Miracle-dz/UniFAR-FTUs huggingface dataset:
An Affective Approach for Behavioral Performance Estimation and Induction

Emotions have a major interactive role in defining how humans interact with their environment by encoding their perception to external events and accordingly, influencing their cognition and decision-making process. Therefore, increasing attention has been directed toward integrating human affective states into system design in order to optimize the quality of task performance. In this work, we seize on the significant correlation between emotions and behavioral performance that is reported in several psychological studies and develop an online closed-loop design framework for Human-Robot Interaction (HRI). The proposed approach monitors the behavioral performance based on the levels of Pleasure, Arousal, and Dominance (PAD) states for the human operator and when required, applies an external stimulus which is selected to induce an improvement in performance. The framework is implemented on an HRI task involving a human operator teleoperating an articulated robotic manipulator. Our statistical analysis shows a significant decrease in pleasure, arousal, and dominance states as the behavioral performance deteriorates $(p<0.05)$. Our closed-loop experiment that uses an audio stimulus to improve emotional state shows a significant improvement in the behavioral performance of certain subjects.
One Algorithm to Evaluate Them All: Unified Linear Algebra Based Approach to Evaluate Both Regular and Context-Free Path Queries

The Kronecker product-based algorithm for context-free path querying (CFPQ) was proposed by Orachev et al. (2020). We reduce this algorithm to operations over Boolean matrices and extend it with the mechanism to extract all paths of interest. We also prove $O(n^3/\log{n})$ time complexity of the proposed algorithm, where n is a number of vertices of the input graph. Thus, we provide the alternative way to construct a slightly subcubic algorithm for CFPQ which is based on linear algebra and incremental transitive closure (a classic graph-theoretic problem), as opposed to the algorithm with the same complexity proposed by Chaudhuri (2008). Our evaluation shows that our algorithm is a good candidate to be the universal algorithm for both regular and context-free path querying.
Dual-Parameterized Quantum Circuit GAN Model in High Energy Physics

Generative models, and Generative Adversarial Networks (GAN) in particular, are being studied as possible alternatives to Monte Carlo simulations. It has been proposed that, in certain circumstances, simulation using GANs can be sped-up by using quantum GANs (qGANs). We present a new design of qGAN, the dual-Parameterized Quantum Circuit (PQC) GAN, which consists of a classical discriminator and two quantum generators which take the form of PQCs. The first PQC learns a probability distribution over N-pixel images, while the second generates normalized pixel intensities of an individual image for each PQC input. With a view to HEP applications, we evaluated the dual-PQC architecture on the task of imitating calorimeter outputs, translated into pixelated images. The results demonstrate that the model can reproduce a fixed number of images with a reduced size as well as their probability distribution and we anticipate it should allow us to scale up to real calorimeter outputs.
One Network Fits All? Modular versus Monolithic Task Formulations in Neural Networks

Can deep learning solve multiple tasks simultaneously, even when they are unrelated and very different? We investigate how the representations of the underlying tasks affect the ability of a single neural network to learn them jointly. We present theoretical and empirical findings that a single neural network is capable of simultaneously learning multiple tasks from a combined data set, for a variety of methods for representing tasks -- for example, when the distinct tasks are encoded by well-separated clusters or decision trees over certain task-code attributes. More concretely, we present a novel analysis that shows that families of simple programming-like constructs for the codes encoding the tasks are learnable by two-layer neural networks with standard training. We study more generally how the complexity of learning such combined tasks grows with the complexity of the task codes; we find that combining many tasks may incur a sample complexity penalty, even though the individual tasks are easy to learn. We provide empirical support for the usefulness of the learning bounds by training networks on clusters, decision trees, and SQL-style aggregation.
Boundedness and volume of generalised pairs

In this paper we investigate boundedness and volumes of generalised pairs, and give applications to usual pairs especially to a class of pairs that we call stable log minimal models. Fixing the dimension and a DCC set controlling coefficients, we will show that the set of volumes of all projective generalised lc pairs (X,B+M) under the given data, satisfies the DCC. Futhermore, we will show that in the klt case, the set of such pairs with ample KX +B +M and fixed volume, forms a bounded family. We prove a result about descent of nef divisors to bounded families. This is the key to proving the above and various other results. We will then apply the above to study projective lc pairs (X,B) with abundant KX+B of arbitrary Kodaira dimension. In particular, we show that the set of Iitaka volumes of such pairs satisfies DCC under some natural boundedness assumptions on the fibres of the Iitaka fibration. We define stable log minimal models which consist of a projective lc pair (X,B) with semi-ample KX + B together with a divisor A ≥ 0 so that KX + B + A is ample and A does not contain any non-klt centre of (X,B). This is a generalisation of both usual stable pairs of general type and stable log Calabi-Yau pairs. Fixing appropriate invariants we show that stable log minimal models form a bounded family. Then we discuss connection with moduli spaces.
Categorification of infinite-dimensional $\mathfrak{sl}_2$-modules and braid group 2-actions I: tensor products

Abstract. This is the first part of a series of two papers aiming to construct a categorification of the braiding on tensor products of Verma modules, and in particular of the Lawrence–Krammer–Bigelow representations. In this part, we categorify all tensor products of Verma modules and integrable modules for quantum sl2. The categorification is given by derived categories of dg versions of KLRW algebras which generalize both the tensor product algebras of Webster, and the dg-algebras used by Lacabanne, the second author and Vaz. We compute a basis for these dgKLRW algebras by using rewriting methods modulo braid-like isotopy, which we develop in an Appendix.
Low-scale resonant leptogenesis in 
SU(5)
 GUT with 
T13
 family symmetry

We investigate low-scale resonant leptogenesis in an SU(5) × T13 model where a single high energy phase in the complex Tribimaximal seesaw mixing produces the yet-to-be-observed low energy Dirac and Majorana CP phases. A fourth right-handed neutrino, required to generate viable light neutrino masses within this scenario, also turns out to be necessary for successful resonant leptogenesis where CP asymmetry is produced by the same high energy phase. We derive a lower bound on the right-handed neutrino mass spectrum in the GeV range, where part of the parameter space, although in tension with Big Bang Nucleosynthesis constrains, can be probed in planned high intensity experiments like DUNE. We also find the existence of a curious upper bound (TeV-scale) on the right-handed neutrino mass spectrum in majority of the parameter space due to significant washout of the resonant asymmetry by lighter right-handed neutrinos. While in most of the parameter space of our model classical Boltzmann equations are sufficient, when right-handed neutrino masses are below the electroweak sphalerons freezeout temperature we resort to the more general density matrix formalism to take into account decays and oscillations of the right-handed neutrinos as well as their helicities which are relevant when they are relativistic. ∗ Email: sheng.fong@ufabc.edu. br † Email: mrahat@ufl.edu ‡ Email: shaikh.saad@unibas.ch 1 ar X iv :2 10 3. 14 69 1v 3 [ he pph ] 2 N ov 2 02 1
Abundance–age relations with red clump stars in open clusters

Context. Precise chemical abundances coupled with reliable ages are key ingredients to understanding the chemical history of our Galaxy. Open clusters (OCs) are useful for this purpose because they provide ages with good precision. Aims. The aim of this work is to investigate the relation between different chemical abundance ratios and age traced by red clump (RC) stars in OCs. Methods. We analyzed a large sample of 209 reliable members in 47 OCs with available high-resolution spectroscopy. We applied a differential line-by-line analysis, performing a comprehensive chemical study of 25 chemical species. This sample is among the largest samples of OCs homogeneously characterized in terms of atmospheric parameters, detailed chemistry, and age. Results. In our metallicity range (−0.2 < [M/H] < +0.2) we find that while most Fe-peak and α elements show a flat dependence on age, the s-process elements show a decreasing trend with increasing age with a remarkable knee at 1 Gyr. For Ba, Ce, Y, Mo, and Zr, we find a plateau at young ages (< 1 Gyr). We investigate the relations between all possible combinations among the computed chemical species and age. We find 19 combinations with significant slopes, including [Y/Mg] and [Y/Al]. The ratio [Ba/α] shows the most significant correlation. Conclusions. We find that the [Y/Mg] relation found in the literature using solar twins is compatible with the one found here in the solar neighborhood. The age–abundance relations in clusters at large distances(d > 1 kpc) show larger scatter than those in clusters in the solar neighborhood, particularly in the outer disk. We conclude that, in addition to pure nucleosynthetic arguments, the complexity of the chemical space introduced by the Galactic dynamics must be taken into account in order to understand these relations, especially outside of the local bubble.
On the Adversarial Robustness of Vision Transformers

Following the success in advancing natural language processing and understanding, transformers are expected to bring revolutionary changes to computer vision. This work provides a comprehensive study on the robustness of vision transformers (ViTs) against adversarial perturbations. Tested on various white-box and transfer attack settings, we find that ViTs possess better adversarial robustness when compared with MLP-Mixer and convolutional neural networks (CNNs) including ConvNeXt, and this observation also holds for certified robustness. Through frequency analysis and feature visualization, we summarize the following main observations contributing to the improved robustness of ViTs: 1) Features learned by ViTs contain less high-frequency patterns that have spurious correlation, which helps explain why ViTs are less sensitive to high-frequency perturbations than CNNs and MLP-Mixer, and there is a high correlation between how much the model learns high-frequency features and its robustness against different frequency-based perturbations. 2) Introducing convolutional or tokens-to-token blocks for learning high-frequency features in ViTs can improve classification accuracy but at the cost of adversarial robustness. 3) Modern CNN designs that borrow techniques from ViTs including activation function, layer norm, larger kernel size to imitate the global attention, and patchify the images as inputs, etc., could help bridge the performance gap between ViTs and CNNs not only in terms of performance, but also certified and empirical adversarial robustness. Moreover, we show adversarial training is also applicable to ViT for training robust models, and sharpness-aware minimization can also help improve robustness, while pre-training with clean images on larger datasets does not significantly improve adversarial robustness.
Alignment of Language Agents

For artificial intelligence to be beneficial to humans the behaviour of AI agents needs to be aligned with what humans want. In this paper we discuss some behavioural issues for language agents, arising from accidental misspecification by the system designer. We highlight some ways that misspecification can occur and discuss some behavioural issues that could arise from misspecification, including deceptive or manipulative language, and review some approaches for avoiding these issues.
Centrality Meets Centroid: A Graph-based Approach for Unsupervised Document Summarization

Unsupervised document summarization has reacquired lots of attention in recent years thanks to its simplicity and data independence. In this paper, we propose a graph-based unsupervised approach for extractive document summarization. Instead of ranking sentences by salience and extracting sentences one by one, our approach works at a summary-level by utilizing graph centrality and centroid. We first extract summary candidates as subgraphs based on centrality from the sentence graph and then select from the summary candidates by matching to the centroid. We perform extensive experiments on two bench-marked summarization datasets, and the results demonstrate the effectiveness of our model compared to state-of-theart baselines.
Joint Resource Management for MC-NOMA: A Deep Reinforcement Learning Approach

This paper presents a novel and effective deep reinforcement learning (DRL)-based approach to addressing joint resource management (JRM) in a practical multi-carrier non-orthogonal multiple access (MC-NOMA) system, where hardware sensitivity and imperfect successive interference cancellation (SIC) are considered. We first formulate the JRM problem to maximize the weighted-sum system throughput. Then, the JRM problem is decoupled into two iterative subtasks: subcarrier assignment (SA, including user grouping) and power allocation (PA). Each subtask is a sequential decision process. Invoking a deep deterministic policy gradient algorithm, our proposed DRL-based JRM (DRL-JRM) approach jointly performs the two subtasks, where the optimization objective and constraints of the subtasks are addressed by a new joint reward and internal reward mechanism. A multi-agent structure and a convolutional neural network are adopted to reduce the complexity of the PA subtask. We also tailor the neural network structure for the stability and convergence of DRL-JRM. Corroborated by extensive experiments, the proposed DRL-JRM scheme is superior to existing alternatives in terms of system throughput and resistance to interference, especially in the presence of many users and strong inter-cell interference. DRL-JRM can flexibly meet individual service requirements of users.
Nonparametric tests for treatment effect heterogeneity in observational studies

We consider the problem of testing for treatment effect heterogeneity in observational studies and propose a nonparametric test based on multisample U$$ U $$ ‐statistics. To account for potential confounders, we use reweighted data where the weights are determined by estimated propensity scores. The proposed method does not require any parametric assumptions on the outcomes and bypasses the need for modelling the treatment effect for each study subgroup. We establish the asymptotic normality for the test statistic and demonstrate its superior numerical performance over several competing approaches via simulation studies. Two real data applications are discussed: an employment programme evaluation study and a mental health study of China's one‐child policy.
Synthesis of Air-Stable Cu Nanoparticles Using Laser Reduction in Liquid

We report the synthesis of air-stable Cu nanoparticles (NPs) using the bottom-up laser reduction in liquid method. Precursor solutions of copper acetlyacetonate in a mixture of methanol and isopropyl alcohol were irradiated with femtosecond laser pulses to produce Cu NPs. The Cu NPs were left at ambient conditions and analyzed at different ages up to seven days. TEM analysis indicates a broad size distribution of spherical NPs surrounded by a carbon matrix, with the majority of the NPs less than 10 nm and small numbers of large particles up to ∼100 nm in diameter. XRD collected over seven days confirmed the presence of fcc-Cu NPs, with some amorphous Cu2O, indicating the stability of the zero-valent Cu phase. Raman, FTIR, and XPS data for oxygen and carbon regions put together indicated the presence of a graphite oxide-like carbon matrix with oxygen functional groups that developed within the first 24 h after synthesis. The Cu NPs were highly active towards the model catalytic reaction of para-nitrophenol reduction in the presence of NaBH4.
The Effects of Sterilization Procedures on the Cutting Efficiency of Endodontic Instruments: A Systematic Review and Network Meta-Analysis

Sterilization processes guarantee the sterility of dental instruments but can negatively affect instrument features by altering their physical and mechanical properties. The endodontic instrumentation can undergo a series of alterations, ranging from corrosion to variation in the cutting angle and then changes in the torsional properties and torsional fatigue resistance. This systematic literature review and meta-analysis aims to investigate alterations to the cutting efficiency of endodontic instruments that are induced by procedures for their disinfection and sterilization. Methodologies adopted for this investigation follow the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines. The following were used as search terms on PubMed and Scopus: “endodontic sterilization”, “endodontic autoclave”, “cyclic fatigue”, “torsional”, “cutting efficiency”, “sterilization”, “surface characteristics”, and “corrosion”. At the end of the selection process, 36 articles were identified, and seven of them are included in this systematic review. The results of a meta-analysis conducted for the use of 10 autoclaving cycles shows a standardized mean difference (SMD) of 0.80 with a p-value equal to 0.04 with respect to effect on cutting efficiency. The network meta-analysis, through direct and indirect comparison between the different autoclave cycles (0, 1, 5, 10, and 15 cycles), revealed that treatment involving 15 autoclave cycles produced the most robust results in terms of having the greatest effects in terms of altered cutting efficiency with a probability of 57.7% and a SUCRA (surface under the cumulative ranking) of 80%. The alterations in the effects on cutting efficiency appear to be triggered after five cycles of sterilization by heat (autoclave). In conclusion, the meta-analysis of the data indicates that the autoclave sterilization protocol must not be repeated more than five times to preserve cutting efficiency. Within the limitations of this review, we can therefore establish that sterilization by autoclaving alone results in steel and NiTi instruments becoming less efficient in cutting after five cycles, as measured by a reduction in cutting efficiency.
Factors Associated with COVID-19 Vaccine Hesitancy

It is critical to develop tailored strategies to increase acceptability of the COVID-19 vaccine and decrease hesitancy. Hence, this study aims to assess and identify factors associated with COVID-19 vaccine hesitancy in Portugal. We used data from a community-based survey, “COVID-19 Barometer: Social Opinion”, which includes data regarding intention to take COVID-19 vaccines, health status, and risk perception in Portugal from September 2020 to January 2021. We used multinomial regression to identify factors associated with intention to delay or refuse to take COVID-19 vaccines. COVID-19 vaccine hesitancy in Portugal was high: 56% would wait and 9% refuse. Several factors were associated with both refusal and delay: being younger, loss of income during the pandemic, no intention of taking the flu vaccine, low confidence in the COVID-19 vaccine and the health service response during the pandemic, worse perception of government measures, perception of the information provided as inconsistent and contradictory, and answering the questionnaire before the release of information regarding the safety and efficacy of COVID-19 vaccines. It is crucial to build confidence in the COVID-19 vaccine as its perceived safety and efficacy were strongly associated with intention to take the vaccine. Governments and health authorities should improve communication and increase trust.
Prognostic Factors Associated with 5-Year Overall Survival in Cervical Cancer Patients Treated with Radical Hysterectomy Followed by Adjuvant Concurrent Chemoradiation Therapy at a Tertiary Care Center in Eastern Europe

Background: This retrospective observational study aims to assess the 5-year overall survival and the prognostic significance of risk factors of patients who underwent radical hysterectomy followed by adjuvant concurrent chemoradiation therapy (CCRT) for FIGO stage IB1-IIB cervical cancer in a tertiary care center in Eastern Europe. Methods: From January 2010 to February 2019, 222 patients with stage IB1-IIB cervical cancer were treated with radical hysterectomy followed by adjuvant CCRT in our institution. The baseline information consisting of demographic and clinicopathologic data, treatment choices, recurrences, and outcome information was collected and examined. The survival rates were illustrated using Kaplan–Meier curves and prognosis analyses were accomplished using Cox multivariate analyses. Results: The 222 participants had a mean age of 51.2 years (28–76). The median follow-up time was 65.5 months (3–128). Tumor characteristics revealed FIGO stage (IB1 2.3%, IB2 35.1%, IB3 16.7%, IIA1 9%, IIA2 8.6%, IIB 28.4%) and the most encountered histologic cell type was squamous cell carcinoma (80.06%) followed by adenocarcinoma (11.3%). At the time of examination, 157 patients (70.07%) were alive, of which 135 (61%) were alive free of disease and 22 (9%) were alive with disease. The multivariate Cox regression analysis acknowledged stage IIB, parametrial involvement, and the presence of lymph node metastases as independent prognostic risk factors, significantly worsening the oncologic outcomes influencing the survival with a P-value of 0.076, 0.0001, and 0.008, respectively. The 5-year overall survival was 69.9%. Conclusions: Altogether, the study enhances the significance of prognostic risk factors on the 5-year overall survival of patients who underwent radical hysterectomy followed by adjuvant CCRT for FIGO stages IB1-IIB cervical cancer, allowing comparisons with other regions.
Molecular-level description of temperature and pressure effects on the viscosity of water

The steep non-Arrhenius temperature dependence at low temperatures of the shear viscosity of water and its backwards-sounding increased fluidity under pressure for temperatures below 33 °C are two of the anomalies of this liquid that have been known for a very long time. The purpose of the present paper is to show how these two important characteristics of water emerge quantitatively from an explicit two-state outer-neighbor mixture model that we have used to explain many other properties of this substance. It will be shown here that both of these viscosity anomalies are directly related to the steep variations with temperature and pressure of the fractional compositions of ice-Ih-type bonding and ice-II-type bonding in the two-state mixture. This compositional dependence has already been obtained in earlier work from the variations of the density and the isothermal compressibility of water with temperature. The viscosity analysis presented here thus helps to unify further all the properties of this liqui.