Miracle-dz/UniFAR-FTUs huggingface dataset:
ELF22: A Context-based Counter Trolling Dataset to Combat Internet Trolls

Online trolls increase social costs and cause psychological damage to individuals. With the proliferation of automated accounts making use of bots for trolling, it is difficult for targeted individual users to handle the situation both quantitatively and qualitatively. To address this issue, we focus on automating the method to counter trolls, as counter responses to combat trolls encourage community users to maintain ongoing discussion without compromising freedom of expression. For this purpose, we propose a novel dataset for automatic counter response generation. In particular, we constructed a pair-wise dataset that includes troll comments and counter responses with labeled response strategies, which enables models fine-tuned on our dataset to generate responses by varying counter responses according to the specified strategy. We conducted three tasks to assess the effectiveness of our dataset and evaluated the results through both automatic and human evaluation. In human evaluation, we demonstrate that the model fine-tuned with our dataset shows a significantly improved performance in strategy-controlled sentence generation.
Understanding the properties and diversity of Galaxies and Quasars through Spectral Decomposition

In the first part of this thesis, we study spectral decompositions of galaxies and quasars (QSOs) by the Karhunen-Lo`eve (KL) transformusing the Sloan Digital Sky Survey (SDSS). Our goal is to understand the average properties and sample variances of the data, with an eyetoward obtaining objective classifications of these objects.The eigencoefficients describing the galaxies naturally place the spectra into several classes defined by the plane formed by the firstthree eigencoefficients of each spectrum. Spectral types, corresponding to different Hubble-types and galaxies with extremeemission lines, are identified for 170,000 spectra and are shown to be complementary to existing spectral classifications. Biasin the spectral classifications due to the aperture spectroscopy in the SDSS is within the signal-to-noise limit for majority of galaxies.We extend the analysis to the decomposition of 17,000 QSO spectra in which the diversity is known to be larger. From a commonality analysis on the eigenspectra sets constructed using different QSO samples, we deduce that QSO spectral classification is redshift and luminosity dependent. The prominent redshift effect is found to be the evolution of the small bump. The luminosity effect is related to the Baldwin effect. We therefore perform the KL transforms in three cases: the local (in the vicinity of emission lines), the intermediate (redshift and luminosity binned) and the global(restframe 900-8000 angstrom) spectral bandpasses. We find that the second order QSO eigenspectra, in both the global and the intermediate spectral bandpasses, represent features from the host galaxies of the QSOs. We discuss the insights the results provide toward classification.In the second part of this thesis, we probe spectroscopic variability of galaxies and narrow line active galactic nuclei (AGNs) usingmulti-epoch observations in the SDSS. We study the galaxy variability per spectral type defined previously in the KL transform and in theOsterbrock diagram. The amplitude of galaxy variability is found to depend on spectral type. We show that the variability in theHII galaxies can be partly due to star formation; and that in the AGNs and the mean eClass type D are probably not related to starbursts.
Multi-Document Summarization with Centroid-Based Pretraining

In Multi-Document Summarization (MDS), the input can be modeled as a set of documents, and the output is its summary. In this paper, we focus on pretraining objectives for MDS. Specifically, we introduce a novel pretraining objective, which involves selecting the ROUGE-based centroid of each document cluster as a proxy for its summary. Our objective thus does not require human written summaries and can be utilized for pretraining on a dataset consisting solely of document sets. Through zero-shot, few-shot, and fully supervised experiments on multiple MDS datasets, we show that our model Centrum is better or comparable to a state-of-the-art model. We make the pretrained and fine-tuned models freely available to the research communityhttps://github.com/ratishsp/centrum.
TeV Dark Matter Searches in the Extragalactic Gamma-ray Sky

High-energetic gamma rays from astrophysical targets constitute a unique probe for annihilation or decay of heavy particle dark matter (DM). After several decades, diverse null detections have resulted in strong constraints for DM particle masses up to the TeV scale. While the gamma-ray signature is expected to be universal from various targets, uncertainties of astrophysical origin strongly affect and weaken the limits. At the same time, spurious signals may originate from non-DM related processes. The many gamma-ray targets in the extragalactic sky being searched for DM play a crucial role to keep these uncertainties under control and to ultimately achieve an unambiguous DM detection. Lately, a large progress has been made in combined analyses of TeV DM candidates towards different targets by using data from various instruments and over a wide range of gamma-ray energies. These approaches not only resulted in an optimal exploitation of existing data and an improved sensitivity, but also helped to level out target- and instrument-related uncertainties. This review gathers all searches in the extragalactic sky performed so far with the space-borne Fermi-Large Area Telescope, the ground-based imaging atmospheric Cherenkov telescopes, and the High-Altitude Water Cherenkov Gamma-Ray Observatory (HAWC). We discuss the different target classes and provide a complete list of all analyses so far.
HPO X ELA: Investigating Hyperparameter Optimization Landscapes by Means of Exploratory Landscape Analysis

Hyperparameter optimization (HPO) is a key component of machine learning models for achieving peak predictive performance. While numerous methods and algorithms for HPO have been proposed over the last years, little progress has been made in illuminating and examining the actual structure of these black-box optimization problems. Exploratory landscape analysis (ELA) subsumes a set of techniques that can be used to gain knowledge about properties of unknown optimization problems. In this paper, we evaluate the performance of five different black-box optimizers on 30 HPO problems, which consist of two-, three- and five-dimensional continuous search spaces of the XGBoost learner trained on 10 different data sets. This is contrasted with the performance of the same optimizers evaluated on 360 problem instances from the black-box optimization benchmark (BBOB). We then compute ELA features on the HPO and BBOB problems and examine similarities and differences. A cluster analysis of the HPO and BBOB problems in ELA feature space allows us to identify how the HPO problems compare to the BBOB problems on a structural meta-level. We identify a subset of BBOB problems that are close to the HPO problems in ELA feature space and show that optimizer performance is comparably similar on these two sets of benchmark problems. We highlight open challenges of ELA for HPO and discuss potential directions of future research and applications.
Personalised recommendations of sleep behaviour with neural networks using sleep diaries captured in Sleepio

SleepioTM is a digital mobile phone and web platform that uses techniques from cognitive behavioural therapy (CBT) to improve sleep in people with sleep difficulty. As part of this process, Sleepio captures data about the sleep behaviour of the users that have consented to such data being processed. For neural networks, the scale of the data is an opportunity to train meaningful models translatable to actual clinical practice. In collaboration with Big Health, the therapeutics company that created and utilizes Sleepio, we have analysed data from a random sample of 401,174 sleep diaries and built a neural network to model sleep behaviour and sleep quality of each individual in a personalised manner. We demonstrate that this neural network is more accurate than standard statistical methods in predicting the sleep quality of an individual based on his/her behaviour from the last 10 days. We compare model performance in a wide range of hyperparameter settings representing various scenarios. We further show that the neural network can be used to produce personalised recommendations of what sleep habits users should follow to maximise sleep quality, and show that these recommendations are substantially better than the ones generated by standard methods. We finally show that the neural network can explain the recommendation given to each participant and calculate confidence intervals for each prediction, all of which are essential for clinicians to be able to adopt such a tool in clinical practice.
DoF-NeRF: Depth-of-Field Meets Neural Radiance Fields

Neural Radiance Field (NeRF) and its variants have exhibited great success on representing 3D scenes and synthesizing photo-realistic novel views. However, they are generally based on the pinhole camera model and assume all-in-focus inputs. This limits their applicability as images captured from the real world often have finite depth-of-field (DoF). To mitigate this issue, we introduce DoF-NeRF, a novel neural rendering approach that can deal with shallow DoF inputs and can simulate DoF effect. In particular, it extends NeRF to simulate the aperture of lens following the principles of geometric optics. Such a physical guarantee allows DoF-NeRF to operate views with different focus configurations. Benefiting from explicit aperture modeling, DoF-NeRF also enables direct manipulation of DoF effect by adjusting virtual aperture and focus parameters. It is plug-and-play and can be inserted into NeRF-based frameworks. Experiments on synthetic and real-world datasets show that, DoF-NeRF not only performs comparably with NeRF in the all-in-focus setting, but also can synthesize all-in-focus novel views conditioned on shallow DoF inputs. An interesting application of DoF-NeRF to DoF rendering is also demonstrated. The source code will be made available at: https://github.com/zijinwuzijin/DoF-NeRF.
Change point detection in dynamic Gaussian graphical models: The impact of COVID-19 pandemic on the U.S. stock market

Reliable estimates of volatility and correlation are fundamental in economics and finance for understanding the impact of macroeconomics events on the market and guiding future investments and policies. Dependence across financial returns is likely to be subject to sudden structural changes, especially in correspondence with major global events, such as the COVID-19 pandemic. In this work, we are interested in capturing abrupt changes over time in the dependence across US industry stock portfolios, over a time horizon that covers the COVID-19 pandemic. The selected stocks give a comprehensive picture of the US stock market. To this end, we develop a Bayesian multivariate stochastic volatility model based on a time-varying sequence of graphs capturing the evolution of the dependence structure. The model builds on the Gaussian graphical models and the random change points literature. In particular, we treat the number, the position of change points, and the graphs as object of posterior inference, allowing for sparsity in graph recovery and change point detection. The high dimension of the parameter space poses complex computational challenges. However, the model admits a hidden Markov model formulation. This leads to the development of an efficient computational strategy, based on a combination of sequential Monte-Carlo and Markov chain Monte-Carlo techniques. Model and computational development are widely applicable, beyond the scope of the application of interest in this work.
One-Shot Medical Landmark Localization by Edge-Guided Transform and Noisy Landmark Refinement

As an important upstream task for many medical applications, supervised landmark localization still requires non-negligible annotation costs to achieve desirable performance. Besides, due to cumbersome collection procedures, the limited size of medical landmark datasets impacts the effectiveness of large-scale self-supervised pre-training methods. To address these challenges, we propose a two-stage framework for one-shot medical landmark localization, which first infers landmarks by unsupervised registration from the labeled exemplar to unlabeled targets, and then utilizes these noisy pseudo labels to train robust detectors. To handle the significant structure variations, we learn an end-to-end cascade of global alignment and local deformations, under the guidance of novel loss functions which incorporate edge information. In stage II, we explore self-consistency for selecting reliable pseudo labels and cross-consistency for semi-supervised learning. Our method achieves state-of-the-art performances on public datasets of different body parts, which demonstrates its general applicability.
MULTIPAR: Supervised Irregular Tensor Factorization with Multi-task Learning

Tensor factorization has received increasing interest due to its intrinsic ability to capture latent factors in multi-dimensional data with many applications such as recommender systems and Electronic Health Records (EHR) mining. PARAFAC2 and its variants have been proposed to address irregular tensors where one of the tensor modes is not aligned, e.g., different users in recommender systems or patients in EHRs may have different length of records. PARAFAC2 has been successfully applied on EHRs for extracting meaningful medical concepts (phenotypes). Despite recent advancements, current models' predictability and interpretability are not satisfactory, which limits its utility for downstream analysis. In this paper, we propose MULTIPAR: a supervised irregular tensor factorization with multi-task learning. MULTIPAR is flexible to incorporate both static (e.g. in-hospital mortality prediction) and continuous or dynamic (e.g. the need for ventilation) tasks. By supervising the tensor factorization with downstream prediction tasks and leveraging information from multiple related predictive tasks, MULTIPAR can yield not only more meaningful phenotypes but also better predictive performance for downstream tasks. We conduct extensive experiments on two real-world temporal EHR datasets to demonstrate that MULTIPAR is scalable and achieves better tensor fit with more meaningful subgroups and stronger predictive performance compared to existing state-of-the-art methods.
Generating Complex 4D Expression Transitions by Learning Face Landmark Trajectories

—In this work, we address the problem of 4D facial expressions generation. This is usually addressed by animating a neutral 3D face to reach an expression peak, and then get back to the neutral state. In the real world though, people show more complex expressions, and switch from one expression to another. We thus propose a new model that generates transitions between different expressions, and synthesizes long and composed 4D expressions. This involves three sub-problems: (i) modeling the temporal dynamics of expressions, (ii) learning transitions between them, and (iii) deforming a generic mesh. We propose to encode the temporal evolution of expressions using the motion of a set of 3D landmarks, that we learn to generate by training a manifold-valued GAN (Motion3DGAN). To allow the generation of composed expressions, this model accepts two labels encoding the starting and the ending expressions. The ﬁnal sequence of meshes is generated by a Sparse2Dense mesh Decoder (S2D-Dec) that maps the landmark displacements to a dense, per-vertex displacement of a known mesh topology. By explicitly working with motion trajectories, the model is totally independent from the identity. Extensive experiments on ﬁve public datasets show that our proposed approach brings signiﬁcant improvements with respect to previous solutions, while retaining good generalization to unseen data. We provide videos of generated 4D facial expressions in this link.
Assimilation of wall-pressure measurements in high-speed flow over a cone

Abstract A nonlinear ensemble-variational data assimilation is performed in order to estimate the unknown flow field over a slender cone at Mach 6, from isolated wall-pressure measurements. The cost functional accounts for discrepancies in wall-pressure spectra and total intensity between the experiment and the prediction using direct numerical simulations, as well as our relative confidence in the measurements and the estimated state. We demonstrate the robustness of the predicted flow by direct propagation of posterior statistics. The approach provides a unique first look at the flow beyond the sensor data, and rigorously accounts for the role of nonlinearity, unlike previous efforts that adopted ad hoc inflow syntheses. Away from the wall, two- and three-dimensional assimilated states both show rope-like structures, qualitatively similar to independent schlieren visualizations. Despite this resemblance, and even though the planar second modes are the most unstable upstream, three-dimensional waves must be included in the assimilation in order to accurately reproduce the wall-pressure measurements recorded in the AFRL Ludwieg Tube facility. The results highlight the importance of three-dimensionality of the field and of the base-state distortion on the instability waves in this experiment, and motivate future measurements that probe the three-dimensional nature of the flow field.
Investigation of the Behavior of Quantum Coherence in Quantum Phase Transitions of Two-Dimensional XY and Ising Models

We investigate the behavior of quantum coherence of the ground states of 2D Heisenberg XY model and 2D Ising model with transverse ﬁeld on square lattices, by using the method of Quantum Renormalization Group (QRG). We show that the non-analytic behavior of quantum coherence near the critical point, can detect quantum phase transition (QPT) of these models. We also use the scaling behavior of maximum derivative of quantum coherence, with system size, to ﬁnd the critical exponent of coherence for both models and also the length exponent of the Ising model. The results are in close agreement with the ones obtained from entanglement analysis, that is while quantum coherence needs less computational calculations in comparison to entanglement approaches.
Joint Active and Passive Beamforming for RIS-Aided MIMO Communications With Low-Resolution Phase Shifts

Practical hardware limitations often impose a reduced number of available phase shifts at the elements of a reconfigurable intelligent surface (RIS). Most works often assume continuous phase-shits at the RIS elements for the transmit and passive beamforming optimization, which can lead to substantial performance loss. Therefore, to harvest the gains of RIS-assisted multi-stream multiple-input multiple-output (MIMO) communications under realistic phase shifts, this letter proposes a problem formulation for the maximization of the achievable rate over the transmit precoder and RIS elements, which avoids an explicit discrete constraint while still incorporating its effect. To efficiently tackle the resulting problem when considering large arrays and RIS panels, an iterative algorithm is derived which comprises a sequence of simple projections. Simulation results demonstrate that the proposed design can be very effective, especially with low-resolution phase-shifts.
LRIP-Net: Low-Resolution Image Prior-Based Network for Limited-Angle CT Reconstruction

In the practical applications of computed tomography (CT) imaging, the projection data may be acquired within a limited-angle range and corrupted by noises due to the limitation of scanning conditions. The noisy incomplete projection data results in the ill-posedness of the inverse problems. Based on the observation that the low-resolution reconstruction problem has better numerical stability, we propose a novel low-resolution image prior-based CT reconstruction model for limited-angle reconstruction. More specifically, we build up a low-resolution reconstruction problem on the down-sampled projection data, and use the reconstructed low-resolution image as prior knowledge for the high-resolution limited-angle CT problem. The constrained minimization problem is then solved by the alternating direction method with all subminimization problems approximated by the convolutional neural networks. Numerical experiments demonstrate that our double-resolution network outperforms both the variational method and popular learning-based reconstruction methods on noisy limited-angle reconstruction problems.
Few Quantum Algorithms on Amplitude Distribution

Amplitude filtering is concerned with identifying basis-states in a superposition whose amplitudes are greater than a specified threshold; probability filtering is defined analogously for probabilities. Given the scarcity of qubits, the focus of this work is to design log-space algorithms for them. Both algorithms follow a similar pattern of estimating the amplitude (or, probability for the latter problem) of each state, in superposition, then comparing each estimate against the threshold for setting up a flag qubit upon success, finally followed by amplitude amplification of states in which the flag is set. We show how to implement each step using very few qubits by designing three subroutines. Our first algorithm performs amplitude amplification even when the"good state'' operator has a small probability of being incorrect -- here we improve upon the space complexity of the previously known algorithms. Our second algorithm performs"true amplitude estimation'' in roughly the same complexity as that of"amplitude estimation'', which actually estimates a probability instead of an amplitude. Our third algorithm is for performing amplitude estimation in parallel (superposition) which is difficult when each estimation branch involves different oracles. As an immediate reward, we observed that the above algorithms for the filtering problems directly improved the upper bounds on the space-bounded query complexity of problems such as non-linearity estimation of Boolean functions and $k$-distinctness.
Aggretriever: A Simple Approach to Aggregate Textual Representations for Robust Dense Passage Retrieval

Pre-trained language models have been successful in many knowledge-intensive NLP tasks. However, recent work has shown that models such as BERT are not “structurally ready” to aggregate textual information into a [CLS] vector for dense passage retrieval (DPR). This “lack of readiness” results from the gap between language model pre-training and DPR fine-tuning. Previous solutions call for computationally expensive techniques such as hard negative mining, cross-encoder distillation, and further pre-training to learn a robust DPR model. In this work, we instead propose to fully exploit knowledge in a pre-trained language model for DPR by aggregating the contextualized token embeddings into a dense vector, which we call agg★. By concatenating vectors from the [CLS] token and agg★, our Aggretriever model substantially improves the effectiveness of dense retrieval models on both in-domain and zero-shot evaluations without introducing substantial training overhead. Code is available at https://github.com/castorini/dhr.
Experimental validation of a system based on particle tracking velocimetry (PTV).

This work describes the starting up of an experimental PTV system, a technique which allows the determination and characterization of the velocity field in a hydraulic device. It has been determined the number of images required to achieve the convergence of velocity to a mean value, a key issue to determine the minimum duration of the test. The curve of spectral density of energy vs frequency in a logarithmic scale has allowed the adjustment of the image acquisition frequency with the PTV system. The relative experimental error in the instant velocity detection was lower than 3-6% (p > 0.01) and lower than 2.01% (p > 0.01) in the mean velocity estimation.