Miracle-dz/UniFAR-FTUs huggingface dataset:
APIDocBooster: An Extract-Then-Abstract Framework Leveraging Large Language Models for Augmenting API Documentation

API documentation is often the most trusted resource for programming. Many approaches have been proposed to augment API documentation by summarizing complementary information from external resources such as Stack Overflow. Existing extractive-based summarization approaches excel in producing faithful summaries that accurately represent the source content without input length restrictions. Nevertheless, they suffer from inherent readability limitations. On the other hand, our empirical study on the abstractive-based summarization method, i.e., GPT-4, reveals that GPT-4 can generate coherent and concise summaries but presents limitations in terms of informativeness and faithfulness. We introduce APIDocBooster, an extract-then-abstract framework that seamlessly fuses the advantages of both extractive (i.e., enabling faithful summaries without length limitation) and abstractive summarization (i.e., producing coherent and concise summaries). APIDocBooster consists of two stages: (1) \textbf{C}ontext-aware \textbf{S}entence \textbf{S}ection \textbf{C}lassification (CSSC) and (2) \textbf{UP}date \textbf{SUM}marization (UPSUM). CSSC classifies API-relevant information collected from multiple sources into API documentation sections. UPSUM first generates extractive summaries distinct from the original API documentation and then generates abstractive summaries guided by extractive summaries through in-context learning. To enable automatic evaluation of APIDocBooster, we construct the first dataset for API document augmentation. Our automatic evaluation results reveal that each stage in APIDocBooster outperforms its baselines by a large margin. Our human evaluation also demonstrates the superiority of APIDocBooster over GPT-4 and shows that it improves informativeness, relevance, and faithfulness by 13.89\%, 15.15\%, and 30.56\%, respectively.
Inferring the Graph of Networked Dynamical Systems under Partial Observability and Spatially Colored Noise

In a Networked Dynamical System (NDS), each node is a system whose dynamics are coupled with the dynamics of neighboring nodes. The global dynamics naturally builds on this network of couplings and it is often excited by a noise input with nontrivial structure. The underlying network is unknown in many applications and should be inferred from observed data. We assume: i) Partial observability -- time series data is only available over a subset of the nodes; ii) Input noise -- it is correlated across distinct nodes while temporally independent, i.e., it is spatially colored. We present a feasibility condition on the noise correlation structure wherein there exists a consistent network inference estimator to recover the underlying fundamental dependencies among the observed nodes. Further, we describe a structure identification algorithm that exhibits competitive performance across distinct regimes of network connectivity, observability, and noise correlation.
Centroidal State Estimation and Control for Hardware-Constrained Humanoid Robots

We introduce novel methods for state estimation, feedforward and feedback control, which specifically target humanoid robots with hardware limitations. Our method com-bines a five-mass model with approximate dynamics of each mass. It enables acquiring an accurate assessment of the centroidal state and Center of Pressure, even when direct forms of force or contact sensing are unavailable. Upon this, we develop a feedforward scheme that operates on the centroidal state, accounting for insufficient joint tracking capabilities. Finally, we implement feedback mechanisms, which compensate for the lack in Degrees of Freedom that our NimbRo-OP2X robot has. The whole approach allows for reactive stepping to maintain balance despite these limitations, which was verified on hardware during RoboCup 2023, in Bordeaux, France.
Upwind summation-by-parts finite differences: error estimates and WENO methodology

High order upwind summation-by-parts finite difference operators have recently been developed. When combining with the simultaneous-approximation-term method to impose boundary conditions, the method converges faster than using traditional summation-by-parts operators. We prove the convergence rate by the normal mode analysis for such methods for a class of hyperbolic partial differential equations. Our analysis shows that the penalty parameter for imposing boundary conditions affects the convergence rate for stable methods. In addition, to solve problems with discontinuous data, we extend the method to also have the weighted essentially nonoscillatory property. The overall method is stable, achieves high order accuracy for smooth problems, and is capable of solving problems with discontinuities.
Extreme Variability Quasars in Their Various States. II. Spectral Variation Revealed with Multiepoch Spectra

We previously built a sample of 14,012 extremely variable quasars (EVQs) based on Sloan Digital Sky Survey (SDSS) and Pan-STARRS1 photometric observations. In this work we present the spectral fitting to their SDSS spectra and study the spectral variation in 1259 EVQs with multiepoch SDSS spectra (after prudently excluding spectra with potentially unreliable spectroscopic photometry). We find a clear “bluer-when-brighter” trend in EVQs, consistent with previous findings of normal quasars and active galactic nuclei. We detect significant intrinsic Baldwin effect (iBeff, i.e., smaller line equivalent width at higher continuum flux in individual active galactic nuclei) in the broad Mg ii and C iv lines of EVQs. Meanwhile, no systematical iBeff is found for the broad Hβ line, which could be attributed to strong host contamination at longer wavelengths. Remarkably, by comparing the iBeff slope of EVQs with archived changing-look quasars, we show that the changing-look quasars identified in the literature are most likely a biased (due to its definition) subpopulation of EVQs, rather than a distinct population of quasars. We also found no significant broad line breathing of Hβ, Mg ii, or C iv, suggesting the broad line breathing in quasars may disappear at longer timescales (∼3000 days).
An essay on deformation measures in isotropic thin shell theories. Bending versus curvature

It has become commonplace for the stored energy function of any realistic shell model to align ``within first order"with the classical Koiter membrane-bending (flexural) shell model. In this paper, we assess whether certain extended Cosserat shell models are consistent with the classical linear Koiter model. In doing this, we observe that there are numerous reasons why a modified version of the classical Koiter model should be considered, a consensus reached not only by Koiter himself but also by Sanders and Budiansky, who independently developed the same theory during the same period. To provide a comprehensive overview of the strain measures employed in our Cosserat shell models, this paper presents them in a unified manner and compares them with the strain measures previously utilized in the literature. We show that all our new strain tensors either generalize (in the case of nonlinear constrained or unconstrained models) or coincide (in the case of the linear constrained model) with the strain tensors recognized as the ``best"or those possessing a well-defined geometric interpretation connected to bending or curvature.
Tuning LayerNorm in Attention: Towards Efficient Multi-Modal LLM Finetuning

This paper introduces an efficient strategy to transform Large Language Models (LLMs) into Multi-Modal Large Language Models (MLLMs). By conceptualizing this transformation as a domain adaptation process, i.e., transitioning from text understanding to embracing multiple modalities, we intriguingly note that, within each attention block, tuning LayerNorm suffices to yield strong performance. Moreover, when benchmarked against other tuning approaches like full parameter finetuning or LoRA, its benefits on efficiency are substantial. For example, when compared to LoRA on a 13B model scale, performance can be enhanced by an average of over 20% across five multi-modal tasks, and meanwhile, results in a significant reduction of trainable parameters by 41.9% and a decrease in GPU memory usage by 17.6%. On top of this LayerNorm strategy, we showcase that selectively tuning only with conversational data can improve efficiency further. Beyond these empirical outcomes, we provide a comprehensive analysis to explore the role of LayerNorm in adapting LLMs to the multi-modal domain and improving the expressive power of the model.
When can you trust feature selection? - I: A condition-based analysis of LASSO and generalised hardness of approximation

The arrival of AI techniques in computations, with the potential for hallucinations and non-robustness, has made trustworthiness of algorithms a focal point. However, trustworthiness of the many classical approaches are not well understood. This is the case for feature selection, a classical problem in the sciences, statistics, machine learning etc. Here, the LASSO optimisation problem is standard. Despite its widespread use, it has not been established when the output of algorithms attempting to compute support sets of minimisers of LASSO in order to do feature selection can be trusted. In this paper we establish how no (randomised) algorithm that works on all inputs can determine the correct support sets (with probability $>1/2$) of minimisers of LASSO when reading approximate input, regardless of precision and computing power. However, we define a LASSO condition number and design an efficient algorithm for computing these support sets provided the input data is well-posed (has finite condition number) in time polynomial in the dimensions and logarithm of the condition number. For ill-posed inputs the algorithm runs forever, hence, it will never produce a wrong answer. Furthermore, the algorithm computes an upper bound for the condition number when this is finite. Finally, for any algorithm defined on an open set containing a point with infinite condition number, there is an input for which the algorithm will either run forever or produce a wrong answer. Our impossibility results stem from generalised hardness of approximation -- within the Solvability Complexity Index (SCI) hierarchy framework -- that generalises the classical phenomenon of hardness of approximation.
Quantum geodesic flows on graphs

We revisit the construction of quantum Riemannian geometries on graphs starting from a hermitian metric compatible connection, which always exists. We use this method to find quantum Levi-Civita connections on the $n$-leg star graph for $n=2,3,4$ and find the same phenomenon as recently found for the $A_n$ Dynkin graph that the metric length for each outbound arrow has to exceed the length in the other direction by a multiple, here $\sqrt{n}$. We then study quantum geodesics on graphs and construct these on the 4-leg graph and on the integer lattice line $\Bbb Z$ with a general edge-symmetric metric
Two sample test for covariance matrices in ultra-high dimension

In this paper, we propose a new test for testing the equality of two population covariance matrices in the ultra-high dimensional setting that the dimension is much larger than the sizes of both of the two samples. Our proposed methodology relies on a data splitting procedure and a comparison of a set of well selected eigenvalues of the sample covariance matrices on the split data sets. Compared to the existing methods, our methodology is adaptive in the sense that (i). it does not require specific assumption (e.g., comparable or balancing, etc.) on the sizes of two samples; (ii). it does not need quantitative or structural assumptions of the population covariance matrices; (iii). it does not need the parametric distributions or the detailed knowledge of the moments of the two populations. Theoretically, we establish the asymptotic distributions of the statistics used in our method and conduct the power analysis. We justify that our method is powerful under very weak alternatives. We conduct extensive numerical simulations and show that our method significantly outperforms the existing ones both in terms of size and power. Analysis of two real data sets is also carried out to demonstrate the usefulness and superior performance of our proposed methodology. An $\texttt{R}$ package $\texttt{UHDtst}$ is developed for easy implementation of our proposed methodology.
Soft Alignment of Modality Space for End-to-end Speech Translation

End-to-end Speech Translation (ST) aims to convert speech into target text within a unified model. The inherent differences between speech and text modalities often impede effective cross-modal and cross-lingual transfer. Existing methods typically employ hard alignment (H-Align) of individual speech and text segments, which can degrade textual representations. To address this, we introduce Soft Alignment (S-Align), using adversarial training to align the representation spaces of both modalities. S-Align creates a modality-invariant space while preserving individual modality quality. Experiments on three languages from the MuST-C dataset show S-Align outperforms H-Align across multiple tasks and offers translation capabilities on par with specialized translation models.
Prime Number Theorems for Polynomials from Homogeneous Dynamics

We establish a new class of examples of the multivariate Bateman-Horn conjecture by using tools from dynamics. These cases include the determinant polynomial on the space of $n\times n$ matrices, the Pfaffian on the space of skew-symmetric $2n\times 2n$ matrices, and the determinant polynomial on the space of symmetric $n\times n$ matrices. In particular, let $(V,F)$ be any pair among the following: $(\textrm{Mat}_n, \det)$, $(\textrm{Skew}_{2n},\textrm{Pff})$, and $(\textrm{Sym}_n, \det). $ We then obtain an asymptotic for $$\pi_{V,F}(T)= \#\{v\in V: \max(|v_i|)\leq T, F(v) \text{ is prime}\},$$ that matches the Bateman-Horn prediction. The key ingredients of our proof are an asymptotic count for integral points on the level sets of $F$ given by Linnik equidistribution, a geometric approximation of the box by cones, and an upper bound sieve to bound the number of prime values missed by the approximation. In the case of the determinant polynomial on symmetric matrices, we must also use the Siegel mass formula to compute the product of local densities for the main term.
APE-then-QE: Correcting then Filtering Pseudo Parallel Corpora for MT Training Data Creation

Automatic Post-Editing (APE) is the task of automatically identifying and correcting errors in the Machine Translation (MT) outputs. We propose a repair-filter-use methodology that uses an APE system to correct errors on the target side of the MT training data. We select the sentence pairs from the original and corrected sentence pairs based on the quality scores computed using a Quality Estimation (QE) model. To the best of our knowledge, this is a novel adaptation of APE and QE to extract quality parallel corpus from the pseudo-parallel corpus. By training with this filtered corpus, we observe an improvement in the Machine Translation system's performance by 5.64 and 9.91 BLEU points, for English-Marathi and Marathi-English, over the baseline model. The baseline model is the one that is trained on the whole pseudo-parallel corpus. Our work is not limited by the characteristics of English or Marathi languages; and is language pair-agnostic, given the necessary QE and APE data.
Variable Importance in High-Dimensional Settings Requires Grouping

Explaining the decision process of machine learning algorithms is nowadays crucial for both model’s performance enhancement and human comprehension. This can be achieved by assessing the variable importance of single variables, even for high-capacity non-linear methods, e.g. Deep Neural Networks (DNNs). While only removal-based approaches, such as Permutation Importance (PI), can bring statistical validity, they return misleading results when variables are correlated. Conditional Permutation Importance (CPI) bypasses PI’s limitations in such cases. However, in high-dimensional settings, where high correlations between the variables cancel their conditional importance, the use of CPI as well as other methods leads to unreliable results, besides prohibitive computation costs. Grouping variables statistically via clustering or some prior knowledge gains some power back and leads to better interpretations. In this work, we introduce BCPI (Block-Based Conditional Permutation Importance), a new generic framework for variable importance computation with statistical guarantees handling both single and group cases. Furthermore, as handling groups with high cardinality (such as a set of observations of a given modality) are both time-consuming and resource-intensive, we also introduce a new stacking approach extending the DNN architecture with sub-linear layers adapted to the group structure. We show that the ensuing approach extended with stacking controls the type-I error even with highly-correlated groups and shows top accuracy across benchmarks. Furthermore, we perform a real-world data analysis in a large-scale medical dataset where we aim to show the consistency between our results and the literature for a biomarker prediction.
NTrack: A Multiple-Object Tracker and Dataset for Infield Cotton Boll Counting

In agriculture, automating the accurate tracking of fruits, vegetables, and fiber is a very tough problem. The issue becomes extremely challenging in dynamic field environments. Yet, this information is critical for making day-to-day agricultural decisions, assisting breeding programs, and much more. To tackle this dilemma, we introduce NTrack, a novel multiple object tracking framework based on the linear relationship between the locations of neighboring tracks. NTrack computes dense optical flow and utilizes particle filtering to guide each tracker. Correspondences between detections and tracks are found through data association via direct observations and indirect cues, which are then combined to obtain an updated observation. Our modular multiple object tracking system is independent of the underlying detection method, thus allowing for the interchangeable use of any off-the-shelf object detector. We show the efficacy of our approach on the task of tracking and counting infield cotton bolls. Experimental results show that our system exceeds contemporary tracking and cotton boll-based counting methods by a large margin. Furthermore, we publicly release the first annotated cotton boll video dataset to the research community.
Rare and exclusive few-body decays of the Higgs, Z, W bosons, and the top quark

We perform an extensive survey of rare and exclusive few-body decays -- defined as those with branching fractions $\mathcal{B} \lesssim 10^{-5}$ and two or three final particles -- of the Higgs, Z, W bosons, and the top quark. Such rare decays can probe physics beyond the Standard Model (BSM), constitute a background for exotic decays into new BSM particles, and provide precise information on quantum chromodynamics factorization with small nonperturbative corrections. We tabulate the theoretical $\mathcal{B}$ values for almost 200 rare decay channels of the four heaviest elementary particles, indicating the current experimental limits in their observation. Among those, we have computed for the first time ultrarare Higgs boson decays into photons and/or neutrinos, H and Z radiative decays into leptonium states, radiative H and Z quark-flavour-changing decays, and semiexclusive top-quark decays into a quark plus a meson, while updating predictions for a few other rare H, Z, and top quark partial widths. The feasibility of measuring each of these unobserved decays is estimated for p-p collisions at the high-luminosity Large Hadron Collider (HL-LHC), and for $e^+e^-$ and p-p collisions at the future circular collider (FCC).
Predicting depinning dynamics of elastic interfaces by machine learning

Predicting the future behaviour of complex systems exhibiting critical-like dynamics is often considered to be an intrinsically hard task. Here, we study the predictability of the depinning dynamics of elastic interfaces in random media driven by a slowly increasing external force, a paradigmatic complex system exhibiting critical avalanche dynamics linked to a continuous non-equilibrium depinning phase transition. To this end, we train a variety of machine learning models to infer the mapping from features of the initial relaxed line shape and the random pinning landscape to predict the sample-dependent staircase-like force-displacement curve that emerges from the depinning process. Even if for a given realization of the quenched random medium the dynamics are in principle deterministic, we find that there is an exponential decay of the predictability with the displacement of the line, quantifying how the system forgets its initial state as it nears the depinning transition from below. Our analysis on how the related displacement scale depends on the system size and the dimensionality of the input descriptor reveals that the onset of the depinning phase transition gives rise to fundamental limits to predictability.
Benchmark of many-body approaches for magnetic dipole transition strength

The low-energy enhancement observed recently in the deexcitation gamma-ray strength functions, suggested to arise due to the magnetic dipole radiation, motivates theoretical efforts to improve the description of M1 strength in available nuclear structure models. Reliable theoretical predictions of nuclear dipole excitations are of interest for different nuclear applications and in particular for nuclear astrophysics, where the calculations of radiative capture cross sections often resort to theoretical strength functions. We aim to benchmark many-body methods in their description of the M1 strength functions, with a special emphasis on the low-energy effects observed in the deexcitation strength. We investigate the zero-temperature and finite-temperature magnetic dipole strength functions computed within the quasiparticle random-phase approximation and compare them to those from exact diagonalizations of the same Hamiltonian in restricted orbital spaces. The study is carried out for a sample of 25 spherical and deformed nuclei which can be described by diagonalization of the respective effective Hamiltonian in three different valence spaces. A reasonable agreement is found for the total photoabsorption strengths while the QRPA distributions are shown to be systematically shifted down in energy with respect to exact results. Photoemission strengths obtained within the FT-QRPA appear insufficient to explain the low-energy enhancement of the M1 strength functions. The problems encountered in QRPA calculations are ascribed to the lack of correlations in the nuclear ground state and to the truncation of the many-body space. In particular, the latter prevents obtaining the sufficiently high level density to produce the low-energy enhancement of the strength function, making the (FT-)QRPA approach unsuitable for predictions of such effects across the nuclear chart.
Global existence and low mach number limit of strong solutions to the full compressible Navier-Stokes equations around the plane Couette flow

In this paper, we study the global existence and low Mach number limit of strong solutions to the 2-D full compressible Navier-Stokes equations around the plane Couette flow in a horizontally periodic layer with non-slip and isothermal boundary conditions. It is shown that the plane Couette flow is asymptotically stable for sufficiently small initial perturbations, provided that the Reynolds number, Mach number and temperature difference between the top and the lower walls are small. For the case that both the top and the lower walls maintain the same temperature, we further prove that such global strong solutions converge to a steady solution of the incompressible Navier-Stokes equations as the Mach number goes to zero.