Miracle-dz/UniFAR-FTUs huggingface dataset:
Patterns of Gender-Specializing Query Reformulation

Users of search systems often reformulate their queries by adding query terms to reflect their evolving information need or to more precisely express their information need when the system fails to surface relevant content. Analyzing these query reformulations can inform us about both system and user behavior. In this work, we study a special category of query reformulations that involve specifying demographic group attributes, such as gender, as part of the reformulated query (e.g., ''olympic 2021 soccer results'' -> ''olympic 2021 women's soccer results"). There are many ways a query, the search results, and a demographic attribute such as gender may relate, leading us to hypothesize different causes for these reformulation patterns, such as under-representation on the original result page or based on the linguistic theory of markedness. This paper reports on an observational study of gender-specializing query reformulations---their contexts and effects---as a lens on the relationship between system results and gender, based on large-scale search log data from Bing. We find that these reformulations sometimes correct for and other times reinforce gender representation on the original result page, but typically yield better access to the ultimately-selected results. The prevalence of these reformulations---and which gender they skew towards---differ by topical context. However, we do not find evidence that either group under-representation or markedness alone adequately explains these reformulations. We hope that future research will use such reformulations as a probe for deeper investigation into gender (and other demographic) representation on the search result page.
Design and analysis of bent functions using M-subspaces

In this article, we provide the first systematic analysis of bent functions $f$ on $\mathbb{F}_2^{n}$ in the Maiorana-McFarland class $\mathcal{MM}$ regarding the origin and cardinality of their $\mathcal{M}$-subspaces, i.e., vector subspaces on which the second-order derivatives of $f$ vanish. By imposing restrictions on permutations $\pi$ of $\mathbb{F}_2^{n/2}$, we specify the conditions, such that Maiorana-McFarland bent functions $f(x,y)=x\cdot \pi(y) + h(y)$ admit a unique $\mathcal{M}$-subspace of dimension $n/2$. On the other hand, we show that permutations $\pi$ with linear structures give rise to Maiorana-McFarland bent functions that do not have this property. In this way, we contribute to the classification of Maiorana-McFarland bent functions, since the number of $\mathcal{M}$-subspaces is invariant under equivalence. Additionally, we give several generic methods of specifying permutations $\pi$ so that $f\in\mathcal{MM}$ admits a unique $\mathcal{M}$-subspace. Most notably, using the knowledge about $\mathcal{M}$-subspaces, we show that using the bent 4-concatenation of four suitably chosen Maiorana-McFarland bent functions, one can in a generic manner generate bent functions on $\mathbb{F}_2^{n}$ outside the completed Maiorana-McFarland class $\mathcal{MM}^\#$ for any even $n\geq 8$. Remarkably, with our construction methods it is possible to obtain inequivalent bent functions on $\mathbb{F}_2^8$ not stemming from two primary classes, the partial spread class $\mathcal{PS}$ and $\mathcal{MM}$. In this way, we contribute to a better understanding of the origin of bent functions in eight variables, since only a small fraction, of which size is about $2^{76}$, stems from $\mathcal{PS}$ and $\mathcal{MM}$, whereas the total number of bent functions on $\mathbb{F}_2^8$ is approximately $2^{106}$.
A Control-Centric Benchmark for Video Prediction

Video is a promising source of knowledge for embodied agents to learn models of the world's dynamics. Large deep networks have become increasingly effective at modeling complex video data in a self-supervised manner, as evaluated by metrics based on human perceptual similarity or pixel-wise comparison. However, it remains unclear whether current metrics are accurate indicators of performance on downstream tasks. We find empirically that for planning robotic manipulation, existing metrics can be unreliable at predicting execution success. To address this, we propose a benchmark for action-conditioned video prediction in the form of a control benchmark that evaluates a given model for simulated robotic manipulation through sampling-based planning. Our benchmark, Video Prediction for Visual Planning ($VP^2$), includes simulated environments with 11 task categories and 310 task instance definitions, a full planning implementation, and training datasets containing scripted interaction trajectories for each task category. A central design goal of our benchmark is to expose a simple interface -- a single forward prediction call -- so it is straightforward to evaluate almost any action-conditioned video prediction model. We then leverage our benchmark to study the effects of scaling model size, quantity of training data, and model ensembling by analyzing five highly-performant video prediction models, finding that while scale can improve perceptual quality when modeling visually diverse settings, other attributes such as uncertainty awareness can also aid planning performance.
Association Rules Mining with Auto-Encoders

Association rule mining is one of the most studied research fields of data mining, with applications ranging from grocery basket problems to explainable classification systems. Classical association rule mining algorithms have several limitations, especially with regards to their high execution times and number of rules produced. Over the past decade, neural network solutions have been used to solve various optimization problems, such as classification, regression or clustering. However there are still no efficient way association rules using neural networks. In this paper, we present an auto-encoder solution to mine association rule called ARM-AE. We compare our algorithm to FP-Growth and NSGAII on three categorical datasets, and show that our algorithm discovers high support and confidence rule set and has a better execution time than classical methods while preserving the quality of the rule set produced.
Learning to Predict Navigational Patterns From Partial Observations

Human beings cooperatively navigate rule-constrained environments by adhering to mutually known navigational patterns, which may be represented as directional pathways or road lanes. Inferring these navigational patterns from incompletely observed environments is required for intelligent mobile robots operating in unmapped locations. However, algorithmically defining these navigational patterns is nontrivial. This letter presents the first self-supervised learning (SSL) method for learning to infer navigational patterns in real-world environments from partial observations only. We explain how geometric data augmentation, predictive world modeling, and an information-theoretic regularizer enable our model to predict an unbiased local directional soft lane probability (DSLP) field in the limit of infinite data. We demonstrate how to infer global navigational patterns by fitting a maximum likelihood graph to the DSLP field. Experiments show that our SSL model outperforms two SOTA supervised lane graph prediction models on the nuScenes dataset. We propose our SSL method as a scalable and interpretable continual learning paradigm for navigation by perception.
Novel phase transition at the Unruh temperature

In recent years, the theory of quantum phase transitions has rapidly developed. These are transitions at a zero temperature which are associated with a change of the theory parameters like couplings. In contrast, the classical phase transitions occur ``within'' the same theory (in particular, with the same couplings) and are associated with a change in temperature. Within the framework of a simple model of Dirac fields in the Euclidean Rindler space, we establish an intermediate case when the phase transition occurs at a finite temperature, but the temperature itself is of a quantum origin (the Unruh temperature). Moreover, the phase transition point is uniquely associated with the behavior of individual levels, namely at the Unruh temperature the two lowest Matsubara modes become singular on the Rindler horizon. This provides a new manifestation of the duality between the thermodynamic description and the geometric approach (the behavior of the quantum levels of particles living on a nontrivial geometric manifold). Although the considered example refers to the physics of black holes, we note the formal similarity of the Unruh temperature with the parameter characterizing quantum transitions in the theory of condensed matter.
On the role of designs in the data-driven approach to quantum statistical inference

Designs, and in particular symmetric, informationally complete (SIC) structures, play an important role in the quantum tomographic reconstruction process and, by extension, in certain interpretations of quantum theory focusing on such a process. This fact is due to the symmetry of the reconstruction formula that designs lead to. However, it is also known that the same tomographic task, albeit with a less symmetric formula, can be accomplished by any informationally complete (non necessarily symmetric) structure. Here we show that, if the tomographic task is replaced by a data-driven inferential approach, the reconstruction, while possible with designs, cannot by accomplished anymore by an arbitrary informationally complete structure. Hence, we propose the data-driven inference as the arena in which the role of designs naturally emerges. Our inferential approach is based on a minimality principle according to which, among all the possible inferences consistent with the data, the weakest should be preferred, in the sense of majorization theory and statistical comparison.
Game-based Platforms for Artificial Intelligence Research

Games have been the perfect test-beds for artificial intelligence research for the characteristics that widely exist in real-world scenarios. Learning and optimisation, decision making in dynamic and uncertain environments, game theory, planning and scheduling, design and education are common research areas shared between games and real-world problems. Numerous open-source games or game-based environments have been implemented for studying artificial intelligence. In addition to single- or multi-player, collaborative or adversarial games, there has also been growing interest in implementing platforms for creative design in recent years. Those platforms provide ideal benchmarks for exploring and comparing artificial intelligence ideas and techniques. This paper reviews the game-based platforms for artificial intelligence research, provides guidance on matching particular types of artificial intelligence with suitable games for testing and matching particular needs in games with suitable artificial intelligence techniques, discusses the research trend induced by the evolution of those platforms, and gives an outlook.
Generative Relevance Feedback with Large Language Models

Current query expansion models use pseudo-relevance feedback to improve first-pass retrieval effectiveness; however, this fails when the initial results are not relevant. Instead of building a language model from retrieved results, we propose Generative Relevance Feedback (GRF) that builds probabilistic feedback models from long-form text generated from Large Language Models. We study the effective methods for generating text by varying the zero-shot generation subtasks: queries, entities, facts, news articles, documents, and essays. We evaluate GRF on document retrieval benchmarks covering a diverse set of queries and document collections, and the results show that GRF methods significantly outperform previous PRF methods. Specifically, we improve MAP between 5-19% and NDCG@10 17-24% compared to RM3 expansion, and achieve state-of-the-art recall across all datasets.
Multi-Camera Visual-Inertial Simultaneous Localization and Mapping for Autonomous Valet Parking

Localization and mapping are key capabilities for self-driving vehicles. In this paper, we build on Kimera and extend it to use multiple cameras as well as external (eg wheel) odometry sensors, to obtain accurate and robust odometry estimates in real-world problems. Additionally, we propose an effective scheme for closing loops that circumvents the drawbacks of common alternatives based on the Perspective-n-Point method and also works with a single monocular camera. Finally, we develop a method for dense 3D mapping of the free space that combines a segmentation network for free-space detection with a homography-based dense mapping technique. We test our system on photo-realistic simulations and on several real datasets collected on a car prototype developed by the Ford Motor Company, spanning both indoor and outdoor parking scenarios. Our multi-camera system is shown to outperform state-of-the art open-source visual-inertial-SLAM pipelines (Vins-Fusion, ORB-SLAM3), and exhibits an average trajectory error under 1% of the trajectory length across more than 8km of distance traveled (combined across all datasets). A video showcasing the system is available at: youtu.be/H8CpzDpXOI8.
Diffusion probabilistic model based accurate and high-degree-of-freedom metasurface inverse design

Abstract Conventional meta-atom designs rely heavily on researchers’ prior knowledge and trial-and-error searches using full-wave simulations, resulting in time-consuming and inefficient processes. Inverse design methods based on optimization algorithms, such as evolutionary algorithms, and topological optimizations, have been introduced to design metamaterials. However, none of these algorithms are general enough to fulfill multi-objective tasks. Recently, deep learning methods represented by generative adversarial networks (GANs) have been applied to inverse design of metamaterials, which can directly generate high-degree-of-freedom meta-atoms based on S-parameters requirements. However, the adversarial training process of GANs makes the network unstable and results in high modeling costs. This paper proposes a novel metamaterial inverse design method based on the diffusion probability theory. By learning the Markov process that transforms the original structure into a Gaussian distribution, the proposed method can gradually remove the noise starting from the Gaussian distribution and generate new high-degree-of-freedom meta-atoms that meet S-parameters conditions, which avoids the model instability introduced by the adversarial training process of GANs and ensures more accurate and high-quality generation results. Experiments have proven that our method is superior to representative methods of GANs in terms of model convergence speed, generation accuracy, and quality.
On the Way to SBOMs: Investigating Design Issues and Solutions in Practice

The increase of software supply chain threats has underscored the necessity for robust security mechanisms, among which the Software Bill of Materials (SBOM) stands out as a promising solution. SBOMs, by providing a machine-readable inventory of software composition details, play a crucial role in enhancing transparency and traceability within software supply chains. This empirical study delves into the practical challenges and solutions associated with the adoption of SBOMs, through an analysis of 4,786 GitHub discussions across 510 SBOM-related projects. Through repository mining and analysis, this research delineates key topics, challenges, and solutions intrinsic to the effective utilization of SBOMs. Furthermore, we shed light on commonly used tools and frameworks for SBOM generation, exploring their respective strengths and limitations. This study underscores a set of findings, for example, there are four phases of the SBOM life cycle, and each phase has a set of SBOM development activities and issues; in addition, this study emphasizes that SBOM play in ensuring resilient software development practices and the imperative of their widespread adoption and integration to bolster supply chain security. The insights of our study provide vital input for future work and practical advancements in this topic.
AutoCure: Automated Tabular Data Curation Technique for ML Pipelines

Machine learning algorithms have become increasingly prevalent in multiple domains, such as autonomous driving, healthcare, and finance. In such domains, data preparation remains a significant challenge in developing accurate models, requiring significant expertise and time investment to explore the huge search space of well-suited data curation and transformation tools. To address this challenge, we present AutoCure, a novel and configuration-free data curation pipeline that improves the quality of tabular data. Unlike traditional data curation methods, AutoCure synthetically enhances the density of the clean data fraction through an adaptive ensemble-based error detection method and a data augmentation module. In practice, AutoCure can be integrated with open source tools, e.g., Auto-sklearn, H2O, and TPOT, to promote the democratization of machine learning. As a proof of concept, we provide a comparative evaluation of AutoCure against 28 combinations of traditional data curation tools, demonstrating superior performance and predictive accuracy without user intervention. Our evaluation shows that AutoCure is an effective approach to automating data preparation and improving the accuracy of machine learning models.
CROP: Towards Distributional-Shift Robust Reinforcement Learning using Compact Reshaped Observation Processing

The safe application of reinforcement learning (RL) requires generalization from limited training data to unseen scenarios. Yet, fulfilling tasks under changing circumstances is a key challenge in RL. Current state-of-the-art approaches for generalization apply data augmentation techniques to increase the diversity of training data. Even though this prevents overfitting to the training environment(s), it hinders policy optimization. Crafting a suitable observation, only containing crucial information, has been shown to be a challenging task itself. To improve data efficiency and generalization capabilities, we propose Compact Reshaped Observation Processing (CROP) to reduce the state information used for policy optimization. By providing only relevant information, overfitting to a specific training layout is precluded and generalization to unseen environments is improved. We formulate three CROPs that can be applied to fully observable observation- and action-spaces and provide methodical foundation. We empirically show the improvements of CROP in a distributionally shifted safety gridworld. We furthermore provide benchmark comparisons to full observability and data-augmentation in two different-sized procedurally generated mazes.
How to design, and tune, a computed torque controller: An introduction and a Matlab example

This note briefly introduces the computed torque control method for trajectory tracking. The method is applicable to fully actuated robots, i.e, those whose inverse dynamics can be solved for any feasible acceleration. This includes many systems, like robot arms or hands, or any tree-like mechanism with all its joints actuated. Using simple explanations, we see how such a controller can be obtained using feedback linearization, and how its gains can be tuned to satisfy a desired settling time for the error signal. We end up discussing the advantages and shortcomings of the controller. A companion Matlab script can be downloaded from https://bit.ly/3QShxYi that implements and tests the controller on a simple actuated pendulum.
Advances on switched systems

During the last three decades,there has been an increasing interest on the modeling,analysis,synthesis,and control of switched systems.The purpose of this paper is to give a survey on the current development of the research on switched systems.The main topics consist of i) common Lyapunov function;ii) stabilization of switched systems;iii) controllability of switched systems.Finally,prospect of development is presented.In addition,some auxiliary knowledge is illustrated in the appendix.
Metataxonomic Analysis of the Uterine Microbiota Associated with Low Fertility in Dairy Cows Using Endometrial Tissues Prior to First Artificial Insemination

We examined the uterine microbiota associated with low fertility in dairy cows derived from four commercial farms via a metataxonomic approach using endometrial tissues prior to the first artificial insemination. The present study provided two new insights into the relevance of uterine microbiota with respect to fertility. ABSTRACT The deterioration in reproductive performance in association with low fertility leads to significant economic losses on dairy farms. The uterine microbiota has begun to attract attention as a possible cause of unexplained low fertility. We analyzed the uterine microbiota associated with fertility by 16S rRNA gene amplicon sequencing in dairy cows. First, the alpha (Chao1 and Shannon) and beta (unweighted and weighted UniFrac) diversities of 69 cows at four dairy farms that had passed the voluntary waiting period before the first artificial insemination (AI) were analyzed with respect to factors including farm, housing style, feeding management, parity, and AI frequency to conception. Significant differences were observed in the farm, housing style, and feeding management, except parity and AI frequency to conception. The other diversity metrics did not show significant differences in the tested factors. Similar results were obtained for the predicted functional profile. Next, the microbial diversity analysis of 31 cows at a single farm using weighted UniFrac distance matrices revealed a correlation with AI frequency to conception but not with parity. In correlation with AI frequency to conception, the predicted function profile appeared to be slightly modified and a single bacterial taxon, Arcobacter, was detected. The bacterial associations related to fertility were estimated. Considering these, the uterine microbiota in dairy cows can be varied depending on the farm management practices and may become one of the measures for low fertility. IMPORTANCE We examined the uterine microbiota associated with low fertility in dairy cows derived from four commercial farms via a metataxonomic approach using endometrial tissues prior to the first artificial insemination. The present study provided two new insights into the relevance of uterine microbiota with respect to fertility. First, the uterine microbiota varied depending on housing style and feeding management. Next, a subtle change was observed in functional profile analysis: a formation of uterine microbiota was detected to be different in correlation with fertility in one farm studied. Considering these insights, an examination system on bovine uterine microbiota is hopefully established based on continuous research on this topic.
Heparin Prowess: Favorable Vascular–Immune Reprogramming in Pancreatic Cancer

Summary Several approaches for overcoming immunotherapy resistance in pancreatic and colorectal cancer syngeneic models were assessed using heparin and immunotherapy. Beneficial responses were attributed to heparin-induced vascular normalization, ensuing CD8+ T-cell infiltration, and M1 macrophage polarization, suggesting the potential for heparin-anchored therapies in cold tumors such as pancreatic cancer. See related article by Wei et al., p. 2525