Miracle-dz/UniFAR-FTUs huggingface dataset:
On t-structures adjacent and orthogonal to weight structures

We study $t$-structures (on triangulated categories) that are closely related to weight structures. A $t$-structure couple $t=(C_{t\le 0},C_{t\ge 0})$ is said to be adjacent to a weight structure $w=(C_{w\le 0}, C_{w\ge 0})$ if $C_{t\ge 0}=C_{w\ge 0}$. For a category $C$ that satisfies the Brown representability property we prove that $t$ that is adjacent to $w$ exists if and only if $w$ is smashing (that is,"respects C-coproducts"). The heart $Ht$ of this $t$ is the category of those functors $Hw^{op}\to Ab$ that respect products (here $Hw$ is the heart of $w$); the result has important applications. We prove several more statements on constructing $t$-structures starting from weight structures; we look for a strictly orthogonal $t$-structure $t$ on some $C'$ (where $C,C'$ are triangulated subcategories of a common $D$) such that $C'_{t\le 0}$ (resp. $C'_{t\ge 0}$) is characterized by the vanishing of morphisms from $C_{w\ge 1}$ (resp. $C_{w\le -1}$). Some of these results generalize properties of semi-orthogonal decompositions proved in the previous paper, and can be applied to various derived categories of (quasi)coherent sheaves on a scheme $X$ that is projective over an affine noetherian one. We also study hearts of orthogonal $t$-structures and their restrictions, and prove some statements on"reconstructing"weight structures from orthogonal $t$-structures.
A Bayesian Approach to OOD Robustness in Image Classification

An important and unsolved problem in computer vision is to ensure that the algorithms are robust to changes in image domains. We address this problem in the scenario where we have access to images from the target domains but no annotations. Motivated by the challenges of the OOD-CV benchmark where we encounter real world Out-of-Domain (OOD) nuisances and occlusion, we introduce a novel Bayesian approach to OOD robustness for object classification. Our work extends Compositional Neural Networks (CompNets), which have been shown to be robust to occlusion but degrade badly when tested on OOD data. We exploit the fact that CompNets contain a generative head defined over feature vectors represented by von Mises-Fisher (vMF) kernels, which correspond roughly to object parts, and can be learned without supervision. We obverse that some vMF kernels are similar between different domains, while others are not. This enables us to learn a transitional dictionary of vMF kernels that are intermediate between the source and target domains and train the generative model on this dictionary using the annotations on the source domain, followed by iterative refinement. This approach, termed Unsupervised Generative Transition (UGT), performs very well in OOD scenarios even when occlusion is present. UGT is evaluated on different OOD benchmarks including the OOD-CV dataset, several popular datasets (e.g., ImageNet-C [9]), artificial image corruptions (including adding occluders), and synthetic-to-real domain transfer, and does well in all scenarios outperforming SOTA alternatives (e.g. up to 10% top-1 accuracy on Occluded OOD-CV dataset).
On the face stratification of the $m=2$ amplituhedron

We define and study the face stratification of the m=2 amplituhedron. We show that the face poset is an upper order ideal in the face poset of the totally nonnegative Grassmannian. Our construction is consistent with earlier work of Lukowski, and we confirm various predictions of Lukowski.
AuG-KD: Anchor-Based Mixup Generation for Out-of-Domain Knowledge Distillation

Due to privacy or patent concerns, a growing number of large models are released without granting access to their training data, making transferring their knowledge inefficient and problematic. In response, Data-Free Knowledge Distillation (DFKD) methods have emerged as direct solutions. However, simply adopting models derived from DFKD for real-world applications suffers significant performance degradation, due to the discrepancy between teachers' training data and real-world scenarios (student domain). The degradation stems from the portions of teachers' knowledge that are not applicable to the student domain. They are specific to the teacher domain and would undermine students' performance. Hence, selectively transferring teachers' appropriate knowledge becomes the primary challenge in DFKD. In this work, we propose a simple but effective method AuG-KD. It utilizes an uncertainty-guided and sample-specific anchor to align student-domain data with the teacher domain and leverages a generative method to progressively trade off the learning process between OOD knowledge distillation and domain-specific information learning via mixup learning. Extensive experiments in 3 datasets and 8 settings demonstrate the stability and superiority of our approach. Code available at https://github.com/IshiKura-a/AuG-KD .
The Virtues of Laziness: Multi-Query Kinodynamic Motion Planning with Lazy Methods

In this work, we introduce LazyBoE, a multi-query method for kinodynamic motion planning with forward propagation. This algorithm allows for the simultaneous exploration of a robot's state and control spaces, thereby enabling a wider suite of dynamic tasks in real-world applications. Our contributions are three-fold: i) a method for discretizing the state and control spaces to amortize planning times across multiple queries; ii) lazy approaches to collision checking and propagation of control sequences that decrease the cost of physics-based simulation; and iii) LazyBoE, a robust kinodynamic planner that leverages these two contributions to produce dynamically-feasible trajectories. The proposed framework not only reduces planning time but also increases success rate in comparison to previous approaches.
Am I the Odd One? Exploring (In)Congruencies in the Realism of Avatars and Virtual Others in Virtual Reality

Virtual humans play a pivotal role in social virtual environments, shaping users' VR experiences. The diversity in available options and users' preferences can result in a heterogeneous mix of appearances among a group of virtual humans. The resulting variety in higher-order anthropomorphic and realistic cues introduces multiple (in)congruencies, eventually impacting the plausibility of the experience. In this work, we consider the impact of (in)congruencies in the realism of a group of virtual humans, including co-located others and one's self-avatar. In a 2 x 3 mixed design, participants embodied either (1) a personalized realistic or (2) a customized stylized self-avatar across three consecutive VR exposures in which they were accompanied by a group of virtual others being either (1) all realistic, (2) all stylized, or (3) mixed. Our results indicate groups of virtual others of higher realism, i.e., potentially more congruent with participants' real-world experiences and expectations, were considered more human-like, increasing the feeling of co-presence and the impression of interaction possibilities. (In)congruencies concerning the homogeneity of the group did not cause considerable effects. Furthermore, our results indicate that a self-avatar's congruence with the participant's real-world experiences concerning their own physical body yielded notable benefits for virtual body ownership and self-identification for realistic personalized avatars. Notably, the incongruence between a stylized self-avatar and a group of realistic virtual others resulted in diminished ratings of self-location and self-identification. We conclude on the implications of our findings and discuss our results within current theories of VR experiences, considering (in)congruent visual cues and their impact on the perception of virtual others, self-representation, and spatial presence.
Empowering Sequential Recommendation from Collaborative Signals and Semantic Relatedness

Sequential recommender systems (SRS) could capture dynamic user preferences by modeling historical behaviors ordered in time. Despite effectiveness, focusing only on the \textit{collaborative signals} from behaviors does not fully grasp user interests. It is also significant to model the \textit{semantic relatedness} reflected in content features, e.g., images and text. Towards that end, in this paper, we aim to enhance the SRS tasks by effectively unifying collaborative signals and semantic relatedness together. Notably, we empirically point out that it is nontrivial to achieve such a goal due to semantic gap issues. Thus, we propose an end-to-end two-stream architecture for sequential recommendation, named TSSR, to learn user preferences from ID-based and content-based sequence. Specifically, we first present novel hierarchical contrasting module, including coarse user-grained and fine item-grained terms, to align the representations of inter-modality. Furthermore, we also design a two-stream architecture to learn the dependence of intra-modality sequence and the complex interactions of inter-modality sequence, which can yield more expressive capacity in understanding user interests. We conduct extensive experiments on five public datasets. The experimental results show that the TSSR could yield superior performance than competitive baselines. We also make our experimental codes publicly available at https://anonymous.4open. science/r/TSSR-2A27/.
White dwarf systems: exoplanets and debris disks

Although there is abundant and diverse observational evidence in support of white dwarf stars hosting planets or debris disks which form in the catastrophic destruction of various planetary bodies, the key processes that explain these observations are still being intensely investigated. The study of white dwarf planetary systems offers a unique perspective on exo-solar composition, that cannot be obtained by any other means. This chapter describes the various observational techniques that are used in order to find and characterize exo-planets and debris disks around white dwarfs. In turn, it discusses how to theoretically interpret these observations by surveying an array of various research tools and models currently employed in this field.
Spatially oscillating correlation functions in $\left(2+1\right)$-dimensional four-fermion models: The mixing of scalar and vector modes at finite density

In this work, we demonstrate that the mixing of scalar and vector condensates produces spatially oscillating, but exponentially damped correlation functions in fermionic theories at finite density and temperature. We find a regime exhibiting this oscillatory behavior in a Gross-Neveu-type model that also features vector interactions within the mean-field approximation. The existence of this regime aligns with expectations based on symmetry arguments, that are also applicable to QCD at finite baryon density. We compute the phase diagram including both homogeneous phases and regions with spatially oscillating, exponentially damped correlation functions at finite temperature and chemical potential for different strengths of the vector coupling. Furthermore, we find that inhomogeneous condensates are disfavored compared to homogeneous ones akin to previous findings without vector interactions. We show that our results are valid for a broad class of $\left(2+1\right)$-dimensional models with local four-fermion interactions.
GRANDE: Gradient-Based Decision Tree Ensembles for Tabular Data

Despite the success of deep learning for text and image data, tree-based ensemble models are still state-of-the-art for machine learning with heterogeneous tabular data. However, there is a significant need for tabular-specific gradient-based methods due to their high flexibility. In this paper, we propose $\text{GRANDE}$, $\text{GRA}$die$\text{N}$t-Based $\text{D}$ecision Tree $\text{E}$nsembles, a novel approach for learning hard, axis-aligned decision tree ensembles using end-to-end gradient descent. GRANDE is based on a dense representation of tree ensembles, which affords to use backpropagation with a straight-through operator to jointly optimize all model parameters. Our method combines axis-aligned splits, which is a useful inductive bias for tabular data, with the flexibility of gradient-based optimization. Furthermore, we introduce an advanced instance-wise weighting that facilitates learning representations for both, simple and complex relations, within a single model. We conducted an extensive evaluation on a predefined benchmark with 19 classification datasets and demonstrate that our method outperforms existing gradient-boosting and deep learning frameworks on most datasets. The method is available under: https://github.com/s-marton/GRANDE
Exploring simplicity bias in 1D dynamical systems

Arguments inspired by algorithmic information theory predict an inverse relation between the probability and complexity of output patterns in a wide range of input-output maps. This phenomenon is known as \emph{simplicity bias}. By viewing the parameters of dynamical systems as inputs, and resulting (digitised) trajectories as outputs, we study simplicity bias in the logistic map, Gauss map, sine map, Bernoulli map, and tent map. We find that the logistic map, Gauss map, and sine map all exhibit simplicity bias upon sampling of map initial values and parameter values, but the Bernoulli map and tent map do not. The simplicity bias upper bound on output pattern probability is used to make \emph{a priori} predictions for the probability of output patterns. In some cases, the predictions are surprisingly accurate, given that almost no details of the underlying dynamical systems are assumed. More generally, we argue that studying probability-complexity relationships may be a useful tool in studying patterns in dynamical systems.
From Files to Streams: Revisiting Web History and Exploring Potentials for Future Prospects

Over the last 30 years, the World Wide Web has changed significantly. In this paper, we argue that common practices to prepare web pages for delivery conflict with many efforts to present content with minimal latency, one fundamental goal that pushed changes in the WWW. To bolster our arguments, we revisit reasons that led to changes of HTTP and compare them systematically with techniques to prepare web pages. We found that the structure of many web pages leverages features of HTTP/1.1 but hinders the use of recent HTTP features to present content quickly. To improve the situation in the future, we propose fine-grained content segmentation. This would allow to exploit streaming capabilities of recent HTTP versions and to render content as quickly as possible without changing underlying protocols or web browsers.
UPS: Towards Foundation Models for PDE Solving via Cross-Modal Adaptation

We introduce UPS (Unified PDE Solver), an effective and data-efficient approach to solve diverse spatiotemporal PDEs defined over various domains, dimensions, and resolutions. UPS unifies different PDEs into a consistent representation space and processes diverse collections of PDE data using a unified network architecture that combines LLMs with domain-specific neural operators. We train the network via a two-stage cross-modal adaptation process, leveraging ideas of modality alignment and multi-task learning. By adapting from pretrained LLMs and exploiting text-form meta information, we are able to use considerably fewer training samples than previous methods while obtaining strong empirical results. UPS outperforms existing baselines, often by a large margin, on a wide range of 1D and 2D datasets in PDEBench, achieving state-of-the-art results on 8 of 10 tasks considered. Meanwhile, it is capable of few-shot transfer to different PDE families, coefficients, and resolutions.
Machine Learning for Soccer Match Result Prediction

Machine learning has become a common approach to predicting the outcomes of soccer matches, and the body of literature in this domain has grown substantially in the past decade and a half. This chapter discusses available datasets, the types of models and features, and ways of evaluating model performance in this application domain. The aim of this chapter is to give a broad overview of the current state and potential future developments in machine learning for soccer match results prediction, as a resource for those interested in conducting future studies in the area. Our main findings are that while gradient-boosted tree models such as CatBoost, applied to soccer-specific ratings such as pi-ratings, are currently the best-performing models on datasets containing only goals as the match features, there needs to be a more thorough comparison of the performance of deep learning models and Random Forest on a range of datasets with different types of features. Furthermore, new rating systems using both player- and team-level information and incorporating additional information from, e.g., spatiotemporal tracking and event data, could be investigated further. Finally, the interpretability of match result prediction models needs to be enhanced for them to be more useful for team management.
Algebraicity in monochromatic homotopy theory

Using Patchkoria--Pstr\k{a}gowski's version of Franke's algebraicity theorem, we prove that the category of $K_p(n)$-local spectra is exotically equivalent to the category of derived $I_n$-complete periodic comodules over the Adams Hopf algebroid $(E_*, E_*E)$ for large primes. This gives a finite prime result analogous to the asymptotic algebraicity for $\mathrm{Sp}_{K_p(n)}$ of Barthel--Schlank--Stapleton.
FocusCLIP: Multimodal Subject-Level Guidance for Zero-Shot Transfer in Human-Centric Tasks

We propose FocusCLIP, integrating subject-level guidance--a specialized mechanism for target-specific supervision--into the CLIP framework for improved zero-shot transfer on human-centric tasks. Our novel contributions enhance CLIP on both the vision and text sides. On the vision side, we incorporate ROI heatmaps emulating human visual attention mechanisms to emphasize subject-relevant image regions. On the text side, we introduce human pose descriptions to provide rich contextual information. For human-centric tasks, FocusCLIP is trained with images from the MPII Human Pose dataset. The proposed approach surpassed CLIP by an average of 8.61% across five previously unseen datasets covering three human-centric tasks. FocusCLIP achieved an average accuracy of 33.65% compared to 25.04% by CLIP. We observed a 3.98% improvement in activity recognition, a 14.78% improvement in age classification, and a 7.06% improvement in emotion recognition. Moreover, using our proposed single-shot LLM prompting strategy, we release a high-quality MPII Pose Descriptions dataset to encourage further research in multimodal learning for human-centric tasks. Furthermore, we also demonstrate the effectiveness of our subject-level supervision on non-human-centric tasks. FocusCLIP shows a 2.47% improvement over CLIP in zero-shot bird classification using the CUB dataset. Our findings emphasize the potential of integrating subject-level guidance with general pretraining methods for enhanced downstream performance.
A unified diagrammatic approach in Liouville space to quantum transport for bosonic and fermionic reservoirs

We present a diagrammatic approach to quantum transport based on a master equation formalism in Liouville space. It can be applied to linear and nonlinear transport in generic multi-level junctions coupled to bosonic or fermionic reservoirs and presents a convenient perturbation expansion in the strength of the coupling between the reservoirs and the junction. The Redfield theory is recovered at second order, with the partial and full secular master equations discussed. Analytical, approximate expressions are provided up to fourth order for the steady-state boson transport that generalize to multi-level systems the known formula for the low-temperature thermal conductance in the spin-boson model. The formalism is applied to the problem of heat transport in a qubit-resonator junction modeled by the quantum Rabi model. Nontrivial transport features emerge as a result of the interplay between the qubit-oscillator detuning and coupling strength. For quasi-degenerate spectra, nonvanishing steady-state coherences cause a suppression of the thermal conductance.
iRoCo: Intuitive Robot Control From Anywhere Using a Smartwatch

This paper introduces iRoCo (intuitive Robot Control) - a framework for ubiquitous human-robot collaboration using a single smartwatch and smartphone. By integrating probabilistic differentiable filters, iRoCo optimizes a combination of precise robot control and unrestricted user movement from ubiquitous devices. We demonstrate and evaluate the effectiveness of iRoCo in practical teleoperation and drone piloting applications. Comparative analysis shows no significant difference between task performance with iRoCo and gold-standard control systems in teleoperation tasks. Additionally, iRoCo users complete drone piloting tasks 32\% faster than with a traditional remote control and report less frustration in a subjective load index questionnaire. Our findings strongly suggest that iRoCo is a promising new approach for intuitive robot control through smartwatches and smartphones from anywhere, at any time. The code is available at www.github. com/wearable-motion-capture
Dimension matters: precision and incompatibility in multi-parameter quantum estimation models

We study the role of probe dimension in determining the bounds of precision and the level of incompatibility in multi-parameter quantum estimation problems. In particular, we focus on the paradigmatic case of unitary encoding generated by $\mathfrak{su}(2)$ and compare precision and incompatibility in the estimation of the same parameters across representations of different dimensions. For two- and three-parameter unitary models, we prove that if the dimension of the probe is smaller than the number of parameters, then simultaneous estimation is not possible (the quantum Fisher matrix is singular). If the dimension is equal to the number of parameters, estimation is possible but the model exhibits maximal (asymptotic) incompatibility. However, for larger dimensions, there is always a state for which the incompatibility vanishes, and the symmetric Cram\'er-Rao bound is achievable. We also critically examine the performance of the so-called asymptotic incompatibility (AI) in characterizing the difference between the Holevo-Cram\'er-Rao bound and the Symmetric Logarithmic Derivative (SLD) one, showing that the AI measure alone may fail to adequately quantify this gap. Assessing the determinant of the Quantum Fisher Information Matrix (QFIM) is crucial for a precise characterization of the model's nature. Nonetheless, the AI measure still plays a relevant role since it encapsulates the non-classicality of the model in one scalar quantity rather than in a matrix form (i.e., the Uhlmann curvature).
Mapping Traditional Bird Knowledge for Urban Bird Conservation in Halifax, Nova Scotia, Canada

viii ACKNOWLEDGEMENTS . ix CHAPTER 1 – INTRODUCTION . 1 CHAPTER 2 – INTRODUCTION TO BIRDS IN THE CITY. 4 2.1. URBANIZATION AND URBAN BIODIVERSITY . 4 2.2. BIRDS AND URBANIZATION . 6 2.3. BIRDS AS INDICATORS OF URBAN ECOLOGICAL INTEGRITY . 11 CHAPTER 3 – MAPPING TRADITIONAL BIRD KNOWLEDGE . 14 3.1. BACKGROUND . 14 3.2. OBJECTIVES . 15 3.3. RESEARCH QUESTIONS . 15 3.4. PROCESS . 16 3.5. METHODS . 16 3.5. 1 Selection of study participants . 16 3.5. 2 Selection of study area . 17 3.5. 4. Semi-structured interviews. 20 3.5. 5 Data Verification . 21 3.5. 6. Focus group . 22 3.5. 7. Transcribing. 23 3.5. 8. Coding process and Nvivo . 23 3.5. 9. Map digitization . 24 3.6. LOCATION AND CHARACTERISTICS OF REGULAR BIRDING SPOTS . 25 3.6. 1. Popular birding spots . 26 3.6. 2. Less well-known sites . 27 3.7. LOCATION OF URBAN BIRD CONSERVATION HOTSPOTS. 30 3.7. 1. Seasonal Effects on Location . 30