Miracle-dz/UniFAR-FTUs huggingface dataset:
High-Level Synthesis for Packet-Processing Pipelines

Compiling high-level programs to target high-speed packet-processing pipelines is a challenging combinatorial optimization problem. The compiler must configure the pipeline’s resources to match the high-level semantics of the program, while packing all of the program’s computation into the pipeline’s limited resources. State of the art approaches tackle individual aspects of this problem. Yet, they miss opportunities to efficiently produce globally high-quality outcomes. We argue that High-Level Synthesis (HLS), previously ap-plied to ASIC/FPGA design, is the right framework to decom-pose the compilation problem for pipelines into smaller pieces with modular solutions. We design an HLS-based compiler that works in three phases. Transformation rewrites programs to use more abundant pipeline resources, avoiding scarce ones. Synthesis breaks complex transactional code into configurations of pipelined compute units. Allocation maps the program’s compute and memory to the hardware resources. We prototype these ideas in a compiler, CaT, which targets the Tofino pipeline and a cycle-accurate simulator of a Verilog hardware model of an RMT pipeline. CaT can handle programs that existing compilers cannot currently run on pipelines, generating code faster than existing compilers, while using fewer pipeline resources.
Deep Narrowband Photometry of the M101 Group: Strong-line Abundances of 720 H ii Regions

We present deep, narrowband imaging of the nearby spiral galaxy M101 and its satellites to analyze the oxygen abundances of their H ii regions. Using Case Western Reserve University’s Burrell Schmidt telescope, we add to the narrowband data set of the M101 Group, consisting of Hα, Hβ, and [O iii] emission lines and the blue [O ii] λ3727 emission line for the first time. This allows for complete spatial coverage of the oxygen abundance of the entire M101 Group. We used the strong-line ratio R 23 to estimate oxygen abundances for the H ii regions in our sample, utilizing three different calibration techniques to provide a baseline estimate of the oxygen abundances. This results in ∼650 H ii regions for M101, 10 H ii regions for NGC 5477, and ∼60 H ii regions for NGC 5474, the largest sample for this Group to date. M101 shows a strong abundance gradient, while the satellite galaxies present little or no gradient. There is some evidence for a flattening of the gradient in M101 beyond R ∼ 14 kpc. Additionally, M101 shows signs of azimuthal abundance variations to the west and southwest. The radial and azimuthal abundance variations in M101 are likely explained by an interaction it had with its most massive satellite, NGC 5474, ∼300 Myr ago combined with internal dynamical effects such as corotation.
Using Features at Multiple Temporal and Spatial Resolutions to Predict Human Behavior in Real Time

When performing complex tasks, humans naturally reason at multiple temporal and spatial resolutions simultaneously. We contend that for an artiﬁcially intelligent agent to eﬀectively model human teammates, i.e., demonstrate computational theory of mind (ToM), it should do the same. In this paper, we present an approach for integrating high and low-resolution spatial and temporal information to predict human behavior in real time and evaluate it on data collected from human subjects performing simulated urban search and rescue (USAR) missions in a Minecraft-based environment. Our model composes neural networks for high and low-resolution feature extraction with a neural network for behavior prediction, with all three networks trained simultaneously. The high-resolution extractor encodes dynamically changing goals robustly by taking as input the Manhattan distance diﬀerence between the humans’ Minecraft avatars and candidate goals in the environment for the latest few actions, computed from a high-resolution gridworld representation. In contrast, the low-resolution extractor encodes participants’ historical behavior using a historical state matrix computed from a low-resolution graph representation. Through supervised learning, our model acquires a robust prior for human behavior prediction, and can eﬀectively deal with long-term observations. Our experimental results demonstrate that our method signiﬁcantly improves prediction accuracy compared to approaches that only use high-resolution information. layer combined with a batch normalization layer as a basic building block for our three neural networks. The output FC layers in the prediction network ( g ( e lr , e hr )) are passed through softmax and sigmoid functions to obtain the probabilities of the agent’s goal ( O gp ) and the likelihood that the next victim is triaged ( O vp ), respectively.
Nonperturbative Loop Quantization of Scalar-Tensor Theories of Gravity

The Hamiltonian formulation of scalar-tensor theories of gravity is derived from their Lagrangian formulation by Hamiltonian analysis. The Hamiltonian formalism marks off two sectors of the theories by the coupling parameter $\omega(\phi)$. In the sector of $\omega(\phi)=-3/2$, the feasible theories are restricted and a new primary constraint generating conformal transformations of spacetime is obtained, while in the other sector of $\omega(\phi)\neq-3/2$, the canonical structure and constraint algebra of the theories are similar to those of general relativity coupled with a scalar field. By canonical transformations, we further obtain the connection dynamical formalism of the scalar-tensor theories with real $su(2)$-connections as configuration variables in both sectors. This formalism enables us to extend the scheme of non-perturbative loop quantum gravity to the scalar-tensor theories. The quantum kinematical framework for the scalar-tensor theories is rigorously constructed. Both the Hamiltonian constraint operator and master constraint operator are well defined and proposed to represent quantum dynamics. Thus loop quantum gravity method is also valid for general scalar-tensor theories.
Free Malliavin-Stein-Dirichlet method: multidimensional semicircular approximations and chaos of a quantum Markov operator

We combine the notion of free Stein kernel and the free Malliavin calculus to provide quantitative bounds under the free (quadratic) Wasserstein distance in the multivariate semicircular approximations for self-adjoint vector-valued multiple Wigner integrals. On the way, we deduce an HSI inequality for a modified non-microstates free entropy with respect to the potential associated with these semicircular families in the case of non-degeneracy of the covariance matrix. The strategy of the proofs is based on functional inequalities involving the free Stein discrepancy. We obtain a bound which depends on the second and fourth free cumulant of each component. We then apply these results to some examples such as the convergence of marginals in the free functional Breuer-Major CLT for the non commutative fractional Brownian motion, and we provide a bound for the free Stein discrepancy with respect to semicircular potentials for q-semicirculars operators. Lastly, we develop an abstract setting on where it is possible to construct a free Stein Kernel with respect to the semicircular potential: the quantum chaos associated to a quantum Markov semigroup whose L generator ∆ can be written as the square of a real closable derivation δ valued into the square integrable bi-processes or into a direct sum of the coarse correspondence. 2010 AMS Classification Numbers: 46L54, 60H07, 60H30.
Towards a Mathematics Formalisation Assistant using Large Language Models

Mathematics formalisation is the task of writing mathematics (i.e., definitions, theorem statements, proofs) in natural language, as found in books and papers, into a formal language that can then be checked for correctness by a program. It is a thriving activity today, however formalisation remains cumbersome. In this paper, we explore the abilities of a large language model (Codex) to help with formalisation in the Lean theorem prover. We find that with careful inputdependent prompt selection and postprocessing, Codex is able to formalise short mathematical statements at undergrad level with nearly 75% accuracy for 120 theorem statements. For proofs quantitative analysis is infeasible and we undertake a detailed case study. We choose a diverse set of 13 theorems at undergrad level with proofs that fit in two-three paragraphs. We show that with a new prompting strategy Codex can formalise these proofs in natural language with at least one out of twelve Codex completion being easy to repair into a complete proof. This is surprising as essentially no aligned data exists for formalised mathematics, particularly for proofs. These results suggest that large language models are a promising avenue towards fully or partially automating formalisation.
Evidence of radius inflation in stars approaching the slow-rotator sequence

Average stellar radii in open clusters can be estimated from rotation periods and projected rotational velocities under the assumption of random orientation of the spin axis. Such estimates are independent of distance, interstellar absorption, and models, but their validity can be limited by missing data (truncation) or data that only represent upper/lower limits (censoring). We present a new statistical analysis method to estimate average stellar radii in the presence of censoring and truncation. We use theoretical distribution functions of the projected stellar radius $R \sin i$ to define a likelihood function in the presence of censoring and truncation. Average stellar radii in magnitude bins are then obtained by a maximum likelihood parametric estimation procedure. This method is capable of recovering the average stellar radius within a few percent with as few as $\approx$ 10 measurements. Here it is applied for the first time to the dataset available for the Pleiades. We find an agreement better than $\approx$ 10 percent between the observed $R$ vs $M_K$ relationship and current standard stellar models for 1.2 $\ge M/M_{\odot} \ge$ 0.85 with no evident bias. Evidence of a systematic deviation at $2\sigma$ level are found for stars with 0.8 $\ge M/M_{\odot} \ge$ 0.6 approaching the slow-rotator sequence. Fast-rotators ($P$ < 2 d) agree with standard models within 15 percent with no systematic deviations in the whole 1.2 $\ge M/M_{\odot} \ge$ 0.5 range. The evidence found of a possible radius inflation just below the lower mass limit of the slow-rotator sequence indicates a possible connection with the transition from the fast to the slow-rotator sequence.
Dynamical friction due to fuzzy dark matter on satellites described by axisymmetric logarithmic potentials

A plausible dark matter candidate is an ultralight bosonic particle referred to as fuzzy dark matter. The equivalent mass-energy of the fuzzy dark matter boson is ∼ 10-22 eV and has a corresponding de Broglie wavelength of kiloparsec scale, thus exhibiting wave behaviour in scales comparable to a galactic core, which could not appear in conventional cold dark matter models. The presence of fuzzy dark matter in galactic clusters will impact the motion of their members through dynamical friction. In this work, we present simulations of the dynamical friction on satellites traversing an initially uniform fuzzy dark matter halo. We focus on the satellites whose shapes are beyond spherical symmetry described by ellipsoidal and logarithmic potentials. We find that the wakes created on the fuzzy dark matter halo due to the passage of such satellites are qualitatively different from those generated by spherically symmetric ones. Furthermore, we quantify the dynamical friction coefficient for such systems, finding that the same satellite may experience a drag differing by a factor of 5 depending on its ellipticity and the direction of motion. Finally, we find that the dynamical friction time-scale is close to Hubble time, assuming a satellite of 1011 M ⊙ traversing at 103 km/s a FDM halo whose mean density is ∼ 106 M ⊙ kpc-3.
A constitutive model for viscosity of dense fiber suspension

We propose a constitutive model to predict the viscosity of fiber suspensions, which undergoes shear thinning, at various volume fractions, aspect ratios, and shear stresses/rates. We calibrate the model using the data from direct numerical simulation and prove the accuracy by predicting experimental measurements from the literature. We use a friction coefficient decreasing with the normal load between the fibers to quantitatively reproduce the experimentally observed shear thinning in fiber suspensions. In this model, the effective normal contact force, which is directly proportional to the bulk shear stress, determines the effective friction coefficient. A rise in the shear stress reduces the effective friction coefficient in the suspension. As a result, the jamming volume fraction increases with the shear stress, resulting in a shear thinning in the suspension viscosity. Moreover, we extend the model to quantify the effects of fiber volume fraction and aspect ratio in the suspension. We calibrate this model using the data from numerical simulations for the rate-controlled shear flow. Once calibrated, we show that the model can be used to predict the relative viscosity for different volume fractions, shear stresses, and aspect ratios. The model predictions are in excellent agreement with the available experimental measurements from the literature. The findings of this study can potentially be used to tune the fiber size and volume fraction for designing the suspension rheology in various applications.
Neutron-powered precursors of kilonovae

The merger of binary neutron stars (NSs) ejects a small quantity of neutron-rich matter, the radioactive decay of which powers a day to week long thermal transient known as a kilonova. Most of the ejecta remains sufficiently dense during its expansion that all neutrons are captured into nuclei during the r-process. However, recent general relativistic merger simulations by Bauswein and collaborators show that a small fraction of the ejected mass (a few per cent, or ~10-4 M⊙) expands sufficiently rapidly for most neutrons to avoid capture. This matter originates from the shocked-heated interface between the merging NSs. Here, we show that the β-decay of these free neutrons in the outermost ejecta powers a 'precursor' to the main kilonova emission, which peaks on a time-scale of ~ few hours following merger at U-band magnitude ~22 (for an assumed distance of 200 Mpc). The high luminosity and blue colours of the neutron precursor render it a potentially important counterpart to the gravitational wave source, that may encode valuable information on the properties of the merging binary (e.g. NS-NS versus NS-black hole) and the NS equation of state. Future work is necessary to assess the robustness of the fast-moving ejecta and the survival of free neutrons in the face of neutrino absorptions, although the precursor properties are robust to a moderate amount of leptonization. Our results provide additional motivation for short latency gravitational wave triggers and rapid follow-up searches with sensitive ground-based telescopes.
A free-form lensing model of A370 revealing stellar mass dominated BCGs, in Hubble Frontier Fields images

JL acknowledges a grant from the Seed Funding for Basic Research of the University of Hong Kong (Project No. 201411159166) for this work. JMD acknowledges support of the projects AYA2015-64508-P (MINECO/FEDER, UE), AYA2012-39475-C02-01 and the consolider project CSD2010-00064 funded by the Ministerio de Economia y Competitividad. JL acknowledges, on behalf of his co-I’s, support from the Research Grants Council of Hong Kong through grant 17319316.
AsyncNeRF: Learning Large-scale Radiance Fields from Asynchronous RGB-D Sequences with Time-Pose Function

Large-scale radiance fields are promising mapping tools for smart transportation applications like autonomous driving or drone delivery. But for large-scale scenes, compact synchronized RGB-D cameras are not applicable due to limited sensing range, and using separate RGB and depth sensors inevitably leads to unsynchronized sequences. Inspired by the recent success of self-calibrating radiance field training methods that do not require known intrinsic or extrinsic parameters, we propose the first solution that self-calibrates the mismatch between RGB and depth frames. We leverage the important domain-specific fact that RGB and depth frames are actually sampled from the same trajectory and develop a novel implicit network called the time-pose function. Combining it with a large-scale radiance field leads to an architecture that cascades two implicit representation networks. To validate its effectiveness, we construct a diverse and photorealistic dataset that covers various RGB-D mismatch scenarios. Through a comprehensive benchmarking on this dataset, we demonstrate the flexibility of our method in different scenarios and superior performance over applicable prior counterparts. Codes, data, and models will be made publicly available.
Mutual information area laws for thermal free fermions

We provide a rigorous and asymptotically exact expression of the mutual information of translationally invariant free fermionic lattice systems in a Gibbs state. In order to arrive at this result, we introduce a novel framework for computing determinants of Töplitz operators with smooth symbols, and for treating Töplitz matrices with system size dependent entries. The asymptotically exact mutual information for a partition of the 1D lattice satisfies an area law, with a prefactor which we compute explicitly. As examples, we discuss the fermionic XX model in one dimension and free fermionic models on the torus in higher dimensions in detail. Special emphasis is put on the discussion of the temperature dependence of the mutual information, scaling like the logarithm of the inverse temperature, hence confirming an expression suggested by conformal field theory. We also comment on the applicability of the formalism to treat open systems driven by quantum noise. In the appendix, we derive useful bounds to the mutual information in terms of purities. Finally, we provide a detailed error analysis for finite system sizes. This analysis is valuable in its own right for the abstract theory of Töplitz determinants.
Classical shadow tomography for continuous variables quantum systems

In this article we develop a continuous variable (CV) shadow tomography scheme with wide ranging applications in quantum optics. Our work is motivated by the increasing experimental and technological relevance of CV systems in quantum information, quantum communication, quantum sensing, quantum simulations, quantum computing and error correction. We introduce two experimentally realisable schemes for obtaining classical shadows of CV (possibly non-Gaussian) quantum states using only randomised Gaussian unitaries and easily implementable Gaussian measurements such as homodyne and heterodyne detection. For both schemes, we show that $N=O\big(\operatorname{poly}\big(\frac{1}{\epsilon},\log\big(\frac{1}{\delta}\big),M_n^{r+\alpha},\log(m)\big)\big)$ samples of an unknown $m$-mode state $\rho$ suffice to learn the expected value of any $r$-local polynomial in the canonical observables of degree $\alpha$, both with high probability $1-\delta$ and accuracy $\epsilon$, as long as the state $\rho$ has moments of order $n>\alpha$ bounded by $M_n$. By simultaneously truncating states and operators in energy and phase space, we are able to overcome new mathematical challenges that arise due to the infinite-dimensionality of CV systems. We also provide a scheme to learn nonlinear functionals of the state, such as entropies over any small number of modes, by leveraging recent energy-constrained entropic continuity bounds. Finally, we provide numerical evidence of the efficiency of our protocols in the case of CV states of relevance in quantum information theory, including ground states of quadratic Hamiltonians of many-body systems and cat qubit states. We expect our scheme to provide good recovery in learning relevant states of 2D materials and photonic crystals.
Collaborative Application Security Testing for DevSecOps: An Empirical Analysis of Challenges, Best Practices and Tool Support

DevSecOps is a software development paradigm that places a high emphasis on the culture of collaboration between developers (Dev), security (Sec) and operations (Ops) teams to deliver secure software continuously and rapidly. Adopting this paradigm effectively, therefore, requires an understanding of the challenges, best practices and available solutions for collaboration among these functional teams. However, collaborative aspects related to these teams have received very little empirical attention in the DevSecOps literature. Hence, we present a study focusing on a key security activity, Application Security Testing (AST), in which practitioners face difficulties performing collaborative work in a DevSecOps environment. Our study made novel use of 48 systematically selected webinars, technical talks and panel discussions as a data source to qualitatively analyse software practitioner discussions on the most recent trends and emerging solutions in this highly evolving field. We find that the lack of features that facilitate collaboration built into the AST tools themselves is a key tool-related challenge in DevSecOps. In addition, the lack of clarity related to role definitions, shared goals, and ownership also hinders Collaborative AST (CoAST). We also captured a range of best practices for collaboration (e.g., Shift-left security), emerging communication methods (e.g., ChatOps), and new team structures (e.g., hybrid teams) for CoAST. Finally, our study identified several requirements for new tool features and specific gap areas for future research to provide better support for CoAST in DevSecOps.
Does Debiasing Inevitably Degrade the Model Performance

Gender bias in language models has attracted sufficient attention because it threatens social justice. However, most of the current debiasing methods degraded the model's performance on other tasks while the degradation mechanism is still mysterious. We propose a theoretical framework explaining the three candidate mechanisms of the language model's gender bias. We use our theoretical framework to explain why the current debiasing methods cause performance degradation. We also discover a pathway through which debiasing will not degrade the model performance. We further develop a causality-detection fine-tuning approach to correct gender bias. The numerical experiment demonstrates that our method is able to lead to double dividends: partially mitigating gender bias while avoiding performance degradation.
ParCNetV2: Oversized Kernel with Enhanced Attention*

Transformers have shown great potential in various computer vision tasks. By borrowing design concepts from transformers, many studies revolutionized CNNs and showed remarkable results. This paper falls in this line of studies. Specifically, we propose a new convolutional neural network, ParCNetV2, that extends the research line of ParCNetV1 by bridging the gap between CNN and ViT. It introduces two key designs: 1) Oversized Convolution (OC) with twice the size of the input, and 2) Bifurcate Gate Unit (BGU) to ensure that the model is input adaptive. Fusing OC and BGU in a unified CNN, ParCNetV2 is capable of flexibly extracting global features like ViT, while maintaining lower latency and better accuracy. Extensive experiments demonstrate the superiority of our method over other convolutional neural networks and hybrid models that combine CNNs and transformers. The code are publicly available at https://github.com/XuRuihan/ParCNetV2.