Miracle-dz/UniFAR-FTUs huggingface dataset:
TacMMs: Tactile Mobile Manipulators for Warehouse Automation

Multi-robot platforms are playing an increasingly important role in warehouse automation for efficient goods transport. This paper proposes a novel customization of a multi-robot system, called Tactile Mobile Manipulators (TacMMs). Each TacMM integrates a soft optical tactile sensor and a mobile robot with a load-lifting mechanism, enabling cooperative transportation in tasks requiring coordinated physical interaction. More specifically, we mount the TacTip (biomimetic optical tactile sensor) on the Distributed Organisation and Transport System (DOTS) mobile robot. The tactile information then helps the mobile robots adjust the relative robot-object pose, thereby increasing the efficiency of load-lifting tasks. This study compares the performance of using two TacMMs with tactile perception with traditional vision-based pose adjustment for load-lifting. The results show that the average success rate of the TacMMs (66%) is improved over a purely visual-based method (34%), with a larger improvement when the mass of the load was non-uniformly distributed. Although this initial study considers two TacMMs, we expect the benefits of tactile perception to extend to multiple mobile robots.
PVP: Personalized Video Prior for Editable Dynamic Portraits using StyleGAN

Portrait synthesis creates realistic digital avatars which enable users to interact with others in a compelling way. Recent advances in StyleGAN and its extensions have shown promising results in synthesizing photorealistic and accurate reconstruction of human faces. However, previous methods often focus on frontal face synthesis and most methods are not able to handle large head rotations due to the training data distribution of StyleGAN. In this work, our goal is to take as input a monocular video of a face, and create an editable dynamic portrait able to handle extreme head poses. The user can create novel viewpoints, edit the appearance, and animate the face. Our method utilizes pivotal tuning inversion (PTI) to learn a personalized video prior from a monocular video sequence. Then we can input pose and expression coefficients to MLPs and manipulate the latent vectors to synthesize different viewpoints and expressions of the subject. We also propose novel loss functions to further disentangle pose and expression in the latent space. Our algorithm shows much better performance over previous approaches on monocular video datasets, and it is also capable of running in real‐time at 54 FPS on an RTX 3080.
An improved kernelization algorithm for Trivially Perfect Editing

In the Trivially Perfect Editing problem one is given an undirected graph $G = (V,E)$ and an integer $k$ and seeks to add or delete at most $k$ edges in $G$ to obtain a trivially perfect graph. In a recent work, Dumas, Perez and Todinca [Algorithmica 2023] proved that this problem admits a kernel with $O(k^3)$ vertices. This result heavily relies on the fact that the size of trivially perfect modules can be bounded by $O(k^2)$ as shown by Drange and Pilipczuk [Algorithmica 2018]. To obtain their cubic vertex-kernel, Dumas, Perez and Todinca [Algorithmica 2023] then showed that a more intricate structure, so-called \emph{comb}, can be reduced to $O(k^2)$ vertices. In this work we show that the bound can be improved to $O(k)$ for both aforementioned structures and thus obtain a kernel with $O(k^2)$ vertices. Our approach relies on the straightforward yet powerful observation that any large enough structure contains unaffected vertices whose neighborhood remains unchanged by an editing of size $k$, implying strong structural properties.
Pulsar timing array stochastic background from light Kaluza-Klein resonances

We investigate the potential of the warped-extradimension framework as an explanation for the recently observed stochastic gravitational background at nHz frequencies in pulsar timing arrays (PTA). Our analysis reveals that the PTA data can be effectively accommodated by a first-order phase transition triggered by a radion at the MeV-GeV scale feebly coupled to the Standard Model. Remarkably, this outcome remains robust irrespective of the specific details of the warped extradimension embedding, providing a foundation for future investigations aiming to develop concrete extradimension descriptions of Nature. We also demonstrate that many existing embeddings are not viable, as their radion and graviton phenomenology clash with a MeV-GeV scale radion. As a possible way-out, we sketch a promising solution involving multiple branes, wherein the light radion, graviton, and ensuing light resonances remain consistent with collider bounds and gravity tests.
Divide and Conquer the EmpiRE: A Community-Maintainable Knowledge Graph of Empirical Research in Requirements Engineering

[Background. ] Empirical research in requirements engineering (RE) is a constantly evolving topic, with a growing number of publications. Several papers address this topic using literature reviews to provide a snapshot of its “current” state and evolution. However, these papers have never built on or updated earlier ones, resulting in overlap and redundancy. The underlying problem is the unavailability of data from earlier works. Researchers need technical infrastructures to conduct sustainable literature reviews. [Aims. ] We examine the use of the Open Research Knowledge Graph (ORKG) as such an infrastructure to build and publish an initial Knowledge Graph of Empirical research in RE (KG-EmpiRE) whose data is openly available. Our long-term goal is to continuously maintain KG-EmpiRE with the research community to synthesize a comprehensive, up-to-date, and long-term available overview of the state and evolution of empirical research in RE. [Method. ] We conduct a literature review using the ORKG to build and publish KG-EmpiRE which we evaluate against competency questions derived from a published vision of empirical research in software (requirements) engineering for 2020–2025. [Results. ] From 570 papers of the IEEE International Requirements Engineering Conference (2000–2022), we extract and analyze data on the reported empirical research and answer 16 out of 77 competency questions. These answers show a positive development towards the vision, but also the need for future improvements. [Conclusions. ] The ORKG is a ready-to-use and advanced infrastructure to organize data from literature reviews as knowledge graphs. The resulting knowledge graphs make the data openly available and maintainable by research communities, enabling sustainable literature reviews.
The State of CO and CO2 Ices in the Kuiper Belt as Seen by JWST

JWST has shown that CO2 and CO are common on the surfaces of objects in the Kuiper Belt and have apparent surface coverages even higher than that of water ice, though water ice is expected to be significantly more abundant in the bulk composition. Using full Mie scattering theory, we show that the high abundance and the unusual spectral behavior around the 4.26 μm ν 1 band of CO2 can be explained by a surface covered in a few micron thick layer of ∼1–2 μm CO2 particles. CO is unstable at the temperatures in the Kuiper Belt, so the CO must be trapped in some more stable species. While hydrate clathrates or amorphous water ice are often invoked as a trapping mechanism for outer solar system ices, the expected spectral shift of the absorption line for a CO hydrate clathrates or trapping in amorphous ice is not seen, nor does the H2O abundance appear to be high enough to explain the depth of the CO absorption line. Instead, we suggest that the CO is created via irradiation of CO2 and trapped in the CO2 grains during this process. The presence of a thin surface layer of CO2 with embedded CO suggests volatile differentiation driving CO2 from the interior as a major process driving the surface appearance of these mid-sized Kuiper Belt objects, but the mechanisms that control the small grain size and depth of the surface layer remain unclear.
OpenPARF: An Open-Source Placement and Routing Framework for Large-Scale Heterogeneous FPGAs with Deep Learning Toolkit

This paper proposes OpenPARF, an open-source placement and routing framework for large-scale FPGA designs1. OpenPARF is implemented with the deep learning toolkit PyTorch and supports massive parallelization on GPU. The framework proposes a novel asymmetric multi-electrostatic field system to solve FPGA placement. It considers fine-grained routing resources inside configurable logic blocks (CLBs) for FPGA routing and supports large-scale irregular routing resource graphs. Experimental results on ISPD 2016 and ISPD 2017 FPGA contest benchmarks and industrial benchmarks demonstrate that OpenPARF can achieve 0.4-12.7% improvement in routed wirelength and more than 2× speedup in placement. We believe that OpenPARF can pave the road for developing FPGA physical design engines and stimulate further research on related topics.
States, symmetries and correlators of $T\bar{T}$ and $ J\bar{T} $ symmetric orbifolds

We derive various properties of symmetric product orbifolds of T\bar TTT‾ and J\bar TJT‾ - deformed CFTs from a field-theoretical perspective. First, we generalise the known formula for the torus partition function of a symmetric orbifold theory in terms of the one of the seed to non-conformal two-dimensional QFTs; specialising this to seed T\bar TTT‾ and J\bar TJT‾ - deformed CFTs reproduces previous results in the literature. Second, we show that the single-trace T\bar TTT‾ and J\bar TJT‾ deformations preserve the Virasoro and Kac-Moody symmetries of the undeformed symmetric product orbifold CFT, including their fractional counterparts, as well as the KdV charges. Finally, we discuss correlation functions in these theories. By extending a previously-proposed basis of operators for J\bar TJT‾ - deformed CFTs to the single-trace case, we explicitly compute the correlation functions of both untwisted and twisted-sector operators and compare them to an appropriate set of holographic correlators. Our derivations are based mainly on Hilbert space techniques and completely avoid the use of conformal invariance, which is not present in these models.
NeuralFuse: Learning to Improve the Accuracy of Access-Limited Neural Network Inference in Low-Voltage Regimes

Deep neural networks (DNNs) have become ubiquitous in machine learning, but their energy consumption remains a notable issue. Lowering the supply voltage is an effective strategy for reducing energy consumption. However, aggressively scaling down the supply voltage can lead to accuracy degradation due to random bit flips in static random access memory (SRAM) where model parameters are stored. To address this challenge, we introduce NeuralFuse, a novel add-on module that addresses the accuracy-energy tradeoff in low-voltage regimes by learning input transformations to generate error-resistant data representations. NeuralFuse protects DNN accuracy in both nominal and low-voltage scenarios. Moreover, NeuralFuse is easy to implement and can be readily applied to DNNs with limited access, such as non-configurable hardware or remote access to cloud-based APIs. Experimental results demonstrate that, at a 1% bit error rate, NeuralFuse can reduce SRAM memory access energy by up to 24% while recovering accuracy by up to 57%. To the best of our knowledge, this is the first model-agnostic approach (i.e., no model retraining) to address low-voltage-induced bit errors. The source code is available at https://github.com/IBM/NeuralFuse.
Fast tree-based algorithms for DBSCAN for low-dimensional data on GPUs

DBSCAN is a well-known density-based clustering algorithm to discover arbitrary shape clusters. While conceptually simple in serial, the algorithm is challenging to efficiently parallelize on manycore GPU architectures. Common pitfalls, such as asynchronous range query calls, result in high thread execution divergence in many implementations. In this paper, we propose a new framework for GPU-accelerated DBSCAN, and describe two tree-based algorithms within that framework. Both algorithms fuse the search for neighbors with updating cluster information, but differ in their treatment of dense regions of the data. We show that the time taken to compute clusters is at most twice that of determination of the neighbors. We compare the proposed algorithms with existing CPU and GPU implementations, and demonstrate their competitiveness and performance using a fast traversal structure (bounding volume hierarchy) for low dimensional data. We also show that the memory usage can be reduced by processing object neighbors dynamically without storing them.
Estimation and variable selection in a joint model of survival times and longitudinal outcomes with random effects

This paper considers a joint survival and mixed-effects model to explain the survival time from longitudinal data and high-dimensional covariates. The longitudinal data is modeled using a nonlinear effects model, where the regression function serves as a link function incorporated into a Cox model as a covariate. In that way, the longitudinal data is related to the survival time at a given time. Additionally, the Cox model takes into account the inclusion of high-dimensional covariates. The main objectives of this research are two-fold: first, to identify the relevant covariates that contribute to explaining survival time, and second, to estimate all unknown parameters of the joint model. For that purpose, we consider the maximization of a Lasso penalized likelihood. To tackle the optimization problem, we implement a pre-conditioned stochastic gradient to handle the latent variables of the nonlinear mixed-effects model associated with a proximal operator to manage the non-differentiability of the penalty. We provide relevant simulations that showcase the performance of the proposed variable selection and parameters' estimation method in the joint modeling of a Cox and logistic model.
On the spectrum of sets made of cores and tubes

We analyze the spectral properties of a particular class of unbounded open sets. These are made of a central bounded ``core'', with finitely many unbounded tubes attached to it. We adopt an elementary and purely variational point of view, studying the compactness (or the defect of compactness) of level sets of the relevant constrained Dirichlet integral. As a byproduct of our argument, we also get exponential decay at infinity of variational eigenfunctions. Our analysis includes as a particular case a planar set (sometimes called ``bookcover''), already encountered in the literature on curved quantum waveguides. J. Hersch suggested that this set could provide the sharp constant in the {\it Makai-Hayman inequality} for the bottom of the spectrum of the Dirichlet-Laplacian of planar simply connected sets. We disprove this fact, by means of a singular perturbation technique.
A semilinear heat equation with singular initial data

We first prove existence and uniqueness of non-negative solutions of the equation in in the range 1 < p < 1 + 2/N, when initial data u(x, 0) = a|x|−2(p−1), x ≠ 0, for a > 0. It is proved that the maximal and minimal solutions are self-similar with the form where g = ga satisfies After uniqueness is proved, the asymptotic behaviour of solutions of is studied. In particular, we show that The case for a = 0 is also considered and a sharp decay rate of the above equation is derived. In the final, we reveal existence of solutions of the first and third equations above, which change sign.
Improving Effectiveness of Programming Assignments with Real-time Formative Feedback

This PhD research explores the problem of building an effective ITS-like system for providing real-time formative feedback for programming assignments given to college/university students. Such system would maximize learning outcomes while minimizing the effort from the tutor to construct such system. It proposes an approach to building such a system and assessing its effectiveness, as well as outlines topics for future research.
Scaffolding to Support Liberal Arts Students Learning to Program on Photographs

Digital photographs are part of liberal arts students' classes (e.g., art, history, and production classes in film and television) and their daily smartphone-based life, in apps like Instagram and Snapchat. Building image filters can be a relevant and engaging context into using computing for humanities students. We have designed a new course for introducing computing in terms of creative expression. We use a scaffolded sequence of programming languages and activities to explore computing on photographs: (a) a teaspoon language for generating image filters, (b) a set of custom Snap blocks for even more sophisticated image filters, and (c) an ebook activity for mapping from Snap to Python. Each stage takes less than 10 minutes to introduce, with a wide variety of possible student activities (for in-class active learning or for later homework). While the tools build on each other, the earliest stage (the teaspoon language) could be used within a single class session in other liberal arts courses.
Automatic Feedback During Coding Exams: Curse or Blessing?

Automatic feedback during a coding exam can be very useful for both the students and the examiners. It can provide a quick and objective evaluation of the code written by the students, which can save a lot of time for the examiners and ensure consistency in grading. However, an exam is also a stressful, emotional situation for students. Therefore, the question arises as to what this immediate feedback triggers in weaker students. Does it help them to recognise their mistakes better or does it tend to prevent them from doing so? How should feedback be designed so that it has the most positive effect possible? What are the key mistakes? These and other points will be discussed in our poster session with the interested participants.
Sensitivity Analysis for Uncertainty Propagation and Robust Design

Three methodologies for uncertainty propagation and robust aerodynamic shape optimization are presented in this paper. These are the perturbation technique based on first and second-order Taylor expansion, the asymptotic expansion based on Laplace integrals and the sparse-grid approach, based on Hermite quadrature integration rules. The up-to fourth-order sensitivity analysis, required for these approaches, with respect to both the uncertain and the shape controlling parameters is presented and the most efficient approach is applied to the robust shape optimization of the transonic flow around a 2D airfoil for minimum drag-to-lift ratio, incorporating uncertainties of the flow conditions.
Building Recommendations for Conducting Equity-Focused, High Quality K-12 Computer Science Education Research

To investigate and identify promising practices in equitable K-12 computer science (CS) education, the capacity for education researchers to conduct this research must be rapidly built globally. Simultaneously, concerns have arisen over the last few years about the quality of research that is being conducted and the lack of equity-focused research. In this working group, we will tackle the research question: In what ways can previous research standards inform high-quality, equity-focused K-12 CS education research? We will use existing research and various standards bodies (e.g., European Educational Research Association, Australian Education Research Organisation, CONSORT, American Psychological Association) to synthesize key features in the context of equity-focused K-12 CS education research. We will then vet these attributes with experts who can provide feedback and refine our recommendations and guidelines. Our working group will select the experts using a strata reflecting a diversity of backgrounds and experiences to support our focus on student populations that have been historically marginalized in computing (e.g., low-income students, rural students, girls, students with disabilities). Our recommendations will directly impact future equitable computing education research by providing guidance on conducting high-quality research such that the findings can be aggregated and impact future policy with evidence-based results. While we recognize that different countries and regions may yield differing answers to this question, our recommendations will be robust enough that researchers in each country or region may choose to use those most appropriate to their context.
Retention in First Stage Undergraduate Computing: Lessons Learned from a Collaborative Learning Intervention

It is challenging to retain computing students through their first stage of undergraduate education. Attrition is high, with many transferring courses or dropping out. This poster explores preliminary findings from an action research project improving continuation in first-stage undergraduate computing. Five years of data from Falmouth University's Games Academy in the UK suggest improvement in first-stage retention from 66.6% in 2017-18 to 91.2% in 2021-22. Findings support prior work on pair programming, media computation, and peer instruction. However, they also highlight the benefits of collaborative learning facilitated by faculty and informed by learning analytics. Peer reviews and pre-submission clinics, student advisor follow-ups, and retrieval via synoptic assessment also contributed to the improvement.
Mapping of intracellular concentrations of macromolecules by two-photon excited fluorescence lifetime imaging

Measurements and monitoring of concentrations of macromolecules in live cells and sub-cellular structures is of tremendous interest in cell biology and translational medicine. In this report we demonstrate a breakthrough potential of FLIM for real-time quantitative mapping of macromolecular distribution in the cell. In our approach we exploit a correlation existing between the fluorescence lifetime of fluorophores, refractive index and local concentrations of cellular macromolecules in the of fluorophore's microenvironment. We show a value of our approach for fundamental cell science and cellular diagnostic assays.
The Death of Neoliberalism or a Mere Camouflage? Polanyian Countermovement in Hungary & Poland

Abstract This article aims to assess the causes of the recent emergence of Polanyian countermovement in Hungary and Poland and their attitudes towards neoliberal orthodoxy prevailing in post-1989 socioeconomic development. This article is structured as follows. Firstly, we approximate the fundaments of neoliberal thinking through the inner workings of Vanberg and Hayek. Secondly, we incorporate the Polanyian framework, focusing primarily on the concept of a countermovement. Lastly, we assess the development of neoliberal features in the political economy of Hungary and Poland, trying to position the emergence of Fidesz and Prawo i Sprawiedliwość (Law and Justice, PiS) into this development. We argue that both countermovements emerged as a result of general discontent with neoliberal development. Whilst the political economy of Orbán’s regime remains critical of neoliberalism mainly in the rhetoric, camouflaging neoliberal orthodoxy with developmental rhetoric, the conservative governments in Poland made a substantial shift from the neoliberal orthodoxy following their rise to power in 2015.