Miracle-dz/UniFAR-FTUs huggingface dataset:
Forecasting with Sparse but Informative Variables: A Case Study in Predicting Blood Glucose

In time-series forecasting, future target values may be affected by both intrinsic and extrinsic effects. When forecasting blood glucose, for example, intrinsic effects can be inferred from the history of the target signal alone (i.e. blood glucose), but accurately modeling the impact of extrinsic effects requires auxiliary signals, like the amount of carbohydrates ingested. Standard forecasting techniques often assume that extrinsic and intrinsic effects vary at similar rates. However, when auxiliary signals are generated at a much lower frequency than the target variable (e.g., blood glucose measurements are made every 5 minutes, while meals occur once every few hours), even well-known extrinsic effects (e.g., carbohydrates increase blood glucose) may prove difficult to learn. To better utilize these sparse but informative variables (SIVs), we introduce a novel encoder/decoder forecasting approach that accurately learns the per-timepoint effect of the SIV, by (i) isolating it from intrinsic effects and (ii) restricting its learned effect based on domain knowledge. On a simulated dataset pertaining to the task of blood glucose forecasting, when the SIV is accurately recorded our approach outperforms baseline approaches in terms of rMSE (13.07 [95% CI: 11.77,14.16] vs. 14.14 [12.69,15.27]). In the presence of a corrupted SIV, the proposed approach can still result in lower error compared to the baseline but the advantage is reduced as noise increases. By isolating their effects and incorporating domain knowledge, our approach makes it possible to better utilize SIVs in forecasting.
M-ENIAC: A machine learning recreation of the first successful numerical weather forecasts

In 1950 the first successful numerical weather forecast was obtained by solving the barotropic vorticity equation using the Electronic Numerical Integrator and Computer (ENIAC), which marked the beginning of the age of numerical weather prediction. Here, we ask the question of how these numerical forecasts would have turned out, if machine learning based solvers had been used instead of standard numerical discretizations. Specifically, we recreate these numerical forecasts using physics-informed neural networks. We show that physics-informed neural networks provide an easier and more accurate methodology for solving meteorological equations on the sphere, as compared to the ENIAC solver.
DRIFT: A Federated Recommender System with Implicit Feedback on the Items

Nowadays there are more and more items available online, this makes it hard for users to find items that they like. Recommender systems aim to find the item who best suits the user, using his historical interactions. Depending on the context, these interactions may be more or less sensitive and collecting them brings an important problem concerning the users' privacy. Federated systems have shown that it is possible to make accurate and efficient recommendations without storing users' personal information. However, these systems use instantaneous feedback from the user. In this report, we propose DRIFT, a federated architecture for recommender systems, using implicit feedback. Our learning model is based on a recent algorithm for recommendation with implicit feedbacks SAROS. We aim to make recommendations as precise as SAROS, without compromising the users' privacy. In this report we show that thanks to our experiments, but also thanks to a theoretical analysis on the convergence. We have shown also that the computation time has a linear complexity with respect to the number of interactions made. Finally, we have shown that our algorithm is secure, and participants in our federated system cannot guess the interactions made by the user, except DOs that have the item involved in the interaction.
Towards a Benchmark for Fog Data Processing

Fog data processing systems provide key abstractions to manage data and event processing in the geo-distributed and heterogeneous fog environment. The lack of standardized benchmarks for such systems, however, hinders their development and deployment, as different approaches cannot be compared quantitatively. Existing cloud data benchmarks are inadequate for fog computing, as their focus on workload specification ignores the tight integration of application and infrastructure inherent in fog computing.In this paper, we outline an approach to a fog-native data processing benchmark that combines workload specifications with infrastructure specifications. This holistic approach allows researchers and engineers to quantify how a software approach performs for a given workload on given infrastructure. Further, by basing our benchmark in a realistic IoT sensor network scenario, we can combine paradigms such as low-latency event processing, machine learning inference, and offline data analytics, and analyze the performance impact of their interplay in a fog data processing system.
Repulsion driven metallic phase in the ground state of the half-filled $t-t^{\prime}$ ionic Hubbard chain

An unusual metallic phase is proven to develop in the one dimensional ionic Hubbard model, at half-filling and zero magnetization, at intermediate electron-electron repulsion $U$ when second neighbors hopping is allowed and tuned close to a topological Lifshitz transition (connected with a change of the Fermi surface in the non-interacting system). The metallic state lies between a band insulator phase at low repulsion and a correlated (Mott-like) insulator phase at high repulsion. In approaching the later, the metal supports spontaneous bond charge dimerization and antiferromagnetic correlations. The ground state of the system, attainable by cold atoms in optical lattices for a wide range of the parameter $U$, is extensively explored by numerically exact density-matrix renormalization-group (DMRG) calculations. A combination of mean field and effective field theory (bosonization) provides an analytical understanding of the physical processes underlying the observed phase transitions.
Soft-Output Deep Neural Network-Based Decoding

Deep neural network (DNN)-based channel decoding is widely considered in the literature. The existing solutions are investigated for the case of hard output, i.e. when the decoder returns the estimated information word. At the same time, soft output decoding is of critical importance for iterative receivers and decoders. In this paper, we focus on the soft-output DNN-based decoding problem. We start with the syndrome-based approach proposed by Bennatan et al. (2018) and modify it to provide soft output in the additive white Gaussian noise (AWGN) channel. The new decoder can be considered as an approximation of the maximum a-posteriori (MAP) decoder with smaller computation complexity. We discuss various regularization functions for joint DNN-MAP training and compare the resulting distributions for [64], [45] (extended Bose-Chaudhuri-Hocquenghem) eBCH code. Finally, to demonstrate the soft-output quality we consider the turbo-product code with [64], [45] eBCH codes as row and column codes. We show that the resulting DNN-based scheme is very close to the MAP-based performance and significantly outperforms the solution based on the Chase decoder. We come to the conclusion that the new method is prospective for the challenging problem of DNN-based decoding of long codes consisting of short component codes.
Full-Duplex Wireless for 6G: Progress Brings New Opportunities and Challenges

The use of in-band full-duplex (FD) enables nodes to simultaneously transmit and receive on the same frequency band, which challenges the traditional assumption in wireless network design. The full-duplex capability enhances spectral efficiency and decreases latency, which are two key drivers pushing the performance expectations of next-generation mobile networks. In less than ten years, in-band FD has advanced from being demonstrated in research labs to being implemented in standards, presenting new opportunities to utilize its foundational concepts. Some of the most significant opportunities include using FD to enable wireless networks to sense the physical environment, integrate sensing and communication applications, develop integrated access and backhaul solutions, and work with smart signal propagation environments powered by reconfigurable intelligent surfaces. However, these new opportunities also come with new challenges for large-scale commercial deployment of FD technology, such as managing self-interference, combating cross-link interference in multi-cell networks, and coexistence of dynamic time division duplex, subband FD and FD networks.
Classification of US Supreme Court Cases Using BERT-Based Techniques

Models based on bidirectional encoder representations from transformers (BERT) produce state of the art (SOTA) results on many natural language processing (NLP) tasks such as named entity recognition (NER), part-of-speech (POS) tagging etc. An interesting phenomenon occurs when classifying long documents such as those from the US supreme court where BERT-based models can be considered difficult to use on a first-pass or out-of-the-box basis. In this paper, we experiment with several BERT-based classification techniques for US supreme court decisions or supreme court database (SCDB) and compare them with the previous SOTA results. We then compare our results specifically with SOTA models for long documents. We compare our results for two classification tasks: (1) a broad classification task with 15 categories and (2) a fine-grained classification task with 279 categories. Our best result produces an accuracy of 80% on the 15 broad categories and 60% on the fine-grained 279 categories which marks an improvement of 8% and 28% respectively from previously reported SOTA results.
Bayes Hilbert Spaces for Posterior Approximation

Performing inference in Bayesian models requires sampling algorithms to draw samples from the posterior. This becomes prohibitively expensive as the size of data sets increase. Constructing approximations to the posterior which are cheap to evaluate is a popular approach to circumvent this issue. This begs the question of what is an appropriate space to perform approximation of Bayesian posterior measures. This manuscript studies the application of Bayes Hilbert spaces to the posterior approximation problem. Bayes Hilbert spaces are studied in functional data analysis in the context where observed functions are probability density functions and their application to computational Bayesian problems is in its infancy. This manuscript shall outline Bayes Hilbert spaces and their connection to Bayesian computation, in particular novel connections between Bayes Hilbert spaces, Bayesian coreset algorithms and kernel-based distances.
Participatory Design of AI with Children: Reflections on IDC Design Challenge

Children growing up in the era of Artificial Intelligence (AI) will be most impacted by the technology across their life span. Participatory Design (PD) is widely adopted by the Interaction Design and Children (IDC) community, which empowers children to bring their interests, needs, and creativity to the design process of future technologies. While PD has drawn increasing attention to human-centered AI design, it remains largely untapped in facilitating the design process of AI technologies relevant to children and their community. In this paper, we report intriguing children's design ideas on AI technologies resulting from the"Research and Design Challenge"of the 22nd ACM Interaction Design and Children (IDC 2023) conference. The diversity of design problems, AI applications and capabilities revealed by the children's design ideas shed light on the potential of engaging children in PD activities for future AI technologies. We discuss opportunities and challenges for accessible and inclusive PD experiences with children in shaping the future of AI-powered society.
Revisiting the Role of Similarity and Dissimilarity in Best Counter Argument Retrieval

This paper studies the task of best counter-argument retrieval given an input argument. Following the definition that the best counter-argument addresses the same aspects as the input argument while having the opposite stance, we aim to develop an efficient and effective model for scoring counter-arguments based on similarity and dissimilarity metrics. We first conduct an experimental study on the effectiveness of available scoring methods, including traditional Learning-To-Rank (LTR) and recent neural scoring models. We then propose Bipolar-encoder, a novel BERT-based model to learn an optimal representation for simultaneous similarity and dissimilarity. Experimental results show that our proposed method can achieve the accuracy@1 of 49.04\%, which significantly outperforms other baselines by a large margin. When combined with an appropriate caching technique, Bipolar-encoder is comparably efficient at prediction time.
Robust Calibrate Proxy Loss for Deep Metric Learning

The mainstream researche in deep metric learning can be divided into two genres: proxy-based and pair-based methods. Proxy-based methods have attracted extensive attention due to the lower training complexity and fast network convergence. However, these methods have limitations as the poxy optimization is done by network, which makes it challenging for the proxy to accurately represent the feature distrubtion of the real class of data. In this paper, we propose a Calibrate Proxy (CP) structure, which uses the real sample information to improve the similarity calculation in proxy-based loss and introduces a calibration loss to constraint the proxy optimization towards the center of the class features. At the same time, we set a small number of proxies for each class to alleviate the impact of intra-class differences on retrieval performance. The effectiveness of our method is evaluated by extensive experiments on three public datasets and multiple synthetic label-noise datasets. The results show that our approach can effectively improve the performance of commonly used proxy-based losses on both regular and noisy datasets.
Assessing Distribution Network Flexibility via Reliability-Based P-Q Area Segmentation

This paper proposes a framework to assess the flexibility of active distribution networks (ADNs) via P-Q area segmentation, considering the reliability of flexible units (FUs). A mixed-integer quadratically constrained programming (MIQCP) model is formulated to analyse flexible active and reactive power support at the interface with transmission networks, explicitly capturing the contributions and reliability of FUs that provide flexibility services within an ADN. The numerical simulations performed for a real 124-bus UK distribution network demonstrate the optimal flexibility provision by different FUs, as well as the corresponding reliability and the impact of network reconfiguration. Distribution system operators (DSOs) can use the proposed framework to identify critical units, select an adequate combination of flexibility volumes, and manage its reliability.
Error mitigation in variational quantum eigensolvers using tailored probabilistic machine learning

Quantum computing technology has the potential to revolutionize the simulation of materials and molecules in the near future. A primary challenge in achieving near-term quantum advantage is effectively mitigating the noise effects inherent in current quantum processing units (QPUs). This challenge is also decisive in the context of quantum-classical hybrid schemes employing variational quantum eigensolvers (VQEs) that have attracted significant interest in recent years. In this work, we present a novel method that employs parametric Gaussian process regression (GPR) within an active learning framework to mitigate noise in quantum computations, focusing on VQEs. Our approach, grounded in probabilistic machine learning, exploits a custom prior based on the VQE ansatz to capture the underlying correlations between VQE outputs for different variational parameters, thereby enhancing both accuracy and efficiency. We demonstrate the effectiveness of our method on a 2-site Anderson impurity model and a 8-site Heisenberg model, using the IBM open-source quantum computing framework, Qiskit, showcasing substantial improvements in the accuracy of VQE outputs while reducing the number of direct QPU energy evaluations. This work contributes to the ongoing efforts in quantum error mitigation and optimization, bringing us a step closer to realizing the potential of quantum computing in quantum matter simulations.
Very-high- and ultrahigh-frequency electric-field detection using high angular momentum Rydberg states

We demonstrate resonant detection of rf electric fields from 240 MHz to 900 MHz (very-high-frequency (VHF) to ultra-high-frequency (UHF)) using electromagnetically induced transparency to measure orbital angular momentum $L=3\rightarrow L'=4$ Rydberg transitions. These Rydberg states are accessible with three-photon infrared optical excitation. By resonantly detecting rf in the electrically small regime, these states enable a new class of atomic receivers. We find good agreement between measured spectra and predictions of quantum defect theory for principal quantum numbers $n=45$ to $70$. Using a super-hetrodyne detection setup, we measure the noise floor at $n=50$ to be $13\,\mathrm{\mu V/m/\sqrt{Hz}}$. Additionally, we utilize data and a numerical model incorporating a five-level master equation solution to estimate the fundamental sensitivity limits of our system.
XAI for transparent wind turbine power curve models

Accurate wind turbine power curve models, which translate ambient conditions into turbine power output, are crucial for wind energy to scale and fulfill its proposed role in the global energy transition. While machine learning (ML) methods have shown significant advantages over parametric, physics-informed approaches, they are often criticised for being opaque 'black boxes', which hinders their application in practice. We apply Shapley values, a popular explainable artificial intelligence (XAI) method, and the latest findings from XAI for regression models, to uncover the strategies ML models have learned from operational wind turbine data. Our findings reveal that the trend towards ever larger model architectures, driven by a focus on test set performance, can result in physically implausible model strategies. Therefore, we call for a more prominent role of XAI methods in model selection. Moreover, we propose a practical approach to utilize explanations for root cause analysis in the context of wind turbine performance monitoring. This can help to reduce downtime and increase the utilization of turbines in the field.
MicroRNA expression in extracellular vesicles as a novel blood‐based biomarker for Alzheimer's disease

Brain cell–derived small extracellular vesicles (sEVs) in blood offer unique cellular and molecular information related to the onset and progression of Alzheimer's disease (AD). We simultaneously enriched six specific sEV subtypes from the plasma and analyzed a selected panel of microRNAs (miRNAs) in older adults with/without cognitive impairment.
Chemically Tunable, Biocompatible, and Cost-Effective Metal–Insulator–Metal Resonators Using Silk Protein and Ultrathin Silver Films

Responsive optical resonators are used in chemical and biological sensing applications because their optical properties can be tuned by interactions with their environment. The use of non-nanostructured designs and biological materials expands the applications of these resonators because of their biocompatibility and low cost. Natural silk protein enables designing cost-effective, biocompatible, and chemically tunable metal–insulator–metal (MIM) resonators for color filters and superabsorbers. Our resonant silk MIM exhibited a red shift of the resonance through swelling, which could be controlled by stimuli such as pH and alcohol concentration. The absorption could be detected even in highly scattering biological tissues. Additionally, deep UV light was applied to generate arbitrary patterns with a different color than the background. The proposed silk MIM resonator, which was tunable, biocompatible, and cost-effective, would be useful as a spectral signature in chemical and biological sensing applications.
Obesity-induced inflammation exacerbates clonal hematopoiesis

Characterized by the accumulation of somatic mutations in blood cell lineages, clonal hematopoiesis of indeterminate potential (CHIP) is frequent in aging and involves the expansion of mutated hematopoietic stem and progenitor cells (HSC/Ps) that leads to an increased risk of hematologic malignancy. However, the risk factors that contribute to CHIP-associated clonal hematopoiesis (CH) are poorly understood. Obesity induces a proinflammatory state and fatty bone marrow (FBM), which may influence CHIP-associated pathologies. We analyzed exome sequencing and clinical data for 47,466 individuals with validated CHIP in the UK Biobank. CHIP was present in 5.8% of the study population and was associated with a significant increase in the waist-to-hip ratio (WHR). Mouse models of obesity and CHIP driven by heterozygosity of Tet2, Dnmt3a, Asxl1, and Jak2 resulted in exacerbated expansion of mutant HSC/Ps due in part to excessive inflammation. Our results show that obesity is highly associated with CHIP and that a proinflammatory state could potentiate the progression of CHIP to more significant hematologic neoplasia. The calcium channel blockers nifedipine and SKF-96365, either alone or in combination with metformin, MCC950, or anakinra (IL-1 receptor antagonist), suppressed the growth of mutant CHIP cells and partially restored normal hematopoiesis. Targeting CHIP-mutant cells with these drugs could be a potential therapeutic approach to treat CH and its associated abnormalities in individuals with obesity.
Amlodipine limits microglia activation and cognitive dysfunction in aged hypertensive mice

Background: SBP and blood pressure variability are independent risk factors for cerebral small vessel disease, a leading cause for stroke and dementia. Calcium-channel blockers are known to reduce blood pressure variability and may thus offer benefit against dementia. Beyond this effect, the impact of calcium-channel blockers on hypertension-induced neuroinflammation, and especially, microglial phenotype remains unknown. We aimed to study the ability of amlopidine to alleviate microglia inflammation, and slow down cognitive dysfunction in aged hypertensive mice. Methods: Hypertensive BPH/2J and normotensive BPN/3J mice were studied until 12 months of age. Hypertensive mice were untreated or received amlodipine (10 mg/kg per day). Blood pressure parameters were measured by telemetry and tail cuff plethysmography. Mice underwent repeated series of cognitive tasks. Brain immunohistochemistry was performed to study blood–brain barrier dysfunction and microglial pro-inflammatory phenotype (CD68+Iba1+ cells; morphological analysis). Results: Amlodipine normalized SBP over the entire life span and decreased blood pressure variability. BPH/2J mice exhibited impaired short-term memory that was prevented by amlodipine at 12 months (discrimination index 0.41 ± 0.25 in amlodipine-treated vs. 0.14 ± 0.15 in untreated BPH/2J mice, P = 0.02). Amlopidine treatment of BPH/2J did not prevent blood–brain barrier leakage, a measure of cerebral small vessel disease, but limited its size. Microglia's inflammatory phenotype in BPH/2J, characterized by an increased number of Iba1+CD68+ cells, increased soma size and shortened processes, was partly reduced by amlodipine. Conclusion: Amlodipine attenuated the short-term memory impairment in aged hypertensive mice. Beyond its blood pressure lowering capacity, amlodipine may be cerebroprotective by modulating neuroinflammation.