Miracle-dz/UniFAR-FTUs huggingface dataset:
Monotone Causality in Opportunistically Stochastic Shortest Path Problems

When traveling through a graph with an accessible deterministic path to a target, is it ever preferable to resort to stochastic node-to-node transitions instead? And if so, what are the conditions guaranteeing that such a stochastic optimal routing policy can be computed efficiently? We aim to answer these questions here by defining a class of Opportunistically Stochastic Shortest Path (OSSP) problems and deriving sufficient conditions for applicability of non-iterative label-setting methods. The usefulness of this framework is demonstrated in two very different contexts: numerical analysis and autonomous vehicle routing. We use OSSPs to derive causality conditions for semi-Lagrangian discretizations of anisotropic Hamilton-Jacobi equations. We also use a Dijkstra-like method to solve OSSPs optimizing the timing and urgency of lane change maneuvers for an autonomous vehicle navigating road networks with a heterogeneous traffic load.
Spectral Phase Transitions in Non-Linear Wigner Spiked Models

We study the asymptotic behavior of the spectrum of a random matrix where a non-linearity is applied entry-wise to a Wigner matrix perturbed by a rank-one spike with independent and identically distributed entries. In this setting, we show that when the signal-to-noise ratio scale as $N^{\frac{1}{2} (1-1/k_\star)}$, where $k_\star$ is the first non-zero generalized information coefficient of the function, the non-linear spike model effectively behaves as an equivalent spiked Wigner matrix, where the former spike before the non-linearity is now raised to a power $k_\star$. This allows us to study the phase transition of the leading eigenvalues, generalizing part of the work of Baik, Ben Arous and Pech\'e to these non-linear models.
Learning Reward for Physical Skills using Large Language Model

Learning reward functions for physical skills are challenging due to the vast spectrum of skills, the high-dimensionality of state and action space, and nuanced sensory feedback. The complexity of these tasks makes acquiring expert demonstration data both costly and time-consuming. Large Language Models (LLMs) contain valuable task-related knowledge that can aid in learning these reward functions. However, the direct application of LLMs for proposing reward functions has its limitations such as numerical instability and inability to incorporate the environment feedback. We aim to extract task knowledge from LLMs using environment feedback to create efficient reward functions for physical skills. Our approach consists of two components. We first use the LLM to propose features and parameterization of the reward function. Next, we update the parameters of this proposed reward function through an iterative self-alignment process. In particular, this process minimizes the ranking inconsistency between the LLM and our learned reward functions based on the new observations. We validated our method by testing it on three simulated physical skill learning tasks, demonstrating effective support for our design choices.
Offline Metrics for Evaluating Explanation Goals in Recommender Systems

Explanations are crucial for improving users' transparency, persuasiveness, engagement, and trust in Recommender Systems (RSs). However, evaluating the effectiveness of explanation algorithms regarding those goals remains challenging due to existing offline metrics' limitations. This paper introduces new metrics for the evaluation and validation of explanation algorithms based on the items and properties used to form the sentence of an explanation. Towards validating the metrics, the results of three state-of-the-art post-hoc explanation algorithms were evaluated for six RSs, comparing the offline metrics results with those of an online user study. The findings show the proposed offline metrics can effectively measure the performance of explanation algorithms and highlight a trade-off between the goals of transparency and trust, which are related to popular properties, and the goals of engagement and persuasiveness, which are associated with the diversification of properties displayed to users. Furthermore, the study contributes to the development of more robust evaluation methods for explanation algorithms in RSs.
FHT-Map: Feature-based Hierarchical Topological Map for Relocalization and Path Planning

Topological maps are favorable for their small storage compared to geometric map. However, they are limited in relocalization and path planning capabilities. To solve this problem, a feature-based hierarchical topological map (FHT-Map) is proposed along with a real-time map construction algorithm for robot exploration. Specifically, the FHT-Map utilizes both RGB cameras and LiDAR information and consists of two types of nodes: main node and support node. Main nodes will store visual information compressed by convolutional neural network and local laser scan data to enhance subsequent relocalization capability. Support nodes retain a minimal amount of data to ensure storage efficiency while facilitating path planning. After map construction with robot exploration, the FHT-Map can be used by other robots for relocalization and path planning. Experiments are conducted in Gazebo simulator, and the results demonstrate that the proposed FHT-Map can effectively improve relocalization and path planning capability compared with other topological maps. Moreover, experiments on hierarchical architecture are implemented to show the necessity of two types of nodes.
A Specialized Semismooth Newton Method for Kernel-Based Optimal Transport

Kernel-based optimal transport (OT) estimators offer an alternative, functional estimation procedure to address OT problems from samples. Recent works suggest that these estimators are more statistically efficient than plug-in (linear programming-based) OT estimators when comparing probability measures in high-dimensions~\citep{Vacher-2021-Dimension}. Unfortunately, that statistical benefit comes at a very steep computational price: because their computation relies on the short-step interior-point method (SSIPM), which comes with a large iteration count in practice, these estimators quickly become intractable w.r. t. sample size $n$. To scale these estimators to larger $n$, we propose a nonsmooth fixed-point model for the kernel-based OT problem, and show that it can be efficiently solved via a specialized semismooth Newton (SSN) method: We show, exploring the problem's structure, that the per-iteration cost of performing one SSN step can be significantly reduced in practice. We prove that our SSN method achieves a global convergence rate of $O(1/\sqrt{k})$, and a local quadratic convergence rate under standard regularity conditions. We show substantial speedups over SSIPM on both synthetic and real datasets.
Optimal Batched Best Arm Identification

We study the batched best arm identification (BBAI) problem, where the learner's goal is to identify the best arm while switching the policy as less as possible. In particular, we aim to find the best arm with probability $1-\delta$ for some small constant $\delta>0$ while minimizing both the sample complexity (total number of arm pulls) and the batch complexity (total number of batches). We propose the three-batch best arm identification (Tri-BBAI) algorithm, which is the first batched algorithm that achieves the optimal sample complexity in the asymptotic setting (i.e., $\delta\rightarrow 0$) and runs only in at most $3$ batches. Based on Tri-BBAI, we further propose the almost optimal batched best arm identification (Opt-BBAI) algorithm, which is the first algorithm that achieves the near-optimal sample and batch complexity in the non-asymptotic setting (i.e., $\delta>0$ is arbitrarily fixed), while enjoying the same batch and sample complexity as Tri-BBAI when $\delta$ tends to zero. Moreover, in the non-asymptotic setting, the complexity of previous batch algorithms is usually conditioned on the event that the best arm is returned (with a probability of at least $1-\delta$), which is potentially unbounded in cases where a sub-optimal arm is returned. In contrast, the complexity of Opt-BBAI does not rely on such an event. This is achieved through a novel procedure that we design for checking whether the best arm is eliminated, which is of independent interest.
Time periodic and almost periodic viscosity solutions of contact Hamilton-Jacobi equations on $\mathbb{T}^n$

This paper concerns with the time periodic viscosity solution problem for a class of evolutionary contact Hamilton-Jacobi equations with time independent Hamiltonians on the torus $\mathbb{T}^n$. Under certain suitable assumptions we show that the equation has a non-trivial $T$-periodic viscosity solution if and only if $T\in D$, where $D$ is a dense subset of $[0,+\infty)$. Moreover, we clarify the structure of $D$. As a consequence, we also study the existence of Bohr almost periodic viscosity solutions.
Skipped Feature Pyramid Network with Grid Anchor for Object Detection

CNN-based object detection methods have achieved significant progress in recent years. The classic structures of CNNs produce pyramid-like feature maps due to the pooling or other re-scale operations. The feature maps in different levels of the feature pyramid are used to detect objects with different scales. For more accurate object detection, the highest-level feature, which has the lowest resolution and contains the strongest semantics, is up-scaled and connected with the lower-level features to enhance the semantics in the lower-level features. However, the classic mode of feature connection combines the feature of lower-level with all the features above it, which may result in semantics degradation. In this paper, we propose a skipped connection to obtain stronger semantics at each level of the feature pyramid. In our method, the lower-level feature only connects with the feature at the highest level, making it more reasonable that each level is responsible for detecting objects with fixed scales. In addition, we simplify the generation of anchor for bounding box regression, which can further improve the accuracy of object detection. The experiments on the MS COCO and Wider Face demonstrate that our method outperforms the state-of-the-art methods.
The Tur\'an and Laguerre inequalities for quasi-polynomial-like functions

This paper deals with both the higher order Tur\'an inequalities and the Laguerre inequalities for quasi-polynomial-like functions -- that are expressions of the form $f(n)=c_l(n)n^l+\cdots+c_d(n)n^d+o(n^d)$, where $d,l\in\mathbb{N}$ and $d\leqslant l$. A natural example of such a function is the $A$-partition function $p_{A}(n)$, which enumerates the number of partitions of $n$ with parts in the fixed finite multiset $A=\{a_1,a_2,\ldots,a_k\}$ of positive integers. For an arbitrary positive integer $d$, we present efficient criteria for both the order $d$ Tur\'an inequality and the $d$th Laguarre inequality for quasi-polynomial-like functions. In particular, we apply these results to deduce non-trivial analogues for $p_A(n)$.
CLMSM: A Multi-Task Learning Framework for Pre-training on Procedural Text

In this paper, we propose CLMSM, a domain-specific, continual pre-training framework, that learns from a large set of procedural recipes. CLMSM uses a Multi-Task Learning Framework to optimize two objectives - a) Contrastive Learning using hard triplets to learn fine-grained differences across entities in the procedures, and b) a novel Mask-Step Modelling objective to learn step-wise context of a procedure. We test the performance of CLMSM on the downstream tasks of tracking entities and aligning actions between two procedures on three datasets, one of which is an open-domain dataset not conforming with the pre-training dataset. We show that CLMSM not only outperforms baselines on recipes (in-domain) but is also able to generalize to open-domain procedural NLP tasks.
The Measure Preserving Martingale Sinkhorn Algorithm

We contribute to the recent studies of the so-called Bass martingale. Backhoff-Veraguas et al. (2020) showed it is the solution to the martingale Benamou-Brenier (mBB) problem, i.e., among all martingales with prescribed initial and terminal distributions it is the one closest to Brownian motion. We link it with the semimartingale optimal transport and deduce an alternative way to arrive at the dual formulation recently obtained in Backhoff-Beraguas et al. (2023). We then consider computational methods to compute the Bass martingale. We propose an approach analogous to the famous Sinkhorn algorithm used to solve the entropic optimal transport problem. Our algorithm, dubbed the measure preserving martingale Sinkhorn (MPMS), is equivalent to the fixed-point method of Conze and Henry-Labordere (2021) and performs very well on a range of examples, including a market data case study.
On the Neural Tangent Kernel of Equilibrium Models

This work studies the neural tangent kernel (NTK) of the deep equilibrium (DEQ) model, a practical ``infinite-depth'' architecture which directly computes the infinite-depth limit of a weight-tied network via root-finding. Even though the NTK of a fully-connected neural network can be stochastic if its width and depth both tend to infinity simultaneously, we show that contrarily a DEQ model still enjoys a deterministic NTK despite its width and depth going to infinity at the same time under mild conditions. Moreover, this deterministic NTK can be found efficiently via root-finding.
Noncommutative spacetimes versus noncommutative spaces of geodesics

The aim of this contribution is twofold. First, we show that when two (or more) different quantum groups share the same noncommutative spacetime, such an ‘ambiguity’ can be resolved by considering together their corresponding noncommutative spaces of geodesics. In any case, the latter play a mathematical/physical role by themselves and, in some cases, they can be interpreted as deformed phase spaces. Second, we explicitly show that noncommutative spacetimes can be reproduced from ‘extended’ noncommutative spaces of geodesics which are those enlarged by the time translation generator. These general ideas are described in detail for the κ-Poincaré and κ-Galilei algebras.
NFT formalised

Non-fungible tokens, NFT, have been used to record ownership of real estate, art, digital assets, and more recently to serve legal notice. They provide an important and accessible non-financial use of cryptocurrency's blockchain but are peculiar because ownership by NFT confers no rights over the asset. This work shows that it is possible to specify that peculiar property by combining functional and epistemic properties. Suitability of the specification is evaluated by proof that the blockchain implementation conforms to it, and by its use in an analysis of serving legal notice.
Ensemble Learning for Graph Neural Networks

Graph Neural Networks (GNNs) have shown success in various fields for learning from graph-structured data. This paper investigates the application of ensemble learning techniques to improve the performance and robustness of Graph Neural Networks (GNNs). By training multiple GNN models with diverse initializations or architectures, we create an ensemble model named ELGNN that captures various aspects of the data and uses the Tree-Structured Parzen Estimator algorithm to determine the ensemble weights. Combining the predictions of these models enhances overall accuracy, reduces bias and variance, and mitigates the impact of noisy data. Our findings demonstrate the efficacy of ensemble learning in enhancing GNN capabilities for analyzing complex graph-structured data. The code is public at https://github.com/wongzhenhao/ELGNN.
A Simple Step-by-Step Guide to Permanent Punctal Occlusion with the Ophthalmic Burr and Suture Technique

Abstract The medical treatment of dry eye disease usually follows a step-wise approach to achieve clinical improvement, ranging from non-surgical interventions with intensive lubrication to permanent surgical punctal occlusion. While frequent lubrication is essential, the intense regime is often too burdensome and difficult to maintain at the required frequency. Punctal plugs are an invaluable alternative approach, but also have limitations, especially in conscious children, in whom inserting and re-inserting punctum plugs in clinic can be challenging. If a patient has permanent and severe dry eye disease and responded well to a trial of temporary punctal plugging, a permanent surgical solution should be considered next. Liu et al showed that a more successful, yet simple technique to achieve permanent occlusion is to combine de-epithelialising the punctum and ampulla with the immediate firm apposition of the de-epithelialised surface using a self-absorbable suture – with a success rate of 92% in a prospective study. This article demonstrates this technique step-by-step in an 8-year-old child with severe chronic dry eye disease following proton beam therapy for orbital rhabdomyosarcoma. She underwent this procedure with excellent results.
Research on ground subsidence mechanism of solution mining by drilling and control countermeasures

In this paper the stability of cavern and overburden of solution mining by drilling were analyzed and calculated,and so did the ground surface deformation and movements.The mechanism of the destabilization of cavern and ground subsidence were discussed.Some methods were suggested to control the destabilization of cavern and ground subsidence from which we can draw lessons for the solution mining by drilling.
A Social Media Website (Supporting Our Valued Adolescents) to Support Treatment Uptake for Adolescents With Depression and/or Anxiety and Their Parents: Protocol for a Pilot Randomized Controlled Trial

Background Few adolescents who experience depression or anxiety connect to mental health treatment. Supporting Our Valued Adolescents (SOVA) is a stakeholder-informed technology intervention that consists of 2 blog-format websites—one for adolescents and another for parents. SOVA is designed to intervene on targets, which may increase the mental health treatment uptake when adolescents with depression or anxiety are identified in primary care settings. Objective This study aims to describe the protocol for a pilot randomized controlled trial designed to refine recruitment and retention strategies, document intervention fidelity and implementation outcomes, and assess changes in health beliefs and knowledge, emotional or informational support, and parent-adolescent communication quality in adolescents and their parents. Methods Adolescents identified with symptoms of depression or anxiety, for which a health care provider recommends treatment, and their parents will be recruited from clinics where adolescents are seen for primary care. Adolescent-parent dyads will be randomized at 1:1 to both receive the SOVA websites and enhanced usual care or enhanced usual care alone. Baseline measures and 6-week and 3-month outcomes will be collected by Web-based self-report surveys and electronic health record review. The main pilot outcome is the 6-week study retention rate. Analyses will also assess changes in health beliefs and knowledge, emotional support, and parent-adolescent communication in both adolescents and their parents. Results The project was funded in 2017. Recruitment commenced in April 2018 and enrollment is ongoing, with completion anticipated at the end of 2019 with subsequent plans for data analysis and publication submission in early 2020. Conclusions The findings of this research will inform the design of a multisite hybrid effectiveness-implementation randomized controlled trial examining the effectiveness and optimal implementation strategies for using SOVA in community primary care settings. Trial Registration ClinicalTrials.gov NCT03318666; https://clinicaltrials.gov/ct2/show/NCT03318666 International Registered Report Identifier (IRRID) PRR1-10.2196/12117
Internet-Based Treatment for Adults with Depressive Symptoms: Randomized Controlled Trial

Background Many depressed people do not receive help for their symptoms, and there are various barriers that impede help-seeking. The Internet may offer interesting alternatives for reaching and helping people with depression. Depression can be treated effectively with Internet-based cognitive behavioral therapy (CBT), but a short intervention based on problem solving therapy (PST) could constitute a worthwhile alternative to CBT. Objective In this study we evaluated the effectiveness of Internet-based CBT and Internet-based PST in comparison to a waiting list control group (WL), and we determined the differences between the two treatments. Methods We conducted a 3-arm randomized controlled trial to compare CBT, PST, and WL. The main inclusion criterion was presence of depressive symptoms (≥ 16 on the Center for Epidemiological Studies Depression scale). CBT and PST consisted of eight and five weekly lessons respectively. Participants were supported by email. Self-report measures of depression, anxiety, and quality of life were completed at pretest and after 5, 8, and 12 weeks. Results A total of 263 participants were randomized to the three conditions (CBT: n=88; PST: n=88; WL: n=87). Of the 263 participants, 184 (70%) completed questionnaires after 5 weeks, 173 (66%) after 8 weeks, and 151 (57%) after 12 weeks. Between-group effect sizes for depressive symptoms were 0.54 for CBT after 8 weeks (95% confidence interval (CI): 0.25 - 0.84) and 0.47 for PST after 5 weeks (95% CI: 0.17 - 0.77). These effects were further improved at 12 weeks (CBT: 0.69, 95% CI: 0.41 - 0.98; PST: 0.65, 95% CI: 0.36 - 0.95). For anxiety, effect sizes were also at a medium level. Effect sizes for quality of life were low. The number of participants showing clinically significant change at 12 weeks was significantly higher for CBT (n = 34, 38.6%) and PST (n = 30, 34.1%), compared to WL (n = 0). Conclusions Both Internet-based treatments are effective in reducing depressive symptoms, although the effect of PST is realized more quickly. Trial Registration International Standard Randomized Controlled Trial Number (ISRCTN): 16823487; http://www.controlled-trials.com/ISRCTN16823487/16823487 (Archived by WebCite at http://www.webcitation.org/5cQsOj7xf).