Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Turn Target-Guided Topic Prediction with Monte Carlo Tree Search

This paper concerns the problem of topic prediction in target-guided conversation, which requires the system to proactively and naturally guide the topic thread of the conversation, ending up with achieving a designated target subject. Existing studies usually resolve the task with a sequence of single-turn topic prediction. Greedy decision is made at each turn since it is impossible to explore the topics in future turns under the single-turn topic prediction mechanism. As a result, these methods often suffer from generating sub-optimal topic threads. In this paper, we formulate the target-guided conversation as a problem of multi-turn topic prediction and model it under the framework of Markov decision process (MDP). To alleviate the problem of generating sub-optimal topic thread, Monte Carlo tree search (MCTS) is employed to improve the topic prediction by conducting long-term planning. At online topic prediction, given a target and a start utterance, our proposed MM-TP (MCTS-enhanced MDP for Topic Prediction) firstly performs MCTS to enhance the policy for predicting the topic for each turn. Then, two retrieval models are respectively used to generate the responses of the agent and the user. Quantitative evaluation and qualitative study showed that MM-TP significantly improved the state-of-the-art baselines.
Named Entity-Factored Transformer for Proper Noun Translation

Subword-based neural machine translation decreases the number of out-of-vocabulary (OOV) words and also keeps the translation quality if input sentences include OOV words. The subword-based NMT decomposes a word into shorter units to solve the OOV problem, but it does not work well for non-compositional proper nouns due to the construction of the shorter unit from words. Furthermore, the lack of translation also occurs in proper noun translation. The proposed method applies the Named Entity (NE) fea-ture vector to Factored Transformer for accurate proper noun translation. The proposed method uses two features which are input sentences in subwords unit and the feature obtained from Named Entity Recognition (NER). The pro-posed method improves the problem of non-compositional proper nouns translation included a low-frequency word. According to the experiments, the proposed method using the best NE feature vector outperformed the baseline sub-word-based transformer model by more than 9.6 points in proper noun accuracy and 2.5 points in the BLEU score.
A Scaled Encoder Decoder Network for Image Captioning in Hindi

Image captioning is a prominent research area in computer vision and natural language processing, which automatically generates natural language descriptions for images. Most of the existing works have focused on developing models for image captioning in the English language. The current paper introduces a novel deep learning architecture based on encoder-decoder with an attention mechanism for image captioning in the Hindi language. For encoder, decoder, and attention, several deep learning-based architectures have been explored. Hindi, the fourth-most spoken language globally, is widely spoken in India and South Asia and is one of India’s official languages. The proposed encoder-decoder architecture utilizes scaling in convolution neural networks to achieve better accuracy than state-of-the-art image captioning methods in Hindi. The proposed method’s performance is compared with state-of-the-art methods in terms of BLEU scores and manual evaluation (in terms of adequacy and fluency). The obtained results demonstrate the efficacy of the proposed method.
An Experiment on Speech-to-Text Translation Systems for Manipuri to English on Low Resource Setting

In this paper, we report the experimental findings of building Speech-to-Text translation systems for Manipuri-English on low resource setting which is first of its kind in this language pair. For this purpose, a new dataset consisting of a Manipuri-English parallel corpus along with the corresponding audio version of the Manipuri text is built. Based on this dataset, a benchmark evaluation is reported for the Manipuri-English Speech-to-Text translation using two approaches: 1) a pipeline model consisting of ASR (Automatic Speech Recognition) and Machine translation, and 2) an end-to-end Speech-to-Text translation. Gaussian Mixture Model-Hidden Markov Model (GMM-HMM) and Time delay neural network (TDNN) Acoustic models are used to build two different pipeline systems using a shared MT system. Experimental result shows that the TDNN model outperforms GMM-HMM model significantly by a margin of 2.53% WER. However, their evaluation of Speech-to-Text translation differs by a small margin of 0.1 BLEU. Both the pipeline translation models outperform the end-to-end translation model by a margin of 2.6 BLEU score.
An Efficient Keyframes Selection Based Framework for Video Captioning

Describing a video is a challenging yet attractive task since it falls into the intersection of computer vision and natural language generation. The attention-based models have reported the best performance. However, all these models follow similar procedures, such as segmenting videos into chunks of frames or sampling frames at equal intervals for visual encoding. The process of segmenting video into chunks or sampling frames at equal intervals causes encoding of redundant visual information and requires additional computational cost since a video consists of a sequence of similar frames and suffers from inescapable noise such as uneven illumination, occlusion and motion effects. In this paper, a boundary-based keyframes selection approach for video description is proposed that allow the system to select a compact subset of keyframes to encode the visual information and generate a description for a video without much degradation. The proposed approach uses 3 4 frames per video and yields competitive performance over two benchmark datasets MSVD and MSR-VTT (in both English and Hindi).
Demonstrating EMMA: Embodied MultiModal Agent for Language-guided Action Execution in 3D Simulated Environments

We demonstrate EMMA, an embodied multimodal agent which has been developed for the Alexa Prize SimBot challenge. The agent acts within a 3D simulated environment for household tasks. EMMA is a unified and multimodal generative model aimed at solving embodied tasks. In contrast to previous work, our approach treats multiple multimodal tasks as a single multimodal conditional text generation problem, where a model learns to output text given both language and visual input. Furthermore, we showcase that a single generative agent can solve tasks with visual inputs of varying length, such as answering questions about static images, or executing actions given a sequence of previous frames and dialogue utterances. The demo system will allow users to interact conversationally with EMMA in embodied dialogues in different 3D environments from the TEACh dataset.
Image Caption Generation Framework for Assamese News using Attention Mechanism

Automatic caption generation is an artificial intelligence problem that falls at the intersection of computer vision and natural language processing. Although significant works have been reported in image captioning, the contribution is limited to English and few major languages with sufficient resources. But, no work on image captioning has been reported in a resource-constrained language like Assamese. With this inspiration, we propose an encoder-decoder based framework for image caption generation in the Assamese news domain. The VGG-16 pre-trained model at the encoder side and LSTM with an attention mechanism are employed at the decoder side to generate the Assamese caption. We train the proposed model on the dataset built in-house consisting of 10,000 images with a single caption for each image. We describe our experimental methodology, quantitative and qualitative results which validate the effectiveness of our model for caption generation. The proposed model shows a BLEU score of 12.1 outperforming the baseline model.
The concept of nation in nineteenth-century Greek fiction through computational literary analysis

How the construction of national consciousness may be captured in the literary production of a whole century? What can the macro-analysis of the 19th-century prose fiction reveal about the formation of the concept of the nation-state of Greece? How could the concept of nationality be detected in literary writing and then interpreted? These are the questions addressed by the research that is published in this paper and which focuses on exploring how the concept of the nation is figured and shaped in 19th-century Greek prose fiction. This paper proposes a methodological approach that combines well-known text mining techniques with computational close reading methods in order to retrieve the nation-related passages and to analyze them linguistically and semantically. The main objective of the paper at hand is to map the frequency and the phraseology of the nation-related references, as well as to explore the phrase patterns in relation to the topic modeling results.
Multitask Learning based Deep Learning Model for Music Artist and Language Recognition

Artist and music language recognitions of music recordings are crucial tasks in the music information retrieval domain. These tasks have many industrial applications and become much important with the advent of music streaming platforms. This work proposed a multitask learning-based deep learning model that leverages the shared latent representation between these two related tasks. Experimentally, we observe that applying multitask learning over a simple few blocks of a convolutional neural network-based model pays off with improvement in the performance. We conduct experiments on a regional music dataset curated for this task and released for others. Results show improvement up to 8.7 percent in AUC-PR, similar improvements observed in AUC-ROC.
Exploring the antecedents of masstige purchase behaviour among different generations

Abstract The present study proposes the conceptual model, which traces the role of consumers’ high-quality consciousness, perceived brand equity, and luxury value perceptions on the intention to purchase masstige brands and tests it for the moderating role of generational cohort membership (X-ers, Y-ers, and Z-ers). To explore the relationship between variables of interest, we used a quantitative survey-based research design and emphasised a single product category – consumer electronics and a single masstige brand – Apple. The online survey questionnaire was developed on the Google platform and distributed among residents of Bosnia and Herzegovina using a snowball sampling technique. In the end, we received 354 usable responses. We used structural equation modelling and multigroup analysis to test the proposed conceptual model. Our findings suggest that quality consciousness is the most significant predictor of consumers’ intentions to buy a masstige brand, followed by perceived brand equity. Also, our results indicate that the relationships between independent variables (consumer-based brand equity, quality consciousness, overall luxury value) and dependent variables (intention to purchase a masstige brand) are contingent on generational differences. The present study deepens our scholarly understanding of masstige consumption and contributes to the theoretical notion of generation cohort theory in the context of masstige consumption.
Prenatal and Perinatal Phthalate Exposure is Associated with Sex-dependent Changes in Hippocampal MiR-15b-5p and MiR-34a-5p Expression and Changes in Testicular Morphology in Rat Offspring

Abstract MicroRNAs are a large group of non-coding nucleic acids, usually 20–22 nt long, which bind to regulatory sections of messenger RNA (mRNA) and inhibit gene expression. However, genome activity is also regulated by hormones. Endocrine disruptors such as those from the phthalate group imitate or block these hormonal effects, and our previous study showed a long-lasting decrease in plasma testosterone levels in rat offspring exposed to a mixture of three phthalates in utero and postnatally. These effects were also observed at the behavioural level. To shed more light on these findings, in this new study we compared testicular tissue morphology between control and phthalatetreated males and investigated possible persistent changes and sex differences in the expression of two hippocampal microRNAs – miR- 15b-5p and miR-34a-5p – participating in the transcription of steroidogenic genes. Histologically observed changes in testicular tissue morphology of phthalate-exposed males compared to control support testosterone drop observed in the previous study. At the microRNA level, we observed more significant changes in phthalate-treated females than in males. However, we are unable to relate these effects to the previously observed behavioural changes.
The Erez–Rosen metric and the role of the quadrupole on light propagation

The gravitational field of a static body with the quadrupole moment is described by an exact solution found by Erez and Rosen. Here, we investigate the role of the quadrupole in the motion, deflection and lensing of a light ray in the above metric. The standard lensing observables such as image positions and magnification have been explicitly obtained in the weak-field and small-quadrupole limit. In this limit, the spacetime metric appears as the natural generalization to quadrupole corrections of the metric form adopted also in current astrometric models. Hence, the corresponding analytical solution of the inverse ray tracing problem and the consistency with other approaches are also discussed.
Is left–right symmetry the key?

In collaboration with Jogesh Pati, Abdus Salam challenged the chiral gauge nature of the Standard Model by paving the road towards the left-right symmetric electro-weak theory. I describe here the logical and historical construction of this theory, by emphasising the pioneering and key role it played for neutrino mass. I show that it is a self-contained and predictive model with the Higgs origin of Majorana neutrino mass, in complete analogy with the SM situation regarding charged fermions.
Causal Effect Estimation Under Linear and Log-Linear Structural Nested Mean Models in the Presence of Unmeasured Confounding

In randomized clinical trials where the effects of post-randomization factors are of interest, the standard regression analyses are biased due to unmeasured confounding. The instrumental variables (IV; Angrist et al., 1996) and G-estimation procedures under structural nested mean models (SNMMs; Robins, 1994) allow one to make valid inference even if unmeasured confounding is present. Two IV approaches, the two-stage predictor substitution (2SPS) and two-stage residual inclusion (2SRI), are typically applied in analysis assuming the exclusion restriction. However, the exclusion restriction may be violated in clinical applications when the mechanism of treatment is assessed under mediation analyses. Accordingly, we focus on estimating the direct effect of the randomized treatment adjusting for a post-randomization mediator. We extend the two IV approaches to estimate the direct effect, and evaluate the corresponding theoretical properties under the linear SNMM. Under certain assumptions, we have shown that the two IV approaches are equivalent to the linear SNMM. We further extend and investigate the validity of these IV methods for estimation under a loglinear SNMM. The results show that the IV estimators are biased in the presence of unmeasured confounding. Therefore, we consider the G-estimation approach as an alternative solution to remove bias under the loglinear SNMM. The method was previously developed under either the exclusion restriction assumption or the sequential ignorability assumption. We present a general framework where these two assumptions are relaxed. In contrast to the IV log-linear regression methods, we have shown that the proposed G-estimators are unbiased in the presence of unmeasured confounding. Degree Type Dissertation Degree Name Doctor of Philosophy (PhD) Graduate Group Epidemiology & Biostatistics First Advisor Thomas R. Ten Have Second Advisor Marshall M. Joffe
Chromospheric Heating by Acoustic Waves Compared to Radiative Cooling. II. Revised Grid of Models

Acoustic and magnetoacoustic waves are considered to be possible agents of chromospheric heating. We present a comparison of deposited acoustic energy flux with total integrated radiative losses in the middle chromosphere of the quiet Sun and a weak plage. The comparison is based on a consistent set of high-resolution observations acquired by the Interferometric Bidimensional Spectrometer instrument in the Ca ii 854.2 nm line. The deposited acoustic-flux energy is derived from Doppler velocities observed in the line core and a set of 1737 non-local thermodynamic equilibrium 1D hydrostatic semi-empirical models, which also provide the radiative losses. The models are obtained by scaling the temperature and column mass of five initial models by Vernazza et al. (1981; VAL) B–F to get the best fit of synthetic to observed profiles. We find that the deposited acoustic-flux energy in the quiet-Sun chromosphere balances 30%–50% of the energy released by radiation. In the plage, it contributes by 50%–60% in locations with vertical magnetic field and 70%–90% in regions where the magnetic field is inclined more than 50° to the solar surface normal.
A 2.4% DETERMINATION OF THE LOCAL VALUE OF THE HUBBLE CONSTANT

We use the Wide Field Camera 3 (WFC3) on the Hubble Space Telescope (HST) to reduce the uncertainty in the local value of the Hubble constant from 3.3% to 2.4%. The bulk of this improvement comes from new near-infrared (NIR) observations of Cepheid variables in 11 host galaxies of recent type Ia supernovae (SNe Ia), more than doubling the sample of reliable SNe Ia having a Cepheid-calibrated distance to a total of 19; these in turn leverage the magnitude-redshift relation based on ∼300 SNe Ia at z < 0.15. All 19 hosts as well as the megamaser system NGC 4258 have been observed with WFC3 in the optical and NIR, thus nullifying cross-instrument zeropoint errors in the relative distance estimates from Cepheids. Other noteworthy improvements include a 33% reduction in the systematic uncertainty in the maser distance to NGC 4258, a larger sample of Cepheids in the Large Magellanic Cloud (LMC), a more robust distance to the LMC based on late-type detached eclipsing binaries (DEBs), HST observations of Cepheids in M31, and new HST-based trigonometric parallaxes for Milky Way (MW) Cepheids. We consider four geometric distance calibrations of Cepheids: (i) megamasers in NGC 4258, (ii) 8 DEBs in the LMC, (iii) 15 MW Cepheids with parallaxes measured with HST/FGS, HST/WFC3 spatial scanning and/or Hipparcos, and (iv) 2 DEBs in M31. The Hubble constant from each is 72.25 ± 2.51, 72.04 ± 2.67, 76.18 ± 2.37, and 74.50 ± 3.27 km s−1 Mpc−1, respectively. Our best estimate of H0 = 73.24 ± 1.74 km s−1 Mpc−1 combines the anchors NGC 4258, MW, and LMC, yielding a 2.4% determination (all quoted uncertainties include fully propagated statistical and systematic components). This value is 3. 4σ higher than 66.93 ± 0.62 km s−1 Mpc−1 predicted by ΛCDM with 3 neutrino flavors having a mass of 0.06 eV and the new Planck data, but the discrepancy reduces to 2. 1σ relative to the prediction of 69.3 ± 0.7 km s−1 Mpc−1 based on the comparably precise combination of WMAP+ACT+SPT+BAO observations, suggesting that systematic uncertainties in CMB radiation measurements may play a role in the tension. If we take the conflict between Planck high-redshift measurements and our local determination of H0 at face value, one plausible explanation could involve an additional source of dark radiation in the early universe in the range of ΔNeff ≈ 0.4–1. We anticipate further significant improvements in H0 from upcoming parallax measurements of long-period MW Cepheids.
A general sensor-fusion and parameters self-calibration framework with applications in mobile robotics

Autonomous robots use sensors to acquire knowledge about the state of the world, in particular to reduce their uncertainty about critical variables related to the assigned tasks. When multiple sensors observe different aspects of the reality, they report noisy, overlapping, possibly contradictory measurements that have to be properly processed to update the robot internal belief. In this work we introduce ROAMFREE, a general, open-source, framework for multi-sensor fusion and parameter self-calibration in mobile robotics. A comprehensive logical sensors library allows to abstract from the actual hardware and processing while preserving modeling accuracy thanks to a rich set of calibration parameters (e.g., sensor geometric placement, biases, gains and distortion matrices). A modular formulation of the information fusion problem has been obtained based on state-of-the-art factor-graph inference techniques; it allows to handle arbitrary number of multi-rate sensors and to adapt to virtually any kind of mobile robot platforms, such as Ackerman steering vehicles, quadrotor unmanned aerial vehicles, omni-directional mobile robots. Different solvers are available to target both high-rate online pose tracking tasks and offline accurate trajectory smoothing and parameter self-calibration. An extensive evaluation of the resulting framework has been performed on different mobile robots. ROAMFREE has already proved its flexibility and out-of-the-box deployment in several, real-world, information fusion and sensor self-calibration problems.
Massive open star clusters using the VVV survey - V. Young clusters with an OB stellar population

The ESO public survey VISTA Variables in the Via Lactea (VVV) has contributed with deep multi-epoch photometry of the Galactic bulge and the adjacent part of the disk over 526 square degrees. More than a hundred cluster candidates have been reported thanks to this survey. We present the fifth article in a series of papers focused on young and massive clusters discovered in the VVV survey. In this paper, we present the physical characterization of five clusters with a spectroscopically confirmed OB-type stellar population. To characterize the clusters, we used near-infrared photometry ($J$, $H,$ and $K_S$) from the VVV survey and near-infrared $K$-band spectroscopy from ISAAC at VLT, following the methodology presented in the previous articles of the series. All clusters in our sample are very young (ages between 1-20 Myr), and their total mass are between $(1.07^{+0.40}_{-0.30})\cdot10^2$ $M_{\odot}$ and $(4.17^{+4.15}_{-2.08})\cdot10^3$ $M_{\odot}$. We observed a relation between the clusters total mass $M_{ecl}$ and the mass of their most massive stellar member $m_{max}$, even for clusters with an age $<$ 10 Myr.