for Multiband Fusion

In multiband fusion, an image with a high spatial and low spectral resolution is combined with an image with a low spatial but high spectral resolution to produce a single multiband image having high spatial and spectral resolutions. This comes up in remote sensing applications such as pansharpening (MS+PAN), hyperspectral sharpening (HS+PAN), and HS-MS fusion (HS+MS). Remote sensing images are textured and have repetitive structures. Motivated by nonlocal patch-based methods for image restoration, we propose a convex regularizer that (i) takes into account long-distance correlations, (ii) penalizes patch variation, which is more effective than pixel variation for capturing texture information, and (iii) uses the higher spatial resolution image as a guide image for weight computation. We come up with an efficient ADMM algorithm for optimizing the regularizer along with a standard least-squares loss function derived from the imaging model. The novelty of our algorithm is that by expressing patch variation as filtering operations and by judiciously splitting the original variables and introducing latent variables, we are able to solve the ADMM subproblems efficiently using FFT-based convolution and soft-thresholding. As far as the reconstruction quality is concerned, our method is shown to outperform state-of-the-art variational and deep learning techniques.
Data-Efficiency with a Single GPU: An Exploration of Transfer Methods for Small Language Models

Multi-task learning (MTL), instruction tuning, and prompting have recently been shown to improve the generalizability of large language models to new tasks. However, the benefits of such methods are less well-documented in smaller language models, with some studies finding contradictory results. In this work, we explore and isolate the effects of (i) model size, (ii) general purpose MTL, (iii) in-domain MTL, (iv) instruction tuning, and (v) few-shot fine-tuning for models with fewer than 500 million parameters. Our experiments in the zero-shot setting demonstrate that models gain 31% relative improvement, on average, from general purpose MTL, with an additional 37.6% relative gain from in-domain MTL. Contradictory to prior works on large models, we find that instruction tuning provides a modest 2% performance improvement for small models.
VP-STO: Via-point-based Stochastic Trajectory Optimization for Reactive Robot Behavior

Achieving reactive robot behavior in complex dynamic environments is still challenging as it relies on being able to solve trajectory optimization problems quickly enough, such that we can replan the future motion at frequencies which are sufficiently high for the task at hand. We argue that current limitations in Model Predictive Control (MPC) for robot manipulators arise from inefficient, high-dimensional trajectory representations and the negligence of time-optimality in the trajectory optimization process. Therefore, we propose a motion optimization framework that optimizes jointly over space and time, generating smooth and timing-optimal robot trajectories in joint-space. While being task-agnostic, our formulation can incorporate additional task-specific requirements, such as collision avoidance, and yet maintain real-time control rates, demonstrated in simulation and real-world robot experiments on closed-loop manipulation. For additional material, please visit https://sites.google. com/oxfordrobotics.institute/vp-sto.
Conformations and dynamics of active star polymers

We study conformations and dynamics of active star polymers. The analysis shows that active star polymers stretching behaviour is quite different from that of active linear chains. The visual inspection of conformations and bond-bond correlations reveal a better coordination for the alignment and coordination of bonds for the star polymers than for the linear counterparts. The architecture substantially affects the chain extension transition at high values of active force. The scaling laws for the shape factor and the arm asphericity ratio established for the passive star polymers coincide with the passive case for active force values below the transition. For the values above the transition range the scaling of these quantities switches to different values.
Lipschitz continuity of quantum-classical conditional entropies with respect to angular distance and related properties

We derive a Lipschitz continuity bound for quantum-classical conditional entropies with respect to angular distance, with a Lipschitz constant that is independent of the dimension of the conditioning system. This bound is sharper in some situations than previous continuity bounds, which were either based on trace distance (where Lipschitz continuity is not possible), or based on angular distance but did not include a conditioning system. However, we find that the bound does not directly generalize to fully quantum conditional entropies. To investigate possible counterexamples in that setting, we study the characterization of states which saturate the Fuchs--van de Graaf inequality and thus have angular distance approximately equal to trace distance. We give an exact characterization of such states in the invertible case. For the noninvertible case, we show that the situation appears to be significantly more elaborate, and seems to be strongly connected to the question of characterizing the set of fidelity-preserving measurements.
Understanding and Supporting Debugging Workflows in Multiverse Analysis

Multiverse analysis—a paradigm for statistical analysis that considers all combinations of reasonable analysis choices in parallel—promises to improve transparency and reproducibility. Although recent tools help analysts specify multiverse analyses, they remain difficult to use in practice. In this work, we identify debugging as a key barrier due to the latency from running analyses to detecting bugs and the scale of metadata processing needed to diagnose a bug. To address these challenges, we prototype a command-line interface tool, Multiverse Debugger, which helps diagnose bugs in the multiverse and propagate fixes. In a qualitative lab study (n=13), we use Multiverse Debugger as a probe to develop a model of debugging workflows and identify specific challenges, including difficulty in understanding the multiverse’s composition. We conclude with design implications for future multiverse analysis authoring systems.
Competing spin-valley entangled and broken symmetry states in the 
N=1
 Landau level of graphene

We study many-body ground states for the partial integer fillings of the $N=1$ Landau level in graphene, by constructing a model that accounts for the lattice scale corrections to the Coulomb interactions. Interestingly, in contrast to the $N=0$ Landau level, this model contains not only pure delta function interactions but also some of its derivatives. Due to this we find several important differences with respect to the $N=0$ Landau level. For example at quarter filling when only a single component is filled, there is a degeneracy lifting of the quantum hall ferromagnets and ground states with entangled spin and valley degrees of freedom can become favourable. Moreover at half-filling of the $N=1$ Landau level, we have found a new phase that is absent in the $N=0$ Landau level, that combines characteristics of the Kekul\'{e} state and an antiferromagnet. We also find that according to the parameters extracted in a recent experiment, at half-filling of the $N=1$ Landau level graphene is expected to be in a delicate competition between an AF and a CDW state, but we also discuss why the models for these recent experiments might be missing some important terms.
Don't Lose Yourself! Empathetic Response Generation via Explicit Self-Other Awareness

As a critical step to achieve human-like chatbots, empathetic response generation has attained increasing interests. Previous attempts are incomplete and not sufficient enough to elicit empathy because they only focus on the initial aspect of empathy to automatically mimic the feelings and thoughts of the user via other-awareness. However, they ignore to maintain and take the own views of the system into account, which is a crucial process to achieve the empathy called self-other awareness. To this end, we propose to generate Empathetic response with explicit Self-Other Awareness (EmpSOA). Specifically, three stages, self-other differentiation, self-other modulation and self-other generation, are devised to clearly maintain, regulate and inject the self-other aware information into the process of empathetic response generation. Both automatic and human evaluations on the benchmark dataset demonstrate the superiority of EmpSOA to generate more empathetic responses.
Controllable Dialogue Simulation with In-Context Learning

Building dialogue systems requires a large corpus of annotated dialogues. Such datasets are usually created via crowdsourcing, which is expensive and time-consuming. In this paper, we propose \textsc{Dialogic}, a novel dialogue simulation method based on large language model in-context learning to automate dataset creation. Seeded with a few annotated dialogues, \textsc{Dialogic} automatically selects in-context examples for demonstration and prompts GPT-3 to generate new dialogues and annotations in a controllable way. Our method can rapidly expand a small set of dialogue data with minimum or zero \textit{human involvement} and \textit{parameter update} and is thus much more cost-efficient and time-saving than crowdsourcing. Experimental results on the MultiWOZ dataset demonstrate that training a model on the simulated dialogues leads to even better performance than using the same amount of human-generated dialogues under the challenging low-resource settings, with as few as 85 dialogues as a seed. When enough data is available, our method can still serve as an effective data augmentation method. Human evaluation results also show that our simulated dialogues have near-human fluency and annotation accuracy. The code and data are available at \textbf{\url{https://github.com/Leezekun/dialogic}}.
The Meaning and Use of Otherwise 175-016 Semantics

Discourse connectives play an important part in every day communication. They are used to relate individual clauses and sentences. This in turn allows us to build a coherent understanding out of these discourse units. This also means that their semantics often cannot be determined purely within the confines of a single sentence. To further complicate matters connectives may or may not relate adjacent discourse units so the arguments of a connective may be hard to find. This essay discusses the semantics of the discourse connective otherwise. A corpus study was performed to cast an empirical light on its meaning and use. Understanding a connective is about understanding what it relates. As such, the focus of this study was on placed on characterising the connective’s arguments and suggesting how they might be identified. This was done with the broad goal of developing techniques for automated argument identification. Before this, however, it is necessary to have an understanding of its the different senses and previous work on the meaning of otherwise.
Universal Adversarial Perturbations: Efficiency on a small image dataset

Although neural networks perform very well on the image classification task, they are still vulnerable to adversarial perturbations that can fool a neural network without visibly changing an input image. A paper has shown the existence of Universal Adversarial Perturbations which when added to any image will fool the neural network with a very high probability. In this paper we will try to reproduce the experience of the Universal Adversarial Perturbations paper, but on a smaller neural network architecture and training set, in order to be able to study the efficiency of the computed perturbation.
Discharge rating curve using bayesian statistics

Discharge in rivers is commonly estimated by the use of ratin g curve constructed from pairs of water level and discharge measurements. Water level meas urements are collected continuously from each river while pairs of discharge and water level are only collected couple times a year due to high cost. The need for accurate estimatio n of discharge is important for constructions as bridges, hydroelectric power plants as we ll as for hydrological models. The methodology currently used by the Icelandic Meteorologica l Office is based on the standard power-law. The power-law is derived from a theoretical basi s and serves as an appropriate model in most cases. However, in some natural settings devia tions from this form arise. The new methodologies presented in this thesis account for t he deviations from the standard power-law by extending it with a smooth B-spline functio n or by assuming two of the are a function of water level and modeled them with B-splines. These methodologies have shown to perform equally well or better than the current meth odology used by the Icelandic Meteorological Office.
Method comparison on confidence interval construction for the slope in a linear measurement model with heteroscedastic errors

In analytical chemistry, the evaluation on performance accuracy of an analytical method is an important issue. When an adjusted or new method (test method) is developed, the linear measurement error model is commonly used to compare it with another reference method. For this routine practice, the measurements on the reference method can be placed on the x‐axis, whereas those of the test method on the y‐axis, then the slope of this linear relationship indicates the agreement between them and also the performance of the test method. Under the assumption that both variables are subject to heteroscedastic measurement errors, a novel approach based on the concepts of a generalized pivotal quantity (GPQ) is proposed to construct confidence intervals for the slope. Its performance is compared with two maximum likelihood estimation (MLE)‐based approaches through simulation studies. It is shown that the proposed GPQ‐based approach is capable of maintaining the empirical coverage probabilities close to the nominal level and yielding reasonable expected lengths. The GPQ‐based approach can be recommended in practical use because of its easy implementation and better performance than the MLE‐based approaches in most simulation scenarios. Two real datasets are given to illustrate the approaches. Copyright © 2011 John Wiley & Sons, Ltd.