Miracle-dz/UniFAR-FTUs huggingface dataset:
SHAPE REPRESENTATION VIA SYMMETRIC POLYNOMIALS: A COMPLETE INVARIANT INSPIRED BY THE BISPECTRUM

We address the representation of two-dimensional shape in its most general form, i.e., arbitrary sets of points, that may arise in multiple situations, e.g., sparse sets of specific landmarks, or dense sets of image edge points. Our goal are recognition tasks, where the key is balancing two contradicting demands: shapes that differ by rigid transformations or point re-labeling should have the same representation (invariance) but geometrically distinct shapes should have different representations (completeness). In the paper, we introduce a new shape representation that marries properties of the elementary symmetric polynomials and the bispectrum. Like the power spectrum, the bispectrum is insensitive to signal shifts; however, unlike the power spectrum, the bispectrum is complete. The elementary symmetric polynomials are complete and invariant to variable relabeling. We show that the elementary symmetric polynomials of the shape points depend on the shape orientation in a way that enables interpreting them in the frequency domain and building from them a bispectrum. The result is a shape representation that is complete and invariant to rigid transformations and point-relabeling. The paper also reports experiments that illustrate the proved properties.
Spread complexity and dynamical transition in two-mode Bose-Einstein condensations

We study the spread complexity in two-mode Bose-Einstein condensations and unveil that the long-time average of the spread complexity $\overline{C}_{K}$ can probe the dynamical transition from self-trapping to Josephson oscillation. When the parameter $\omega$ increases over a critical value $\omega_c$, we reveal that the spread complexity exhibits a sharp transition from lower to higher value, with the corresponding phase space trajectory changing from self-trapping to Josephson oscillation. Moreover, we scrutinize the eigen-spectrum and uncover the relation between the dynamical transition and the excited state quantum phase transition, which is characterized by the emergence of singularity in the density of states at critical energy $E_{c}$. In the thermodynamical limit, the cross point of $E_{c}(\omega)$ and the initial energy $E_{0}(\omega)$ determines the dynamical transition point $\omega_c$. Finally, we show that the different dynamical behavior for the initial state at a fixed point can be distinguished by the long-time average of the spread complexity, when the fixed point changes from unstable to stable.
Network Calculus Characterization of Congestion Control for Time-Varying Traffic

Models for the dynamics of congestion control generally involve systems of coupled differential equations. Universally, these models assume that traffic sources saturate the maximum transmissions allowed by the congestion control method. This is not suitable for studying congestion control of intermittent but bursty traffic sources. In this paper, we present a characterization of congestion control for arbitrary time-varying traffic that applies to rate-based as well as window-based congestion control. We leverage the capability of network calculus to precisely describe the input-output relationship at network elements for arbitrary source traffic. We show that our characterization can closely track the dynamics of even complex congestion control algorithms.
Image Classification with Rotation-Invariant Variational Quantum Circuits

Variational quantum algorithms are gaining attention as an early application of Noisy Intermediate-Scale Quantum (NISQ) devices. One of the main problems of variational methods lies in the phenomenon of Barren Plateaus, present in the optimization of variational parameters. Adding geometric inductive bias to the quantum models has been proposed as a potential solution to mitigate this problem, leading to a new field called Geometric Quantum Machine Learning. In this work, an equivariant architecture for variational quantum classifiers is introduced to create a label-invariant model for image classification with $C_4$ rotational label symmetry. The equivariant circuit is benchmarked against two different architectures, and it is experimentally observed that the geometric approach boosts the model's performance. Finally, a classical equivariant convolution operation is proposed to extend the quantum model for the processing of larger images, employing the resources available in NISQ devices.
Prediagnosis biomarkers of insulin-like growth factor-1, insulin, and interleukin-6 dysregulation and multiple myeloma risk in the Multiple Myeloma Cohort Consortium.

Insulin-like growth factor-1 (IGF-1), insulin, and IL-6 are dysregulated in multiple myeloma pathogenesis and may also contribute to multiple myeloma etiology. To examine their etiologic role, we prospectively analyzed concentrations of serologic markers in 493 multiple myeloma cases and 978 controls from 8 cohorts in the Multiple Myeloma Cohort Consortium. We computed odds ratios (ORs) and 95% confidence intervals (CIs) for multiple myeloma per 1-SD increase in biomarker concentration using conditional logistic regression. We examined heterogeneity by time since blood collection (≤ 3, 4- ≤ 6, and > 6 years) in stratified models. Fasting IGF binding protein-1 concentration was associated with multiple myeloma risk within 3 years (OR, 95% CI per 1-SD increase: 2.3, 1.4-3.8, P = .001) and soluble IL-6 receptor level was associated within 6 years after blood draw (OR (≤ 3 years), 95% CI, 1.4, 1.1-1.9, P = . 01; OR(4- ≤ 6 years), 95% CI, 1.4, 1.1-1.7, P = . 002). No biomarker was associated with longer-term multiple myeloma risk (ie, > 6 years). Interactions with time were statistically significant (IGF binding protein-1, P-heterogeneity = .0016; sIL6R, P-heterogeneity = .016). The time-restricted associations probably reflect the bioactivity of tumor and microenvironment cells in transformation from monoclonal gammopathy of undetermined significance or smoldering multiple myeloma to clinically manifest multiple myeloma.
A Collection of Pragmatic-Similarity Judgments over Spoken Dialog Utterances

Automatic measures of similarity between utterances are invaluable for training speech synthesizers, evaluating machine translation, and assessing learner productions. While there exist measures for semantic similarity and prosodic similarity, there are as yet none for pragmatic similarity. To enable the training of such measures, we developed the first collection of human judgments of pragmatic similarity between utterance pairs. Each pair consisting of an utterance extracted from a recorded dialog and a re-enactment of that utterance. Re-enactments were done under various conditions designed to create a variety of degrees of similarity. Each pair was rated on a continuous scale by 6 to 9 judges. The average inter-judge correlation was as high as 0.72 for English and 0.66 for Spanish. We make this data available at https://github.com/divettemarco/PragSim .
AI for bureaucratic productivity: Measuring the potential of AI to help automate 143 million UK government transactions

There is currently considerable excitement within government about the potential of artificial intelligence to improve public service productivity through the automation of complex but repetitive bureaucratic tasks, freeing up the time of skilled staff. Here, we explore the size of this opportunity, by mapping out the scale of citizen-facing bureaucratic decision-making procedures within UK central government, and measuring their potential for AI-driven automation. We estimate that UK central government conducts approximately one billion citizen-facing transactions per year in the provision of around 400 services, of which approximately 143 million are complex repetitive transactions. We estimate that 84% of these complex transactions are highly automatable, representing a huge potential opportunity: saving even an average of just one minute per complex transaction would save the equivalent of approximately 1,200 person-years of work every year. We also develop a model to estimate the volume of transactions a government service undertakes, providing a way for government to avoid conducting time consuming transaction volume measurements. Finally, we find that there is high turnover in the types of services government provide, meaning that automation efforts should focus on general procedures rather than services themselves which are likely to evolve over time. Overall, our work presents a novel perspective on the structure and functioning of modern government, and how it might evolve in the age of artificial intelligence.
Construction of a Japanese Financial Benchmark for Large Language Models

With the recent development of large language models (LLMs), models that focus on certain domains and languages have been discussed for their necessity. There is also a growing need for benchmarks to evaluate the performance of current LLMs in each domain. Therefore, in this study, we constructed a benchmark comprising multiple tasks specific to the Japanese and financial domains and performed benchmark measurements on some models. Consequently, we confirmed that GPT-4 is currently outstanding, and that the constructed benchmarks function effectively. According to our analysis, our benchmark can differentiate benchmark scores among models in all performance ranges by combining tasks with different difficulties.
Collaborative AI Teaming in Unknown Environments via Active Goal Deduction

With the advancements of artificial intelligence (AI), we're seeing more scenarios that require AI to work closely with other agents, whose goals and strategies might not be known beforehand. However, existing approaches for training collaborative agents often require defined and known reward signals and cannot address the problem of teaming with unknown agents that often have latent objectives/rewards. In response to this challenge, we propose teaming with unknown agents framework, which leverages kernel density Bayesian inverse learning method for active goal deduction and utilizes pre-trained, goal-conditioned policies to enable zero-shot policy adaptation. We prove that unbiased reward estimates in our framework are sufficient for optimal teaming with unknown agents. We further evaluate the framework of redesigned multi-agent particle and StarCraft II micromanagement environments with diverse unknown agents of different behaviors/rewards. Empirical results demonstrate that our framework significantly advances the teaming performance of AI and unknown agents in a wide range of collaborative scenarios.
Real-time Threat Detection Strategies for Resource-constrained Devices

As more devices connect to the internet, it becomes crucial to address their limitations and basic security needs. While much research focuses on utilizing ML and DL to tackle security challenges, there is often a tendency to overlook the practicality and feasibility of implementing these methods in real-time settings. This oversight stems from the constrained processing power and memory of certain devices (IoT devices), as well as concerns about the generalizability of these approaches. Focusing on the detection of DNS-tunneling attacks in a router as a case study, we present an end-to-end process designed to effectively address these challenges. The process spans from developing a lightweight DNS-tunneling detection model to integrating it into a resource-constrained device for real-time detection. Through our experiments, we demonstrate that utilizing stateless features for training the ML model, along with features chosen to be independent of the network configuration, leads to highly accurate results. The deployment of this carefully crafted model, optimized for embedded devices across diverse environments, resulted in high DNS-tunneling attack detection with minimal latency. With this work, we aim to encourage solutions that strike a balance between theoretical advancements and the practical applicability of ML approaches in the ever-evolving landscape of device security.
iSpLib: A Library for Accelerating Graph Neural Networks using Auto-tuned Sparse Operations

Core computations in Graph Neural Network (GNN) training and inference are often mapped to sparse matrix operations such as sparse-dense matrix multiplication (SpMM). These sparse operations are harder to optimize by manual tuning because their performance depends significantly on the sparsity of input graphs, GNN models, and computing platforms. To address this challenge, we present iSpLib, a PyTorch-based C++ library equipped with auto-tuned sparse operations. iSpLib expedites GNN training with a cache-enabled backpropagation that stores intermediate matrices in local caches. The library offers a user-friendly Python plug-in that allows users to take advantage of our optimized PyTorch operations out-of-the-box for any existing linear algebra-based PyTorch implementation of popular GNNs (Graph Convolution Network, GraphSAGE, Graph Inference Network, etc.) with only two lines of additional code. We demonstrate that iSpLib obtains up to 27x overall training speedup compared to the equivalent PyTorch 2.1. 0 and PyTorch Geometric 2.4. 0 implementations on the CPU. Our library is publicly available at https://github.com/HipGraph/iSpLib (https://doi.org/10.5281/zenodo.10806511).
Topoi with enough points

We extend Deligne's original argument showing that locally coherent topoi have enough points, clarified using collage diagrams. We show that our refinement of Deligne's technique can be adapted to recover every existing result of this kind, including the most recent results about $\kappa$-coherent $\kappa$-topoi. Our presentation allows us to relax the cardinality assumptions typically imposed on the sites involved. We show that a larger class of locally finitely presentable toposes have enough points and that a closed subtopos of a topos with enough points has enough points.
Few-Shot Adversarial Prompt Learning on Vision-Language Models

The vulnerability of deep neural networks to imperceptible adversarial perturbations has attracted widespread attention. Inspired by the success of vision-language foundation models, previous efforts achieved zero-shot adversarial robustness by aligning adversarial visual features with text supervision. However, in practice, they are still unsatisfactory due to several issues, including heavy adaptation cost, suboptimal text supervision, and uncontrolled natural generalization capacity. In this paper, to address these issues, we propose a few-shot adversarial prompt framework where adapting input sequences with limited data makes significant adversarial robustness improvement. Specifically, we achieve this by providing adversarially correlated text supervision that is end-to-end learned from adversarial examples. We also propose a novel training objective that enhances the consistency of multi-modal features while encourages differentiated uni-modal features between natural and adversarial examples. The proposed framework gives access to learn adversarial text supervision, which provides superior cross-modal adversarial alignment and matches state-of-the-art zero-shot adversarial robustness with only 1% training data.
Anti-aging treatments slow propagation of synucleinopathy by restoring lysosomal function

ABSTRACT Aging is the major risk factor for neurodegenerative diseases that are also associated with impaired proteostasis, resulting in abnormal accumulation of protein aggregates. However, the role of aging in development and progression of disease remains elusive. Here, we used Caenorhabditis elegans models to show that aging-promoting genetic variations accelerated the rate of cell-to-cell transmission of SNCA/α-synuclein aggregates, hallmarks of Parkinson disease, and the progression of disease phenotypes, such as nerve degeneration, behavioral deficits, and reduced life span. Genetic and pharmacological anti-aging manipulations slowed the spread of aggregates and the associated phenotypes. Lysosomal degradation was significantly impaired in aging models, while anti-aging treatments reduced the impairment. Transgenic expression of hlh-30p::hlh-30, the master controller of lysosomal biogenesis, alleviated intercellular transmission of aggregates in the aging model. Our results demonstrate that the rate of aging closely correlates with the rate of aggregate propagation and that general anti-aging treatments can slow aggregate propagation and associated disease progression by restoring lysosomal function.
Cell Variational Information Bottleneck Network

In this work, we propose Cell Variational Information Bottleneck Network (cellVIB), a convolutional neural network using information bottleneck mechanism, which can be combined with the latest feedforward network architecture in an end-to-end training method. Our Cell Variational Information Bottleneck Network is constructed by stacking VIB cells, which generate feature maps with uncertainty. As layers going deeper, the regularization effect will gradually increase, instead of directly adding excessive regular constraints to the output layer of the model as in Deep VIB. Under each VIB cell, the feedforward process learns an independent mean term and an standard deviation term, and predicts the Gaussian distribution based on them. The feedback process is based on reparameterization trick for effective training. This work performs an extensive analysis on MNIST dataset to verify the effectiveness of each VIB cells, and provides an insightful analysis on how the VIB cells affect mutual information. Experiments conducted on CIFAR-10 also prove that our cellVIB is robust against noisy labels during training and against corrupted images during testing. Then, we validate our method on PACS dataset, whose results show that the VIB cells can significantly improve the generalization performance of the basic model. Finally, in a more complex representation learning task, face recognition, our network structure has also achieved very competitive results.
Protected group bias and stereotypes in Large Language Models

As modern Large Language Models (LLMs) shatter many state-of-the-art benchmarks in a variety of domains, this paper investigates their behavior in the domains of ethics and fairness, focusing on protected group bias. We conduct a two-part study: first, we solicit sentence continuations describing the occupations of individuals from different protected groups, including gender, sexuality, religion, and race. Second, we have the model generate stories about individuals who hold different types of occupations. We collect>10k sentence completions made by a publicly available LLM, which we subject to human annotation. We find bias across minoritized groups, but in particular in the domains of gender and sexuality, as well as Western bias, in model generations. The model not only reflects societal biases, but appears to amplify them. The model is additionally overly cautious in replies to queries relating to minoritized groups, providing responses that strongly emphasize diversity and equity to an extent that other group characteristics are overshadowed. This suggests that artificially constraining potentially harmful outputs may itself lead to harm, and should be applied in a careful and controlled manner.
Robust Model Based Reinforcement Learning Using $\mathcal{L}_1$ Adaptive Control

We introduce $\mathcal{L}_1$-MBRL, a control-theoretic augmentation scheme for Model-Based Reinforcement Learning (MBRL) algorithms. Unlike model-free approaches, MBRL algorithms learn a model of the transition function using data and use it to design a control input. Our approach generates a series of approximate control-affine models of the learned transition function according to the proposed switching law. Using the approximate model, control input produced by the underlying MBRL is perturbed by the $\mathcal{L}_1$ adaptive control, which is designed to enhance the robustness of the system against uncertainties. Importantly, this approach is agnostic to the choice of MBRL algorithm, enabling the use of the scheme with various MBRL algorithms. MBRL algorithms with $\mathcal{L}_1$ augmentation exhibit enhanced performance and sample efficiency across multiple MuJoCo environments, outperforming the original MBRL algorithms, both with and without system noise.
Time-efficient, high-resolution 3T whole-brain relaxometry using Cartesian 3D MR-STAT with CSF suppression

Purpose: Current 3D Magnetic Resonance Spin TomogrAphy in Time-domain (MR-STAT) protocols use transient-state, gradient-spoiled gradient-echo sequences that are prone to cerebrospinal fluid (CSF) pulsation artifacts when applied to the brain. This study aims at developing a 3D MR-STAT protocol for whole-brain relaxometry that overcomes the challenges posed by CSF-induced ghosting artifacts. Method: We optimized the flip-angle train within the Cartesian 3D MR-STAT framework to achieve two objectives: (1) minimization of the noise level in the reconstructed quantitative maps, and (2) reduction of the CSF-to-white-matter signal ratio to suppress CSF signal and the associated pulsation artifacts. The optimized new sequence was tested on a gel/water-phantom to evaluate the accuracy of the quantitative maps, and on healthy volunteers to explore the effectiveness of the CSF artifact suppression and robustness of the new protocol. Results: A new optimized sequence with both high parameter encoding capability and low CSF intensity was proposed and initially validated in the gel/water-phantom experiment. From in-vivo experiments with five volunteers, the proposed CSF-suppressed sequence shows no CSF ghosting artifacts and overall greatly improved image quality for all quantitative maps compared to the baseline sequence. Statistical analysis indicated low inter-subject and inter-scan variability for quantitative parameters in gray matter and white matter (1.6%-2.4% for T1 and 2.0%-4.6% for T2), demonstrating the robustness of the new sequence. Conclusion: We presented a new 3D MR-STAT sequence with CSF suppression that effectively eliminates CSF pulsation artifacts. The new sequence ensures consistently high-quality, 1mm^3 whole-brain relaxometry within a rapid 5.5-minute scan time.
Packaging Up Media Mix Modeling: An Introduction to Robyn's Open-Source Approach

While attribution of user behavior across apps and websites had led to unseen levels of determinism in digital advertising measurement, privacy-centric changes to the digital data landscape are bringing probabilistic techniques such as marketing and media mix modeling en vogue again. Many small and midsize advertisers lack the scale and resources to invest in advanced proprietary modeling efforts that would usually require specific expertise and a team of several data scientists. To facilitate broad successful adoption of media mix modeling for digital advertising measurement, marketing data scientists at Meta started the open-source computational package Robyn. This article presents architectural components and choices in Robyn and discusses how Robyn aims to be packaged against biases and for organizational acceptance. As an open-source package with wide adoption and a highly active community, Robyn undergoes continual development. In this vein, what is described in this article should not be seen as conclusive solutions but as an outline of pathways that the Robyn community has embarked on. The article aims to provide a structured introduction to these pathways as a basis for feedback from marketing data scientists, to ensure Robyn's ongoing development aligns with users' needs.