Miracle-dz/UniFAR-FTUs huggingface dataset:
Conversations in Galician: a Large Language Model for an Underrepresented Language

The recent proliferation of Large Conversation Language Models has highlighted the economic significance of widespread access to this type of AI technologies in the current information age. Nevertheless, prevailing models have primarily been trained on corpora consisting of documents written in popular languages. The dearth of such cutting-edge tools for low-resource languages further exacerbates their underrepresentation in the current economic landscape, thereby impacting their native speakers. This paper introduces two novel resources designed to enhance Natural Language Processing (NLP) for the Galician language. We present a Galician adaptation of the Alpaca dataset, comprising 52,000 instructions and demonstrations. This dataset proves invaluable for enhancing language models by fine-tuning them to more accurately adhere to provided instructions. Additionally, as a demonstration of the dataset utility, we fine-tuned LLaMA-7B to comprehend and respond in Galician, a language not originally supported by the model, by following the Alpaca format. This work contributes to the research on multilingual models tailored for low-resource settings, a crucial endeavor in ensuring the inclusion of all linguistic communities in the development of Large Language Models. Another noteworthy aspect of this research is the exploration of how knowledge of a closely related language, in this case, Portuguese, can assist in generating coherent text when training resources are scarce. Both the Galician Alpaca dataset and Cabuxa-7B are publicly accessible on our Huggingface Hub, and we have made the source code available to facilitate replication of this experiment and encourage further advancements for underrepresented languages.
Initial correlations in open quantum systems are always detectable

Consider an open quantum system which interacts with its environment. Assuming that the experimenter has access only to the system, an interesting question is whether it is possible to detect initial correlations between the system and the environment by performing measurements only on the system. Various methods have been proposed to detect correlations by local measurements on the system. After reviewing these methods, we will show that initial correlations between the system and the environment are always detectable. In particular, we will show that one can always find a unitary evolution, for the whole system-environment, such that the trace distance method, proposed to witness correlations locally, succeeds. We also find the condition for existence of the optimal unitary evolution, for which the entire correlation is locally detectable. Next, we address the case where the system and the environment interact through a time-independent Hamiltonian. For this case we will see that if the initial correlation can be detected locally at some time t , then it can be detected for almost all the other times too. On the other hand, we see that one can find cases for which initial correlations between the system and the environment always remain undetectable even though the unitary evolution, generated by the Hamiltonian, is not factorized.
A Simple Interpretable Transformer for Fine-Grained Image Classification and Analysis

We present a novel usage of Transformers to make image classification interpretable. Unlike mainstream classifiers that wait until the last fully-connected layer to incorporate class information to make predictions, we investigate a proactive approach, asking each class to search for itself in an image. We realize this idea via a Transformer encoder-decoder inspired by DEtection TRansformer (DETR). We learn ``class-specific'' queries (one for each class) as input to the decoder, enabling each class to localize its patterns in an image via cross-attention. We name our approach INterpretable TRansformer (INTR), which is fairly easy to implement and exhibits several compelling properties. We show that INTR intrinsically encourages each class to attend distinctively; the cross-attention weights thus provide a faithful interpretation of the prediction. Interestingly, via ``multi-head'' cross-attention, INTR could identify different ``attributes'' of a class, making it particularly suitable for fine-grained classification and analysis, which we demonstrate on eight datasets. Our code and pre-trained model are publicly accessible at https://github.com/Imageomics/INTR.
Homogeneous crystallization in four-dimensional Lennard-Jones liquids

We report the first observation of homogeneous crystallization in simulated high-dimensional ($d>3$) liquids that follow physically realistic dynamics and have system sizes that are large enough to eliminate the possibility that crystallization was induced by the periodic boundary conditions. Supercooled four-dimensional (4D) Lennard-Jones liquids maintained at zero pressure and constant temperatures $0.59<T<0.63$ crystallized within $\sim 2 \times 10^4\tau$, where $\tau$ is the LJ time unit. WCA liquids that were maintained at the same densities and temperatures at which their LJ counterparts nucleated did not crystallize even after $2.5\times 10^5\tau$, showing that the presence of long-ranged attractive interactions dramatically speeds up 4D crystallization, much as it does in 3D. However, the overlap of the liquid and crystalline phases' local-bond-order distributions is much smaller for LJ than for WCA systems, which is the opposite of the 3D trend. This implies that the widely accepted hypothesis that increasing geometrical frustration rapidly suppresses crystallization as the spatial dimension $d$ increases is only generally valid in the absence of attractive interparticle forces.
Characterization in Geant4 of different PET configurations

Positron Emission Tomography (PET) is a Nuclear Medicine technique that creates images that allow the study of metabolic activity and organ function using radiopharmaceuticals. Continuous improvement of scintillation detectors for radiation in PET as well as improvement in electronic detectors (e.g., SiPMs) and signal processing, makes the field of PET a fast and changing environment. If industry desires to build new systems implementing these technological improvements, it is of its interest to develop modelling strategies that can provide information on how to build them, reducing time and material costs. Bearing this in mind three different PET configurations, were simulated in Geant4, to determine which one presented the best performance according to quality parameters such as spatial resolution (SR), coincident time resolution (CTR) and acceptance value (A). This was done with three different (in size) pairs of LYSO crystals + SiPM detectors. It was found that the 2 Modules system presented worst results than the two Ring detector configurations. Between the Ring configurations the first was marginally better than the second.
Newvision: application for helping blind people using deep learning

As able-bodied people, we often take our vision for granted. For people who are visually impaired, however, their disability can have a significant impact on their daily lives. We are developing proprietary headgear that will help visually impaired people navigate their surroundings, identify objects and people, read text, and avoid obstacles. The headgear will use a combination of computer vision, distance estimation with ultrasonic sensors, voice recognition, and voice assistants to provide users with real-time information about their environment. Users will be able to interact with the headgear through voice commands, such as ''What is that? '' to identify an object or ''Navigate to the front door'' to find their way around. The headgear will then provide the user with a verbal description of the object or spoken navigation instructions. We believe that this headgear has the potential to make a significant difference in the lives of visually impaired people, allowing them to live more independently and participate more fully in society.
DP-DCAN: Differentially Private Deep Contrastive Autoencoder Network for Single-cell Clustering

Single-cell RNA sequencing (scRNA-seq) is important to transcriptomic analysis of gene expression. Recently, deep learning has facilitated the analysis of high-dimensional single-cell data. Unfortunately, deep learning models may leak sensitive information about users. As a result, Differential Privacy (DP) is increasingly used to protect privacy. However, existing DP methods usually perturb whole neural networks to achieve differential privacy, and hence result in great performance overheads. To address this challenge, in this paper, we take advantage of the uniqueness of the autoencoder that it outputs only the dimension-reduced vector in the middle of the network, and design a Differentially Private Deep Contrastive Autoencoder Network (DP-DCAN) by partial network perturbation for single-cell clustering. Since only partial network is added with noise, the performance improvement is obvious and twofold: one part of network is trained with less noise due to a bigger privacy budget, and the other part is trained without any noise. Experimental results of six datasets have verified that DP-DCAN is superior to the traditional DP scheme with whole network perturbation. Moreover, DP-DCAN demonstrates strong robustness to adversarial attacks. The code is available at https://github.com/LFD-byte/DP-DCAN.
A Topological Data Analysis of the CHIME/FRB Catalogues

In this paper, we use Topological Data Analysis (TDA), a mathematical approach for studying data shape, to analyse Fast Radio Bursts (FRBs). Applying the Mapper algorithm, we visualise the topological structure of a large FRB sample. Our findings reveal three distinct FRB populations based on their inferred source properties, and show a robust structure indicating their morphology and energy. We also identify potential non-repeating FRBs that might become repeaters based on proximity in the Mapper graph. This work showcases TDA's promise in unraveling the origin and nature of FRBs.
Federated Learning via Active RIS Assisted Over-the-Air Computation

In this paper, we propose leveraging the active reconfigurable intelligence surface (RIS) to support reliable gradient aggregation for over-the-air computation (AirComp) enabled federated learning (FL) systems. An analysis of the FL convergence property reveals that minimizing gradient aggregation errors in each training round is crucial for narrowing the convergence gap. As such, we formulate an optimization problem, aiming to minimize these errors by jointly optimizing the transceiver design and RIS configuration. To handle the formulated highly non-convex problem, we devise a two-layer alternative optimization framework to decompose it into several convex subproblems, each solvable optimally. Simulation results demonstrate the superiority of the active RIS in reducing gradient aggregation errors compared to its passive counterpart.
Deep Bayesian Reinforcement Learning for Spacecraft Proximity Maneuvers and Docking

In the pursuit of autonomous spacecraft proximity maneuvers and docking(PMD), we introduce a novel Bayesian actor-critic reinforcement learning algorithm to learn a control policy with the stability guarantee. The PMD task is formulated as a Markov decision process that reflects the relative dynamic model, the docking cone and the cost function. Drawing from the principles of Lyapunov theory, we frame the temporal difference learning as a constrained Gaussian process regression problem. This innovative approach allows the state-value function to be expressed as a Lyapunov function, leveraging the Gaussian process and deep kernel learning. We develop a novel Bayesian quadrature policy optimization procedure to analytically compute the policy gradient while integrating Lyapunov-based stability constraints. This integration is pivotal in satisfying the rigorous safety demands of spaceflight missions. The proposed algorithm has been experimentally evaluated on a spacecraft air-bearing testbed and shows impressive and promising performance.
The Music Meta Ontology: a flexible semantic model for the interoperability of music metadata

The semantic description of music metadata is a key requirement for the creation of music datasets that can be aligned, integrated, and accessed for information retrieval and knowledge discovery. It is nonetheless an open challenge due to the complexity of musical concepts arising from different genres, styles, and periods -- standing to benefit from a lingua franca to accommodate various stakeholders (musicologists, librarians, data engineers, etc.). To initiate this transition, we introduce the Music Meta ontology, a rich and flexible semantic model to describe music metadata related to artists, compositions, performances, recordings, and links. We follow eXtreme Design methodologies and best practices for data engineering, to reflect the perspectives and the requirements of various stakeholders into the design of the model, while leveraging ontology design patterns and accounting for provenance at different levels (claims, links). After presenting the main features of Music Meta, we provide a first evaluation of the model, alignments to other schema (Music Ontology, DOREMUS, Wikidata), and support for data transformation.
A seven-step guide to spatial, agent-based modelling of tumour evolution

Spatial agent-based models are increasingly used to investigate the evolution of solid tumours subject to localised cell-cell interactions and microenvironmental heterogeneity. Here we present a non-technical step by step guide to developing such a model from first principles, aimed at both aspiring modellers and other biologists and oncologists who wish to understand the assumptions and limitations of this approach. Stressing the importance of tailoring the model structure to that of the biological system, we describe methods of increasing complexity, from the basic Eden growth model up to off-lattice simulations with diffusible factors. We examine choices that unavoidably arise in model design, such as implementation, parameterisation, visualisation, and reproducibility. Each topic is illustrated with examples drawn from recent research studies and state of the art modelling platforms. We emphasise the benefits of simpler models that aim to match the complexity of the phenomena of interest, rather than that of the entire biological system.
Governance Considerations Relating to Social Impact Assessments for Mining Development in African Communities

This paper explores how social impact assessments (SIAs) can be applied productively in mining development in African societies characterised by weak regulatory structures and ethnic diversity. It evaluates the nature of the social risks associated with mining development in Africa and examines the concept of SIA as a tool for mitigating those risks. Most importantly, it considers the factors that should be taken into account in the design and implementation of SIA programs in ethnically diverse African regions. One of the paper's central theses is that within-group perceptions of procedural justice are instrumental to the success of participatory processes for SIAs in mining development in Africa.
Graph-controlled Permutation Mixers in QAOA for the Flexible Job-Shop Problem

One of the most promising attempts towards solving optimization problems with quantum computers in the noisy intermediate scale era of quantum computing are variational quantum algorithms. The Quantum Alternating Operator Ansatz provides an algorithmic framework for constrained, combinatorial optimization problems. As opposed to the better known standard QAOA protocol, the constraints of the optimization problem are built into the mixing layers of the ansatz circuit, thereby limiting the search to the much smaller Hilbert space of feasible solutions. In this work we develop mixing operators for a wide range of scheduling problems including the flexible job shop problem. These mixing operators are based on a special control scheme defined by a constraint graph model. After describing an explicit construction of those mixing operators, they are proven to be feasibility preserving, as well as exploring the feasible subspace.
Moderate deviations for the mildly stationary autoregressive model with dependent errors

In this paper, we consider the normalized least squares estimator of the parameter in a mildly stationary first-order autoregressive model with dependent errors which are modeled as a mildly stationary AR(1) process. By martingale methods, we establish the moderate deviations for the least squares estimators of the regressor and error, which can be applied to understand the near-integrated second order autoregressive processes. As an application, we obtain the moderate deviations for the Durbin-Watson statistic.
MINT: Multiplier-less INTeger Quantization for Energy Efficient Spiking Neural Networks

We propose Multiplier-less INTeger (MINT) quantization, a uniform quantization scheme that efficiently compresses weights and membrane potentials in spiking neural networks (SNNs). Unlike previous SNN quantization methods, MINT quantizes memory-intensive membrane potentials to an extremely low precision (2-bit), significantly reducing the memory footprint. MINT also shares the quantization scaling factor between weights and membrane potentials, eliminating the need for multipliers required in conventional uniform quantization. Experimental results show that our method matches the accuracy of full-precision models and other state-of-the-art SNN quantization techniques while surpassing them in memory footprint reduction and hardware cost efficiency at deployment. For example, 2-bit MINT VGG-16 achieves 90.6% accuracy on CIFAR-10, with roughly 93.8% reduction in memory footprint from the full-precision model and 90% reduction in computation energy compared to vanilla uniform quantization at deployment.11Code is available at https://github.com/Intelligent-Computing-Lab-Yale/MINT-Quantization
Reconfigurable Intelligent Surface-Enabled Gridless DoA Estimation System for NLoS Scenarios

—The conventional direction-of-arrival (DoA) estimation approaches are effective only when the line-of-sight (LoS) link is available. In non-line-of-sight (NLoS) scenarios, the spatial angle can not be captured and thus the DoA estimation performance would be significantly degraded. A novel reconfigurable intelligent surface (RIS)-enabled gridless DoA estimation system for NLoS scenarios is proposed to address this challenge, where the RIS establishes a virtual LoS link between the base station and the targets. To extract the statistics of the signal, the RIS-enabled signal model in the covariance domain is proposed. We estimate the noise variance by constraining the Frobenius norm of the measurement error matrix to enhance the robustness to noise. Additionally, we reconstruct the Hermitian Toeplitz matrix by addressing the atom norm minimization (ANM) problem on the covariance-based matrix. To reduce the computation, an efficient iterative approach is designed to solve the ANM problem via the alternating direction method of multipliers. Numerical experiments validate the superiority of the proposed system over the benchmark in terms of computational efficiency and multi-source DoA estimation precision.
A conserved mechanism for JNK-mediated loss of Notch function in advanced prostate cancer

Dysregulated Notch signaling is a common feature of cancer; however, its effects on tumor initiation and progression are highly variable, with Notch having either oncogenic or tumor-suppressive functions in various cancers. To better understand the mechanisms that regulate Notch function in cancer, we studied Notch signaling in a Drosophila tumor model, prostate cancer–derived cell lines, and tissue samples from patients with advanced prostate cancer. We demonstrated that increased activity of the Src-JNK pathway in tumors inactivated Notch signaling because of JNK pathway–mediated inhibition of the expression of the gene encoding the Notch S2 cleavage protease, Kuzbanian, which is critical for Notch activity. Consequently, inactive Notch accumulated in cells, where it was unable to transcribe genes encoding its target proteins, many of which have tumor-suppressive activities. These findings suggest that Src-JNK activity in tumors predicts Notch activity status and that suppressing Src-JNK signaling could restore Notch function in tumors, offering opportunities for diagnosis and targeted therapies for a subset of patients with advanced prostate cancer. Description Analysis of Notch signaling suggests a tumor-suppressive role for Notch in Drosophila tumors and in advanced prostate cancer. Editor’s summary Notch signaling plays a prominent but context-dependent role in the progression of prostate cancer because of its effects on cell proliferation, differentiation, and survival. Wang et al. used a Drosophila tumor model to identify modulators of Notch signaling in tumors. The activation of the kinase JNK blocked the expression of a protease required for Notch activity, resulting in the accumulation of inactive Notch, whereas inhibition of JNK rescued Notch signaling. High JNK activity correlated with low Notch activity in samples from patients with advanced human prostate cancer, supporting a tumor-suppressive role for Notch. Thus, suppressing JNK signaling could restore Notch activity in some advanced prostate cancer tumors. —Amy E. Baek
Aerosol optical depth retrieval over snow using AATSR data

Aerosol observations over the Arctic are important because of the effects of aerosols on Arctic climate, such as their direct and indirect effects on the Earth's radiation balance and on snow albedo. Although information on aerosol properties is available from ground-based measurements, passive remote sensing using satellite measurements would offer the advantage of large spatial coverage with good temporal resolution, even though, due to light limitations, this is only available during the Arctic summer. However, aerosol optical depth (AOD) retrieval over the Arctic region is a great challenge due to the high reflectance of snow and ice and due to the high solar zenith angle. In this article, we describe a retrieval algorithm using Advanced Along-Track Scanning Radiometer (AATSR) data, a radiometer flying on the European Space Agency (ESA) Environmental Satellite (ENVISAT), which offers two views (near nadir and at 55° forward) at seven wavelengths in the visible thermal-infrared (VIS-TIR). The main idea of the Dual-View Multi-Spectral (DVMS) approach is to use the dual view to separate contributions to reflectance measured at the top of the atmosphere (TOA) due to atmospheric aerosol and the underlying surface. The algorithm uses an analytical snow bidirectional reflectance distribution function (BRDF) model for the estimation of the ratio of snow reflectances in the nadir and forward views, as well as an estimate of the atmospheric contribution to TOA reflectance obtained using the dark pixel method over the adjacent ocean surface, assuming that this value applies over nearby land surfaces in the absence of significant sources across the coastline. An iteration involving all four AATSR wavebands in the visible near-infrared (VIS-NIR) is used to retrieve the relevant information. The method is illustrated for AATSR overpasses over Greenland with clear sky in April 2009. Comparison of the retrieved AOD with AErosol Robotic Network (AERONET) data shows a correlation coefficient of 0.75. The AODs retrieved from AATSR using the DVMS approach and those obtained from AERONET data show similar temporal trends, but the AERONET results are more variable and the highest AOD values are mostly missed by the DVMS approach. Limitations of the DVMS method are discussed. The pure-snow BRDF model needs further correction in order to obtain a better estimation for mixtures of snow and ice.