Miracle-dz/UniFAR-FTUs huggingface dataset:
Force-Ultrasound Fusion: Bringing Spine Robotic-US to the Next “Level”

Spine injections are commonly performed in several clinical procedures. The localization of the target vertebral level (i.e. the position of a vertebra in a spine) is typically done by back palpation or under X-ray guidance, yielding either higher chances of procedure failure or exposure to ionizing radiation. Preliminary studies have been conducted in the literature, suggesting that ultrasound imaging may be a precise and safe alternative to X-ray for spine level detection. However, ultrasound data are noisy and complicated to interpret. In this study, a robotic-ultrasound approach for automatic vertebral level detection is introduced. The method relies on the fusion of ultrasound and force data, thus providing both “tactile” and visual feedback during the procedure, which results in higher performances in presence of data corruption. A robotic arm automatically scans the volunteer’s back along the spine by using force-ultrasound data to locate vertebral levels. The occurrences of vertebral levels are visible on the force trace as peaks, which are enhanced by properly controlling the force applied by the robot on the patient back. Ultrasound data are processed with a Deep Learning method to extract a 1D signal modelling the probabilities of having a vertebra at each location along the spine. Processed force and ultrasound data are fused using both a non deep learning method and a Temporal Convolutional Network to compute the locations of the vertebral levels. The benefits of fusing force and image signals for the identification of vertebrae locations are showcased through extensive evaluation.
Rethinking the Route Towards Weakly Supervised Object Localization

Weakly supervised object localization (WSOL) aims to localize objects with only image-level labels. Previous methods often try to utilize feature maps and classification weights to localize objects using image level annotations indirectly. In this paper, we demonstrate that weakly supervised object localization should be divided into two parts: class-agnostic object localization and object classification. For class-agnostic object localization, we should use class-agnostic methods to generate noisy pseudo annotations and then perform bounding box regression on them without class labels. We propose the pseudo supervised object localization (PSOL) method as a new way to solve WSOL. Our PSOL models have good transferability across different datasets without fine-tuning. With generated pseudo bounding boxes, we achieve 58.00% localization accuracy on ImageNet and 74.74% localization accuracy on CUB-200, which have a large edge over previous models.
Analytical model for electrohydrodynamic thrust

Electrohydrodynamic (EHD) thrust is produced when ionized fluid is accelerated in an electric field due to the momentum transfer between the charged species and neutral molecules. We extend the previously reported analytical model that couples space charge, electric field and momentum transfer to derive thrust force in one-dimensional planar coordinates. The electric current density in the model can be expressed in the form of Mott–Gurney law. After the correction for the drag force, the EHD thrust model yields good agreement with the experimental data from several independent studies. The EHD thrust expression derived from the first principles can be used in the design of propulsion systems and can be readily implemented in the numerical simulations.
Measuring Spatial Subdivisions in Urban Mobility with Mobile Phone Data

Urban population grows constantly. By 2050 two thirds of the world population will reside in urban areas. This growth is faster and more complex than the ability of cities to measure and plan for their sustainability. To understand what makes a city inclusive for all, we define a methodology to identify and characterize spatial subdivisions: areas with over- and under-representation of specific population groups, named hot and cold spots respectively. Using aggregated mobile phone data, we apply this methodology to the city of Barcelona to assess the mobility of three groups of people: women, elders, and tourists. We find that, within the three groups, cold spots have a lower diversity of amenities and services than hot spots. Also, cold spots of women and tourists tend to have lower population income. These insights apply to the floating population of Barcelona, thus augmenting the scope of how inclusiveness can be analyzed in the city.
Graphcore C2 Card performance for image-based deep learning application: A Report

Recently, Graphcore has introduced an IPU Processor for accelerating machine learning applications. The architecture of the processor has been designed to achieve state of the art performance on current machine intelligence models for both training and inference. In this paper, we report on a benchmark in which we have evaluated the performance of IPU processors on deep neural networks for inference. We focus on deep vision models such as ResNeXt. We report the observed latency, throughput and energy efficiency.
Towards new cross-validation-based estimators for Gaussian process regression: efficient adjoint computation of gradients

We consider the problem of estimating the parameters of the covariance function of a Gaussian process by cross-validation. We suggest using new cross-validation criteria derived from the literature of scoring rules. We also provide an efficient method for computing the gradient of a cross-validation criterion. To the best of our knowledge, our method is more efficient than what has been proposed in the literature so far. It makes it possible to lower the complexity of jointly evaluating leave-one-out criteria and their gradients.
Time integration of tree tensor networks

Dynamical low-rank approximation by tree tensor networks is studied for the data-sparse approximation to large time-dependent data tensors and unknown solutions of tensor differential equations. A time integration method for tree tensor networks of prescribed tree rank is presented and analyzed. It extends the known projector-splitting integrators for dynamical low-rank approximation by matrices and Tucker tensors and is shown to inherit their favorable properties. The integrator is based on recursively applying the Tucker tensor integrator. In every time step, the integrator climbs up and down the tree: it uses a recursion that passes from the root to the leaves of the tree for the construction of initial value problems on subtree tensor networks using appropriate restrictions and prolongations, and another recursion that passes from the leaves to the root for the update of the factors in the tree tensor network. The integrator reproduces given time-dependent tree tensor networks of the specified tree rank exactly and is robust to the typical presence of small singular values in matricizations of the connection tensors, in contrast to standard integrators applied to the differential equations for the factors in the dynamical low-rank approximation by tree tensor networks.
Revisiting Compact RDF Stores Based on k2-Trees

We present a new compact representation to efficiently store and query large RDF datasets in main memory. Our proposal, called BMatrix, is based on the k2-tree, a data structure devised to represent binary matrices in a compressed way, and aims at improving the results of previous state-of-the-art alternatives, especially in datasets with a relatively large number of predicates. We introduce our technique, together with some improvements on the basic k2-tree that can be applied to our solution in order to boost compression. Experimental results in the flagship RDF dataset DBPedia show that our proposal achieves better compression than existing alternatives, while yielding competitive query times, particularly in the most frequent triple patterns and in queries with unbound predicate, in which we outperform existing solutions.
Limitations of Greed: Influence Maximization in Undirected Networks Re-visited

We consider the influence maximization problem (selecting $k$ seeds in a network maximizing the expected total influence) on undirected graphs under the linear threshold model. On the one hand, we prove that the greedy algorithm always achieves a $(1 - (1 - 1/k)^k + \Omega(1/k^3))$-approximation, showing that the greedy algorithm does slightly better on undirected graphs than the generic $(1- (1 - 1/k)^k)$ bound which also applies to directed graphs. On the other hand, we show that substantial improvement on this bound is impossible by presenting an example where the greedy algorithm can obtain at most a $(1- (1 - 1/k)^k + O(1/k^{0.2}))$ approximation. This result stands in contrast to the previous work on the independent cascade model. Like the linear threshold model, the greedy algorithm obtains a $(1-(1-1/k)^k)$-approximation on directed graphs in the independent cascade model. However, Khanna and Lucier showed that, in undirected graphs, the greedy algorithm performs substantially better: a $(1-(1-1/k)^k + c)$ approximation for constant $c > 0$. Our results show that, surprisingly, no such improvement occurs in the linear threshold model. Finally, we show that, under the linear threshold model, the approximation ratio $(1 - (1 - 1/k)^k)$ is tight if 1) the graph is directed or 2) the vertices are weighted. In other words, under either of these two settings, the greedy algorithm cannot achieve a $(1 - (1 - 1/k)^k + f(k))$-approximation for any positive function $f(k)$. The result in setting 2) is again in a sharp contrast to Khanna and Lucier's $(1 - (1 - 1/k)^k + c)$-approximation result for the independent cascade model, where the $(1 - (1 - 1/k)^k + c)$ approximation guarantee can be extended to the setting where vertices are weighted. We also discuss extensions to more generalized settings including those with edge-weighted graphs.
Unsupervised Semantic Attribute Discovery and Control in Generative Models

This work focuses on the ability to control via latent space factors semantic image attributes in generative models, and the faculty to discover mappings from factors to attributes in an unsupervised fashion. The discovery of controllable semantic attributes is of special importance, as it would facilitate higher level tasks such as unsupervised representation learning to improve anomaly detection, or the controlled generation of novel data for domain shift and imbalanced datasets. The ability to control semantic attributes is related to the disentanglement of latent factors, which dictates that latent factors be "uncorrelated" in their effects. Unfortunately, despite past progress, the connection between control and disentanglement remains, at best, confused and entangled, requiring clarifications we hope to provide in this work. To this end, we study the design of algorithms for image generation that allow unsupervised discovery and control of semantic attributes.We make several contributions: a) We bring order to the concepts of control and disentanglement, by providing an analytical derivation that connects mutual information maximization, which promotes attribute control, to total correlation minimization, which relates to disentanglement. b) We propose hybrid generative model architectures that use mutual information maximization with multi-scale style transfer. c) We introduce a novel metric to characterize the performance of semantic attributes control. We report experiments that appear to demonstrate, quantitatively and qualitatively, the ability of the proposed model to perform satisfactory control while still preserving competitive visual quality. We compare to other state of the art methods (e.g., Frechet inception distance (FID)= 9.90 on CelebA and 4.52 on EyePACS).
Bayesian Social Influence in the Online Realm.

Our opinions, which things we like or dislike, depend on the opinions of those around us. Nowadays, we are influenced by the opinions of online strangers, expressed in comments and ratings on online platforms. Here, we perform novel "academic A/B testing" experiments with over 2,500 participants to measure the extent of that influence. In our experiments, the participants watch and evaluate videos on mirror proxies of YouTube and Vimeo. We control the comments and ratings that are shown underneath each of these videos. Our study shows that from 5$\%$ up to 40$\%$ of subjects adopt the majority opinion of strangers expressed in the comments. Using Bayes' theorem, we derive a flexible and interpretable family of models of social influence, in which each individual forms posterior opinions stochastically following a logit model. The variants of our mixture model that maximize Akaike information criterion represent two sub-populations, i.e., non-influenceable and influenceable individuals. The prior opinions of the non-influenceable individuals are strongly correlated with the external opinions and have low standard error, whereas the prior opinions of influenceable individuals have high standard error and become correlated with the external opinions due to social influence. Our findings suggest that opinions are random variables updated via Bayes' rule whose standard deviation is correlated with opinion influenceability. Based on these findings, we discuss how to hinder opinion manipulation and misinformation diffusion in the online realm.
Hybrid-Liouvillian formalism connecting exceptional points of non-Hermitian Hamiltonians and Liouvillians via postselection of quantum trajectories

Exceptional points (EPs) are degeneracies of classical and quantum open systems, which are studied in many areas of physics including optics, optoelectronics, plasmonics, and condensed matter physics. In the semiclassical regime, open systems can be described by phenomenological effective non-Hermitian Hamiltonians (NHHs) capturing the effects of gain and loss in terms of imaginary fields. The EPs that characterize the spectra of such Hamiltonians (HEPs) describe the time evolution of a system without quantum jumps. It is well known that a full quantum treatment describing more generic dynamics must crucially take into account such quantum jumps. In a recent paper [F. Minganti et al., Phys. Rev. A 100, 062131 (2019)], we generalized the notion of EPs to the spectra of Liouvillian superoperators governing open system dynamics described by Lindblad master equations. Intriguingly, we found that in situations where a classical-to-quantum correspondence exists, the two types of dynamics can yield different EPs. In a recent experimental work [M. Naghiloo et al., Nat. Phys. 15, 1232 (2019)], it was shown that one can engineer a non-Hermitian Hamiltonian in the quantum limit by postselecting on certain quantum jump trajectories. This raises an interesting question concerning the relation between Hamiltonian and Lindbladian EPs, and quantum trajectories. We discuss these connections by introducing a hybrid-Liouvillian superoperator, capable of describing the passage from an NHH (when one postselects only those trajectories without quantum jumps) to a true Liouvillian including quantum jumps (without postselection). Beyond its fundamental interest, our approach allows to intuitively relate the effects of postselection and finite-efficiency detectors.
Information Directed Sampling for Linear Partial Monitoring

Partial monitoring is a rich framework for sequential decision making under uncertainty that generalizes many well known bandit models, including linear, combinatorial and dueling bandits. We introduce information directed sampling (IDS) for stochastic partial monitoring with a linear reward and observation structure. IDS achieves adaptive worst-case regret rates that depend on precise observability conditions of the game. Moreover, we prove lower bounds that classify the minimax regret of all finite games into four possible regimes. IDS achieves the optimal rate in all cases up to logarithmic factors, without tuning any hyper-parameters. We further extend our results to the contextual and the kernelized setting, which significantly increases the range of possible applications.
An extended proto-cluster of galaxies surrounding SPT2349$-$56 at $z\,{=}\,4.3$

The $z\,{=}\,4.3$ overdensity SPT2349$-$56 has been proposed as one of the most actively star-forming proto-clusters known, and we have undertaken an extensive ALMA spectroscopic follow-up programme of this system to search for far infrared-luminous proto-cluster members via their [CII] 158$\,\mu$m and CO(4-3) line emission. In addition to robustly detecting the 14 previously published galaxies in this structure, we identify a further 15 associated galaxies at $z\,{=}\,4.3$. These proto-cluster members are distributed into a central core containing 23 galaxies extending out to 300$\,$kpc in diameter, and a northern extension, offset from the core by 400$\,$kpc and containing three galaxies. We discovered three additional galaxies in a red Herschel-SPIRE source 2$\,$Mpc from the main structure, suggesting the existence of many other sources at the same redshift as SPT2349$-$56 that are not yet detected in the limited coverage of our data. An analysis of the velocity distribution within the core indicates that this region may be a nearly virialized structure with a mass of (9$\pm$5)$\,{\times}\,$10$^{12}\,$M$_{\odot}$, while the two offset substructures show significant velocity offsets from the central structure. We calculate the [CII] and far-infrared number counts, and find evidence for a break in the [CII] luminosity function around 4$\,{\times}\,10^9\,$L$_{\odot}$. We estimate the average SFR density within the region of SPT2349$-$56 containing single-dish emission (a proper diametre of 720$\,$kpc), assuming spherical symmetry, to be roughly 40,000$\,$M$_{\odot}\,$yr$^{-1}\,$Mpc$^{-3}$; this is an order of magnitude greater than the most extreme examples seen in simulations.
Heat-flow reversal in a trapped-ion simulator

We propose a trapped-ion platform to simulate a reconfigurable spin-spin Hamiltonian related to quantum thermodynamic processes. Starting from an experimental model describing two trapped-ions under slightly off-resonant first sideband driving with individually controlled driving phases, we follow an operational quantum optics approach to show that it produces an effective model appearing in recent quantum thermodynamics proposals. We show that projection into the vibrational ground state manifold allows full analytic treatment. As a practical example, we take experimental data from a $^{187}$Yb$^{+}$ trap and numerically simulate the reversal of heat flow between two thermal spins controlled by their quantum correlations.
THU0477 Effect of Recent Spinal Cord Injury on The OPG/RANKL System and Its Relationship with Bone Loss and Antiosteoporotic Response To Denosumab Therapy. Preliminary Results

Background Spinal cord injury (SCI) has been associated with a marked increase in bone loss and bone remodeling, especially short-term after injury. However, the pathogenesis and clinical management of this process remain unclear, as well as the role of the key regulators of bone remodeling, the OPG/RANKL system. Objectives To analyze the role of the regulators of bone remodeling, OPG and RANKL, in the bone loss associated with recent SCI as well as the effect of antiosteoporotic therapy with denosumab in these bone regulators in a prospective study. Methods Twenty-three male patients (aged 18 - 67 years [mean 36 ± 16 years]) with recent (<6 months) complete SCI were prospectively included (43.5% paraplegic, 56.5% tetraplegic). Patients were followed every 6 months. All patients received calcium and vitamin D supplementation. Antiosteoporotic therapy with denosumab was recommended when patients developed densitometric osteoporosis during follow-up. Serum levels of OPG and RANKL (Biomedica, Vienna, Austria), bone turnover markers (PINP, bone ALP, sCTX) and bone mineral density (BMD) at the lumbar spine and total hip were assessed at baseline (99±30 days after SCI), prior to initiating antiosteoporotic treatment (14±4 months post-SCI) and during antiosteoporotic therapy with denosumab (6 months after initiating treatment). The results were compared with a healthy control group. Results At baseline, SCI patients showed a significant increase in RANKL serum levels compared to controls (3.4±1.7 vs. 2.3±1.6 pg/mL, p=0.022) which correlated with days-since-SCI (r=0.589, p=0.005) and became undetectable after denosumab treatment in 67% of the patients (p=0.001). OPG serum levels were similar to controls at baseline (87.7±38.7 vs. 71.7±25.4, p=0.1) and did not change with denosumab treatment. No differences were observed in RANKL and OPG levels on comparing tetraplegic vs. paraplegic patients. Neither were RANKL or OPG levels related to the increase in bone loss and bone turnover markers after SCI. Patients with undetectable RANKL serum levels after denosumab treatment did not present further sublesional bone loss but increased total hip BMD (+2.5±1.3%, p=0.005), and bone markers markedly decreased (PINP: -53%, p=0.007; sCTX: -68%, p=0.005). Conclusions This study shows that short-term after SCI there is an increase in RANKL serum levels which become undetectable after denosumab treatment. The preventive effect of denosumab on sublesional bone loss further suggests a contributory role of RANKL in this clinical process. Acknowledgement Work funded by a Grant from the SEIOMM (Spanish Society for Bone and Mineral Research). Disclosure of Interest None declared
New research progress in pharmacological activities of baicalin

Objective To review the new research progress in some pharmacological activities of baicalin.Methods Reviews were done based on relative references about baicalin.Results Except for traditional effects of baicalin on sedation,hypotension,clearance of free radical and antiarrhythmia,baicalin was proved to possess the therapeutic effects on arteriosclerosis caused by chlamydia pneumoniae,protective effects on ischemia reperfusion injury of brain,heart and hepatic injury induced by different causes,human periodontal ligament fibroblasts;baicalin could significantly inhibit trichomonas vaginalis in vitro.Conclusions This review provides a reference for the research and development of baicalin.
Application of biotechnology on the prevention and cure of lactose intolerance

Primary lactase deficiency and lactose intolerance(malabsorption) affect hundreds of millions people worldwide.In this paper,the application of biotechnology on the prevention and cure of lactose intolerance(malabsorption) was discussed from three aspects including lactase production using genetic engineering technique,low lactose milk production using bioreactor and gene therapy for lactose intolerance(malabsorption). In addition,it is suggested that oral genetic modified probiotic pharmaceutics might be a practical and feasible strategy for lactase deficiency.