Miracle-dz/UniFAR-FTUs huggingface dataset:
The application of Large Language Models to the phenotype-based prioritization of causative genes in rare disease patients

Computational methods for identifying gene-disease associations can use both genomic and phenotypic information to prioritize genes and variants that may be associated with genetic diseases. Phenotype-based methods commonly rely on comparing phenotypes observed in a patient with a database of genotype-to-phenotype associations using a measure of semantic similarity, and are primarily limited by the quality and completeness of this database as well as the quality of phenotypes assigned to a patient. Genotype-to-phenotype associations used by these methods are largely derived from literature and coded using phenotype ontologies. Large Language Models (LLMs) have been trained on large amounts of text and have shown their potential to answer complex questions across multiple domains. Here, we demonstrate that LLMs can prioritize disease-associated genes as well, or better than, dedicated bioinformatics methods relying on calculated phenotype similarity. The LLMs use only natural language information as background knowledge and do not require ontology-based phenotyping or structured genotype-to-phenotype knowledge. We use a cohort of undiagnosed patients with rare diseases and show that LLMs can be used to provide diagnostic support that helps in identifying plausible candidate genes.
Comparing natural language processing representations of disease sequences for prediction in the electronic healthcare record

Natural language processing (NLP) is increasingly being applied to obtain unsupervised representations of electronic healthcare record (EHR) data, but their performance for the prediction of clinical endpoints remains unclear. Here we use primary care EHRs from 6,286,233 people with Multiple Long-Term Conditions in England to generate vector representations of sequences of disease development using two input strategies (212 disease categories versus 9,462 diagnostic codes) and different NLP algorithms (Latent Dirichlet Allocation, doc2vec and two transformer models designed for EHRs). We also develop a new transformer architecture, named EHR-BERT, which incorporates socio-demographic information. We then compare use of each of these representations to predict mortality, healthcare use and new disease diagnosis. We find that representations generated using disease categories perform similarly to those using diagnostic codes, suggesting models can equally manage smaller or larger vocabularies. Sequence-based algorithms perform consistently better than bag-of-words methods, with the highest performance for EHR-BERT.
Taming assembly bias for primordial non-Gaussianity

Primordial non-Gaussianity of the local type induces a strong scale-dependent bias on the clustering of halos in the late-time Universe. This signature is particularly promising to provide constraints on the non-Gaussianity parameter f NL from galaxy surveys, as the bias amplitude grows with scale and becomes important on large, linear scales. However, there is a well-known degeneracy between the real prize, the f NL parameter, and the (non-Gaussian) assembly bias i.e., the halo formation history-dependent contribution to the amplitude of the signal, which could seriously compromise the ability of large-scale structure surveys to constrain f NL. We show how the assembly bias can be modeled and constrained, thus almost completely recovering the power of galaxy surveys to competitively constrain primordial non-Gaussianity. In particular, studying hydrodynamical simulations, we find that a proxy for the halo properties that determine assembly bias can be constructed from photometric properties of galaxies. Using a prior on the assembly bias guided by this proxy degrades the statistical errors on f NL only mildly compared to an ideal case where the assembly bias is perfectly known. The systematic error on f NL that the proxy induces can be safely kept under control.
Revolutionizing Customer Interactions: Insights and Challenges in Deploying ChatGPT and Generative Chatbots for FAQs

In the rapidly evolving domain of artificial intelligence, chatbots have emerged as a potent tool for various applications ranging from e-commerce to healthcare. This research delves into the intricacies of chatbot technology, from its foundational concepts to advanced generative models like ChatGPT. We present a comprehensive taxonomy of existing chatbot approaches, distinguishing between rule-based, retrieval-based, generative, and hybrid models. A specific emphasis is placed on ChatGPT, elucidating its merits for frequently asked questions (FAQs)-based chatbots, coupled with an exploration of associated Natural Language Processing (NLP) techniques such as named entity recognition, intent classification, and sentiment analysis. The paper further delves into the customization and fine-tuning of ChatGPT, its integration with knowledge bases, and the consequent challenges and ethical considerations that arise. Through real-world applications in domains such as online shopping, healthcare, and education, we underscore the transformative potential of chatbots. However, we also spotlight open challenges and suggest future research directions, emphasizing the need for optimizing conversational flow, advancing dialogue mechanics, improving domain adaptability, and enhancing ethical considerations. The research culminates in a call for further exploration in ensuring transparent, ethical, and user-centric chatbot systems.
Fumbling in Babel: An Investigation into ChatGPT's Language Identification Ability

Recently, ChatGPT has emerged as a powerful NLP tool that can carry out several tasks. However, the range of languages ChatGPT can handle remains largely a mystery. In this work, we investigate ChatGPT's language identification abilities. For this purpose, we compile Babel-670, a benchmark comprising $670$ languages representing $23$ language families. Languages in Babel-670 run the gamut between the very high-resource to the very low-resource and are spoken in five continents. We then study ChatGPT's (both GPT-3.5 and GPT-4) ability to (i) identify both language names and language codes (ii) under both zero- and few-shot conditions (iii) with and without provision of label set. When compared to smaller finetuned language identification tools, we find that ChatGPT lags behind. Our empirical analysis shows the reality that ChatGPT still resides in a state of potential enhancement before it can sufficiently serve diverse communities.
Optical polarisation study of Galactic Open clusters

Dust is a ubiquitous component in our Galaxy. It accounts for only $1\%$ mass of the ISM but still is an essential part of the Galaxy. It affects our view of the Galaxy by obscuring the starlight at shorter wavelengths and re-emitting in longer wavelengths. Studying the dust distribution in the Galaxy at longer wavelengths may cause discrepancies due to distance ambiguity caused by unknown Galactic potential. However, another aspect of dust, i.e., the polarisation of the background starlight, when combined with distance information, will help to give direct observational evidence of the number of dust clouds encountered in the line of sight. We observed 15 open clusters distributed at increasing distances in three lines of sight using two Indian national facilities. The measured polarisation results used to scrutinize the dust distribution and orientation of the local plane of sky magnetic fields towards selected directions. The analysis of the stars observed towards the distant cluster King 8 cluster shows two foreground layers at a distance of $\sim 500$ pc and $\sim$ 3500 pc. Similar analysis towards different clusters also results in multiple dust layers.
Visual Environment Assessment for Safe Autonomous Quadrotor Landing

Autonomous identification and evaluation of safe landing zones are of paramount importance for ensuring the safety and effectiveness of aerial robots in the event of system failures, low battery, or the successful completion of specific tasks. In this paper, we present a novel approach for detection and assessment of potential landing sites for safe quadrotor landing. Our solution efficiently integrates 2D and 3D environmental information, eliminating the need for external aids such as GPS and computationally intensive elevation maps. The proposed pipeline combines semantic data derived from a Neural Network (NN), to extract environmental features, with geometric data obtained from a disparity map, to extract critical geometric attributes such as slope, flatness, and roughness. We define several cost metrics based on these attributes to evaluate safety, stability, and suitability of regions in the environments and identify the most suitable landing area. Our approach runs in real-time on quadrotors equipped with limited computational capabilities. Experimental results conducted in diverse environments demonstrate that the proposed method can effectively assess and identify suitable landing areas, enabling the safe and autonomous landing of a quadrotor.
Multi-Task Learning Approach for Unified Biometric Estimation from Fetal Ultrasound Anomaly Scans

Precise estimation of fetal biometry parameters from ultrasound images is vital for evaluating fetal growth, monitoring health, and identifying potential complications reliably. However, the automated computerized segmentation of the fetal head, abdomen, and femur from ultrasound images, along with the subsequent measurement of fetal biometrics, remains challenging. In this work, we propose a multi-task learning approach to classify the region into head, abdomen and femur as well as estimate the associated parameters. We were able to achieve a mean absolute error (MAE) of 1.08 mm on head circumference, 1.44 mm on abdomen circumference and 1.10 mm on femur length with a classification accuracy of 99.91\% on a dataset of fetal Ultrasound images. To achieve this, we leverage a weighted joint classification and segmentation loss function to train a U-Net architecture with an added classification head. The code can be accessed through \href{https://github.com/BioMedIA-MBZUAI/Multi-Task-Learning-Approach-for-Unified-Biometric-Estimation-from-Fetal-Ultrasound-Anomaly-Scans.git}{\texttt{Github}
Runtime Verification of Learning Properties for Reinforcement Learning Algorithms

Reinforcement learning (RL) algorithms interact with their environment in a trial-and-error fashion. Such interactions can be expensive, inefficient, and timely when learning on a physical system rather than in a simulation. This work develops new runtime verification techniques to predict when the learning phase has not met or will not meet qualitative and timely expectations. This paper presents three verification properties concerning the quality and timeliness of learning in RL algorithms. With each property, we propose design steps for monitoring and assessing the properties during the system's operation.
NGEL-SLAM: Neural Implicit Representation-based Global Consistent Low-Latency SLAM System

Neural implicit representations have emerged as a promising solution for providing dense geometry in Simultaneous Localization and Mapping (SLAM). However, existing methods in this direction fall short in terms of global consistency and low latency. This paper presents NGEL-SLAM to tackle the above challenges. To ensure global consistency, our system leverages a traditional feature-based tracking module that incorporates loop closure. Additionally, we maintain a global consistent map by representing the scene using multiple neural implicit fields, enabling quick adjustment to the loop closure. Moreover, our system allows for fast convergence through the use of octree-based implicit representations. The combination of rapid response to loop closure and fast convergence makes our system a truly low-latency system that achieves global consistency. Our system enables rendering high-fidelity RGB-D images, along with extracting dense and complete surfaces. Experiments on both synthetic and real-world datasets suggest that our system achieves state-of-the-art tracking and mapping accuracy while maintaining low latency.
Mutating etcd Towards Edge Suitability

In the edge environment servers are no longer being co-located away from clients, instead they are being co-located with clients away from other servers, focusing on reliable and performant operation. Orchestration platforms, such as Kubernetes, are a key system being transitioned to the edge but they remain unsuited to the environment, stemming primarily from their critical key-value stores. In this work we derive requirements from the edge environment showing that, fundamentally, the design of distributed key-value datastores, such as etcd, is unsuited to meet them. Using these requirements, we explore the design space for distributed key-value datastores and implement two successive mutations of etcd for different points: mergeable-etcd and dismerge, trading linearizability for causal consistency based on CRDTs. mergeable-etcd retains the linear revision history but encounters inherent shortcomings, whilst dismerge embraces the causal model. Both stores are local-first, maintaining reliable performance under network partitions and variability, drastically surpassing etcd's performance, whilst maintaining competitive performance in reliable settings.
From GPT-3 to GPT-4: On the Evolving Efficacy of LLMs to Answer Multiple-choice Questions for Programming Classes in Higher Education

We explore the evolving efficacy of three generative pre-trained transformer (GPT) models in generating answers for multiple-choice questions (MCQ) from introductory and intermediate Python programming courses in higher education. We focus on the differences in capabilities of the models prior to the release of ChatGPT (Nov '22), at the time of the release, and today (i.e., Aug '23). Recent studies have established that the abilities of the OpenAI's GPT models to handle assessments originally designed for humans keep increasing as the newer more capable models are released. However, the qualitative differences in the capabilities and limitations of these models to reason about and/or analyze programming MCQs have been under-explored. We evaluated three OpenAI's GPT models on formative and summative MCQ assessments from three Python courses (530 questions) focusing on the qualitative differences in the evolving efficacy of the subsequent models. This study provides further evidence and insight into the trajectory of the current developments where there already exists a technology that can be utilized by students to collect passing scores, with no effort whatsoever, on what today counts as viable programming knowledge and skills assessments. This study could be leveraged by educators and institutions to better understand the recent technological developments in order to adapt the design of programming assessments as well as to fuel the necessary discussions into how assessments in future programming classes should be updated.
Does Starting Deep Learning Homework Earlier Improve Grades?

Intuitively, students who start a homework assignment earlier and spend more time on it should receive better grades on the assignment. However, existing literature on the impact of time spent on homework is not clear-cut and comes mostly from K-12 education. It is not clear that these prior studies can inform coursework in deep learning due to differences in demographics, as well as the computational time needed for assignments to be completed. We study this problem in a post-hoc study of three semesters of a deep learning course at the University of Maryland, Baltimore County (UMBC), and develop a hierarchical Bayesian model to help make principled conclusions about the impact on student success given an approximate measure of the total time spent on the homework, and how early they submitted the assignment. Our results show that both submitting early and spending more time positively relate with final grade. Surprisingly, the value of an additional day of work is apparently equal across students, even when some require less total time to complete an assignment.
ICXML: An In-Context Learning Framework for Zero-Shot Extreme Multi-Label Classification

This paper focuses on the task of Extreme Multi-Label Classification (XMC) whose goal is to predict multiple labels for each instance from an extremely large label space. While existing research has primarily focused on fully supervised XMC, real-world scenarios often lack complete supervision signals, highlighting the importance of zero-shot settings. Given the large label space, utilizing in-context learning approaches is not trivial. We address this issue by introducing In-Context Extreme Multilabel Learning (ICXML), a two-stage framework that cuts down the search space by generating a set of candidate labels through incontext learning and then reranks them. Extensive experiments suggest that ICXML advances the state of the art on two diverse public benchmarks.
Score-based generative models learn manifold-like structures with constrained mixing

How do score-based generative models (SBMs) learn the data distribution supported on a low-dimensional manifold? We investigate the score model of a trained SBM through its linear approximations and subspaces spanned by local feature vectors. During diffusion as the noise decreases, the local dimensionality increases and becomes more varied between different sample sequences. Importantly, we find that the learned vector field mixes samples by a non-conservative field within the manifold, although it denoises with normal projections as if there is an energy function in off-manifold directions. At each noise level, the subspace spanned by the local features overlap with an effective density function. These observations suggest that SBMs can flexibly mix samples with the learned score field while carefully maintaining a manifold-like structure of the data distribution.
From Feast to Famine: A Systematic Study of Accretion onto Oblique Pulsars with 3D GRMHD Simulations

Disk-fed accretion onto neutron stars can power a wide range of astrophysical sources ranging from X-ray binaries, to accretion-powered millisecond pulsars, ultraluminous X-ray sources, and gamma-ray bursts. A crucial parameter controlling the gas–magnetosphere interaction is the strength of the stellar dipole. In addition, coherent X-ray pulsations in many neutron star systems indicate that the star's dipole moment is oblique relative to its rotation axis. Therefore, it is critical to systematically explore the 2D parameter space of the star's magnetic field strength and obliquity, which is what this work does, for the first time, in the framework of 3D general-relativistic magnetohydrodynamics. If the accretion disk carries its own vertical magnetic field, this introduces an additional factor: the relative polarity of the disk and stellar magnetic fields. We find that depending on the strength of the stellar dipole and the star–disk relative polarity, the neutron star's jet power can either increase or decrease with increasing obliquity. For weak dipole strength (equivalently, high accretion rate), the parallel polarity results in a positive correlation between jet power and obliquity, whereas the antiparallel orientation displays the opposite trend. For stronger dipoles, the relative-polarity effect disappears, and jet power always decreases with increasing obliquity. The influence of the relative polarity gradually disappears as obliquity increases. Highly oblique pulsars tend to have an increased magnetospheric radius, a lower mass accretion rate, and enter the propeller regime at lower magnetic moments than aligned stars.
DeepEMD: A Transformer-based Fast Estimation of the Earth Mover's Distance

The Earth Mover's Distance (EMD) is the measure of choice between point clouds. However the computational cost to compute it makes it prohibitive as a training loss, and the standard approach is to use a surrogate such as the Chamfer distance. We propose an attention-based model to compute an accurate approximation of the EMD that can be used as a training loss for generative models. To get the necessary accurate estimation of the gradients we train our model to explicitly compute the matching between point clouds instead of EMD itself. We cast this new objective as the estimation of an attention matrix that approximates the ground truth matching matrix. Experiments show that this model provides an accurate estimate of the EMD and its gradient with a wall clock speed-up of more than two orders of magnitude with respect to the exact Hungarian matching algorithm and one order of magnitude with respect to the standard approximate Sinkhorn algorithm, allowing in particular to train a point cloud VAE with the EMD itself. Extensive evaluation show the remarkable behaviour of this model when operating out-of-distribution, a key requirement for a distance surrogate. Finally, the model generalizes very well to point clouds during inference several times larger than during training.
On some elusive aspects of databases hindering AI based discovery: A case study on superconducting materials

It stands to reason that the amount and the quality of big data is of key importance for setting up accurate AI-driven models. Nonetheless, we believe there are still critical roadblocks in the inherent generation of databases, that are often underestimated and poorly discussed in the literature. In our view, such issues can seriously hinder the AI-based discovery process, even when high quality, sufficiently large and highly reputable data sources are available. Here, considering superconducting and thermoelectric materials as two representative case studies, we specifically discuss three aspects, namely intrinsically biased sample selection, possible hidden variables, disparate data age. Importantly, to our knowledge, we suggest and test a first strategy capable of detecting and quantifying the presence of the intrinsic data bias.
Examining LLMs' Uncertainty Expression Towards Questions Outside Parametric Knowledge

Can large language models (LLMs) express their uncertainty in situations where they lack sufficient parametric knowledge to generate reasonable responses? This work aims to systematically investigate LLMs' behaviors in such situations, emphasizing the trade-off between honesty and helpfulness. To tackle the challenge of precisely determining LLMs' knowledge gaps, we diagnostically create unanswerable questions containing non-existent concepts or false premises, ensuring that they are outside the LLMs' vast training data. By compiling a benchmark, UnknownBench, which consists of both unanswerable and answerable questions, we quantitatively evaluate the LLMs' performance in maintaining honesty while being helpful. Using a model-agnostic unified confidence elicitation approach, we observe that most LLMs fail to consistently refuse or express uncertainty towards questions outside their parametric knowledge, although instruction fine-tuning and alignment techniques can provide marginal enhancements. Moreover, LLMs' uncertainty expression does not always stay consistent with the perceived confidence of their textual outputs.