Miracle-dz/UniFAR-FTUs huggingface dataset:
VisRec: A Semi-Supervised Approach to Radio Interferometric Data Reconstruction

Radio telescopes produce visibility data about celestial objects, but these data are sparse and noisy. As a result, images created on raw visibility data are of low quality. Recent studies have used deep learning models to reconstruct visibility data to get cleaner images. However, these methods rely on a substantial amount of labeled training data, which requires significant labeling effort from radio astronomers. Addressing this challenge, we propose VisRec, a model-agnostic semi-supervised learning approach to the reconstruction of visibility data. Specifically, VisRec consists of both a supervised learning module and an unsupervised learning module. In the supervised learning module, we introduce a set of data augmentation functions to produce diverse training examples. In comparison, the unsupervised learning module in VisRec augments unlabeled data and uses reconstructions from non-augmented visibility data as pseudo-labels for training. This hybrid approach allows VisRec to effectively leverage both labeled and unlabeled data. This way, VisRec performs well even when labeled data is scarce. Our evaluation results show that VisRec outperforms all baseline methods in reconstruction quality, robustness against common observation perturbation, and generalizability to different telescope configurations.
Misconfiguration in O-RAN: Analysis of the impact of AI/ML

User demand on network communication infrastructure has never been greater with applications such as extended reality, holographic telepresence, and wireless brain-computer interfaces challenging current networking capabilities. Open RAN (O-RAN) is critical to supporting new and anticipated uses of 6G and beyond. It promotes openness and standardisation, increased flexibility through the disaggregation of Radio Access Network (RAN) components, supports programmability, flexibility, and scalability with technologies such as Software-Defined Networking (SDN), Network Function Virtualization (NFV), and cloud, and brings automation through the RAN Intelligent Controller (RIC). Furthermore, the use of xApps, rApps, and Artificial Intelligence/Machine Learning (AI/ML) within the RIC enables efficient management of complex RAN operations. However, due to the open nature of O-RAN and its support for heterogeneous systems, the possibility of misconfiguration problems becomes critical. In this paper, we present a thorough analysis of the potential misconfiguration issues in O-RAN with respect to integration and operation, the use of SDN and NFV, and, specifically, the use of AI/ML. The opportunity for AI/ML to be used to identify these misconfigurations is investigated. A case study is presented to illustrate the direct impact on the end user of conflicting policies amongst xApps along with a potential AI/ML-based solution to this problem. This research presents a first analysis of the impact of AI/ML on misconfiguration challenges in O-RAN
Run-time Introspection of 2D Object Detection in Automated Driving Systems Using Learning Representations

Reliable detection of various objects and road users in the surrounding environment is crucial for the safe operation of automated driving systems (ADS). Despite recent progresses in developing highly accurate object detectors based on Deep Neural Networks (DNNs), they still remain prone to detection errors, which can lead to fatal consequences in safety-critical applications such as ADS. An effective remedy to this problem is to equip the system with run-time monitoring, named as introspection in the context of autonomous systems. Motivated by this, we introduce a novel introspection solution, which operates at the frame level for DNN-based 2D object detection and leverages neural network activation patterns. The proposed approach pre-processes the neural activation patterns of the object detector's backbone using several different modes. To provide extensive comparative analysis and fair comparison, we also adapt and implement several state-of-the-art (SOTA) introspection mechanisms for error detection in 2D object detection, using one-stage and two-stage object detectors evaluated on KITTI and BDD datasets. We compare the performance of the proposed solution in terms of error detection, adaptability to dataset shift, and, computational and memory resource requirements. Our performance evaluation shows that the proposed introspection solution outperforms SOTA methods, achieving an absolute reduction in the missed error ratio of 9% to 17% in the BDD dataset.
On the family of elliptic curves $y^2=x^3-m^2x + (pqr)^2$

In this article, we consider a family of elliptic curves defined by $E_{m}: y^2= x^3 -m^2 x + (pqr)^2 $ where $m $ is a positive integer and $p, q, ~\text{and}~ r$ are distinct odd primes and study the torsion as well the rank of $E_{m}(\mathbb{Q})$. More specifically, we proved that if $m \not \equiv 0 \pmod{3}, m \not \equiv 0 \pmod{4} ~\text{and}~ m \equiv 2 \pmod {2^{k}}$ where $k \geq 5$, then the torsion subgroup of $E_{m}(\mathbb{Q})$ is trivial and lower bound of the $\mathbb{Q}$ rank of this family of elliptic curves is $2$.
Distilling Text Style Transfer With Self-Explanation From LLMs

Text Style Transfer (TST) seeks to alter the style of text while retaining its core content. Given the constraints of limited parallel datasets for TST, we propose CoTeX, a framework that leverages large language models (LLMs) alongside chain-of-thought (CoT) prompting to facilitate TST. CoTeX distills the complex rewriting and reasoning capabilities of LLMs into more streamlined models capable of working with both non-parallel and parallel data. Through experimentation across four TST datasets, CoTeX is shown to surpass traditional supervised fine-tuning and knowledge distillation methods, particularly in low-resource settings. We conduct a comprehensive evaluation, comparing CoTeX against current unsupervised, supervised, in-context learning (ICL) techniques, and instruction-tuned LLMs. Furthermore, CoTeX distinguishes itself by offering transparent explanations for its style transfer process.
Different Debt: An Addition to the Technical Debt Dataset and a Demonstration Using Developer Personality

Background: The"Technical Debt Dataset"(TDD) is a comprehensive dataset on technical debt (TD) in the main branches of more than 30 Java projects. However, some TD items produced by SonarQube are not included for many commits, for instance because the commits failed to compile. This has limited previous studies using the dataset. Aims and Method: In this paper, we provide an addition to the dataset that includes an analysis of 278,320 commits of all branches in a superset of 37 projects using Teamscale. We then demonstrate the utility of the dataset by exploring the relationship between developer personality by replicating a prior study. Results: The new dataset allows us to use a larger sample than prior work could, and we analyze the personality of 111 developers and 5,497 of their commits. The relationships we find between developer personality and the introduction and removal of TD differ from those found in prior work. Conclusions: We offer a dataset that may enable future studies into the topic of TD and we provide additional insights on how developer personality relates to TD.
Indirect measurement of atomic magneto-optical rotation via Hilbert transform

The Kramers-Kronig relations are a pivotal foundation of linear optics and atomic physics, embedding a physical connection between the real and imaginary components of any causal response function. A mathematically equivalent, but simpler, approach instead utilises the Hilbert transform. In a previous study, the Hilbert transform was applied to absorption spectra in order to infer the sole refractive index of an atomic medium in the absence of an external magnetic field. The presence of a magnetic field causes the medium to become birefringent and dichroic, and therefore it is instead characterised by two refractive indices. In this study, we apply the same Hilbert transform technique to independently measure both refractive indices of a birefringent atomic medium, leading to an indirect measurement of atomic magneto-optical rotation. Key to this measurement is the insight that inputting specific light polarisations into an atomic medium induces absorption associated with only one of the refractive indices. We show this is true in two configurations, commonly referred to in literature as the Faraday and Voigt geometries, which differ by the magnetic field orientation with respect to the light wavevector. For both cases, we measure the two refractive indices independently for a Rb thermal vapour in a 0.6 T magnetic field, finding excellent agreement with theory. This study further emphasises the application of the Hilbert transform to the field of quantum and atomic optics in the linear regime.
Requirements for building effective Hamiltonians using quantum-enhanced density matrix downfolding

Density matrix downfolding (DMD) is a technique for regressing low-energy effective Hamiltonians from quantum many-body Hamiltonians. One limiting factor in the accuracy of classical implementations of DMD is the presence of difficult-to-quantify systematic errors attendant to sampling the observables of quantum many-body systems on an approximate low-energy subspace. We propose a hybrid quantum-classical protocol for circumventing this limitation, relying on the prospective ability of quantum computers to efficiently prepare and sample from states in well-defined low-energy subspaces with systematically improvable accuracy. We introduce three requirements for when this is possible, including a notion of compressibility that quantifies features of Hamiltonians and low-energy subspaces thereof for which quantum DMD might be efficient. Assuming that these requirements are met, we analyze design choices for our protocol and provide resource estimates for implementing quantum-enhanced DMD on both the doped 2-D Fermi-Hubbard model and an ab initio model of a cuprate superconductor.
Defining Expertise: Applications to Treatment Effect Estimation

Decision-makers are often experts of their domain and take actions based on their domain knowledge. Doctors, for instance, may prescribe treatments by predicting the likely outcome of each available treatment. Actions of an expert thus naturally encode part of their domain knowledge, and can help make inferences within the same domain: Knowing doctors try to prescribe the best treatment for their patients, we can tell treatments prescribed more frequently are likely to be more effective. Yet in machine learning, the fact that most decision-makers are experts is often overlooked, and"expertise"is seldom leveraged as an inductive bias. This is especially true for the literature on treatment effect estimation, where often the only assumption made about actions is that of overlap. In this paper, we argue that expertise - particularly the type of expertise the decision-makers of a domain are likely to have - can be informative in designing and selecting methods for treatment effect estimation. We formally define two types of expertise, predictive and prognostic, and demonstrate empirically that: (i) the prominent type of expertise in a domain significantly influences the performance of different methods in treatment effect estimation, and (ii) it is possible to predict the type of expertise present in a dataset, which can provide a quantitative basis for model selection.
Nearest-Neighbours Estimators for Conditional Mutual Information

The conditional mutual information quantifies the conditional dependence of two random variables. It has numerous applications; it forms, for example, part of the definition of transfer entropy, a common measure of the causal relationship between time series. It does, however, require a lot of data to estimate accurately and suffers the curse of dimensionality, limiting its application in machine learning and data science. However, the Kozachenko-Leonenko approach can address this problem: it is possible, in this approach to define a nearest-neighbour estimator which depends only on the distance between data points and not on the dimension of the data. Furthermore, the bias can be calculated analytically for this estimator. Here this estimator is described and is tested on simulated data.
The Causal Roadmap and Simulations to Improve the Rigor and Reproducibility of Real-Data Applications

The Causal Roadmap outlines a systematic approach to asking and answering questions of cause-and-effect: define quantity of interest, evaluate needed assumptions, conduct statistical estimation, and carefully interpret results. It is paramount that the algorithm for statistical estimation and inference be carefully pre-specified to optimize its expected performance for the specific real-data application. Simulations that realistically reflect the application, including key characteristics such as strong confounding and dependent or missing outcomes, can help us gain a better understanding of an estimator's applied performance. We illustrate this with two examples, using the Causal Roadmap and realistic simulations to inform estimator selection and full specification of the Statistical Analysis Plan. First, in an observational longitudinal study, outcome-blind simulations are used to inform nuisance parameter estimation and variance estimation for longitudinal targeted maximum likelihood estimation (TMLE). Second, in a cluster-randomized controlled trial with missing outcomes, treatment-blind simulations are used to ensure control for Type-I error in Two-Stage TMLE. In both examples, realistic simulations empower us to pre-specify an estimator that is expected to have strong finite sample performance and also yield quality-controlled computing code for the actual analysis. Together, this process helps to improve the rigor and reproducibility of our research.
A Successive Decoding Strategy for Channels With Memory

This paper presents a new technique for communication over channels with memory where the channel state is unknown at the transmitter and receiver. A deep interleaver combined with successive decoding decomposes a channel with memory into an array of parallel memoryless channels on which a conventional coding system can operate individually. The problems of joint channel estimation and decoding thus are separated without loss of capacity. This technique achieves channel capacity and so may be used to evaluate the capacities of different channels. A general information-theoretic framework is developed and applied to intersymbol interference (ISI), finite-state Markov, and Rayleigh-fading channels. A full system implementation, which performs within 1.1 dB of the channel capacity upper bound, is presented for the Rayleigh-fading channel
High resolution live cell imaging to define ultrastructural and dynamic features of the halotolerant yeast Debaryomyces hansenii

Although some budding yeasts have proved tractable and intensely studied models, others are more recalcitrant. Debaryomyces hansenii, an important yeast species in food and biotechnological industries with curious physiological characteristics, has proved difficult to manipulate genetically and remains poorly defined. To remedy this, we have combined live cell fluorescent dyes with high resolution imaging techniques to define the sub-cellular features of D. hansenii, such as the mitochondria, nuclei, vacuoles and the cell wall. Using these tools, we define biological processes like the cell cycle, organelle inheritance and different membrane trafficking pathways of D. hansenii for the first time. Beyond this, reagents designed to study Saccharomyces cerevisiae proteins were used to access proteomic information about D. hansenii. Finally, we optimised the use of label free holotomography to image yeast, defining the physical parameters and visualising sub-cellular features like membranes and vacuoles. Not only does this work shed light on D. hansenii but this combinatorial approach serves as a template for how other cell biological systems, which are not amenable to standard genetic procedures, can be studied.
Factors that influence the caste ratio in a bacterial division of labour

Colonies of the bacteria Streptomyces coelicolor divide labour between cells that specialize on growth and sporulation and cells that specialize on antibiotic production. This division of labour arises due to costly chromosome deletions in the antibiotic overproducers. However, little is known about when and where these mutations occur or whether their frequency – which we liken to the caste ratio in social insects – is phenotypically plastic. To elucidate changes in the proportions of specialized cells (measured as the mutation frequency), we sampled S. coelicolor colonies grown under different conditions. Temporally, mutation frequency increased linearly with colony age and size. Spatially, mutations accumulated disproportionately in the colony center, despite greater growth and sporulation at the periphery. Exposing colonies to sub-inhibitory concentrations of some antibiotics, a competitive cue in Streptomyces, increased mutation frequencies. Finally, direct competition with other Streptomyces that naturally produce antibiotics increased mutation frequencies, while also increasing spore production. Our findings provide insights into the intrinsic and environmental factors driving division of labor in Streptomyces colonies by showing that mutation frequencies are dynamic and responsive to the competitive environment. These results show that chromosome deletions are phenotypically plastic and suggest that Streptomyces can flexibly adjust their caste ratio.
FHOD-1/profilin-mediated actin assembly protects sarcomeres against contraction-induced deformation in C. elegans

Formin HOmology Domain 2-containing (FHOD) proteins are a subfamily of actin-organizing formins that are important for proper striated muscle development in many animals. We had shown previously that absence of the sole FHOD protein, FHOD-1, in C. elegans results in thin body-wall muscles with misshapen dense bodies, structures that serve as sarcomere Z-lines in body-wall muscle. However, the mechanism of FHOD-1 action was unclear. Using mutations that target the actin assembly activity of FHOD-1, we demonstrate here that actin polymerization by FHOD-1 is required for its function in muscle development. Moreover, we show FHOD-1 cooperates with profilin PFN-3 to promote dense body morphogenesis, and profilins PFN-2 and PFN-3 to promote body-wall muscle growth. This is the first demonstration that a FHOD protein works with profilin partners in striated muscle development. Utilizing worms expressing fluorescently tagged dense body proteins PAT-3/β-integrin and ATN-1/α-actinin, we demonstrate dense bodies in fhod-1 and pfn-3 mutants are less stable than in wild type animals, having a higher proportion of dynamic protein, and becoming highly distorted by prolonged muscle contraction. We also observe the accumulation of actin depolymerization factor/cofilin homolog UNC-60B/cofilin in the body-wall muscle of these mutants. We suggest such accumulations may indicate targeting of thin filaments dislodged from unstable dense bodies disassembly. Similar failures in sarcomere assembly might account for the slow growth of body-wall muscle in fhod-1 mutants compared to wild type animals, and the coincident muscle weakness that we measure in these animals. Overall, these results show the importance of FHOD protein-mediated actin assembly to forming stable sarcomere Z-lines, and identify profilin as a new contributor to FHOD activity in striated muscle development.
Dual agonism of sodium iodide symporter function in vivo

New approaches are urgently needed to enhance the radioiodide (RAI) ablation of aggressive and metastatic thyroid cancer. We recently discovered that valosin-containing protein inhibitors (VCPi) such as clotrimazole and disulfiram transiently block sodium iodide symporter (NIS) proteasomal degradation, hence promoting RAI uptake. However, poor bioavailability diminishes their potential impact in vivo. Following 3D modelling and iterative drug design we appraised 26 novel analogues of clotrimazole, as well as albumin nano-encapsulated copper-diethyldithiocarbamate [Cu(DDC)2-alb] – a stabilised reformulation of a disulfiram metabolite. While several clotrimazole analogues specifically increased RAI uptake, the greatest impact was observed with Cu(DDC)2-alb in thyroid cancer cells as well as human primary thyrocytes from patients with thyroid hyperplasia. NanoBRET assays revealed that Cu(DDC)2 enhanced the plasma membrane accumulation of NIS in living cells. In BALB/c mice, both intraperitoneal and intravenous administration of Cu(DDC)2-alb significantly enhanced thyroidal 99mTc-uptake. RNA-Seq revealed the surprising observation that Cu(DDC)2-alb induced key thyroid transcription factors. Accordingly, expression of PAX8 and NKX2.1 was upregulated in thyroid glands from drug treated mice, with NIS levels correlating closely to 99mTc-uptake. As Cu(DDC)2 inhibits the VCP cofactor NPL4, with VCP being critical to the proteostatic processing of NIS protein, we identify a new dual agonist of RAI uptake in vivo, with the potential to directly impact RAI therapy for patients with aggressive thyroid cancer.
Next Generation Neuropeptide Y Receptor Small Molecule Agonists Inhibit Mosquito Biting Behavior

Female Aedes aegypti mosquitoes can spread disease-causing pathogens when they bite humans to obtain blood nutrients required for egg production. Following a complete blood meal, host-seeking is suppressed until eggs are laid. Neuropeptide Y-like Receptor 7 (NPYLR7) plays a role in endogenous host-seeking suppression and previous work identified small molecule NPYLR7 agonists that suppress host-seeking and blood feeding when fed to mosquitoes at high micromolar doses. Using structure activity relationship analysis and structure-guided design we synthesized 128 compounds with similarity to known NPYLR7 agonists. Although in vitro potency (EC50) was not strictly predictive of in vivo effect, we identified 3 compounds that suppressed blood feeding from a live host when fed to mosquitoes at a 1 μM dose, a 100-fold improvement over the original reference compound. Exogenous activation of NPYLR7 represents an innovative vector control strategy to block mosquito biting behavior and prevent mosquito/human host interactions that lead to pathogen transmission.
Cognate Antigen Engagement Induces HIV-1 Expression In CD4+ T Cells From People On Long-Term ART

Despite antiretroviral therapy (ART), HIV-1 persists in latently-infected CD4+ T cells, preventing cure. Antigens drive the proliferation of infected cells, precluding latent reservoir decay. However, the relationship between antigen recognition and HIV-1 gene expression is poorly understood since most studies of latency reversal use agents that induce non-specific global T cell activation. Here, we isolated rare CD4+ T cells responding to cytomegalovirus (CMV) or HIV-1 Gag antigens from participants on long-term ART and assessed T cell activation and HIV-1 RNA expression upon co-culture with autologous dendritic cells (DCs) presenting cognate antigens. Physiological presentation of cognate antigens induced broad T cell activation (median 42-fold increase in CD154+CD69+ cells) and significantly increased HIV-1 transcription (median 4-fold), mostly through the induction of rare cells with higher viral expression. Thus, despite low proviral inducibility, physiologic antigen recognition can promote HIV-1 expression, potentially contributing to spontaneous reservoir activity on ART and viral rebound upon ART interruption.