Miracle-dz/UniFAR-FTUs huggingface dataset:
Introducing a Symmetry-Breaking Coupler into a Dielectric Metasurface Enables Robust High-Q Quasibound States in the Continuum and Efficient Nonlinear Frequency Conversion

Dielectric metasurfaces supporting quasi-bound states in the continuum (quasi-BICs) exhibit very high quality factor resonances and electric field confinement. However, accessing the high- Q end of the quasi-BIC regime usually requires marginally distorting the metasurface design from a BIC condition, pushing the needed nanoscale fabrication precision to the limit. This work introduces a novel concept for generating high- Q quasi-BICs, which strongly relaxes this requirement by incorporating a relatively large perturbative element close to high-symmetry points of an undistorted BIC metasurface, acting as a coupler to the radiation continuum. We validate this approach by adding a ⁓100 nm diameter cylinder between two reflection-symmetry points separated by a 300 nm gap in an elliptical disk metasurface unit cell, using gallium phosphide as the dielectric. We find that high- Q resonances emerge when the cylindrical coupler is placed at any position between such symmetry points. We further explore this metasurface’s second harmonic generation capability in the optical range. Displacing the coupler as much as a full diameter from a BIC condition produces record-breaking normalized conversion efficiencies >10 2 W -1 . The strategy of enclosing a disruptive element between multiple high-symmetry points in a BIC metasurface could be applied to construct robust high- Q quasi-BICs in many geometrical designs.
Lombard Effect for Bilingual Speakers in Cantonese and English: importance of spectro-temporal features

For a better understanding of the mechanisms underlying speech perception and the contribution of different signal features, computational models of speech recognition have a long tradition in hearing research. Due to the diverse range of situations in which speech needs to be recognized, these models need to be generalizable across many acoustic conditions, speakers, and languages. This contribution examines the importance of different features for speech recognition predictions of plain and Lombard speech for English in comparison to Cantonese in stationary and modulated noise. While Cantonese is a tonal language that encodes information in spectro-temporal features, the Lombard effect is known to be associated with spectral changes in the speech signal. These contrasting properties of tonal languages and the Lombard effect form an interesting basis for the assessment of speech recognition models. Here, an automatic speech recognition-based ASR model using spectral or spectro-temporal features is evaluated with empirical data. The results indicate that spectro-temporal features are crucial in order to predict the speaker-specific speech recognition threshold SRT$_{50}$ in both Cantonese and English as well as to account for the improvement of speech recognition in modulated noise, while effects due to Lombard speech can already be predicted by spectral features.
Gradient boosting for convex cone predict and optimize problems

Prediction models are typically optimized independently from decision optimization. A smart predict then optimize (SPO) framework optimizes prediction models to minimize downstream decision regret. In this paper we present dboost, the first general purpose implementation of smart gradient boosting for `predict, then optimize' problems. The framework supports convex quadratic cone programming and gradient boosting is performed by implicit differentiation of a custom fixed-point mapping. Experiments comparing with state-of-the-art SPO methods show that dboost can further reduce out-of-sample decision regret.
Two Rings and a Marginally Resolved, 5 au Disk around LkCa 15 Identified via Near-infrared Sparse Aperture Masking Interferometry

Sparse aperture masking interferometry (SAM) is a high-resolution observing technique that allows for imaging at and beyond a telescope’s diffraction limit. The technique is ideal for searching for stellar companions at small separations from their host star; however, previous analyses of SAM observations of young stars surrounded by dusty disks have had difficulties disentangling planet and extended disk emission. We analyze VLT/SPHERE-IRDIS SAM observations of the transition disk LkCa 15, model the extended disk emission, probe for planets at small separations, and improve contrast limits for planets. We fit geometrical models directly to the interferometric observables and recover previously observed extended disk emission. We use dynamic nested sampling to estimate uncertainties on our model parameters and to calculate evidences to perform model comparison. We compare our extended disk emission models against point-source models to robustly conclude that the system is dominated by extended emission within 50 au. We report detections of two previously observed asymmetric rings at ∼17 and ∼45 au. The peak brightness location of each model ring is consistent with the previous observations. We also, for the first time with imaging, robustly recover an elliptical Gaussian inner disk, previously inferred via SED fitting. This inner disk has an FWHM of 5 au and a similar inclination and orientation to the outer rings. Finally, we recover no clear evidence for candidate planets. By modeling the extended disk emission, we are able to place a lower limit on the near-infrared companion contrast of at least 1000.
A slow-replicating mutant induces synchronized replication rates and oscillatory viral dynamics in an HIV mathematical model

HIV infection is an incurable disease that leads to the destruction of immunity. Its clinical course is divided into the acute, latency, and acquired immunodeﬁciency syndrome (AIDS) phases. Nowak and May proposed a simple mathematical model that considered viral mutation and found the parameter condition for the long-term development of AIDS [M. A. Nowak, R. M. May. J. Theor. Biol 159, 329-342 (1992)]. However, the mechanism of the acute and latency phase remains unclear from their study. In the present paper, we consider the same model and investigate its capability to qualitatively reproduce the time course of an actual HIV infection. We ﬁnd that the three phases robustly appear when there are two viral mutants with quite different replication rates. The viral dynamics have the following two components: the damped oscillation that forms the initial viral peak and the followingslow exponentialgrowththat resembles the latency and AIDS phase. We analyze the obtained dynamics utilizing linear stability analysis and subsequently generalize for the many mutants case in which one mutant has a considerably lower replication rate ε than the others. We demonstrate that (i) all replication rates synchronize to the same value of O ( ε ) and (ii) an oscillation appears if the initial value of the slowest-replicatingmutant is sufﬁciently small. In particular, the synchronizationof replicationrates emphasizes the role of a slow-replicating mutant in maintaining the latency. Our ﬁndings explain the pathogenesis of HIV from a mathematical aspect, possibly providing a clue for the cure of HIV infection.
An integrated ISM fuzzy MICMAC approach for modelling the supply chain knowledge flow enablers

The aim of this study is to identify supply chain knowledge flow enablers (SCKFEs) to inspect interrelationships among these enablers and classify these enablers into driving power and dependence power using an integrated interpretive structural modelling (ISM) and fuzzy Matriced Impacts Croisés Multiplication Appliquée á un Classement (MICMAC) methodology. While the ISM methodology analyses the interactions among the SCKFEs, fuzzy MICMAC analysis is employed to obtain insights into the dependencies among the SCKFEs. A total of 34 SCKFEs were identified through the literature review and expert opinion. As an example, an Indian manufacturing organisation is selected that is willing to adopt the successful knowledge flow for improving supply chain (SC) performance to overcome the intense competition among the SC versus SC. The research shows SCKFEs having high driving power and low dependence have strategic importance because of their driving nature, while the SCKFEs having high dependence and low driving power are more performance orientated. Therefore, it is the responsibility of SC executives to address the high driving power SCKFEs for the enhancement of SC performance. This categorisation provides a useful tool to top management to differentiate between independent and dependent SCKFEs and their mutual relationships, helping them focus on those key SCKFEs that are most significant. This gives a clear picture to SC practitioners and decision-makers about number of SCKFEs, interrelationship and dependencies existing among them.
An Exploratory Study of Attestation Mechanisms for Trusted Execution Environments

Attestation is a fundamental building block to establish trust over software systems. When used in conjunction with trusted execution environments, it guarantees that genuine code is executed even when facing strong attackers, paving the way for adoption in several sensitive application domains. This paper reviews existing remote attestation principles and compares the functionalities of current trusted execution environments as Intel SGX, Arm TrustZone and AMD SEV, as well as emerging RISC-V solutions.
Studying Alignment in a Collaborative Learning Activity via Automatic Methods: The Link Between What We Say and Do

A dialogue is successful when there is alignment between the speakers, at different linguistic levels. In this work, we consider the dialogue occurring between interlocutors engaged in a collaborative learning task, where they are evaluated on how well they performed and how much they learnt. Our main contribution is to propose new automatic measures to study alignment; focusing on lexical alignment, and a new alignment context that we introduce termed as behavioural alignment (when an instruction given by one interlocutor was followed with concrete actions in a physical environment by another). Thus we propose methodologies to create a link between what was said, and what was done as a consequence. To do so, we focus on expressions related to the task in the situated activity. These expressions are minimally required by the interlocutors to make progress in the task. We then observe how these local alignment contexts build to dialogue level phenomena; success in the task. What distinguishes our approach from other works, is the treatment of alignment as a procedure that occurs in stages. Since we utilise a dataset of spontaneous speech dialogues elicited from children, a second contribution of our work is to study how spontaneous speech phenomena (such as when interlocutors say "uh", "oh" .) are used in the process of alignment. Lastly, we make public the dataset to study alignment in educational dialogues. Our results show that all teams lexically and behaviourally align to some degree regardless of their performance and learning, and our measures capture that teams that did not succeed in the task were simply slower to collaborate. Thus we find that teams that performed better, were faster to align. Furthermore, our methodology captures a productive, collaborative period that includes the time where the interlocutors came up with their best solutions. We also find that well-performing teams verbalise the marker "oh" more when they are behaviourally aligned, compared to other times in the dialogue; showing that this marker is an important cue in alignment. To the best of our knowledge, we are the first to study the role of "oh" as an information management marker in a behavioural context (i.e. in connection to actions taken in a physical environment), compared to only a verbal one. Our measures contribute to the research in the field of educational dialogue and the intersection between dialogue and collaborative learning research.
Justice in interaction design: preventing manipulation in interfaces

Designers incorporate values in the design process that raise risks for vulnerable groups. Persuasion in user interfaces can quickly turn into manipulation and become potentially harmful for those groups in the realm of intellectual disabilities, class, or health, requiring proactive responsibility approaches in design. Here we introduce the Capability Sensitive Design Approach and explain how it can be used proactively to inform designers' decisions when it comes to evaluating justice in their designs preventing the risk of manipulation.
Infinite hidden Markov models for multiple multivariate time series with missing data

Exposure to air pollution is associated with increased morbidity and mortality. Recent technological advancements permit the collection of time‐resolved personal exposure data. Such data are often incomplete with missing observations and exposures below the limit of detection, which limit their use in health effects studies. In this paper, we develop an infinite hidden Markov model for multiple asynchronous multivariate time series with missing data. Our model is designed to include covariates that can inform transitions among hidden states. We implement beam sampling, a combination of slice sampling and dynamic programming, to sample the hidden states, and a Bayesian multiple imputation algorithm to impute missing data. In simulation studies, our model excels in estimating hidden states and state‐specific means and imputing observations that are missing at random or below the limit of detection. We validate our imputation approach on data from the Fort Collins Commuter Study. We show that the estimated hidden states improve imputations for data that are missing at random compared to existing approaches. In a case study of the Fort Collins Commuter Study, we describe the inferential gains obtained from our model including improved imputation of missing data and the ability to identify shared patterns in activity and exposure among repeated sampling days for individuals and among distinct individuals.
Finitistic dimensions over commutative DG-rings

We study the small and big ﬁnitistic projective, injective and ﬂat dimensions over a non-positively graded commutative noetherian DG-ring A with bounded cohomology. Our main results generalize results of Bass and Raynaud-Gruson to this derived setting, showing that any bounded DG-module M of ﬁnite ﬂat dimension satisﬁes proj dim A ( M ) ≤ dim(H 0 ( A )) − inf( M ). We further construct DG-modules of prescribed projective dimension, and deduce that the big ﬁnitistic projective dimension satisﬁes the inequalities dim(H 0 ( A )) − amp( A ) ≤ FPD ( A ) ≤ dim(H 0 ( A )). It is further shown that this result is optimal, in the sense that there are examples that achieve either bound. As an application, new vanishing results for the derived Hochschild (co)homology of homologically smooth algebras are deduced.
Quasifibrations in configuration Lie groupoids and orbifold braid groups

In [19] we studied a Fadell-Neuwirth type fibration theorem for orbifolds, and gave a short exact sequence of fundamental groups of configuration Lie groupoids of Lie groupoids corresponding to the genus zero 2-dimensional orbifolds with cone points, and at least one puncture. In this paper we extend this work to all genus $\geq 1$, 2-dimensional orbifolds with cone points. As a consequence, we prove the Farrell-Jones Isomorphism conjecture for the fundamental groups of the associated configuration Lie groupoids. This answers a substantial part of a question we posed in [[18], Problem]. In [19] we also showed that for all global quotient type orbifolds, the fibration theorem does not hold. Here, we give some nontrivial examples of orbifolds where a Fadell-Neuwirth type quasifibration theorem holds. Finally, we state an Asphericity conjecture and a Quasifibration conjecture for orbifolds.
Synthesizing analytical SQL queries from computation demonstration

Analytical SQL is widely used in modern database applications and data analysis. However, its partitioning and grouping operators are challenging for novice users. Unfortunately, programming by example, shown effective on standard SQL, are less attractive because examples for analytical queries are more laborious to solve by hand. To make demonstrations easier to author, we designed a new end-user specification, programming by computation demonstration, that allows the user to demonstrate the task using a (possibly incomplete) cell-level computation trace. This specification is exploited in a new abstraction-based synthesis algorithm to prove that a partially formed query cannot be completed to satisfy the specification, allowing us to prune the search tree. We implemented our approach in a tool named Sickle and tested it on 80 real-world analytical SQL tasks. Results show that even from small demonstrations, Sickle can solve 76 tasks, in 12.8 seconds on average, while the prior approaches can solve only 60 tasks and are on average 22.5 times slower. Furthermore, our user study with 13 participants reveals that our specification increases user efficiency and confidence on challenging tasks.
Farmer-Bot: An Interactive Bot for Farmers

— The Indian Agricultural sector generates huge employment accounting for over 54% of country’s workforce. Its overall stand in GDP is close to 14%. However, this sector has been plagued by knowledge and infrastructure deficit, especially in the rural sectors. Like other sectors, the Indian Agricultural sector has seen rapid digitization with use of technology and Kisan Call Center (KCC) is one such example. It is a Government of India initiative launched on 21st January 2004 which is a synthesis of two hitherto separate sectors the Information Technology and Agriculture sector. However, studies have shown to have constrains to KCC beneficiaries, especially in light of network congestion and incomplete knowledge of the call center representatives. With the advent of new technologies, like first-generation SMS based and next-generation social media tools like WhatsApp, farmers in India are digitally more connected to the agricultural information services. Previous studies have shown that the KCC dataset can be used as a viable alternative for Chat-bot. We will base our study with the available KCC dataset to build an NLP model by getting the semantic similarity of the queries made by farmers in the past and use it to automatically answer future queries. We will attempt to make a WhatsApp based chat-bot to easily communicate with farmers using RASA as a tool.
Long-term Evolution of a Supernova Remnant Hosting a Double Neutron Star Binary

An ultra-stripped supernova (USSN) is a type of core-collapse supernova explosion proposed to be a candidate formation site of a double neutron star (DNS) binary. We investigate the dynamical evolution of an ultra-stripped supernova remnant (USSNR), which should host a DNS at its center. By accounting for the mass-loss history of the progenitor binary using a model developed by a previous study, we construct the large-scale structure of the circumstellar medium (CSM) up to a radius ∼100 pc, and simulate the explosion and subsequent evolution of a USSN surrounded by such a CSM environment. We find that the CSM encompasses an extended region characterized by a hot plasma with a temperature ∼108 K located around the termination shock of the wind from the progenitor binary (∼10 pc), and the USSNR blast wave is drastically weakened while penetrating through this hot plasma. Radio continuum emission from a young USSNR is sufficiently bright to be detectable if it inhabits our galaxy but faint compared to the observed Galactic supernova remnants (SNRs), and thereafter declines in luminosity through adiabatic cooling. Within our parameter space, USSNRs typically exhibit a low radio luminosity and surface brightness compared to the known Galactic SNRs. Due to the small event rate of USSNe and their relatively short observable life span, we calculate that USSNRs account for only ∼0.1%–1% of the total SNR population. This is consistent with the fact that no SNR hosting a DNS binary has been discovered in the Milky Way so far.
Iterative Inner/outer Approximations for Scalable Semidefinite Programs using Block Factor-width-two Matrices

In this paper, we propose iterative inner/outer approximations based on a recent notion of block factor-width-two matrices for solving semidefinite programs (SDPs). Our inner/outer approximating algorithms generate a sequence of upper/lower bounds of increasing accuracy for the optimal SDP cost. The block partition in our algorithms offers flexibility in terms of both numerical efficiency and solution quality, which includes the approach of scaled diagonally dominance (SDD) approximation as a special case. We discuss both the theoretical results and numerical implementation in detail. Our main theorems guarantee that the proposed iterative algorithms generate monotonically decreasing upper and increasing lower bounds. Extensive numerical results confirm our findings.
A Frustratingly Simple Approach for End-to-End Image Captioning

Image Captioning is a fundamental task to join vision and language, concerning about cross-modal understanding and text generation. Recent years witness the emerging attention on image captioning. Most of existing works follow a traditional two-stage training paradigm. Before training the captioning models, an extra object detector is utilized to recognize the objects in the image at first. However, they require sizeable datasets with fine-grained object annotation for training the object detector, which is a daunting task. In addition, the errors of the object detectors are easy to propagate to the following captioning models, degenerating models' performance. To alleviate such defects, we propose a frustratingly simple but highly effective end-to-end image captioning framework, Visual Conditioned GPT (VC-GPT), by connecting the pre-trained visual encoder (CLIP-ViT) and language decoder (GPT2). Different from the vanilla connection method that directly inserts the cross-attention modules into GPT2, we come up with a self-ensemble cross-modal fusion mechanism that comprehensively considers both the single- and cross-modal knowledge. As a result, we do not need extra object detectors for model training. Experimental results conducted on three popular image captioning benchmarks (MSCOCO, Flickr30k and NoCaps) demonstrate that our VC-GPT achieves either the best or the second-best performance across all evaluation metrics over extensive baseline systems.
Detection and Mitigation of Algorithmic Bias via Predictive Rate Parity

Recently, numerous studies have demonstrated the presence of bias in machine learning powered decision-making systems. Although most deﬁnitions of algorithmic bias have solid mathematical foundations, the corresponding bias detection techniques often lack statistical rigor, especially for non-iid data. We ﬁll this gap in the literature by presenting a rigorous non-parametric testing procedure for bias according to Predictive Rate Parity, a commonly considered notion of algorithmic bias. We adapt traditional asymptotic results for nonparametric estimators to test for bias in the presence of dependence commonly seen in user-level data generated by technology industry applications and illustrate how these approaches can be lever-aged for mitigation. We further propose modiﬁcations of this methodology to address bias measured through marginal outcome disparities in classiﬁcation settings and extend notions of predictive rate parity to multi-objective models. Experimental results on real data show the efﬁcacy of the proposed detection and mitigation methods.