Miracle-dz/UniFAR-FTUs huggingface dataset:
Exact and efficient phylodynamic simulation from arbitrarily large populations

Many biological studies involve inferring the genealogical history of a sample of individuals from a large population and interpreting the reconstructed tree. Such an ascertained tree typically represents only a small part of a comprehensive population tree and is distorted by survivorship and sampling biases. Inferring evolutionary parameters from ascertained trees requires modeling both the underlying population dynamics and the ascertainment process. A crucial component of this phylodynamic modeling involves tree simulation, which is used to benchmark probabilistic inference methods. To simulate an ascertained tree, one must first simulate the full population tree and then prune unobserved lineages. Consequently, the computational cost is determined not by the size of the final simulated tree, but by the size of the population tree in which it is embedded. In most biological scenarios, simulations of the entire population are prohibitively expensive due to computational demands placed on lineages without sampled descendants. Here, we address this challenge by proving that, for any partially ascertained process from a general multi-type birth-death-mutation-sampling (BDMS) model, there exists an equivalent pure birth process (i.e., no death) with mutation and complete sampling. The final trees generated under these processes have exactly the same distribution. Leveraging this property, we propose a highly efficient algorithm for simulating trees under a general BDMS model. Our algorithm scales linearly with the size of the final simulated tree and is independent of the population size, enabling simulations from extremely large populations beyond the reach of current methods but essential for various biological applications. We anticipate that this unprecedented speedup will significantly advance the development of novel inference methods that require extensive training data.
Optimization of foreground moment deprojection for semi-blind CMB polarization reconstruction

Upcoming Cosmic Microwave Background (CMB) experiments, aimed at measuring primordial CMB B-modes, require exquisite control of Galactic foreground contamination. Minimum-variance techniques, like the Needlet Internal Linear Combination (NILC), have proven effective in reconstructing the CMB polarization signal and mitigating foregrounds across diverse sky models without suffering from mismodelling errors. Still, residual contamination may bias the recovered CMB polarization at large angular scales when confronted with the most complex foreground scenarios. By adding constraints to NILC to deproject moments of the Galactic emission, the Constrained Moment ILC (cMILC) method has proven to enhance foreground subtraction, albeit with an associated increase in overall noise variance. Faced with this trade-off between foreground bias reduction and overall variance minimization, there is still no recipe on which moments to deproject and which are better suited for blind variance minimization. To address this, we introduce the optimized cMILC (ocMILC) pipeline, which performs full optimization of the required number and set of foreground moments to deproject, pivot parameter values, and deprojection coefficients across the sky and angular scales, depending on the actual sky complexity, available frequency coverage, and experiment sensitivity. The optimal number of deprojected moments, before paying significant noise penalty, is determined through a data diagnosis inspired by the Generalized NILC (GNILC) method. Validated on B-mode simulations of the PICO space mission concept with four challenging foreground models, ocMILC exhibits lower foreground contamination compared to NILC and cMILC at all angular scales, with limited noise penalty. This multi-layer optimization enables the ocMILC pipeline to achieve unbiased posteriors of the tensor-to-scalar ratio, regardless of foreground complexity.
H\"ormander type Fourier multiplier theorem and Nikolskii inequality on quantum tori, and applications

In this paper, we study H\"ormander type Fourier multiplier theorem and the Nikolskii inequality on quantum tori. On the way to obtain these results, we also prove some classical inequalities such as Paley, Hausdorff-Young, Hausdorff-Young-Paley, Hardy-Littlewood, and Logarithmic Sobolev inequalities on quantum tori. As applications we establish embedding theorems between Sobolev, Besov spaces as well as embeddings between Besov and Wiener and Beurling spaces on quantum tori. We also analyse $\beta$-versions of Wiener and Beurling spaces and their embeddings, and interpolation properties of all these spaces on quantum tori. As an applications of the analysis, we also derive a version of the Nash inequality, and the time decay for solutions of a heat type equation.
Neural Automated Writing Evaluation with Corrective Feedback

The utilization of technology in second language learning and teaching has become ubiquitous. For the assessment of writing specifically, automated writing evaluation (AWE) and grammatical error correction (GEC) have become immensely popular and effective methods for enhancing writing proficiency and delivering instant and individualized feedback to learners. By leveraging the power of natural language processing (NLP) and machine learning algorithms, AWE and GEC systems have been developed separately to provide language learners with automated corrective feedback and more accurate and unbiased scoring that would otherwise be subject to examiners. In this paper, we propose an integrated system for automated writing evaluation with corrective feedback as a means of bridging the gap between AWE and GEC results for second language learners. This system enables language learners to simulate the essay writing tests: a student writes and submits an essay, and the system returns the assessment of the writing along with suggested grammatical error corrections. Given that automated scoring and grammatical correction are more efficient and cost-effective than human grading, this integrated system would also alleviate the burden of manually correcting innumerable essays.
Path Planning for a Cooperative Navigation Aid Vehicle to Assist Multiple Agents Intermittently

This paper considers the problem of planning a path for a single underwater cooperative navigation aid (CNA) vehicle to intermittently aid a set of N agents to minimize average navigation uncertainty. Both the CNA and agents are modeled as constant-velocity vehicles. The agents traverse along known nominal trajectories and the CNA plans a path to sequentially intercept them. Navigation aiding is modeled by a scalar discrete time Kalman filter. During path planning, the CNA considers surfacing to reduce its own navigation uncertainty. A greedy planning algorithm is proposed that uses a heuristic based on an optimal time-to-aid, overall navigation uncertainty reduction, and transit time, to assign agents to the CNA. The approach is compared to an optimal (exhaustive enumeration) algorithm through a Monte Carlo experiment with randomized agent nominal trajectories and initial navigation uncertainty.
Modulation of chiral anomaly and bilinear magnetoconductivity in Weyl semimetals by impurity-resonance states

The phenomenon of nonlinear transport has attracted tremendous interest within the condensed matter community. We present a theoretical framework for nonlinear transport based on the nonequilibrium retarded Green's function, and examine the impact of disorder on nonlinear magnetotransport in Weyl semimetals (WSMs). It is demonstrated that bilinear magnetoconductivity can be induced in disordered WSMs by several mechanisms, including impurity-induced tilting of the Weyl cones, Lorentz-force-induced normal orbital magnetic moment, and chiral anomaly arising from the Berry-curvature-induced anomalous orbital magnetic moment. Additionally, we observe that the localization of Weyl fermions by impurity scattering will lead to resonant dips in both the chiral chemical potential and magnetoconductivity when the Fermi energy approaches the impurity resonance states. Our findings offer a theoretical proposition for modulating nonreciprocal transport in topological semimetals.
Highly-Efficient Persistent FIFO Queues

In this paper, we study the question whether techniques employed, in a conventional system, by state-of-the-art concurrent algorithms to avoid contended hot spots are still efficient for recoverable computing in settings with Non-Volatile Memory (NVM). We focus on concurrent FIFO queues that have two end-points, head and tail, which are highly contended. We present a persistent FIFO queue implementation that performs a pair of persistence instructions per operation (enqueue or dequeue). The algorithm achieves to perform these instructions on variables of low contention by employing Fetch&Increment and using the state-of-the-art queue implementation by Afek and Morrison (PPoPP'13). These result in performance that is up to 2x faster than state-of-the-art persistent FIFO queue implementations.
A Multi-Agent Model for Opinion Evolution under Cognitive Biases

We generalize the DeGroot model for opinion dynamics to better capture realistic social scenarios. We introduce a model where each agent has their own individual cognitive biases. Society is represented as a directed graph whose edges indicate how much agents influence one another. Biases are represented as the functions in the square region $[-1,1]^2$ and categorized into four sub-regions based on the potential reactions they may elicit in an agent during instances of opinion disagreement. Under the assumption that each bias of every agent is a continuous function within the region of receptive but resistant reactions ($\mathbf{R}$), we show that the society converges to a consensus if the graph is strongly connected. Under the same assumption, we also establish that the entire society converges to a unanimous opinion if and only if the source components of the graph-namely, strongly connected components with no external influence-converge to that opinion. We illustrate that convergence is not guaranteed for strongly connected graphs when biases are either discontinuous functions in $\mathbf{R}$ or not included in $\mathbf{R}$. We showcase our model through a series of examples and simulations, offering insights into how opinions form in social networks under cognitive biases.
Prescribing Large Language Models for Perioperative Care: What's The Right Dose for Pre-trained Models?

Postoperative risk predictions can inform effective perioperative care management and planning. We aimed to assess whether clinical large language models (LLMs) can predict postoperative risks using clinical texts with various training strategies. The main cohort involved 84,875 records from Barnes Jewish Hospital (BJH) system between 2018 and 2021. Methods were replicated on Beth Israel Deaconess's MIMIC dataset. Both studies had mean duration of follow-up based on the length of postoperative ICU stay less than 7 days. For the BJH dataset, outcomes included 30-day mortality, pulmonary embolism (PE) and pneumonia. Three domain adaptation and finetuning strategies were implemented for BioGPT, ClinicalBERT and BioClinicalBERT: self-supervised objectives; incorporating labels with semi-supervised fine-tuning; and foundational modelling through multi-task learning. Model performance was compared using the area under the receiver operating characteristic curve (AUROC) and the area under the precision recall curve (AUPRC) for classification tasks, and mean squared error (MSE) and R2 for regression tasks. Pre-trained LLMs outperformed traditional word embeddings, with absolute maximal gains of 38.3% for AUROC and 14% for AUPRC. Adapting models further improved performance: (1) self-supervised finetuning by 3.2% for AUROC and 1.5% for AUPRC; (2) semi-supervised finetuning by 1.8% for AUROC and 2% for AUPRC, compared to self-supervised finetuning; (3) foundational modelling by 3.6% for AUROC and 2.6% for AUPRC, compared to self-supervised finetuning. Pre-trained clinical LLMs offer opportunities for postoperative risk predictions in unforeseen data, with peaks in foundational models indicating the potential of task-agnostic learning towards the generalizability of LLMs in perioperative care.
SKT5SciSumm - A Hybrid Generative Approach for Multi-Document Scientific Summarization

Summarization for scientific text has shown significant benefits both for the research community and human society. Given the fact that the nature of scientific text is distinctive and the input of the multi-document summarization task is substantially long, the task requires sufficient embedding generation and text truncation without losing important information. To tackle these issues, in this paper, we propose SKT5SciSumm - a hybrid framework for multi-document scientific summarization (MDSS). We leverage the Sentence-Transformer version of Scientific Paper Embeddings using Citation-Informed Transformers (SPECTER) to encode and represent textual sentences, allowing for efficient extractive summarization using k-means clustering. We employ the T5 family of models to generate abstractive summaries using extracted sentences. SKT5SciSumm achieves state-of-the-art performance on the Multi-XScience dataset. Through extensive experiments and evaluation, we showcase the benefits of our model by using less complicated models to achieve remarkable results, thereby highlighting its potential in advancing the field of multi-document summarization for scientific text.
Quantum process tomography of structured optical gates with convolutional neural networks

The characterization of a unitary gate is experimentally accomplished via Quantum Process Tomography, which combines the outcomes of different projective measurements to reconstruct the underlying operator. The process matrix is typically extracted from maximum-likelihood estimation. Recently, optimization strategies based on evolutionary and machine-learning techniques have been proposed. Here, we investigate a deep-learning approach that allows for fast and accurate reconstructions of space-dependent SU(2) operators, only processing a minimal set of measurements. We train a convolutional neural network based on a scalable U-Net architecture to process entire experimental images in parallel. Synthetic processes are reconstructed with average fidelity above 90%. The performance of our routine is experimentally validated on complex polarization transformations. Our approach further expands the toolbox of data-driven approaches to Quantum Process Tomography and shows promise in the real-time characterization of complex optical gates.
Can LLM Generate Culturally Relevant Commonsense QA Data? Case Study in Indonesian and Sundanese

Large Language Models (LLMs) are increasingly being used to generate synthetic data for training and evaluating models. However, it is unclear whether they can generate a good quality of question answering (QA) dataset that incorporates knowledge and cultural nuance embedded in a language, especially for low-resource languages. In this study, we investigate the effectiveness of using LLMs in generating culturally relevant commonsense QA datasets for Indonesian and Sundanese languages. To do so, we create datasets for these languages using various methods involving both LLMs and human annotators. Our experiments show that the current best-performing LLM, GPT-4 Turbo, is capable of generating questions with adequate knowledge in Indonesian but not in Sundanese, highlighting the performance discrepancy between medium- and lower-resource languages. We also benchmark various LLMs on our generated datasets and find that they perform better on the LLM-generated datasets compared to those created by humans.
On the upper and lower covariances under multiple probabilities

In this paper, we define the upper (resp. lower) covariance under multiple probabilities via a corresponding max-min-max (resp. min-max-min) optimization problem and the related properties of covariances are obtained. In particular, we propose a fast algorithm of calculation for upper and lower covariances under the finite number of probabilities. As an application, our algorithm can be used to solve a class of quadratic programming problem exactly, and we obtain a probabilistic representation of such quadratic programming problem.
Time vs. Frequency Domain DPD for Massive MIMO: Methods and Performance Analysis

The use of up to hundreds of antennas in massive multi-user (MU) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) poses a complexity challenge for digital predistortion (DPD) aiming to linearize the nonlinear power amplifiers (PAs). While the complexity for conventional time domain (TD) DPD scales with the number of PAs, frequency domain (FD) DPD has a complexity scaling with the number of user equipments (UEs). In this work, we provide a comprehensive analysis of different state-of-the-art TD and FD-DPD schemes in terms of complexity and linearization performance in both rich scattering and line-of-sight (LOS) channels. We also propose a novel low-complexity FD convolutional neural network (CNN) DPD. The analysis shows that FD-DPD, particularly the proposed FD CNN, is preferable in LOS scenarios with few users, due to the favorable trade-off between complexity and linearization performance. On the other hand, in scenarios with more users or isotropic scattering channels, significant intermodulation distortions among UEs degrade FD-DPD performance, making TD-DPD more suitable.
A novel image space formalism of Fourier domain interpolation neural networks for noise propagation analysis

Purpose: To develop an image space formalism of multi-layer convolutional neural networks (CNNs) for Fourier domain interpolation in MRI reconstructions and analytically estimate noise propagation during CNN inference. Theory and Methods: Nonlinear activations in the Fourier domain (also known as k-space) using complex-valued Rectifier Linear Units are expressed as elementwise multiplication with activation masks. This operation is transformed into a convolution in the image space. After network training in k-space, this approach provides an algebraic expression for the derivative of the reconstructed image with respect to the aliased coil images, which serve as the input tensors to the network in the image space. This allows the variance in the network inference to be estimated analytically and to be used to describe noise characteristics. Monte-Carlo simulations and numerical approaches based on auto-differentiation were used for validation. The framework was tested on retrospectively undersampled invivo brain images. Results: Inferences conducted in the image domain are quasi-identical to inferences in the k-space, underlined by corresponding quantitative metrics. Noise variance maps obtained from the analytical expression correspond with those obtained via Monte-Carlo simulations, as well as via an auto-differentiation approach. The noise resilience is well characterized, as in the case of classical Parallel Imaging. Komolgorov-Smirnov tests demonstrate Gaussian distributions of voxel magnitudes in variance maps obtained via Monte-Carlo simulations. Conclusion: The quasi-equivalent image space formalism for neural networks for k-space interpolation enables fast and accurate description of the noise characteristics during CNN inference, analogous to geometry-factor maps in traditional parallel imaging methods.
Results of the follow-up of ANTARES neutrino alerts

High-energy neutrinos could be produced in the interaction of charged cosmic rays with matter or radiation surrounding astrophysical sources. To look for transient sources associated with neutrino emission, a follow-up program of neutrino alerts has been operating within the ANTARES Collaboration since 2009. This program, named TAToO, has triggered robotic optical telescopes (MASTER, TAROT, ROTSE and the SVOM ground based telescopes) immediately after the detection of any relevant neutrino candidate and scheduled several observations in the weeks following the detection. A subset of ANTARES events with highest probabilities of being of cosmic origin has also been followed by the Swift and the INTEGRAL satellites, the Murchison Widefield Array radio telescope and the H.E. S.S. high-energy gamma-ray telescope. The results of twelve years of observations are reported. No optical counterpart has been significantly associated with an ANTARES candidate neutrino signal during image analysis. Constraints on transient neutrino emission have been set. In September 2015, ANTARES issued a neutrino alert and during the follow-up, a potential transient counterpart was identified by Swift and MASTER. A multi-wavelength follow-up campaign has allowed to identify the nature of this source and has proven its fortuitous association with the neutrino. The return of experience is particularly important for the design of the alert system of KM3NeT, the next generation neutrino telescope in the Mediterranean Sea.
Curriculum Learning Meets Directed Acyclic Graph for Multimodal Emotion Recognition

Emotion recognition in conversation (ERC) is a crucial task in natural language processing and affective computing. This paper proposes MultiDAG+CL, a novel approach for Multimodal Emotion Recognition in Conversation (ERC) that employs Directed Acyclic Graph (DAG) to integrate textual, acoustic, and visual features within a unified framework. The model is enhanced by Curriculum Learning (CL) to address challenges related to emotional shifts and data imbalance. Curriculum learning facilitates the learning process by gradually presenting training samples in a meaningful order, thereby improving the model's performance in handling emotional variations and data imbalance. Experimental results on the IEMOCAP and MELD datasets demonstrate that the MultiDAG+CL models outperform baseline models. We release the code for MultiDAG+CL and experiments: https://github.com/vanntc711/MultiDAG-CL
$\zeta$-QVAE: A Quantum Variational Autoencoder utilizing Regularized Mixed-state Latent Representations

A major challenge in near-term quantum computing is its application to large real-world datasets due to scarce quantum hardware resources. One approach to enabling tractable quantum models for such datasets involves compressing the original data to manageable dimensions while still representing essential information for downstream analysis. In classical machine learning, variational autoencoders (VAEs) facilitate efficient data compression, representation learning for subsequent tasks, and novel data generation. However, no model has been proposed that exactly captures all of these features for direct application to quantum data on quantum computers. Some existing quantum models for data compression lack regularization of latent representations, thus preventing direct use for generation and control of generalization. Others are hybrid models with only some internal quantum components, impeding direct training on quantum data. To bridge this gap, we present a fully quantum framework, $\zeta$-QVAE, which encompasses all the capabilities of classical VAEs and can be directly applied for both classical and quantum data compression. Our model utilizes regularized mixed states to attain optimal latent representations. It accommodates various divergences for reconstruction and regularization. Furthermore, by accommodating mixed states at every stage, it can utilize the full-data density matrix and allow for a"global"training objective. Doing so, in turn, makes efficient optimization possible and has potential implications for private and federated learning. In addition to exploring the theoretical properties of $\zeta$-QVAE, we demonstrate its performance on representative genomics and synthetic data. Our results consistently indicate that $\zeta$-QVAE exhibits similar or better performance compared to matched classical models.