Miracle-dz/UniFAR-FTUs huggingface dataset:
Empowering consumers to PREVENT diet-related diseases through OMICS sciences (PREVENTOMICS): protocol for a parallel double-blinded randomised intervention trial to investigate biomarker-based nutrition plans for weight loss

Introduction Personalised nutrition holds immense potential over conventional one-size-fits-all approaches for preventing and treating diet-related diseases, such as obesity. The current study aims to examine whether a personalised nutritional plan produces more favourable health outcomes than a standard approach based on general dietary recommendations in subjects with overweight or obesity and elevated waist circumference. Methods and analysis This project is a 10-week parallel, double-blinded randomised intervention trial. We plan to include 100 adults aged 18–65 years interested in losing weight, with body mass index ≥27 but<40 kg/m2 and elevated waist circumference (males >94 cm; females >80 cm). Participants will be categorised into one of five predefined ‘clusters’ based on their individual metabolic biomarker profile and genetic background, and will be randomised in a 1:1 ratio to one of two groups: (1) personalised plan group that will receive cluster-specific meals every day for 6 days a week, in conjunction with a personalised behavioural change programme via electronic push notifications; or (2) control group that will receive meals following the general dietary recommendations in conjunction with generic health behaviour prompts. The primary outcome is the difference between groups (personalised vs control) in the change in fat mass from baseline. Secondary outcomes include changes in weight and body composition, fasting blood glucose and insulin, lipid profile, adipokines, inflammatory biomarkers, and blood pressure. Other outcomes involve measures of physical activity and sleep patterns, health-related quality of life, dietary intake, eating behaviour, and biomarkers of food intake. The effect of the intervention on the primary outcome will be analysed by means of linear mixed models. Ethics and dissemination The protocol has been approved by the Ethics Committee of the Capital Region, Copenhagen, Denmark. Study findings will be disseminated through peer-reviewed publications, conference presentations and media outlets. Trial registration number NCT04590989.
Near-Optimal Weighted Matrix Completion

Recent work in the matrix completion literature has shown that prior knowledge of a matrix's row and column spaces can be successfully incorporated into reconstruction programs to substantially benefit matrix recovery. This paper proposes a novel methodology that exploits more general forms of known matrix structure in terms of subspaces. The work derives reconstruction error bounds that are informative in practice, providing insight to previous approaches in the literature while introducing novel programs that severely reduce sampling complexity. The main result shows that a family of weighted nuclear norm minimization programs incorporating a $M_1 r$-dimensional subspace of $n\times n$ matrices (where $M_1\geq 1$ conveys structural properties of the subspace) allow accurate approximation of a rank $r$ matrix aligned with the subspace from a near-optimal number of observed entries (within a logarithmic factor of $M_1 r)$. The result is robust, where the error is proportional to measurement noise, applies to full rank matrices, and reflects degraded output when erroneous prior information is utilized. Numerical experiments are presented that validate the theoretical behavior derived for several example weighted programs.
Energy Efficient Large Scale Antenna Systems for 5G Communications and Beyond

The increasing popularity of mobile devices has fueled an exponential growth in data traffic. This phenomenon has led to the development of systems that achieve higher spectral efficiencies, at the cost of higher power consumptions. Consequently, the investigation on solutions that allow to increase the maximum throughput together with the energy efficiency becomes crucial for modern wireless systems. This thesis aims to improve the trade-off between performances and power consumption with special focus toward multiuser multiple-antenna communications, due to their promising benefits in terms of spectral efficiency. Research envisaged massive Multi-Input-Multi-Output (MIMO) systems as the main technology to meet these data traffic demands, as very large arrays lead to unprecedented data throughputs and beamforming gains. However, larger arrays lead to increased power consumption and hardware complexity, as each radiating element requires a radio frequency chain, which is accountable for the highest percentage of the total power consumption. Nonetheless, the availability of a large number of antennas unveils the possibility to wisely select a subset of radiating elements. This thesis shows that multiuser interference can be exploited to increase the received power, with significant circuit power savings at the base station. Similarly, millimeter-wave communications experienced raising interest among the scientific community because of their multi-GHz bandwidth and their ability to place large arrays in limited physical spaces. Millimeter-wave systems inherit same benefits and weaknesses of massive MIMO communications. However, antenna selection is not viable in millimeter-wave communications because they rely on high beamforming gains. Therefore, this thesis proposes a scheme that is able to reduce the number of radio frequency chains required, while achieving close-to-optimal performances. Analytical and numerical results show that the proposed techniques are able to improve the overall energy efficiency with respect to the state-of-the-art, hence proving to be valid candidates for practical implementations of modern communication systems.
Synaptic plasticity in hippocampal CA1 neurons of mice lacking inositol-1,4,5-trisphosphate receptor-binding protein released with IP3 (IRBIT)

In hippocampal CA1 neurons of wild-type mice, a short tetanus (15 or 20 pulses at 100 Hz) or a standard tetanus (100 pulses at 100 Hz) to a naive input pathway induces long-term potentiation (LTP) of the responses. Low-frequency stimulation (LFS; 1000 pulses at 1 Hz) 60 min after the standard tetanus reverses LTP (depotentiation [DP]), while LFS applied 60 min prior to the standard tetanus suppresses LTP induction (LTP suppression). We investigated LTP, DP, and LTP suppression of both field excitatory postsynaptic potentials and population spikes in CA1 neurons of mice lacking the inositol 1,4,5-trisphosphate (IP3) receptor (IP3R)-binding protein released with IP3 (IRBIT). The mean magnitudes of LTP induced by short and standard tetanus were not different in mutant and wild-type mice. In contrast, DP and LTP suppression were attenuated in mutant mice, whereby the mean magnitude of responses after LFS or tetanus were significantly greater than in wild-type mice. These results suggest that, in hippocampal CA1 neurons, IRBIT is involved in DP and LTP suppression, but is not essential for LTP. The attenuation of DP and LTP suppression in mice lacking IRBIT indicates that this protein, released during or after priming stimulations, determines the direction of LTP expression after the delivery of subsequent stimulations.
Improved Convergence Rate of Stochastic Gradient Langevin Dynamics with Variance Reduction and its Application to Optimization

The stochastic gradient Langevin Dynamics is one of the most fundamental algorithms to solve sampling problems and non-convex optimization appearing in several machine learning applications. Especially, its variance reduced versions have nowadays gained particular attention. In this paper, we study two variants of this kind, namely, the Stochastic Variance Reduced Gradient Langevin Dynamics and the Stochastic Recursive Gradient Langevin Dynamics. We prove their convergence to the objective distribution in terms of KL-divergence under the sole assumptions of smoothness and Log-Sobolev inequality which are weaker conditions than those used in prior works for these algorithms. With the batch size and the inner loop length set to $\sqrt{n}$, the gradient complexity to achieve an $\epsilon$-precision is $\tilde{O}((n+dn^{1/2}\epsilon^{-1})\gamma^2 L^2\alpha^{-2})$, which is an improvement from any previous analyses. We also show some essential applications of our result to non-convex optimization.
CEE inner TOF prototype design and preliminary test results

The Cooling Storage Ring (CSR) External-target Experiment (CEE) is the first multi-purpose nuclear physics experimental device to operate in the GeV energy range at the Heavy-Ion Research Facility (HIRFL-CSR) in Lanzhou, China.The primary goals of the CEE are to study the bulk properties of dense matter and to understand the quantum chromo-dynamic (QCD) phase diagram by measuring the charged particles produced in heavy-ion collisions at the target region with large acceptance. An inner time of flight (iTOF) system has been proposed to measure the multiplicity, angular distribution, and time information of the charged particles. Herein, we introduce the performance requirements of iTOF according to calculations and GEANT4 simulations. The proposed system is characterized by high granularity and time performance, hence, the conceptual design of the iTOF wall adopts high granularity Multi-gap Resistive Plate Chambers (MRPC) with a time resolution around 30 ps. To evaluate the MRPC design, the cosmic ray test was performed. A timing resolution better than 28 ps and an efficiency better than 98% have been achieved for MIPs, as interpreted by the cosmic ray GEANT4 simulation of time jitter components.
Automatic identification of chemical moieties

In recent years, the prediction of quantum mechanical observables with machine learning methods has become increasingly popular. Message-passing neural networks (MPNNs) solve this task by constructing atomic representations, from which the properties of interest are predicted. Here, we introduce a method to automatically identify chemical moieties (molecular building blocks) from such representations, enabling a variety of applications beyond property prediction, which otherwise rely on expert knowledge. The required representation can either be provided by a pretrained MPNN, or be learned from scratch using only structural information. Beyond the data-driven design of molecular fingerprints, the versatility of our approach is demonstrated by enabling the selection of representative entries in chemical databases, the automatic construction of coarse-grained force fields, as well as the identification of reaction coordinates.
STeP-CiM: Strain-Enabled Ternary Precision Computation-In-Memory Based on Non-Volatile 2D Piezoelectric Transistors

We proposed 2D piezoelectric FET (PeFET)–based compute-enabled non-volatile memory for ternary deep neural networks (DNNs). PeFETs hinge on ferroelectricity for bit storage and piezoelectricity for bit sensing, exhibiting inherently amenable features for computation-in-memory of dot products of weights and inputs in the signed ternary regime. PeFETs consist of a material with ferroelectric and piezoelectric properties coupled with a transition metal dichalcogenide channel. We utilized (a) ferroelectricity to store binary bits (0/1) in the form of polarization (−P/+P) and (b) polarization-dependent piezoelectricity to read the stored state by means of strain-induced bandgap change in the transition metal dichalcogenide channel. The unique read mechanism of PeFETs enables us to expand the traditional association of +P (−P) with low (high) resistance states to their dual high (low) resistance depending on read voltage. Specifically, we demonstrated that +P (−P) stored in PeFETs can be dynamically configured in (a) a low (high) resistance state for positive read voltages and (b) their dual high (low) resistance states for negative read voltages, without afflicting a read disturb. Such a feature, which we named as polarization-preserved piezoelectric effect reversal with dual voltage polarity (PiER), is unique to PeFETs and has not been shown in hitherto explored memories. We leveraged PiER to propose a Strain-enabled Ternary Precision Computation-in-Memory (STeP-CiM) cell with capabilities of computing the scalar product of the stored weight and input, both of which are represented with signed ternary precision. Furthermore, using multi-word line assertion of STeP-CiM cells, we achieved massively parallel computation of dot products of signed ternary inputs and weights. Our array-level analysis showed 91% lower delay and improvements of 15% and 91% in energy for in-memory multiply-and-accumulate operations compared to near-memory design approaches based on 2D FET–based SRAM and PeFET, respectively. We also analyzed the system-level implications of STeP-CiM by deploying it in a ternary DNN accelerator. STeP-CiM exhibits 6.11× to 8.91× average improvement in performance and 3.2 × average improvement in energy over SRAM-based near-memory design. We also compared STeP-CiM to near-memory design based on PeFETs showing 5.67× to 6.13× average performance improvement and 6.07 × average energy savings.
A Metaheuristic Algorithm for Large Maximum Weight Independent Set Problems

Motivated by a real-world vehicle routing application, we consider the maximum-weight independent set problem: Given a node-weighted graph, ﬁnd a set of independent (mutually nonadjacent) nodes whose node-weight sum is maximum. Some of the graphs airsing in this application are large, having hundreds of thousands of nodes and hundreds of millions of edges. To solve instances of this size, we develop a new local search algorithm, which is a metaheuristic in the greedy randomized adaptive search (GRASP) framework. This algorithm, which we call METAMIS, uses a wider range of simple local search operations than previously described in the literature. We introduce data structures that make these operations eﬃcient. A new variant of path-relinking is introduced to escape local optima and so is a new alternating augmenting-path local search move that improves algorithm performance. We compare an implementation of our algorithm with a state-of-the-art openly available code on public benchmark sets, including some large instances with hundreds of millions of vertices. Our algorithm is, in general, competitive and outperforms this openly available code on large vehicle routing instances. We hope that our results will lead to even better MWIS algorithms.
Reshaping the Jaynes-Cummings ladder with Majorana bound states

We study the optical properties of a hybrid device composed by a quantum dot (QD) resonantly coupled to a photonic mode of an optical microcavity and a Majorana nanowire: a topological superconducting segment hosting Majorana bound states (MBSs) at the opposite ends. In the regime of strong light-matter coupling, it is demonstrated that the leakage of the Majorana mode into the QD opens new optical transitions between polaritonic states formed due to hybridisation of material excitation with cavity photons, which leads to the reshaping of the Jaynes-Cummings ladder and can lead to the formation of a robust single-peak at cavity eigenfrequency in the emission spectrum. Moreover, weak satellite peaks in the low and high frequency regions are revealed for the distinct cases of highly isolated MBSs, overlapped MBSs and MBSs not well localized at the nanowire ends.
Foveation-based Deep Video Compression without Motion Search

The requirements of much larger file sizes, different storage formats, and immersive viewing conditions of VR pose significant challenges to the goals of acquiring, transmitting, compressing, and displaying high-quality VR content. At the same time, the great potential of deep learning to advance progress on the video compression problem has driven a significant research effort. Because of the high bandwidth requirements of VR, there has also been significant interest in the use of space-variant, foveated compression protocols. We have integrated these techniques to create an end-to-end deep learning video compression framework. A feature of our new compression model is that it dispenses with the need for expensive search-based motion prediction computations. This is accomplished by exploiting statistical regularities inherent in video motion expressed by displaced frame differences. Foveation protocols are desirable since only a small portion of a video viewed in VR may be visible as a user gazes in any given direction. Moreover, even within a current field of view (FOV), the resolution of retinal neurons rapidly decreases with distance (eccentricity) from the projected point of gaze. In our learning based approach, we implement foveation by introducing a Foveation Generator Unit (FGU) that generates foveation masks which direct the allocation of bits, significantly increasing compression efficiency while making it possible to retain an impression of little to no additional visual loss given an appropriate viewing geometry. Our experiment results reveal that our new compression model, which we call the Foveated MOtionless VIdeo Codec (Foveated MOVI-Codec), is able to efficiently compress videos without computing motion, while outperforming foveated version of both H.264 and H.265 on the widely used UVG dataset and on the HEVC Standard Class B Test Sequences.
HDSDF: Hybrid Directional and Signed Distance Functions for Fast Inverse Rendering

Implicit neural representations of 3D shapes form strong priors that are useful for various applications, such as single and multiple view 3D reconstruction. A downside of existing neural representations is that they require multiple network evaluations for rendering, which leads to high computational costs. This limitation forms a bottleneck particularly in the context of inverse problems, such as image-based 3D reconstruction. To address this issue, in this paper (i) we propose a novel hybrid 3D object representation based on a signed distance function (SDF) that we augment with a directional distance function (DDF), so that we can predict distances to the object surface from any point on a sphere enclosing the object. Moreover, (ii) using the proposed hybrid representation we address the multi-view consistency problem common in existing DDF representations. We evaluate our novel hybrid representation on the task of single-view depth reconstruction and show that our method is several times faster compared to competing methods, while at the same time achieving better reconstruction accuracy.
AxIoU: An Axiomatically Justified Measure for Video Moment Retrieval

Evaluation measures have a crucial impact on the direction of research. Therefore, it is of utmost importance to develop appropriate and reliable evaluation measures for new applications where conventional measures are not well suited. Video Moment Retrieval (VMR) is one such application, and the current practice is to use R@K, $\theta$ for evaluating VMR systems. However, this measure has two disadvantages. First, it is rank-insensitive: It ignores the rank positions of successfully localised moments in the top-K ranked list by treating the list as a set. Second, it binarizes the Intersection over Union (IoU) of each retrieved video moment using the threshold $\theta$ and thereby ignoring fine-grained localisation quality of ranked moments. We propose an alternative measure for evaluating VMR, called Average Max IoU (AxIoU), which is free from the above two problems. We show that AxIoU satisfies two important axioms for VMR evaluation, namely, Invariance against Redundant Moments and Monotonicity with respect to the Best Moment, and also that R@ K, $\theta$ satisfies the first axiom only. We also empirically examine how Ax-IoU agrees with R@K, $\theta$, as well as its stability with respect to change in the test data and human-annotated temporal boundaries.
Robust Generation Dispatch With Strategic Renewable Power Curtailment and Decision-Dependent Uncertainty

As renewable energy sources replace traditional power sources (such as thermal generators), uncertainty grows while there are fewer controllable units. To reduce operational risks and avoid frequent real-time emergency controls, a preparatory schedule of renewable generation curtailment is required. This paper proposes a novel two-stage robust generation dispatch (RGD) model, where the preparatory curtailment threshold is optimized in the pre-dispatch stage. The curtailment threshold will then influence the variation range of real-time renewable power outputs, resulting in a decision-dependent uncertainty (DDU) set. In the re-dispatch stage, the controllable units adjust their outputs within their reserve contributions to maintain power balancing. To overcome the difficulty in solving the RGD with DDU, an adaptive column-and-constraint generation (AC&CG) algorithm is developed. We prove that the proposed algorithm can generate the optimal solution in a finite number of iterations. Numerical studies show the advantages of the proposed model and algorithm and validate their practicability and scalability.
Phase-Aware Deep Speech Enhancement: It's All About The Frame Length

Algorithmic latency in speech processing is dominated by the frame length used for Fourier analysis, which in turn limits the achievable performance of magnitude-centric approaches. As previous studies suggest the importance of phase grows with decreasing frame length, this work presents a systematic study on the contribution of phase and magnitude in modern deep neural network (DNN)-based speech enhancement at different frame lengths. Results indicate that DNNs can successfully estimate phase when using short frames, with similar or better overall performance compared to using longer frames. Thus, interestingly, modern phase-aware DNNs allow for low-latency speech enhancement at high quality.
Skew and sphere fibrations

A great sphere ﬁbration is a sphere bundle with total space S n and ﬁbers which are great k -spheres. Given a smooth great sphere ﬁbration, the central projection to any tangent hyperplane yields a nondegenerate ﬁbration of R n by pairwise skew, aﬃne copies of R k (though not all nondegenerate ﬁbrations can arise in this way). Here we study the topology and geometry of nondegenerate ﬁbrations, we show that every nondegenerate ﬁbration satisﬁes a notion of Continuity at Inﬁnity, and we prove several classiﬁcation results. These results allow us to determine, in certain dimensions, precisely which nondegenerate ﬁbrations correspond to great sphere ﬁbrations via the central projection. We use this correspondence to reprove a number of recent results about sphere ﬁbrations in the simpler, more explicit setting of nondegenerate ﬁbrations. For example, we show that every germ of a nondegenerate ﬁbration extends to a global ﬁbration, and we study the relationship between nondegenerate line ﬁbrations and contact structures in odd-dimensional Euclidean space. We conclude with a number of partial results, in hopes that the continued study of nondegenerate ﬁbrations, together with their correspondence with sphere ﬁbrations, will yield new insights towards the unsolved classiﬁcation problems for sphere ﬁbrations.