Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring Shallow-Depth Boson Sampling: Towards Scalable Quantum Supremacy

Boson sampling is a sampling task proven to be hard to simulate efficiently using classical computers under plausible assumptions, which makes it an appealing candidate for quantum supremacy. However, due to a large noise rate for near-term quantum devices, it is still unclear whether those noisy devices maintain the quantum advantage for much larger quantum systems. Since the noise rate typically grows with the circuit depth, an alternative is to find evidence of simulation hardness at the shallow-depth quantum circuit. To find the evidence, one way is to identify the minimum depth required for the average-case hardness of approximating output probabilities, which is considered a necessary condition for the state-of-the-art technique to prove the simulation hardness of boson sampling. In this work, we analyze the output probability distribution of shallow-depth boson sampling for Fock-states and Gaussian states, and examine the limitation of the average-case hardness argument at this shallow-depth regime for geometrically local architectures. We propose a shallow-depth linear optical circuit architecture that can overcome the problems associated with geometrically local architectures. Our numerical results suggest that this architecture demonstrates possibilities of average-case hardness properties in a shallow-depth regime, through its resemblance to the global Haar-random boson sampling circuit. This result implies that the corresponding architecture has the potential to be utilized for scalable quantum supremacy with its shallow-depth boson sampling.
Frequency & Channel Attention for computationally efficient sound event detection

We explore on various attention methods on frequency and channel dimensions for sound event detection (SED) in order to enhance performance with minimal increase in computational cost while leveraging domain knowledge to address the frequency dimension of audio data. We have introduced frequency dynamic convolution (FDY conv) in a previous work to release the translational equivariance issue associated with 2D convolution on the frequency dimension of 2D audio data. Although this approach demonstrated state-of-the-art SED performance, it resulted in a model with 150% more trainable parameters. To achieve comparable SED performance with computationally efficient methods for practicality, we explore on lighter alternative attention methods. In addition, we focus on attention methods applied to frequency and channel dimensions. Joint application Squeeze-and-excitation (SE) module and time-frame frequency-wise SE (tfwSE) to apply attention on both frequency and channel dimensions shows comparable performance to SED model with FDY conv with only 2.7% more trainable parameters compared to the baseline model. In addition, we performed class-wise comparison of various attention methods to further discuss various attention methods' characteristics.
Covering convection with a thermal blanket: numerical simulation and stochastic modelling

Adding moving boundaries to convective fluids is known to result in non-trivial and surprising dynamics, leading to spectacular geoformations ranging from kilometre-scale karst terrains to planetary-scale plate tectonics. On the one hand, the moving solid alters the surrounding flow field, but on the other hand, the flow modifies the motion and shape of the solid. This leads to a two-way coupling that is significant in the study of fluid–structure interactions and in the understanding of geomorphologies. In this work, we investigate the coupling between a floating plate and the convective fluid below it. Through numerical experiments, we show that the motion of this plate is driven by the flow beneath. However, the flow structure is also modified by the presence of the plate, leading to the ‘thermal blanket’ effect where the trapped heat beneath the plate results in buoyant and upwelling flows that in turn push the plate away. By analysing this two-way coupling between moving boundary and fluid, we are able to capture the dynamical behaviours of this plate through a low-dimensional stochastic model. Geophysically, the thermal blanket effect is believed to drive the continental drift, therefore understanding this mechanism has significance beyond fluid dynamics.
Properties of Star Formation of the Large Magellanic Cloud As Probed by Young Stellar Objects

We perform a systematic study of the evolutionary stages and stellar masses of young stellar objects (YSOs) in the Large Magellanic Cloud (LMC) to investigate the properties of star formation in the galaxy. There are 4825 sources in our YSO sample, which are constructed by combining the previous studies identifying YSOs in the LMC. Spectral energy distributions of the YSOs from optical to infrared wavelengths were fitted with a model consisting of stellar, polycyclic aromatic hydrocarbon, and dust emissions. We utilize the stellar-to-dust luminosity ratios thus derived to study the evolutionary stages of the sources; younger YSOs are expected to show lower stellar-to-dust luminosity ratios. We find that most of the YSOs are associated with the interstellar gas across the galaxy, which are younger with more gas, suggesting that more recent star formation is associated with larger amounts of the interstellar medium (ISM). N157 shows a hint of higher stellar-to-dust luminosity ratios between active star-forming regions in the LMC, suggesting that recent star formation in N157 is possibly in later evolutionary stages. We also find that the stellar mass function tends to be bottom-heavy in supergiant shells (SGSs), indicating that gas compression by SGSs may be ineffective in compressing the ISM enough to trigger massive star formation. There is no significant difference in the stellar mass function between YSOs likely associated with the interface between colliding SGSs and those with a single SGS, suggesting that gas compression by collisions between SGSs may also be ineffective for massive star formation.
Galaxy and Mass Assembly (GAMA): Stellar-to-dynamical Mass Relation. I. Constraining the Precision of Stellar Mass Estimates

In this empirical work, we aim to quantify the systematic uncertainties in stellar-mass (M ⋆) estimates made from spectral energy distribution (SED) fitting through stellar population synthesis (SPS) for galaxies in the local Universe by using the dynamical mass (M dyn) estimator as an SED-independent check on stellar mass. We first construct a statistical model of the high-dimensional space of galaxy properties; including size (R e ), velocity dispersion (σ e ), surface brightness (I e ), mass-to-light ratio (M ⋆/L), rest-frame color, Sérsic index (n), and dynamical mass (M dyn), and accounting for selection effects and covariant errors. We disentangle the correlations among galaxy properties and find that the variation in M ⋆/M dyn is driven by σ e , Sérsic index and color. We use these parameters to calibrate an SED-independent M ⋆ estimator, Mˆ⋆ . We find the random scatter of the relation M⋆−Mˆ⋆ to be 0.108 dex and 0.147 dex for quiescent and star-forming galaxies, respectively. Finally, we inspect the residuals as a function of SPS parameters (dust, age, metallicity, and star formation rate) and spectral indices (Hα, Hδ, and D n 4000). For quiescent galaxies, ∼65% of the scatter can be explained by the uncertainty in SPS parameters, with dust and age being the largest sources of uncertainty. For star-forming galaxies, while age and metallicity are the leading factors, SPS parameters account for only ∼13% of the scatter. These results leave us with remaining unmodelled scatters of 0.055 dex and 0.122 dex for quiescent and star-forming galaxies, respectively. This can be interpreted as a conservative limit on the precision in M ⋆ that can be achieved via simple SPS modeling.
Two new measurement methods for explicit determination of complex permittivity

This paper presents two new measurement methods for explicit determination of complex permittivity. For the first time, these methods combine the explicit algorithm with a simplified yet accurate error-correction technique. The combination is made possible by the use of one sample of single length and another of double length. For low-loss materials, one of the methods is valid for any sample length and independent of sample positions, but needs a prior estimate of the permittivity, while the other requires no such estimate, but avoidance of the single length being multiples of half-wavelength in the sample. For high-loss materials, both methods may need the estimate. Advantages of each method can be taken if both methods are used simultaneously. Experimental results from the proposed methods show excellent agreement with those from a recent iterative method. Errors arising from small deviations from the double length are also analyzed and presented. The validity, explicitness, and simple error-correction capability make the new methods very useful.
New Perspectives and Systematic Approaches for Analyzing Negative Damping-Induced Sustained Oscillation

Sustained oscillations (SOs) are commonly observed in systems dominated by converters. Under specific conditions, even though the origin of SOs can be identified through negative damping modes using conventional linear analysis, utilizing the describing function to compute harmonic amplitude and frequency remains incomplete. This is because a) it can not cover the cases where hard limits are not triggered, and b) it can not provide a complete trajectory for authentic linear analysis to confirm the presence of SO. Hence, two analytical methods are proposed by returning to the essential principle of harmonic balance. a) A dedicated approach is proposed to solve steady-state harmonics via Newton–Raphson iteration with carefully chosen initial values. The method encompasses all potential hard limit triggered cases. b) By employing extended multiharmonic linearization theory and considering loop impedance, an authentic linear analysis of SO is conducted. The analysis indicates that the initial negative damping modes transform into multiple positive damping modes as SO develops. Simulation validations are performed on a two-level voltage source converter using both PSCAD and RT-LAB. Additionally, valuable insights into the work are addressed considering the modularity and scalability of the proposed methods.
Textbooks Are All You Need

We introduce phi-1, a new large language model for code, with significantly smaller size than competing models: phi-1 is a Transformer-based model with 1.3B parameters, trained for 4 days on 8 A100s, using a selection of ``textbook quality"data from the web (6B tokens) and synthetically generated textbooks and exercises with GPT-3.5 (1B tokens). Despite this small scale, phi-1 attains pass@1 accuracy 50.6% on HumanEval and 55.5% on MBPP. It also displays surprising emergent properties compared to phi-1-base, our model before our finetuning stage on a dataset of coding exercises, and phi-1-small, a smaller model with 350M parameters trained with the same pipeline as phi-1 that still achieves 45% on HumanEval.
Understanding the Challenges of Deploying Live-Traceability Solutions

Software traceability is the process of establishing and maintaining relationships between artifacts in a software system. This process is crucial to many engineering processes, particularly for safety critical projects; however, it is labor-intensive and error-prone. Automated traceability has been a long awaited tool for project managers of these systems, and due to the semantic similarities between linked artifacts, NLP techniques, such as transformer models, may be leveraged to accomplish this task. SAFA.ai is a startup focusing on fine-tuning project-specific models that deliver automated traceability in a near real-time environment. The following paper describes the challenges that characterize commercializing software traceability and highlights possible future directions.
Classical spin models of the windmill lattice and their relevance for 
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We investigate classical Heisenberg models on the distorted windmill lattice and discuss their applicability to the spin-$1/2$ spin liquid candidate PbCuTe$_2$O$_6$. We first consider a general Heisenberg model on this lattice with antiferromagnetic interactions $J_n$ ($n=1,2,3,4$) up to fourth neighbors. Setting $J_1=J_2$ (as approximately realized in PbCuTe$_2$O$_6$) we map out the classical ground state phase diagram in the remaining parameter space and identify a competition between $J_3$ and $J_4$ that opens up interesting magnetic scenarios. Particularly, these couplings tune the ground states from coplanar commensurate or non-coplanar incommensurate magnetically ordered states to highly degenerate ground state manifolds with subextensive or extensive degeneracies. In the latter case, we uncover an unusual classical spin liquid defined on a lattice of corner sharing octahedra. We then focus on the particular set of interaction parameters $J_n$ that has previously been proposed for PbCuTe$_2$O$_6$ and investigate the system's incommensurate magnetic ground state order and finite temperature multistage ordering mechanism. We perform extensive finite temperature simulations of the system's dynamical spin structure factor and compare it with published neutron scattering data for PbCuTe$_2$O$_6$ at low temperatures. Our results demonstrate that thermal fluctuations in the classical model can largely explain the signal distribution in the measured spin structure factor but we also identify distinct differences. Our investigations make use of a variety of different analytical and numerical approaches for classical spin systems, such as Luttinger-Tisza, classical Monte Carlo, iterative minimization, and molecular dynamics simulations.
A remark on the multiplicity of monotone periodic orbits

Abstract We give a new proof of the fact that an area-preserving monotone twist map of the annulus with one p/q-periodic orbit on which the map preserves the ordering on the angular coordinate (i.e. Birkhoff or monotone periodic orbits) actually has a second such orbit distinct from the first.
A De Lellis-M\"uller type estimate on the Minkowski lightcone

We prove an analogue statement to an estimate by De Lellis-M\"uller in $\mathbb{R}^3$ on the standard Minkowski lightcone. More precisely, we show that under some additional assumptions, any spacelike cross section of the standard lightcone is $W^{2,2}$-close to a round surface provided the trace-free part of a scalar second fundamental form $A$ is sufficiently small in $L^2$. To determine the correct intrinsically round cross section of reference, we define an associated $4$-vector, which transforms equivariantly under Lorentz transformations in the restricted Lorentz group. A key step in the proof consists of a geometric, scaling invariant estimate, and we give two different proofs. One utilizes a recent characterization of singularity models of null mean curvature flow along the standard lightcone by the author, while the other is heavily inspired by an almost-Schur lemma by De Lellis-Topping.
Tilings in quasi-random $k$-partite hypergraphs

Given $k\ge 2$ and two $k$-graphs ($k$-uniform hypergraphs) $F$ and $H$, an $F$-factor in $H$ is a set of vertex disjoint copies of $F$ that together cover the vertex set of $H$. Lenz and Mubayi were first to study the $F$-factor problems in quasi-random $k$-graphs with a minimum degree condition. Recently, Ding, Han, Sun, Wang and Zhou gave the density threshold for having all $3$-partite $3$-graphs factors in quasi-random $3$-graphs with vanishing minimum codegree condition $\Omega(n)$. In this paper, we consider embedding factors when the host $k$-graph is $k$-partite and quasi-random with partite minimum codegree condition. We prove that if $p>1/2$ and $F$ is a $k$-partite $k$-graph with each part having $m$ vertices, then for $n$ large enough and $m\mid n$, any $p$-dense $k$-partite $k$-graph with each part having $n$ vertices and partite minimum codegree condition $\Omega(n)$ contains an $F$-factor. We also present a construction showing that $1/2$ is best possible. Furthermore, for $1\leq \ell \leq k-2$, by constructing a sequence of $p$-dense $k$-partite $k$-graphs with partite minimum $\ell$-degree $\Omega(n^{k-\ell})$ having no $K_k(m)$-factor, we show that the partite minimum codegree constraint can not be replaced by other partite minimum degree conditions. On the other hand, we prove that $n/2$ is the asymptotic partite minimum codegree threshold for having all fixed $k$-partite $k$-graph factors in sufficiently large host $k$-partite $k$-graphs even without quasi-randomness.
Convergence of Empirical Optimal Transport in Unbounded Settings

In compact settings, the convergence rate of the empirical optimal transport cost to its population value is well understood for a wide class of spaces and cost functions. In unbounded settings, however, hitherto available results require strong assumptions on the ground costs and the concentration of the involved measures. In this work, we pursue a decomposition-based approach to generalize the convergence rates found in compact spaces to unbounded settings under generic moment assumptions that are sharp up to an arbitrarily small $\epsilon>0$. Hallmark properties of empirical optimal transport on compact spaces, like the recently established adaptation to lower complexity, are shown to carry over to the unbounded case.
Evaluating Graph Neural Networks for Link Prediction: Current Pitfalls and New Benchmarking

Link prediction attempts to predict whether an unseen edge exists based on only a portion of edges of a graph. A flurry of methods have been introduced in recent years that attempt to make use of graph neural networks (GNNs) for this task. Furthermore, new and diverse datasets have also been created to better evaluate the effectiveness of these new models. However, multiple pitfalls currently exist that hinder our ability to properly evaluate these new methods. These pitfalls mainly include: (1) Lower than actual performance on multiple baselines, (2) A lack of a unified data split and evaluation metric on some datasets, and (3) An unrealistic evaluation setting that uses easy negative samples. To overcome these challenges, we first conduct a fair comparison across prominent methods and datasets, utilizing the same dataset and hyperparameter search settings. We then create a more practical evaluation setting based on a Heuristic Related Sampling Technique (HeaRT), which samples hard negative samples via multiple heuristics. The new evaluation setting helps promote new challenges and opportunities in link prediction by aligning the evaluation with real-world situations. Our implementation and data are available at https://github.com/Juanhui28/HeaRT
On sufficient conditions for spanning structures in dense graphs

We study structural conditions in dense graphs that guarantee the existence of vertex‐spanning substructures such as Hamilton cycles. It is easy to see that every Hamiltonian graph is connected, has a perfect fractional matching and, excluding the bipartite case, contains an odd cycle. A simple consequence of the Robust Expander Theorem of Kühn, Osthus and Treglown tells us that any large enough graph that robustly satisfies these properties must already be Hamiltonian. Our main result generalises this phenomenon to powers of cycles and graphs of sublinear bandwidth subject to natural generalisations of connectivity, matchings and odd cycles. This answers a question of Ebsen, Maesaka, Reiher, Schacht and Schülke and solves the embedding problem that underlies multiple lines of research on sufficient conditions for spanning structures in dense graphs. As applications, we recover and establish Bandwidth Theorems in a variety of settings including Ore‐type degree conditions, Pósa‐type degree conditions, deficiency‐type conditions, locally dense and inseparable graphs, multipartite graphs as well as robust expanders.
Investigation of transport properties of graphene Dirac fluid by holographic two-current axion coupling model

Recently, there has been great interest in the phenomenon of severe violation of the Wiedemann-Franz law in graphene Dirac fluids around 75 K, due to the strong coupling relativistic plasma near the neutral point, where traditional perturbation theory fails. To explain this phenomenon, this article proposes a holographic dual two-current axion coupling model, describing the interaction between electrons and holes in graphene near the charge neutrality point (CNP) and revealing the related physical mechanism. The study shows that the holographic two-current model aligns with experimental results at $100\mu m^{-2}$,and correctly predicts conductivity as temperature increases. Additionally, the article explores the behavior of $\beta+\gamma$ and its impact on conductivity and thermal conductivity. The results suggest a frictional effect between electrons and holes. Consequently, this study provides us with a clearer understanding of the properties of graphene Dirac fluids and further confirms the reliability of the holographic duality method.
Magnificent Minified Models

This paper concerns itself with the task of taking a large trained neural network and 'compressing' it to be smaller by deleting parameters or entire neurons, with minimal decreases in the resulting model accuracy. We compare various methods of parameter and neuron selection: dropout-based neuron damage estimation, neuron merging, absolute-value based selection, random selection, OBD (Optimal Brain Damage). We also compare a variation on the classic OBD method that slightly outperformed all other parameter and neuron selection methods in our tests with substantial pruning, which we call OBD-SD. We compare these methods against quantization of parameters. We also compare these techniques (all applied to a trained neural network), with neural networks trained from scratch (random weight initialization) on various pruned architectures. Our results are only barely consistent with the Lottery Ticket Hypothesis, in that fine-tuning a parameter-pruned model does slightly better than retraining a similarly pruned model from scratch with randomly initialized weights. For neuron-level pruning, retraining from scratch did much better in our experiments.
Study on agronomic characters and fiber development of two materials of naturally colored cotton

A field experiment was conducted in the Experimental Station of SCRC in 2000-2001 to investigate the agronomic characters and fiber development of two materials of naturally colored cotton (green and brown fiber), using 33B (white fiber) as the check Results showed that the colored cotton had evident advantages in vegetative growth, development and earliness of maturity in comparision with the check 33B However, both boll weight and lint percentage of colored cottons were much lower than those of 33B, which resulted in a 50%-60% decrease in unit yield of lint compared with 33B Fiber quality of colored cottons was poor, of which green cotton fiber was almost without textile value Quite big difference in fiber development between colored cottons and 33B was observed, with colored cotton having some characters, such as shorter period of fiber elongation, lower speed in fiber elongation, slowly secondary wall thickening and shorter overlapping period of fiber elongation and secondary wall thickening It is suggested that poor lint quality of colored cottons was closely correlated to their fiber development