Miracle-dz/UniFAR-FTUs huggingface dataset:
MEGH: A parametric class of general hazard models for clustered survival data

In many applications of survival data analysis, the individuals are treated in different medical centres or belong to different clusters defined by geographical or administrative regions. The analysis of such data requires accounting for between-cluster variability. Ignoring such variability would impose unrealistic assumptions in the analysis and could affect the inference on the statistical models. We develop a novel parametric mixed-effects general hazard (MEGH) model that is particularly suitable for the analysis of clustered survival data. The proposed structure generalises the mixed-effects proportional hazards and mixed-effects accelerated failure time structures, among other structures, which are obtained as special cases of the MEGH structure. We develop a likelihood-based algorithm for parameter estimation in general subclasses of the MEGH model, which is implemented in our R package MEGH. We propose diagnostic tools for assessing the random effects and their distributional assumption in the proposed MEGH model. We investigate the performance of the MEGH model using theoretical and simulation studies, as well as a real data application on leukaemia.
Real-Time BDI Agents: a model and its implementation

The BDI model proved to be effective for the developing of applications requiring high-levels of autonomy and to deal with the complexity and unpredictability of real-world scenarios. The model, however, has significant limitations in reacting and handling contingencies within the given real-time constraints. Without an explicit representation of time, existing real-time BDI implementations overlook the temporal implications during the agent’s decision process that may result in delays or unresponsiveness of the system when it gets overloaded. In this paper, we redefine the BDI agent control loop inspired by traditional and well establish algorithms for real-time systems to ensure a proper reaction of agents and their effective application in typical real-time domains. Our model proposes an effective real-time management of goals, plans, and actions with respect to time constraints and resources availability. We propose an implementation of the model for a resource-collection video-game and we validate the approach against a set of significant scenarios.
Budgeted Classification with Rejection: An Evolutionary Method with Multiple Objectives

Classification systems are often deployed in resource-constrained settings where labels must be assigned to inputs on a budget of time, memory, etc. Budgeted, sequential classifiers (BSCs) address these scenarios by processing inputs through a sequence of partial feature acquisition and evaluation steps with early-exit options. This allows for an efficient evaluation of inputs that prevents unneeded feature acquisition. To approximate an intractable combinatorial problem, current approaches to budgeted classification rely on well-behaved loss functions that account for two primary objectives (processing cost and error). These approaches offer improved efficiency over traditional classifiers but are limited by analytic constraints in formulation and do not manage additional performance objectives. Notably, such methods do not explicitly account for an important aspect of real-time detection systems—the fraction of “accepted” predictions satisfying a confidence criterion imposed by a risk-averse monitor. We propose a problem-specific genetic algorithm to build budgeted, sequential classifiers with confidence-based reject options. Three objectives—accuracy, processing time/cost, and coverage—are considered. The algorithm emphasizes Pareto efficiency while accounting for a notion of aggregate performance via a unique scalarization. Experiments show our method can quickly find globally Pareto optimal solutions in very large search spaces and is competitive with existing approaches while offering advantages for selective, budgeted deployment scenarios.
Understanding Distance Measures Among Elections

Motivated by putting empirical work based on (synthetic) election data on a more solid mathematical basis, we analyze six distances among elections, including, e.g., the challenging-to-compute but very precise swap distance and the distance used to form the so-called map of elections. Among the six, the latter seems to strike the best balance between its computational complexity and expressiveness.
COSPLAY: Concept Set Guided Personalized Dialogue Generation Across Both Party Personas

Maintaining a consistent persona is essential for building a human-like conversational model. However, the lack of attention to the partner makes the model more egocentric: they tend to show their persona by all means such as twisting the topic stiffly, pulling the conversation to their own interests regardless, and rambling their persona with little curiosity to the partner. In this work, we propose COSPLAY(COncept Set guided PersonaLized dialogue generation Across both partY personas) that considers both parties as a "team": expressing self-persona while keeping curiosity toward the partner, leading responses around mutual personas, and finding the common ground. Specifically, we first represent self-persona, partner persona and mutual dialogue all in the concept sets. Then, we propose the Concept Set framework with a suite of knowledge-enhanced operations to process them such as set algebras, set expansion, and set distance. Based on these operations as medium, we train the model by utilizing 1) concepts of both party personas, 2) concept relationship between them, and 3) their relationship to the future dialogue. Extensive experiments on a large public dataset, Persona-Chat, demonstrate that our model outperforms state-of-the-art baselines for generating less egocentric, more human-like, and higher quality responses in both automatic and human evaluations.
Visual Spatial Reasoning

Spatial relations are a basic part of human cognition. However, they are expressed in natural language in a variety of ways, and previous work has suggested that current vision-and-language models (VLMs) struggle to capture relational information. In this paper, we present Visual Spatial Reasoning (VSR), a dataset containing more than 10k natural text-image pairs with 66 types of spatial relations in English (e.g., under, in front of, facing). While using a seemingly simple annotation format, we show how the dataset includes challenging linguistic phenomena, such as varying reference frames. We demonstrate a large gap between human and model performance: The human ceiling is above 95%, while state-of-the-art models only achieve around 70%. We observe that VLMs’ by-relation performances have little correlation with the number of training examples and the tested models are in general incapable of recognising relations concerning the orientations of objects.1
Data-driven emotional body language generation for social robotics

humanoid robots with the ability to generate bodily expressions of can improve human-robot interaction and collaboration, since humans attribute, and perhaps subconsciously anticipate, such traces to perceive an agent as engaging, trustworthy, and socially present. Robotic emotional body language needs to be believable, nuanced and relevant to the context. We implemented a deep learning data-driven framework that learns from a few hand-designed robotic bodily expressions and can generate numerous new ones of similar believability and lifelikeness. The framework uses the Conditional Variational Autoencoder model and a sampling approach based on the geometric properties of the model’s latent space to condition the generative process on targeted levels of valence and arousal. The evaluation study found that the anthropomorphism and animacy of the generated expressions are not perceived differently from the hand-designed ones, and the emotional conditioning was adequately differentiable between most levels except the pairs of neutral-positive valence and low-medium arousal. Furthermore, an exploratory analysis of the results reveals a possible impact of the conditioning on the perceived dominance of the robot, as well as on the participants’ attention.
What do we really know about State of the Art NER?

Named Entity Recognition (NER) is a well researched NLP task and is widely used in real world NLP scenarios. NER research typically focuses on the creation of new ways of training NER, with relatively less emphasis on resources and evaluation. Further, state of the art (SOTA) NER models, trained on standard datasets, typically report only a single performance measure (F-score) and we don’t really know how well they do for different entity types and genres of text, or how robust are they to new, unseen entities. In this paper, we perform a broad evaluation of NER using a popular dataset, that takes into consideration various text genres and sources constituting the dataset at hand. Additionally, we generate six new adversarial test sets through small perturbations in the original test set, replacing select entities while retaining the context. We also train and test our models on randomly generated train/dev/test splits followed by an experiment where the models are trained on a select set of genres but tested genres not seen in training. These comprehensive evaluation strategies were performed using three SOTA NER models. Based on our results, we recommend some useful reporting practices for NER researchers, that could help in providing a better understanding of a SOTA model’s performance in future.
A Sequential MPC Approach to Reactive Planning for Bipedal Robots Using Safe Corridors in Highly Cluttered Environments

This letter presents a sequential Model Predictive Control (MPC) approach to reactive motion planning for bipedal robots in highly cluttered environments with moving obstacles. The approach relies on a directed convex decomposition of the free space, which provides a safe corridor in the form of an ordered collection of mutually intersecting obstacle-free polytopes and waypoints. These are subsequently used to define a corresponding sequence of MPC programs that drive the system to a goal location avoiding static and moving obstacles. This way, the planner focuses on the free space in the vicinity of the robot, thus alleviating the need to consider all the obstacles simultaneously and reducing computational time. We verify the efficacy of our approach in high-fidelity simulations with the bipedal robot Digit, demonstrating robust reactive planning and trajectory realization amidst static and moving obstacles.
Study of maximum electron energy of sub-PeV pulsar wind nebulae by multiwavelength modelling

Recently, the Large High Altitude Air Shower Observatory (LHAASO) reported the discovery of 12 ultrahigh-energy (UHE; ϵ ≥ 100 TeV) gamma-ray sources located in the Galactic plane. A few of these UHE gamma-ray emitting regions are in spatial coincidence with pulsar wind nebulae (PWNe). We consider a sample of five sources; two of them are LHAASO sources (LHAASO J1908+0621 and LHAASO J2226+6057) and the remaining three are GeV-TeV gamma-ray emitters. In addition, X-rays, radio observations or upper limits are also available for these objects. We study multiwavelength radiation from these sources by considering a PWN origin, where the emission is powered by spin-down luminosity of the associated pulsars. In this leptonic emission model, the electron population is calculated at different times under the radiative (synchrotron and inverse-Compton) and adiabatic cooling. We also include the onset of the reverberation phase for the PWN, by assuming radially symmetric expansion. However, in this work, we find that multiwavelength emission can be interpreted before the onset of this phase. The maximum energy of the electrons based on the spectral fit is found to be above 0.1 PeV and close to 1 PeV. For LHAASO J2226+6057, using its observations in radio to UHE gamma-rays, we find that UHE gamma-rays can be interpreted using electrons with maximum energy of 1 PeV. We estimate the upper limits on the minimum Lorentz factor of the electrons and it also infers the minimum value of the pair-multiplicity of charged pairs.
ELQA: A Corpus of Questions and Answers about the English Language

We introduce a community-sourced dataset for English Language Question Answering (ELQA), which consists of more than 180k questions and answers on numerous topics about English language such as grammar, meaning, fluency, and etymology. The ELQA corpus will enable new NLP applications for language learners. We introduce three tasks based on the ELQA corpus: 1) answer quality classification, 2) semantic search for finding similar questions, and 3) answer generation. We present baselines for each task along with analysis, showing the strengths and weaknesses of current transformer-based models. The ELQA corpus and scripts are publicly available for fu-ture studies . 1
Quality Assurance in the Context of Contemporary Software Practice

After the formal introduction of the Agile Manifesto in 2001 (Beck et al., 2001), previously conventional life-cycle models were at first slowly and then very rapidly augmented, or replaced outright, by approaches that emphasised rapid iterations or increments of idea-developmentdeployment cycles (Boehm et al., 2019a; Bruel et al., 2019). In the last decade this has been followed by adoption of ‘continuous *’ (or ‘continuous everything’) thinking (Fitzgerald and Stol, 2017), and rapid continuous data-driven software engineering (Gerostathopoulos et al., 2019; Konersmann et al., 2020). This was seen first in activities such as continuous integration and continuous delivery (CI/CD) of software features, releases, products and services. Subsequently, the traditional development phases of requirements, design, implementation and the like, and the often separately treated catch-all phase called maintenance, have been melded into a continuous deployment pipeline of new features alongside bug fixes, enhancements and refactorings (Cheriyan et al., 2020; Kersten, 2019).
A word-hyperbolic special monoid with undecidable Diophantine problem

The Diophantine problem for a monoid M is the decision problem to decide whether any given system of equations has a solution in M . In this note, we give a simple example of a context-free, word-hyperbolic, ﬁnitely presented, special monoid M with trivial group of units, and such that the Diophantine problem is undecidable in M . This answers two questions asked by Garreta & Gray in 2019, and shows that the decidability of the Diophantine problem in hyperbolic groups, as proved by Dahmani & Guirardel, does not generalise to word-hyperbolic monoids.
Thermalization by off-shell processes: The virtues of small virtuality

We study the thermalization of a scalar field $\Phi$ coupled to two other scalar fields $\chi_{1,2}$ that constitute a bath in thermal equilibrium. For a range of masses the $\Phi$ propagator features threshold and infrared divergences, a vanishing residue at the (quasi) particle pole and vanishing \emph{on-shell} decay rates thereby preventing the equilibration of $\Phi$ with the bath via on-shell processes. Inspired by the theory of quantum open systems we obtain a quantum master equation for the reduced density matrix of $\Phi$ that includes the time dependence of bath correlations, yielding time dependent rates in the dynamics of relaxation and allowing virtual processes of small virtuality $\propto 1/t$ at long time $t$. These \emph{off-shell} processes lead to thermalization despite vanishing S-matrix rates. In the case of threshold divergences we find that a thermal fixed point is approached as $e^{-\sqrt{t/t^*}}$ with the relaxation time $t^*$ becoming shorter at high temperature as a consequence of stimulated emission and absorption. In the infrared case, the thermal fixed point is approached as $e^{-\gamma(t)}$, where $\gamma(t)$ features a crossover between a $\propto \ln(t)$ and a $\propto t$ behavior for $t \gg 1/T$. The vanishing of the residue and the crossover in relaxational dynamics in this case is strikingly reminiscent of the orthogonality catastrophe in heavy impurity systems. The results yield more general lessons on thermalization via virtual processes.
Crude Oil-related Events Extraction and Processing: A Transfer Learning Approach

One of the challenges in event extraction via traditional supervised learning paradigm is the need for a sizeable annotated dataset to achieve satisfactory model performance. It is even more challenging when it comes to event extraction in the ﬁnance and economics domain, a domain with considerably fewer resources. This paper presents a complete framework for extracting and processing crude oil-related events found in CrudeOilNews corpus Lee et al. (2022), addressing the issue of annotation scarcity and class imbalance by leveraging on the eﬀectiveness of transfer learning. Apart from event extraction, we place special emphasis on event properties (Polarity, Modality, and Intensity) classiﬁcation to determine the factual certainty of each event. We build baseline models ﬁrst by supervised learning and then exploit Transfer Learning methods to boost event extraction model performance despite the limited amount of annotated data and severe class imbalance. This is done via methods within the transfer learning framework such as Domain Adaptive Pre-training, Multi-task Learning and Sequential Transfer Learning. Based on experiment results, we are able to improve all event extraction sub-task models both in F1 and MCC 1 -score as compared to baseline models trained via the standard supervised learning. Accurate and holistic event extraction from crude oil news is very useful for downstream tasks such as understanding event chains and learning event-event relations, which can be used for other downstream tasks such as commodity price prediction, summarisation, etc. to support a wide range of business decision making.
Adversarial Plannning

Planning algorithms are used in computational systems to direct autonomous behavior. In a canonical application, for example, planning for autonomous vehicles is used to automate the static or continuous planning towards performance, resource management, or functional goals (e.g., arriving at the destination, managing fuel fuel consumption). Existing planning algorithms assume non-adversarial settings; a least-cost plan is developed based on available environmental information (i.e., the input instance). Yet, it is unclear how such algorithms will perform in the face of adversaries attempting to thwart the planner. In this paper, we explore the security of planning algorithms used in cyber- and cyber-physical systems. We present two $\textit{adversarial planning}$ algorithms-one static and one adaptive-that perturb input planning instances to maximize cost (often substantially so). We evaluate the performance of the algorithms against two dominant planning algorithms used in commercial applications (D* Lite and Fast Downward) and show both are vulnerable to extremely limited adversarial action. Here, experiments show that an adversary is able to increase plan costs in 66.9% of instances by only removing a single action from the actions space (D* Lite) and render 70% of instances from an international planning competition unsolvable by removing only three actions (Fast Forward). Finally, we show that finding an optimal perturbation in any search-based planning system is NP-hard.
Local adaptation, phenotypic plasticity, and species coexistence

Understanding the mechanisms of species coexistence has always been a fundamental topic in ecology. Classical theory predicts that interspecific competition may select for traits that stabilize niche differences, although recent work shows that this is not strictly necessary. Here, we ask whether adaptive phenotypic plasticity could allow species coexistence (i.e., some stability at an equilibrium point) without ecological differentiation in habitat use. We used individual-based stochastic simulations defining a landscape composed of spatially uncorrelated or autocorrelated environmental patches, where two species with the same competitive strategies, not able to coexist without some form of phenotypic plasticity, expanded their ranges in the absence of a competition—colonization trade-off (a well-studied mechanism for species diversity). Each patch is characterized by a random environmental value that determines the optimal phenotype of its occupants. In such a scenario, only local adaptation and gene flow (migration) may interact to promote genetic variation and coexistence in the metapopulation. Results show that a competitively inferior species with adaptive phenotypic plasticity can coexist in a same patch with a competitively superior, non-plastic species, provided the migration rates and variances of the patches' environmental values are sufficiently large.
Local Topology Inference of Mobile Robotic Networks Under Formation Control

The interaction topology is critical for efficient cooperation of mobile robotic networks (MRNs). We focus on the local topology inference problem of MRNs under formation control, where an inference robot with a limited observation range can maneuver among the formation robots. This problem faces new challenges brought by the highly coupled influence of unobservable formation robots, inaccessible formation inputs, and unknown interaction range. The novel idea here is to advocate a range-shrink strategy to perfectly avoid the influence of unobservable robots while filtering the input. To that end, we develop consecutive algorithms to determine a feasible constant robot subset from the changing robot set within the observation range, and estimate the formation input offline and the interaction range online. Then, an ordinary least squares-based local topology estimator is designed with the previously inferred information. Resorting to the concentration measure, we prove the convergence rate and accuracy of the proposed estimator, taking the estimation errors of previous steps into account. Extensions on nonidentical observation slots and more complicated scenarios are also analyzed. Comprehensive simulation tests and method comparisons corroborate the theoretical findings.
HarmoF0: Logarithmic Scale Dilated Convolution for Pitch Estimation

Sounds, especially music, contain various harmonic components scattered in the frequency dimension. It is difficult for normal convolutional neural networks to ob-serve these overtones. This paper introduces a multiple rates dilated causal convolution (MRDC-Conv) method to capture the harmonic structure in logarithmic scale spectrograms efficiently. The harmonic is helpful for pitch estimation, which is important for many sound processing applications. We propose HarmoF0, a fully convolutional network, to evaluate the MRDC-Conv and other dilated convolutions in pitch estimation. The re-sults show that this model outperforms the DeepF0, yields state-of-the-art performance in three datasets, and simultaneously reduces more than 90% parameters. We also find that it has stronger noise resistance and fewer octave errors.
Hydrogen Infrastructure Market Readiness: Opportunities and Potential for Near-term Cost Reductions; Proceedings of the Hydrogen Infrastructure Market Readiness Workshop and Summary of Feedback Provided through the Hydrogen Station Cost Calculator

Recent progress with fuel cell electric vehicles (FCEVs) has focused attention on hydrogen infrastructure as a critical commercialization barrier. With major automakers focused on 2015 as a target timeframe for global FCEV commercialization, the window of opportunity is short for establishing a sufficient network of hydrogen stations to support large-volume vehicle deployments. This report describes expert feedback on the market readiness of hydrogen infrastructure technology from two activities.