Miracle-dz/UniFAR-FTUs huggingface dataset:
Evolution of topics in central bank speech communication

This paper studies the content of central bank speech communication from 1997 through 2020 and asks the following questions: (i) What global topics do central banks talk about? (ii) How do these topics evolve over time? I turn to natural language processing, and more specifically Dynamic Topic Models, to answer these questions. The analysis consists of an aggregate study of nine major central banks and a case study of the Federal Reserve, which allows for region specific control variables. I show that: (i) Central banks address a broad range of topics. (ii) The topics are well captured by Dynamic Topic Models. (iii) The global topics exhibit strong and significant autoregressive properties not easily explained by financial control variables.
Error Compensated Loopless SVRG, Quartz, and SDCA for Distributed Optimization

The communication of gradients is a key bottleneck in distributed training of large scale machine learning models. In order to reduce the communication cost, gradient compression (e.g., sparsification and quantization) and error compensation techniques are often used. In this paper, we propose and study three new efficient methods in this space: error compensated loopless SVRG method (EC-LSVRG), error compensated Quartz (EC-Quartz), and error compensated SDCA (EC-SDCA). Our method is capable of working with any contraction compressor (e.g., TopK compressor), and we perform analysis for convex optimization problems in the composite case and smooth case for EC-LSVRG. We prove linear convergence rates for both cases and show that in the smooth case the rate has a better dependence on the parameter associated with the contraction compressor. Further, we show that in the smooth case, and under some certain conditions, error compensated loopless SVRG has the same convergence rate as the vanilla loopless SVRG method. Then we show that the convergence rates of EC-Quartz and EC-SDCA in the composite case are as good as EC-LSVRG in the smooth case. Finally, numerical experiments are presented to illustrate the efficiency of our methods.
LOTR: Face Landmark Localization Using Localization Transformer

This paper presents a novel Transformer-based facial landmark localization network named Localization Transformer (LOTR). The proposed framework is a direct coordinate regression approach leveraging a Transformer network to better utilize the spatial information in a feature map. An LOTR model consists of three main modules: 1) a visual backbone that converts an input image into a feature map, 2) a Transformer module that improves the feature representation from the visual backbone, and 3) a landmark prediction head that directly predicts landmark coordinates from the Transformer’s representation. Given cropped-and-aligned face images, the proposed LOTR can be trained end-to-end without requiring any post-processing steps. This paper also introduces a loss function named smooth-Wing loss, which addresses the gradient discontinuity of the Wing loss, leading to better convergence than standard loss functions such as L1, L2, and Wing loss. Experimental results on the JD landmark dataset provided by the First Grand Challenge of 106-Point Facial Landmark Localization indicate the superiority of LOTR over the existing methods on the leaderboard and two recent heatmap-based approaches. On the WFLW dataset, the proposed LOTR framework demonstrates promising results compared with several state-of-the-art methods. Additionally, we report an improvement in the performance of state-of-the-art face recognition systems when using our proposed LOTRs for face alignment.
Plants as flagship species in tourism destination: a case study at Mount Mahawu Tomohon, North Sulawesi, Indonesia.

This study aims to identify the plants as a flagship species in tourism destination based on the perception of tourists. Field survey was conducted in Mt. Mahawu nature-based tourism area in Tomohon, North Sulawesi, Indonesia. Field survey was done by distributed questionnaire to the 196 respondents. Respondent asked to identify the preferred main tourism attraction in Mt. Mahawu and respondent’s perception of diversity of plant species that could potentially be used as a flagship tourist destination. Among the numerous natural tourism object in Mt. Mahawu, this research explained that plants was one of the most interesting tourism attraction in Mt. Mahawu. The important plants species were found in research area as a tourist flagships, both in term of tourism interesting object and conservation issues, encompasses Nepenthes maxima Reinw. ex Nees, Blechnum capense (L.) Schltdl. , Pinus merkusii Jungh. & de Vriese, Phajus sp. , Tabernaemontana pandacaqui Poir, Macaranga minahassae Whitmore, Swietenia macrophylla King, Bulbophyllum lobii Lindl, Euphorbia cotinifolia L. and Shefflera elliptica (Blume) Harms. The conservation effort to preserve such species was important in order to enhance tourism destination competitiveness in Mt. Mahawu.
Rapid Replanning in Consecutive Pick-and-Place Tasks with Lazy Experience Graph

—In an environment where a manipulator needs to execute multiple consecutive tasks, the act of object manoeuvre will change the underlying conﬁguration space, affecting all subsequent tasks. Previously free conﬁgurations might now be occupied by the manoeuvred objects, and previously occupied space might now open up new paths. We propose Lazy Tree-based Replanner ( LTR *)—a novel hybrid planner that inher- its the rapid planning nature of existing anytime incremental sampling-based planners. At the same time, it allows subsequent tasks to leverage prior experience via a lazy experience graph. Previous experience is summarised in a lazy graph structure, and LTR * is formulated to be robust and beneﬁcial regard-less of the extent of changes in the workspace. Our hybrid approach attains a faster speed in obtaining an initial solution than existing roadmap-based planners and often with a lower cost in trajectory length. Subsequent tasks can utilise the lazy experience graph to speed up ﬁnding a solution and take advant- age of the optimised graph to minimise the cost objective. We provide proofs of probabilistic completeness and almost-surely asymptotic optimal guarantees. Experimentally, we show that in repeated pick-and-place tasks, LTR * attains a high gain in performance when planning for subsequent tasks.
Neural network–based pore flow field prediction in porous media using super resolution

Direct pore-scale simulations of fluid flow through porous media are computationally expensive to perform for realistic systems. Previous works have demonstrated using the geometry of the microstructure of porous media to predict the velocity fields therein based on neural networks. However, such trained neural networks do not perform well for unseen porous media with a large degree of heterogeneity. In this study we propose that incorporating a coarse velocity field in the input of neural networks is an effective way to improve the prediction performance. The coarse velocity field can be simulated with a low computational cost and provides global information to regularize the ill-posedness of the learning problem, which is usually caused by the use of local geometries due to the computational resource constraints. We show that incorporating the coarse-mesh velocity field significantly improves the prediction accuracy of the fine-mesh velocity field by comparison to the prediction that relies on geometric information alone, especially for the porous medium with a large interior vuggy pore space. We also show the flexibility of training the network in using coarse velocity fields with various resolutions. The results suggest that even using coarse velocity field with a very low resolution, the predictions are still enhanced and close to the ground truths. The feasibility of the method is further demonstrated by testing the trained network on real rocks. This study highlights the merits of incorporating a coarse-mesh velocity field into the input for neural networks, which provides global, physics-based information for the model, thereby improving the model's generalization capability.
Not All Comments Are Equal: Insights into Comment Moderation from a Topic-Aware Model

Moderation of reader comments is a significant problem for online news platforms. Here, we experiment with models for automatic moderation, using a dataset of comments from a popular Croatian newspaper. Our analysis shows that while comments that violate the moderation rules mostly share common linguistic and thematic features, their content varies across the different sections of the newspaper. We therefore make our models topic-aware, incorporating semantic features from a topic model into the classification decision. Our results show that topic information improves the performance of the model, increases its confidence in correct outputs, and helps us understand the model’s outputs.
Finding quadruply imaged quasars with machine learning. I. Methods

Strongly lensed quadruply imaged quasars (quads) are extraordinary objects. They are very rare in the sky and yet they provide unique information about a wide range of topics, including the expansion history and the composition of the Universe, the distribution of stars and dark matter in galaxies, the host galaxies of quasars, and the stellar initial mass function. Finding them in astronomical images is a classic ‘needle in a haystack’ problem, as they are outnumbered by other (contaminant) sources by many orders of magnitude. To solve this problem, we develop state-of-the-art deep learning methods and train them on realistic simulated quads based on real images of galaxies taken from the Dark Energy Survey, with realistic source and deflector models, including the chromatic effects of microlensing. The performance of the best methods on a mixture of simulated and real objects is excellent, yielding area under the receiver operating curve in the range 0.86 to 0.89. Recall is close to 100 per cent down to total magnitude i ∼ 21 indicating high completeness, while precision declines from 85 per cent to 70 per cent in the range i ∼ 17–21. The methods are extremely fast: training on 2 million samples takes 20 hours on a GPU machine, and 108 multi-band cutouts can be evaluated per GPU-hour. The speed and performance of the method pave the way to apply it to large samples of astronomical sources, bypassing the need for photometric pre-selection that is likely to be a major cause of incompleteness in current samples of known quads.
Near-Field Wideband Beamforming for Extremely Large Antenna Array

The natural integration of extremely large antenna arrays (ELAAs) and terahertz (THz) communications can potentially achieve Tbps data rates in 6G networks. However, due to the extremely large array aperture and wide bandwidth, a new phenomenon called"near-field beam split"emerges. This phenomenon causes beams at different frequencies to focus on distinct physical locations, leading to a significant gain loss of beamforming. To address this challenging problem, we first harness a piecewise-far-field channel model to approximate the complicated near-field wideband channel. In this model, the entire large array is partitioned into several small sub-arrays. While the wireless channel's phase discrepancy across the entire array is modeled as near-field spherical, the phase discrepancy within each sub-array is approximated as far-field planar. Built on this approximation, a phase-delay focusing (PDF) method employing delay phase precoding (DPP) architecture is proposed. Our PDF method could compensate for the intra-array far-field phase discrepancy and the inter-array near-field phase discrepancy via the joint control of phase shifters and time delayers, respectively. Theoretical and numerical results are provided to demonstrate the efficiency of the proposed PDF method in mitigating the near-field beam split effect.Finally, we define and derive a novel metric termed the"effective Rayleigh distance"by the evaluation of beamforming gain loss. Compared to classical Rayleigh distance, the effective Rayleigh distance is more accurate in determining the near-field range for practical communications.
Towards a Fairness-Aware Scoring System for Algorithmic Decision-Making

Scoring systems, as a type of predictive model, have significant advantages in interpretability and transparency and facilitate quick decision-making. As such, scoring systems have been extensively used in a wide variety of industries such as healthcare and criminal justice. However, the fairness issues in these models have long been criticized, and the use of big data and machine learning algorithms in the construction of scoring systems heightens this concern. In this paper, we propose a general framework to create fairness-aware, data-driven scoring systems. First, we develop a social welfare function that incorporates both efficiency and group fairness. Then, we transform the social welfare maximization problem into the risk minimization task in machine learning, and derive a fairness-aware scoring system with the help of mixed integer programming. Lastly, several theoretical bounds are derived for providing parameter selection suggestions. Our proposed framework provides a suitable solution to address group fairness concerns in the development of scoring systems. It enables policymakers to set and customize their desired fairness requirements as well as other application-specific constraints. We test the proposed algorithm with several empirical data sets. Experimental evidence supports the effectiveness of the proposed scoring system in achieving the optimal welfare of stakeholders and in balancing the needs for interpretability, fairness, and efficiency.
The GRAVITY young stellar object survey. II. First spatially resolved observations of the CO bandhead emission in a high-mass YSO

The inner regions of the discs of high-mass young stellar objects (HMYSOs) are still poorly known due to the small angular scales and the high visual extinction involved. We deploy near-infrared (NIR) spectro-interferometry to probe the inner gaseous disc in HMYSOs and investigate the origin and physical characteristics of the CO bandhead emission (2.3-2.4 $\mu$m). We present the first GRAVITY/VLTI observations at high spectral (R=4000) and spatial (mas) resolution of the CO overtone transitions in NGC 2024 IRS2. The continuum emission is resolved in all baselines and is slightly asymmetric, displaying small closure phases ($\leq$8$^{\circ}$). Our best ellipsoid model provides a disc inclination of 34$^{\circ}$$\pm$1$^{\circ}$, a disc major axis position angle of 166$^{\circ}$$\pm$1$^{\circ}$, and a disc diameter of 3.99$\pm$0.09 mas (or 1.69$\pm$0.04 au, at a distance of 423 pc). The small closure phase signals in the continuum are modelled with a skewed rim, originating from a pure inclination effect. For the first time, our observations spatially and spectrally resolve the first four CO bandheads. Changes in visibility, as well as differential and closure phases across the bandheads are detected. Both the size and geometry of the CO-emitting region are determined by fitting a bidimensional Gaussian to the continuum-compensated CO bandhead visibilities. The CO-emitting region has a diameter of 2.74$\pm^{0.08}_{0.07}$ mas (1.16$\pm$0.03 au), and is located in the inner gaseous disc, well within the dusty rim, with inclination and $PA$ matching the dusty disc geometry, which indicates that both dusty and gaseous discs are coplanar. Physical and dynamical gas conditions are inferred by modelling the CO spectrum. Finally, we derive a direct measurement of the stellar mass of $M_*\sim$14.7$^{+2}_{-3.6}$ M$_{\odot}$ by combining our interferometric and spectral modelling results.
Generalization in Text-based Games via Hierarchical Reinforcement Learning

Deep reinforcement learning provides a promising approach for text-based games in studying natural language communication between humans and artificial agents. However, the generalization still remains a big challenge as the agents depend critically on the complexity and variety of training tasks. In this paper, we address this problem by introducing a hierarchical framework built upon the knowledge graph-based RL agent. In the high level, a meta-policy is executed to decompose the whole game into a set of subtasks specified by textual goals, and select one of them based on the KG. Then a sub-policy in the low level is executed to conduct goal-conditioned reinforcement learning. We carry out experiments on games with various difficulty levels and show that the proposed method enjoys favorable generalizability.
I Know You Can't See Me: Dynamic Occlusion-Aware Safety Validation of Strategic Planners for Autonomous Vehicles Using Hypergames

A particular challenge for both autonomous and human driving is dealing with risk associated with dynamic occlusion, i.e., occlusion caused by other vehicles in traffic. Based on the theory of hypergames, we develop a novel multi-agent dynamic occlusion risk (DOR) measure for assessing situational risk in dynamic occlusion scenarios. Furthermore, we present a white-box, scenario-based, accelerated safety validation framework for assessing safety of strategic planners in AV. Based on evaluation over a large naturalistic database, our proposed validation method achieves a 4000% speedup compared to direct validation on naturalistic data, a more diverse coverage, and ability to generalize beyond the dataset and generate commonly observed dynamic occlusion crashes in traffic in an automated manner.
Learning PAC-Bayes Priors for Probabilistic Neural Networks

Recent works have investigated deep learning models trained by optimising PAC-Bayes bounds, with priors that are learnt on subsets of the data. This combination has been shown to lead not only to accurate classifiers, but also to remarkably tight risk certificates, bearing promise towards self-certified learning (i.e. use all the data to learn a predictor and certify its quality). In this work, we empirically investigate the role of the prior. We experiment on 6 datasets with different strategies and amounts of data to learn data-dependent PAC-Bayes priors, and we compare them in terms of their effect on test performance of the learnt predictors and tightness of their risk certificate. We ask what is the optimal amount of data which should be allocated for building the prior and show that the optimum may be dataset dependent. We demonstrate that using a small percentage of the prior-building data for validation of the prior leads to promising results. We include a comparison of underparameterised and overparameterised models, along with an empirical study of different training objectives and regularisation strategies to learn the prior distribution.
ShapeMap 3-D: Efficient shape mapping through dense touch and vision

Knowledge of 3-D object shape is of great importance to robot manipulation tasks, but may not be readily available in unstructured environments. While vision is often occluded during robot-object interaction, high-resolution tactile sensors can give a dense local perspective of the object. However, tactile sensors have limited sensing area and the shape representation must faithfully approximate non-contact areas. In addition, a key challenge is efficiently incorporating these dense tactile measurements into a 3-D mapping framework. In this work, we propose an incremental shape mapping method using a GelSight tactile sensor and a depth camera. Local shape is recovered from tactile images via a learned model trained in simulation. Through efficient inference on a spatial factor graph informed by a Gaussian process, we build an implicit surface representation of the object. We demonstrate visuo-tactile mapping in both simulated and real-world experiments, to incrementally build 3-D reconstructions of household objects.
Homography augumented momentum constrastive learning for SAR image retrieval

Deep learning-based image retrieval has been emphasized in computer vision. Representation embedding extracted by deep neural networks (DNNs) not only aims at containing semantic information of the image, but also can manage large-scale image retrieval tasks. In this work, we propose a deep learning-based image retrieval approach using homography transformation augmented contrastive learning to perform large-scale synthetic aperture radar (SAR) image search tasks. Moreover, we propose a training method for the DNNs induced by contrastive learning that does not require any labeling procedure. This may enable tractability of large-scale datasets with relative ease. Finally, we verify the performance of the proposed method by conducting experiments on the polarimetric SAR image datasets.
A Horizon Study for Cosmic Explorer: Science, Observatories, and Community

This Horizon Study describes a next-generation ground-based gravitational-wave observatory: Cosmic Explorer. With ten times the sensitivity of Advanced LIGO, Cosmic Explorer will push gravitational-wave astronomy towards the edge of the observable universe ($z \sim 100$). The goals of this Horizon Study are to describe and evaluate design concepts for Cosmic Explorer; to plan for the United States' leadership in gravitational-wave astronomy; and to envisage the role of Cosmic Explorer in the international effort to build a"Third-Generation"(3G) observatory network that will make discoveries transformative across astronomy, physics, and cosmology.
Probing Gas Kinematics and PDR Structure around O-type Stars in the Sh 2-305 H ii Region

We report an observational study of the Galactic H ii region Sh 2-305/S305 using the [C ii] 158 μm line data, which are used to examine the gas dynamics and structure of photodissociation regions. The integrated [C ii] emission map at [39.4, 49.5] km s−1 spatially traces two shell-like structures (i.e., inner and outer neutral shells) having a total mass of ∼565 M ⊙. The inner neutral shell encompasses an O9.5V star at its center and has a compact ring-like appearance. However, the outer shell is seen with more extended and diffuse [C ii] emission, hosting an O8.5V star at its center, and surrounds the inner neutral shell. The velocity channel maps and position–velocity diagrams confirm the presence of a compact [C ii] shell embedded in the diffuse outer shell, and both the shells seem to expand with vexp∼1.3 km s−1. The outer shell appears to be older than the inner shell, hinting that these shells are formed sequentially. The [C ii] profiles are examined toward S305, which are either double peaked or blue skewed and have the brighter redshifted component. The redshifted and blueshifted components spatially trace the inner and outer neutral shell geometry, respectively. The ionized, neutral, and molecular zones in S305 are seen adjacent to one another around the O-type stars. The regularly spaced dense molecular and dust clumps (mass ∼10–103 M ⊙) are investigated around the neutral shells, which might have originated as a result of gravitational instability in the shell of collected materials.