Miracle-dz/UniFAR-FTUs huggingface dataset:
Higgs boson rapidity distribution in bottom annihilation at NNLL and beyond

We present precise resummed predictions for Higgs boson rapidity distribution through bottom quark annihilation at next-to-next-to-leading logarithmic (NNLL) accuracy matched to next-to-next-to-leading order (NNLO) and at next-to-next-to-next-to-leading logarithmic (N3LL) accuracy matched to next-to-next-to-next-to-leading order soft-virtual (N3LOsv) in the strong coupling. Exploiting the universal behavior of soft radiation near the threshold, we determine the analytic expressions for the process-dependent and universal perturbative ingredients for threshold resummation in double singular limits of partonic threshold variables $z_1,z_2$. Subsequently, the threshold resummation is performed in the double Mellin space within the standard QCD framework. The new third-order process-dependent non-logarithmic coefficients are determined using three-loop bottom quark form factor and third-order quark soft distribution function in rapidity distribution. The effect of these new resummed coefficients are studied at the $13$ TeV LHC. We observe a better perturbative convergence in the resummed predictions on the Higgs rapidity spectrum in bottom quark annihilation. We also find that the NNLL and N3LL corrections are sizeable which typically are of the order of $-2.5\%$ and $-1.5\%$ over the respective available fixed orders with the scale uncertainty remaining at the same level as the fixed order.
Improving neural network representations using human similarity judgments

Deep neural networks have reached human-level performance on many computer vision tasks. However, the objectives used to train these networks enforce only that similar images are embedded at similar locations in the representation space, and do not directly constrain the global structure of the resulting space. Here, we explore the impact of supervising this global structure by linearly aligning it with human similarity judgments. We find that a naive approach leads to large changes in local representational structure that harm downstream performance. Thus, we propose a novel method that aligns the global structure of representations while preserving their local structure. This global-local transform considerably improves accuracy across a variety of few-shot learning and anomaly detection tasks. Our results indicate that human visual representations are globally organized in a way that facilitates learning from few examples, and incorporating this global structure into neural network representations improves performance on downstream tasks.
Self-Supervised Audio Teacher-Student Transformer for Both Clip-Level and Frame-Level Tasks

Self-supervised learning (SSL) has emerged as a popular approach for learning audio representations. One goal of audio self-supervised pre-training is to transfer knowledge to downstream audio tasks, generally including clip-level and frame-level tasks. While frame-level tasks are important for fine-grained acoustic scene/event understanding, prior studies primarily evaluate on clip-level downstream tasks. In order to tackle both clip-level and frame-level tasks, this paper proposes Audio Teacher-Student Transformer (ATST), with a clip-level version (named ATST-Clip) and a frame-level version (named ATST-Frame), responsible for learning clip-level and frame-level representations, respectively. Both methods use a Transformer encoder and a teacher-student training scheme. We have carefully designed a view creation strategy for ATST-Clip and ATST-Frame. Specifically, ATST-Clip uses segment-wise data augmentations, and ATST-Frame integrates frame-wise data augmentations and masking. Experimental results show that our ATST-Frame model obtains state-of-the-art (SOTA) performances on most of the clip-level and frame-level downstream tasks. Especially, it outperforms other models by a large margin on the frame-level sound event detection task. In addition, the performance can be further improved by combining the two models through knowledge distillation.
Unbalanced Optimal Transport for Unbalanced Word Alignment

Monolingual word alignment is crucial to model semantic interactions between sentences.In particular, null alignment, a phenomenon in which words have no corresponding counterparts, is pervasive and critical in handling semantically divergent sentences. Identification of null alignment is useful on its own to reason about the semantic similarity of sentences by indicating there exists information inequality. To achieve unbalanced word alignment that values both alignment and null alignment, this study shows that the family of optimal transport (OT), i.e., balanced, partial, and unbalanced OT, are natural and powerful approaches even without tailor-made techniques.Our extensive experiments covering unsupervised and supervised settings indicate that our generic OT-based alignment methods are competitive against the state-of-the-arts specially designed for word alignment, remarkably on challenging datasets with high null alignment frequencies.
An Empirical Analysis of Parameter-Efficient Methods for Debiasing Pre-Trained Language Models

The increasingly large size of modern pre-trained language models not only makes them inherit more human-like biases from the training corpora, but also makes it computationally expensive to mitigate such biases. In this paper, we investigate recent parameter-efficient methods in combination with counterfactual data augmentation (CDA) for bias mitigation. We conduct extensive experiments with prefix tuning, prompt tuning, and adapter tuning on different language models and bias types to evaluate their debiasing performance and abilities to preserve the internal knowledge of a pre-trained model. We find that the parameter-efficient methods (i) are effective in mitigating gender bias, where adapter tuning is consistently the most effective one and prompt tuning is more suitable for GPT-2 than BERT, (ii) areless effective when it comes to racial and religious bias, which may be attributed to the limitations of CDA, and (iii) can perform similarly to or sometimes better than full fine-tuning with improved time and memory efficiency, as well as maintain the internal knowledge in BERT and GPT-2, evaluated via fact retrieval and downstream fine-tuning.
The asymptotics of r(4,t)

For integers $s,t \geq 2$, the Ramsey numbers $r(s,t)$ denote the minimum $N$ such that every $N$-vertex graph contains either a clique of order $s$ or an independent set of order $t$. In this paper we prove \[ r(4,t) = \Omega\Bigl(\frac{t^3}{\log^4 \! t}\Bigr) \quad \quad \mbox{ as }t \rightarrow \infty\] which determines $r(4,t)$ up to a factor of order $\log^2 \! t$, and solves a conjecture of Erd\H{o}s.
Partial Inference in Structured Prediction

In this paper, we examine the problem of partial inference in the context of structured prediction. Using a generative model approach, we consider the task of maximizing a score function with unary and pairwise potentials in the space of labels on graphs. Employing a two-stage convex optimization algorithm for label recovery, we analyze the conditions under which a majority of the labels can be recovered. We introduce a novel perspective on the Karush-Kuhn-Tucker (KKT) conditions and primal and dual construction, and provide statistical and topological requirements for partial recovery with provable guarantees.
PyTrial: Machine Learning Software and Benchmark for Clinical Trial Applications

Clinical trials are conducted to test the effectiveness and safety of potential drugs in humans for regulatory approval. Machine learning (ML) has recently emerged as a new tool to assist in clinical trials. Despite this progress, there have been few efforts to document and benchmark ML4Trial algorithms available to the ML research community. Additionally, the accessibility to clinical trial-related datasets is limited, and there is a lack of well-defined clinical tasks to facilitate the development of new algorithms. To fill this gap, we have developed PyTrial that provides benchmarks and open-source implementations of a series of ML algorithms for clinical trial design and operations. In this paper, we thoroughly investigate 34 ML algorithms for clinical trials across 6 different tasks, including patient outcome prediction, trial site selection, trial outcome prediction, patient-trial matching, trial similarity search, and synthetic data generation. We have also collected and prepared 23 ML-ready datasets as well as their working examples in Jupyter Notebooks for quick implementation and testing. PyTrial defines each task through a simple four-step process: data loading, model specification, model training, and model evaluation, all achievable with just a few lines of code. Furthermore, our modular API architecture empowers practitioners to expand the framework to incorporate new algorithms and tasks effortlessly. The code is available at https://github.com/RyanWangZf/PyTrial.
A Comprehensive Model of Snow Crystal Faceting

Crystal faceting can emerge via two broad physical mechanisms: anisotropic attachment kinetics on growing crystals and anisotropic surface energies on near-equilibrium crystals. For the case of the ice/vapor system, anisotropic attachment kinetics is the dominant faceting mechanism, while the possible occurrence of equilibrium faceting has been debated for many decades. In this investigation we examine ice/vapor faceting at low supersaturations over the temperature range -15C<T<0C, where evidence of a roughening transition has been previously reported. Our findings indicate that a comprehensive attachment kinetics model can explain all the experimental data to date, while assuming an essentially isotropic surface energy (which is supported by other considerations). Specifically, our kinetic model naturally explains the observed disappearance of prism faceting on slowly growing ice crystals in vacuum at T>-2C, thus suggesting that snow crystal faceting is caused by anisotropic attachment kinetics even at extremely slow growth rates.
A Meta-Analytic Review of Studies of the Effectiveness of Small-Group Learning Methods on Statistics Achievement

This meta-analytic study focused on the quantitative integration and synthesis of the accumulated pedagogical research in undergraduate statistics education literature. These accumulated research studies compared the academic achievement of students who had been instructed using one of the various forms of small-group learning methods to those who had been instructed using lecture-based instruction. The meta-analytic results showed that cooperative, collaborative, and inquiry-based learning methods were used in college-level statistics courses. The results also showed that cooperative and collaborative learning methods supported the effectiveness of the small-group learning methods in improving students' academic achievement with an overall average effect-size of 0.60. In contrast, the effectiveness of inquiry-based learning was close to zero. This significant positive average effect-size indicated that using small-group learning methods in statistics classrooms could increase the achievement of college students, increasing the scores on a statistics exam from the 50th to the 73rd percentile. In addition, the multilevel analysis revealed that the effect sizes were influenced significantly by the publication-year of the studies, with the most recently published studies having lower effect sizes. The major implication of this study is that evidence-based research supports the effectiveness of active small-group learning methods in promoting students' achievement in statistics.
A Mirror Descent Perspective on Classical and Quantum Blahut-Arimoto Algorithms

The Blahut-Arimoto algorithm is a well known method to compute classical channel capacities and rate-distortion functions. Recent works have extended this algorithm to compute various quantum analogs of these quantities. In this paper, we show how these Blahut-Arimoto algorithms are special instances of mirror descent, which is a well-studied generalization of gradient descent for constrained convex optimization. Using new convex analysis tools, we show how relative smoothness and strong convexity analysis recovers known sublinear and linear convergence rates for Blahut-Arimoto algorithms. This mirror descent viewpoint allows us to derive related algorithms with similar convergence guarantees to solve problems in information theory for which Blahut-Arimoto-type algorithms are not directly applicable. We apply this framework to compute energy-constrained classical and quantum channel capacities, classical and quantum rate-distortion functions, and approximations of the relative entropy of entanglement, all with provable convergence guarantees.
The Progenitor of the Peculiar Galaxy NGC 3077

We present a study of the structural properties and metallicity distribution of the nearby peculiar galaxy NGC 3077. Using data from our survey of the M81 Group with the Hyper Suprime-Cam on board the Subaru Telescope, we construct deep color–magnitude diagrams that are used to probe the old red giant branch (RGB) population of NGC 3077. We map these stars out to and beyond the nominal tidal radius, which allows us to derive the structural properties and stellar content of the peripheral regions. We show that NGC 3077 has an extended stellar halo and pronounced “S-shaped” tidal tails that diverge from the radial profile of the inner region. The average metallicity of the old population in NGC 3077 is estimated from individual RGBs to be [M/H] = −0.98 ± 0.26, which decreases with the distance from the galaxy center as [M/H] = −0.17 dex Rh−1 . The metallicity of the S-shaped structure is similar to that of the regions lying at r ∼ 4 × R h ( ∼ 30 kpc), indicating that the stellar constituents of the tidal tails have come from the outer envelope of NGC 3077. These results suggest that this peculiar galaxy was probably a rather normal dwarf elliptical galaxy before the tidal interaction with M81 and M82. We also examine the evidence in our data set for the six recently reported ultrafaint dwarf candidates around NGC 3077. We recover a spatial overdensity of sources coinciding with only one of these.
Hardness of Deceptive Certificate Selection

Recent progress towards theoretical interpretability guarantees for AI has been made with classifiers that are based on interactive proof systems. A prover selects a certificate from the datapoint and sends it to a verifier who decides the class. In the context of machine learning, such a certificate can be a feature that is informative of the class. For a setup with high soundness and completeness, the exchanged certificates must have a high mutual information with the true class of the datapoint. However, this guarantee relies on a bound on the Asymmetric Feature Correlation of the dataset, a property that so far is difficult to estimate for high-dimensional data. It was conjectured in W\"aldchen et al. that it is computationally hard to exploit the AFC, which is what we prove here. We consider a malicious prover-verifier duo that aims to exploit the AFC to achieve high completeness and soundness while using uninformative certificates. We show that this task is $\mathsf{NP}$-hard and cannot be approximated better than $\mathcal{O}(m^{1/8 - \epsilon})$, where $m$ is the number of possible certificates, for $\epsilon>0$ under the Dense-vs-Random conjecture. This is some evidence that AFC should not prevent the use of interactive classification for real-world tasks, as it is computationally hard to be exploited.
Fifty Years of ISCA: A data-driven retrospective on key trends

Computer Architecture, broadly, involves optimizing hardware and software for current and future processing systems. Although there are several other top venues to publish Computer Architecture research, including ASPLOS, HPCA, and MICRO, ISCA (the International Symposium on Computer Architecture) is one of the oldest, longest running, and most prestigious venues for publishing Computer Architecture research. Since 1973, except for 1975, ISCA has been organized annually. Accordingly, this year will be the 50th year of ISCA. Thus, we set out to analyze the past 50 years of ISCA to understand who and what has been driving and innovating computing systems thus far. Our analysis identifies several interesting trends that reflect how ISCA, and Computer Architecture in general, has grown and evolved in the past 50 years, including minicomputers, general-purpose uniprocessor CPUs, multiprocessor and multi-core CPUs, general-purpose GPUs, and accelerators.
Flexible Distribution Alignment: Towards Long-tailed Semi-supervised Learning with Proper Calibration

Long-tailed semi-supervised learning (LTSSL) represents a practical scenario for semi-supervised applications, challenged by skewed labeled distributions that bias classifiers. This problem is often aggravated by discrepancies between labeled and unlabeled class distributions, leading to biased pseudo-labels, neglect of rare classes, and poorly calibrated probabilities. To address these issues, we introduce Flexible Distribution Alignment (FlexDA), a novel adaptive logit-adjusted loss framework designed to dynamically estimate and align predictions with the actual distribution of unlabeled data and achieve a balanced classifier by the end of training. FlexDA is further enhanced by a distillation-based consistency loss, promoting fair data usage across classes and effectively leveraging underconfident samples. This method, encapsulated in ADELLO (Align and Distill Everything All at Once), proves robust against label shift, significantly improves model calibration in LTSSL contexts, and surpasses previous state-of-of-art approaches across multiple benchmarks, including CIFAR100-LT, STL10-LT, and ImageNet127, addressing class imbalance challenges in semi-supervised learning. Our code will be made available upon paper acceptance.
Neutrino Mass and Proton Lifetime in a Realistic Supersymmetric SO(10) Model

Title of dissertation: NEUTRINO MASS AND PROTON LIFETIME IN A REALISTIC SUPERSYMMETRIC SO(10) MODEL Matthew Severson Doctor of Philosophy, 2015 Dissertation directed by: Professor Rabindra Mohapatra Department of Physics This work presents a complete analysis of fermion fitting and proton decay in a supersymmetric SO(10) model previously suggested by Dutta, Mimura, and Mohapatra. A key question in any grand unified theory is whether it satisfies the stringent experimental lower limits on the partial lifetimes of the proton. In more generic models, substantial fine-tuning is required among GUT-scale parameters to satisfy the limits. In the proposed model, the 10, 126, and 120 Yukawa couplings contributing to fermion masses have restricted textures intended to give favorable results for proton lifetime, while still giving rise to a realistic fermion sector, without the need for fine-tuning, even for large tan β, and for either type-I or type-II dominance in the neutrino mass matrix. In this thesis, I investigate the above hypothesis at a strict numerical level of scrutiny; I obtain a valid fit for the entire fermion sector for both types of seesaw dominance, including θ13 in good agreement with the most recent data. For the case with type-II seesaw, I find that, using the Yukawa couplings fixed by the successful fermion sector fit, proton partial lifetime limits are readily satisfied for all but one of the pertinent decay modes for nearly arbitrary values of the triplet-Higgs mixing parameters, with the Kν̄ mode requiring a minor O(10−1) cancellation in order to satisfy its limit. I also find a maximum partial lifetime for that mode of τ(Kν̄) ∼ 10 years. For the type-I seesaw case, I find that Kν̄ decay mode is satisfied for any values of the triplet mixing parameters giving no major enhancement, and all other modes are easily satisfied for arbitrary mixing values; I also find a maximum partial lifetime for Kν̄ of nearly 10 years, which is largely sub-dominant to gauge boson decay channels. NEUTRINO MASS AND PROTON LIFETIME IN A REALISTIC SUPERSYMMETRIC SO(10) MODEL
Size Dependence of the Bouncing Barrier in Protoplanetary Dust Growth

Understanding the collisional behavior of dust aggregates is essential in the context of planet formation. It is known that low-velocity collisions of dust aggregates result in bouncing rather than sticking when the filling factor of colliding dust aggregates is higher than a threshold value. However, a large discrepancy between numerical and experimental results on the threshold filling factor was reported so far. In this study, we perform numerical simulations using soft-sphere discrete element methods and demonstrate that the sticking probability decreases with increasing aggregate radius. Our results suggest that the large discrepancy in the threshold filling factor may reflect the difference in the size of dust aggregates in earlier numerical simulations and laboratory experiments.
Intervention Generalization: A View from Factor Graph Models

One of the goals of causal inference is to generalize from past experiments and observational data to novel conditions. While it is in principle possible to eventually learn a mapping from a novel experimental condition to an outcome of interest, provided a sufficient variety of experiments is available in the training data, coping with a large combinatorial space of possible interventions is hard. Under a typical sparse experimental design, this mapping is ill-posed without relying on heavy regularization or prior distributions. Such assumptions may or may not be reliable, and can be hard to defend or test. In this paper, we take a close look at how to warrant a leap from past experiments to novel conditions based on minimal assumptions about the factorization of the distribution of the manipulated system, communicated in the well-understood language of factor graph models. A postulated $\textit{interventional factor model}$ (IFM) may not always be informative, but it conveniently abstracts away a need for explicit unmeasured confounding and feedback mechanisms, leading to directly testable claims. We derive necessary and sufficient conditions for causal effect identifiability with IFMs using data from a collection of experimental settings, and implement practical algorithms for generalizing expected outcomes to novel conditions never observed in the data.
The permuton limit of random recursive separable permutations

We introduce and study a simple Markovian model of random separable permutations. Our first main result is the almost sure convergence of these permutations towards a random limiting object in the sense of permutons, which we call the recursive separable permuton. We then prove several results on this new limiting object: a characterization of its distribution via a fixed-point equation, a combinatorial formula for its expected pattern densities, an explicit integral formula for its intensity measure, and lastly, we prove that its distribution is absolutely singular with respect to that of the Brownian separable permuton, which is the large size limit of uniform random separable permutations.