Miracle-dz/UniFAR-FTUs huggingface dataset:
Topology of matching complexes of complete graphs via discrete Morse theory

We denote the abstract simplicial complex containing all matchings in the complete graph of order $n$ by $M_n$. Bouc first studied the topological properties of $M_n$ in connection with the Quillen complexes. Later Bj\"orner et al. showed that $M_n$ is homotopically $(\nu_n-1)$-connected, where $\nu_n=\lfloor{\frac{n+1}{3}}\rfloor-1$, and conjectured that this connectivity bound is sharp. Shareshian and Wachs settled the conjecture by inductively showing that the $\nu_n$-dimensional homology group of $M_n$ is nontrivial, with Bouc's"hand"calculation of $H_1(M_7)$ serving as the pivotal base step. In general, the topology of $M_n$ is not very well-understood, even for a small $n$. In the present article, we look into the topology of $M_n$, and $M_7$ in particular, in the light of discrete Morse theory as developed by Forman. This theory gives a combinatorial generalization of the classical notion of a smooth gradient vector field, called (discrete) gradient vector field. Similar to the smooth case, the critical simplices capture the topology of the complex. We first construct a gradient vector field on $M_n$ that doesn't admit any critical simplices up to dimension $\nu_n-1$, except one $0$-simplex, which also leads to the aforementioned $(\nu_n-1)$-connectedness of $M_n$ in a purely combinatorial way. However, for an efficient homology computation, we need to work with a gradient vector field that admits a low number of critical simplices. An optimal gradient vector field is one with the least number of critical simplices, but the problem of finding one is an NP-hard problem. We improve the gradient vector field constructed on $M_7$ in particular to a near-optimal one, and then with the help of this improved gradient vector field, compute the homology groups of $M_7$ in an efficient and algorithmic manner. We also augment this near-optimal gradient vector field to an optimal one.
“My Unconditional Homework Buddy:” Exploring Children’s Preferences for a Homework Companion Robot

We aim to design robotic educational support systems that can promote socially and intellectually meaningful learning experiences for students while they complete school work outside of class. To pursue this goal, we conducted participatory design studies with 10 children (aged 10–12) to explore their design needs for robot-assisted homework. We investigated children’s current ways of doing homework, the type of support they receive while doing homework, and co-designed the speech and expressiveness of a homework companion robot. Children and parents attending our design sessions explained that an emotionally expressive social robot as a homework aid can support students’ motivation and engagement, as well as their affective state. Children primarily perceived the robot as a dedicated assistant at home, capable of forming meaningful friendships, or a shared classroom learning resource. We present key design recommendations to support students’ homework experiences with a learning companion robot.
A Preconditioned Riemannian Gradient Descent Algorithm for Low-Rank Matrix Recovery

The low-rank matrix recovery problem often arises in various fields, including signal processing, machine learning, and imaging science. The Riemannian gradient descent (RGD) algorithm has proven to be an efficient algorithm for solving this problem. In this paper, we present a preconditioned Riemannian gradient descent (PRGD) for low-rank matrix recovery. The preconditioner, noted for its simplicity and computational efficiency, is constructed by weighting the (i,j)-th entry of the gradient matrix according to the norms of the i-th row and the j-th column. We establish the theoretical recovery guarantee for PRGD under the restricted isometry property assumption. Experimental results indicate that PRGD can accelerate RGD by up to tenfold in solving low-rank matrix recovery problems such as matrix completion.
Boosted Ensembles of Qubit and Continuous Variable Quantum Support Vector Machines for B Meson Flavor Tagging

The recent physical realization of quantum computers with hundreds of noisy qubits has given birth to an intense search for useful applications of their unique capabilities. One area that has received particular attention is quantum machine learning (QML), the study of machine learning algorithms running natively on quantum computers. In this work, QML methods are developed and applied to B meson flavor tagging, an important component of experiments which probe CP violation in order to better understand the matter‐antimatter asymmetry of the universe. One simulate boosted ensembles of quantum support vector machines (QSVMs) based on both conventional qubit‐based and continuous variable architectures, attaining effective tagging efficiencies of 28.0% and 29.2%, respectively, comparable with the leading published result of 30.0% using classical machine learning algorithms. The ensemble nature of the classifier is of particular importance, doubling the effective tagging efficiency of a single QSVM, which is find to be highly prone to overfitting. These results are obtained despite the constraint of working with QSVM architectures that are classically simulable, and it finds evidence that QSVMs beyond the simulable regime may be able to realize even higher performance, when sufficiently powerful quantum hardware is developed to execute them.
An efficient doubly-robust imputation framework for longitudinal dropout, with an application to an Alzheimer’s clinical trial

We develop a novel doubly-robust (DR) imputation framework for longitudinal studies with monotone dropout, motivated by the informative dropout that is common in FDA-regulated trials for Alzheimer's disease. In this approach, the missing data are first imputed using a doubly-robust augmented inverse probability weighting (AIPW) estimator, then the imputed completed data are substituted into a full-data estimating equation, and the estimate is obtained using standard software. The imputed completed data may be inspected and compared to the observed data, and standard model diagnostics are available. The same imputed completed data can be used for several different estimands, such as subgroup analyses in a clinical trial, allowing for reduced computation and increased consistency across analyses. We present two specific DR imputation estimators, AIPW-I and AIPW-S, study their theoretical properties, and investigate their performance by simulation. AIPW-S has substantially reduced computational burden compared to many other DR estimators, at the cost of some loss of efficiency and the requirement of stronger assumptions. Simulation studies support the theoretical properties and good performance of the DR imputation framework. Importantly, we demonstrate their ability to address time-varying covariates, such as a time by treatment interaction. We illustrate using data from a large randomized Phase III trial investigating the effect of donepezil in Alzheimer's disease, from the Alzheimer's Disease Cooperative Study (ADCS) group.
Charged scalar fields and superconductivity in AdS4/CFT3.

In this thesis I shall examine the energy behaviour of a massive charged scalar field in an Anti de-Sitter-Reissner-Nordstrom space-time background using the AdS/CFT correspondence. After obtaining the equations of motion for the system I numerically solve for the vacuum expectation values of the energy momentum tensor of the field on an AdS4 background. It will be shown that the vev condenses as the temperature of the black hole is lowered, suggesting superconductivity. I then add a time dependent part to the scalar field and examine how it behaves on the AdS4 boundary with respect to a varying frequency and momentum. The boundary field shows evidence of the emergence of quasiparticle pairs at large momentum. The relevant Greens functions are then calculated numerically and presented as a sequence of surface and contour plots. From these we are able to determine the dispersion relations for the field. I shall also find pseudo-particle masses at zero momentum, and consequently see the lowest energy bound which a particle in this field can adopt.
Mitigating crosstalk errors by randomized compiling: Simulation of the BCS model on a superconducting quantum computer

We develop and apply an extension of the randomized compiling (RC) protocol that includes a special treatment of neighboring qubits and dramatically reduces crosstalk effects caused by the application of faulty gates on superconducting qubits in IBMQ quantum computers (\texttt{ibm\_lagos} and \texttt{ibmq\_ehningen}). Crosstalk errors, stemming from CNOT two-qubit gates, are a crucial source of errors on numerous quantum computing platforms. For the IBMQ machines, their magnitude is often overlooked-9. Our RC protocol turns coherent noise due to crosstalk into a depolarising noise channel that can then be treated using established error mitigation schemes, such as noise estimation circuits. We apply our approach to the quantum simulation of the non-equilibrium dynamics of the Bardeen-Cooper-Schrieffer (BCS) Hamiltonian for superconductivity, a particularly challenging model to simulate on quantum hardware because of the long-range interaction of Cooper pairs. With 135 CNOT gates, we work in a regime where crosstalk, as opposed to either trotterization or qubit decoherence, dominates the error. Our twirling of neighboring qubits is shown to dramatically improve the noise estimation protocol without the need to add new qubits or circuits and allows for a quantitative simulation of the BCS model.
Single-Shot Implicit Morphable Faces with Consistent Texture Parameterization

There is a growing demand for the accessible creation of high-quality 3D avatars that are animatable and customizable. Although 3D morphable models provide intuitive control for editing and animation, and robustness for single-view face reconstruction, they cannot easily capture geometric and appearance details. Methods based on neural implicit representations, such as signed distance functions (SDF) or neural radiance fields, approach photo-realism, but are difficult to animate and do not generalize well to unseen data. To tackle this problem, we propose a novel method for constructing implicit 3D morphable face models that are both generalizable and intuitive for editing. Trained from a collection of high-quality 3D scans, our face model is parameterized by geometry, expression, and texture latent codes with a learned SDF and explicit UV texture parameterization. Once trained, we can reconstruct an avatar from a single in-the-wild image by leveraging the learned prior to project the image into the latent space of our model. Our implicit morphable face models can be used to render an avatar from novel views, animate facial expressions by modifying expression codes, and edit textures by directly painting on the learned UV-texture maps. We demonstrate quantitatively and qualitatively that our method improves upon photo-realism, geometry, and expression accuracy compared to state-of-the-art methods.
Correcting for Interference in Experiments: A Case Study at Douyin

Interference is a ubiquitous problem in experiments conducted on two-sided content marketplaces, such as Douyin (China’s analog of TikTok). In many cases, creators are the natural unit of experimentation, but creators interfere with each other through competition for viewers’ limited time and attention. “Naive” estimators currently used in practice simply ignore the interference, but in doing so incur bias on the order of the treatment effect. We formalize the problem of inference in such experiments as one of policy evaluation. Off-policy estimators, while unbiased, are impractically high variance. We introduce a novel Monte-Carlo estimator, based on “Differences-in-Qs” (DQ) techniques, which achieves bias that is second-order in the treatment effect, while remaining sample-efficient to estimate. On the theoretical side, our contribution is to develop a generalized theory of Taylor expansions for policy evaluation, which extends DQ theory to all major MDP formulations. On the practical side, we implement our estimator on Douyin’s experimentation platform, and in the process develop DQ into a truly “plug-and-play” estimator for interference in real-world settings: one which provides robust, low-bias, low-variance treatment effect estimates; admits computationally cheap, asymptotically exact uncertainty quantification; and reduces MSE by 99% compared to the best existing alternatives in our applications.
Hybrid AHS: A Hybrid of Kalman Filter and Deep Learning for Acoustic Howling Suppression

Deep learning has been recently introduced for efficient acoustic howling suppression (AHS). However, the recurrent nature of howling creates a mismatch between offline training and streaming inference, limiting the quality of enhanced speech. To address this limitation, we propose a hybrid method that combines a Kalman filter with a self-attentive recurrent neural network (SARNN) to leverage their respective advantages for robust AHS. During offline training, a pre-processed signal obtained from the Kalman filter and an ideal microphone signal generated via teacher-forced training strategy are used to train the deep neural network (DNN). During streaming inference, the DNN's parameters are fixed while its output serves as a reference signal for updating the Kalman filter. Evaluation in both offline and streaming inference scenarios using simulated and real-recorded data shows that the proposed method efficiently suppresses howling and consistently outperforms baselines.
Cognitive Reframing of Negative Thoughts through Human-Language Model Interaction

A proven therapeutic technique to overcome negative thoughts is to replace them with a more hopeful “reframed thought. ” Although therapy can help people practice and learn this Cognitive Reframing of Negative Thoughts, clinician shortages and mental health stigma commonly limit people’s access to therapy. In this paper, we conduct a human-centered study of how language models may assist people in reframing negative thoughts. Based on psychology literature, we define a framework of seven linguistic attributes that can be used to reframe a thought. We develop automated metrics to measure these attributes and validate them with expert judgements from mental health practitioners. We collect a dataset of 600 situations, thoughts and reframes from practitioners and use it to train a retrieval-enhanced in-context learning model that effectively generates reframed thoughts and controls their linguistic attributes. To investigate what constitutes a “high-quality” reframe, we conduct an IRB-approved randomized field study on a large mental health website with over 2,000 participants. Amongst other findings, we show that people prefer highly empathic or specific reframes, as opposed to reframes that are overly positive. Our findings provide key implications for the use of LMs to assist people in overcoming negative thoughts.
Diffuse and Localized Functional Dysconnectivity in Schizophrenia: a Bootstrapped Top-Down Approach

Schizophrenia is a brain disorder leading to detached mind’s normally integrated processes. Hence, the exploration of the symptoms in relation to functional connectivity (FC) had great relevance in the field. Connectivity can be investigated on different levels, going from global features to single edges between pairs of regions, revealing diffuse and localized dysconnection patterns. In this context, schizophrenia is characterized by a different global integration with reduced connectivity in specific areas of the brain, part of the Default Mode Network (DMN). However, the assessment of FC presents various sources of uncertainty. This study proposes a multi-level approach for more robust group-comparison. FC data between 74 AAL brain areas of 15 healthy controls (HC) and 12 subjects with chronic schizophrenia (SZ) were used. Multi-level analyses were carried out by the previously published SPIDER-NET tool. Graph topological indexes were evaluated to assess global abnormalities. Robustness was augmented by bootstrapped (BOOT) data and the stability was evaluated by removing one (RST1) or two subjects (RST2). The DMN subgraph was extracted and specifically evaluated. Changes relevant to the overall local indexes were also analyzed. Finally, the connection weights were explored to enhance common strongest activations/deactivations. At a global level, expected trends of the indexes were found and the significance of modularity (p = 0.043) was not confirmed by BOOT (p = 0.133). The robustness assessment tests (both RST1 and RST2) highlighted more stable results for BOOT compared to the direct data testing. Conversely, significant results were found in the analysis at lower levels. The DMN highlighted reduced connectivity and strength as well as increased deactivation in the SZ group. At local level, 13 areas were found to be significantly different (p < 0.05) in the groups, highlighting a greater divergence in the frontal lobe. These results were confirmed analyzing the single negative edges, suggesting inverted connectivity between prefronto-temporal areas. In conclusion, multi-level analysis supported by BOOT is highly recommended when analyzing FC, especially when diffuse and localized dysconnections must be investigated in limited samples.
SuperNOVA: Design Strategies and Opportunities for Interactive Visualization in Computational Notebooks

Computational notebooks, such as Jupyter Notebook, have become data scientists' de facto programming environments. Many visualization researchers and practitioners have developed interactive visualization tools that support notebooks, yet little is known about the appropriate design of these tools. To address this critical research gap, we investigate the design strategies in this space by analyzing 163 notebook visualization tools. Our analysis encompasses 64 systems from academic papers and 105 systems sourced from a pool of 55k notebooks containing interactive visualizations that we obtain via scraping 8.6 million notebooks on GitHub. Through this study, we identify key design implications and trade-offs, such as leveraging multimodal data in notebooks as well as balancing the degree of visualization-notebook integration. Furthermore, we provide empirical evidence that tools compatible with more notebook platforms have a greater impact. Finally, we develop SuperNOVA, an open-source interactive browser to help researchers explore existing notebook visualization tools. SuperNOVA is publicly accessible at: https://poloclub.github. io/supernova/.
Towards End-to-End Semi-Supervised Table Detection with Deformable Transformer

Table detection is the task of classifying and localizing table objects within document images. With the recent development in deep learning methods, we observe remarkable success in table detection. However, a significant amount of labeled data is required to train these models effectively. Many semi-supervised approaches are introduced to mitigate the need for a substantial amount of label data. These approaches use CNN-based detectors that rely on anchor proposals and post-processing stages such as NMS. To tackle these limitations, this paper presents a novel end-to-end semi-supervised table detection method that employs the deformable transformer for detecting table objects. We evaluate our semi-supervised method on PubLayNet, DocBank, ICADR-19 and TableBank datasets, and it achieves superior performance compared to previous methods. It outperforms the fully supervised method (Deformable transformer) by +3.4 points on 10\% labels of TableBank-both dataset and the previous CNN-based semi-supervised approach (Soft Teacher) by +1.8 points on 10\% labels of PubLayNet dataset. We hope this work opens new possibilities towards semi-supervised and unsupervised table detection methods.
Guidance & Control Networks for Time-Optimal Quadcopter Flight

Reaching fast and autonomous flight requires computationally efficient and robust algorithms. To this end, we train Guidance&Control Networks to approximate optimal control policies ranging from energy-optimal to time-optimal flight. We show that the policies become more difficult to learn the closer we get to the time-optimal 'bang-bang' control profile. We also assess the importance of knowing the maximum angular rotor velocity of the quadcopter and show that over- or underestimating this limit leads to less robust flight. We propose an algorithm to identify the current maximum angular rotor velocity onboard and a network that adapts its policy based on the identified limit. Finally, we extend previous work on Guidance&Control Networks by learning to take consecutive waypoints into account. We fly a 4x3m track in similar lap times as the differential-flatness-based minimum snap benchmark controller while benefiting from the flexibility that Guidance&Control Networks offer.
Breaking the Reptation Trap: Escape Dynamics of Semi-Flexible Polymers in Crowded Networks

Semi-flexible polymers in crowded environments exhibit complex dynamics that play a crucial role in various biological and material design processes. Based on the classic reptation theory, it is generally believed that semiflexible polymers are trapped within static confinements. Here we demonstrate that semi-flexible polymers are indeed trapped in short-lived kinetic cages. We have developed a novel scaling law for rotational diffusion through the examination of the polymer network at the level of an individual nanofiber and quantify the onset of entanglement and entanglement itself by introducing an effective tube diameter. We also show that understanding these dynamics is critical for interpreting the macroscopic behavior of such systems, as evidenced by our micro-rheology analysis. The insight from our study provides a rectified microscopic understanding of the dynamics of semi-flexible polymers in complex environments, yielding new principles to better understand the macroscopic behavior of crowded systems, from biophysics to materials science.
Revisiting Graph Contrastive Learning for Anomaly Detection

Combining Graph neural networks (GNNs) with contrastive learning for anomaly detection has drawn rising attention recently. Existing graph contrastive anomaly detection (GCAD) methods have primarily focused on improving detection capability through graph augmentation and multi-scale contrast modules. However, the underlying mechanisms of how these modules work have not been fully explored. We dive into the multi-scale and graph augmentation mechanism and observed that multi-scale contrast modules do not enhance the expression, while the multi-GNN modules are the hidden contributors. Previous studies have tended to attribute the benefits brought by multi-GNN to the multi-scale modules. In the paper, we delve into the misconception and propose Multi-GNN and Augmented Graph contrastive framework MAG, which unified the existing GCAD methods in the contrastive self-supervised perspective. We extracted two variants from the MAG framework, L-MAG and M-MAG. The L-MAG is the lightweight instance of the MAG, which outperform the state-of-the-art on Cora and Pubmed with the low computational cost. The variant M-MAG equipped with multi-GNN modules further improve the detection performance. Our study sheds light on the drawback of the existing GCAD methods and demonstrates the potential of multi-GNN and graph augmentation modules. Our code is available at https://github.com/liuyishoua/MAG-Framework.