Miracle-dz/UniFAR-FTUs huggingface dataset:
Elastic Anisotropy Governs the Decay of Cell-induced Displacements

The unique nonlinear mechanics of the fibrous extracellular matrix (ECM) facilitates long-range cell-cell mechanical communications that would be impossible on linear elastic substrates. Past research has described the contribution of two separated effects on the range of force transmission, including ECM elastic non-linearity and fiber alignment. However, the relation between these different effects is unclear, and how they combine to dictate force transmission range is still elusive. Here, we combine discrete fiber simulations with continuum modeling to study the decay of displacements induced by a contractile cell in fibrous networks. We demonstrate that fiber non-linearity and fiber reorientation both contribute to the strain-induced anisotropy of the elastic moduli of the cell local environment. This elastic anisotropy is a parameter that governs the slow decay of the displacements, and it depends on the magnitude of applied strain, either an external tension or an internal contraction as a model of the cell. Furthermore, we show that accounting for artificially-prescribed elastic anisotropy dictates the displacement decay induced by a contracting cell. Our findings unify previous single effects into a mechanical theory that explains force transmission in fibrous networks. This work provides important insights into biological processes that involve the coordinated action of distant cells mediated by the ECM, such that occur in morphogenesis, wound healing, angiogenesis, and cancer metastasis. It may also provide design parameters for biomaterials to control force transmission between cells, as a way to guide morphogenesis in tissue engineering.
Scalability Issues in CORBA

Scalability is recognised as a primary factor to be considered in the design of distributed systems. The scalability of object-oriented middleware CORBA is becoming a major concern as it has emerged as a standard architecture for distributed object computing. In this paper, a systematic scalability analysis of the basic components of the CORBA specification is attempted. From this analysis, the Portable Object Adapter (POA) and the Implementation Repository (IR) are identified to influence the scale of a CORBA-based system. The specification of the POA provides enough feasibility for the application designer to handle scalability. The existing implementations of IR have a tradeoff between scalability and object migration. A scalable design of the IR is proposed which allows individual objects to migrate without compromising scalability. A performance comparison of the proposed model with existing IR designs is made using a simulation study.
PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization

We introduce Pixel-aligned Implicit Function (PIFu), an implicit representation that locally aligns pixels of 2D images with the global context of their corresponding 3D object. Using PIFu, we propose an end-to-end deep learning method for digitizing highly detailed clothed humans that can infer both 3D surface and texture from a single image, and optionally, multiple input images. Highly intricate shapes, such as hairstyles, clothing, as well as their variations and deformations can be digitized in a unified way. Compared to existing representations used for 3D deep learning, PIFu produces high-resolution surfaces including largely unseen regions such as the back of a person. In particular, it is memory efficient unlike the voxel representation, can handle arbitrary topology, and the resulting surface is spatially aligned with the input image. Furthermore, while previous techniques are designed to process either a single image or multiple views, PIFu extends naturally to arbitrary number of views. We demonstrate high-resolution and robust reconstructions on real world images from the DeepFashion dataset, which contains a variety of challenging clothing types. Our method achieves state-of-the-art performance on a public benchmark and outperforms the prior work for clothed human digitization from a single image.
The Softwarised Network Data Zoo

More and more management and orchestration approaches for (software) networks are based on machine learning paradigms and solutions. These approaches depend not only on their program code to operate properly, but also require enough input data to train their internal models. However, such training data is barely available for the software networking domain and most presented solutions rely on their own, sometimes not even published, data sets. This makes it hard, or even infeasible, to reproduce and compare many of the existing solutions. As a result, it ultimately slows down the adoption of machine learning approaches in softwarised networks.To this end, we introduce the “softwarised network data zoo” (SNDZoo), an open collection of software networking data sets aiming to streamline and ease machine learning research in the software networking domain. We present a general methodology to collect, archive, and publish those data sets for use by other researchers and, as an example, eight initial data sets, focusing on the performance of virtualised network functions.
An Interactive Discriminative Epilepsy Prediction Tool Using Support Vector Machine

— Epilepsy is a neurological condition that from time to time produces brief disturbances in the normal electrical functions of the brain. An epileptic seizure strikes when the energy pulses occur more rapidly as many as 500 per second for a short time due to an electrical abnormality in the brain. Magnetic Resonance Imaging (MRI) is a non-invasive scanning technique based on nuclear magnetic resonance. It is one of several tests used to diagnose epilepsy, in addition to blood tests and an electroencephalogram (EEG). When a patient is tested for epilepsy, the MRI may detect brain abnormalities through its images. The radiologist has to examine large volume of MRI images that leads to inaccurate and time consuming process. This paper presents an automated discriminative epilepsy prediction tool. The interactive tool has been developed by implementing one against one multiclass support vector machine. The feature set with number of promising quantitative features derived from the MRI images has been used for learning the model. The prediction model is evaluated using 10-fold cross validation and observed that the model based on SVM with RBF kernel produces 98% of prediction accuracy.
Radio Map Based Path Planning for Cellular-Connected UAV

In this paper, we study the path planning for a cellular-connected unmanned aerial vehicle (UAV) to minimize its flying distance from given initial to final locations, while ensuring a target link quality in terms of the large-scale channel gain with each of its associated ground base stations (GBSs) during the flight. To this end, we propose the use of radio map that provides the information on the large-scale channel gains between each GBS and uniformly sampled locations on a three- dimensional (3D) grid over the region of interest, which are assumed to be time-invariant due to the generally static and large-size obstacles therein (e.g., buildings). Based on the given radio maps of the GBSs, we first obtain the optimal UAV path by solving an equivalent shortest path problem (SPP) in graph theory. To reduce the computation complexity of the optimal solution, we further propose a grid quantization method whereby the grid points in each GBS's radio map are more coarsely sampled by exploiting the spatial channel correlation over neighboring grids. Then, we solve the approximate SPP over the reduced-size radio map (graph) more efficiently. Numerical results show that the proposed solutions can effectively minimize the flying distance of the UAV subject to its communication quality constraint. Moreover, a flexible trade-off between performance and complexity can be achieved by adjusting the quantization ratio for the radio map.
Climate Change and Human Disturbance Can Lead to Local Extinction of Alpine Rock Ptarmigan: New Insight from the Western Italian Alps

Alpine grouses are particularly vulnerable to climate change due to their adaptation to extreme conditions and to their relict distributions in the Alps where global warming has been particularly marked in the last half century. Grouses are also currently threatened by habitat modification and human disturbance, and an assessment of the impact of multiple stressors is needed to predict the fate of Alpine populations of these birds in the next decades. We estimated the effect of climate change and human disturbance on a rock ptarmigan population living in the western Italian Alps by combining an empirical population modelling approach and stochastic simulations of the population dynamics under the a1B climate scenario and two different disturbance scenarios, represented by the development of a ski resort, through 2050.The early appearance of snow-free ground in the previous spring had a favorable effect on the rock ptarmigan population, probably through a higher reproductive success. On the contrary, delayed snowfall in autumn had a negative effect possibly due to a mismatch in time to molt to white winter plumage which increases predation risk. The regional climate model PROTHEUS does not foresee any significant change in snowmelt date in the study area, while the start date of continuous snow cover is expected to be significantly delayed. The net effect in the stochastic projections is a more or less pronounced (depending on the model used) decline in the studied population. The addition of extra-mortality due to collision with ski-lift wires led the population to fatal consequences in most projections. Should these results be confirmed by larger studies the conservation of Alpine populations would deserve more attention. To counterbalance the effects of climate change, the reduction of all causes of death should be pursued, through a strict preservation of the habitats in the present area of occurrence.
Association of total protein intake with bone mineral density and bone loss in men and women from the Framingham Offspring Study

Abstract Objective To examine (i) the association of percentage of total energy intake from protein (protein intake %) with bone mineral density (BMD, g/cm2) and bone loss at the femoral neck, trochanter and lumbar spine (L2–L4) and (ii) Ca as an effect modifier. Setting The Framingham Offspring Study. Subjects Men (n 1280) and women (n 1639) completed an FFQ in 1992–1995 or 1995–1998 and underwent baseline BMD measurement by dual-energy X-ray absorptiometry in 1996–2000. Men (n 495) and women (n 680) had follow-up BMD measured in 2002–2005. Design Cohort study using multivariable regression to examine the association of protein intake % with each BMD, adjusting for covariates. Statistical interaction between protein intake % and Ca (total, dietary, supplemental) intake was examined. Results The mean age at baseline was 61 (sd 9) years. In the cross-sectional analyses, protein intake % was positively associated with all BMD sites (P range: 0·02–0·04) in women but not in men. Significant interactions were observed with total Ca intake (<800 mg/d v. ≥800 mg/d) in women at all bone sites (P range: 0·002–0·02). Upon stratification, protein intake % was positively associated with all BMD sites (P range: 0·04–0·10) in women with low Ca intakes but not in those with high Ca intakes. In the longitudinal analyses, in men, higher protein intake % was associated with more bone loss at the trochanter (P = 0·01) while no associations were seen in women, regardless of Ca intake. Conclusions This suggests that greater protein intake benefits women especially those with lower Ca intakes. However, protein effects are not significant for short-term changes in bone density. Contrastingly, in men, higher protein intakes lead to greater bone loss at the trochanter. Longer follow-up is required to examine the impact of protein on bone loss.
Massive Parallelization Boosts Big Bayesian Multidimensional Scaling

Abstract Big Bayes is the computationally intensive co-application of big data and large, expressive Bayesian models for the analysis of complex phenomena in scientific inference and statistical learning. Standing as an example, Bayesian multidimensional scaling (MDS) can help scientists learn viral trajectories through space-time, but its computational burden prevents its wider use. Crucial MDS model calculations scale quadratically in the number of observations. We partially mitigate this limitation through massive parallelization using multi-core central processing units, instruction-level vectorization and graphics processing units (GPUs). Fitting the MDS model using Hamiltonian Monte Carlo, GPUs can deliver more than 100-fold speedups over serial calculations and thus extend Bayesian MDS to a big data setting. To illustrate, we employ Bayesian MDS to infer the rate at which different seasonal influenza virus subtypes use worldwide air traffic to spread around the globe. We examine 5392 viral sequences and their associated 14 million pairwise distances arising from the number of commercial airline seats per year between viral sampling locations. To adjust for shared evolutionary history of the viruses, we implement a phylogenetic extension to the MDS model and learn that subtype H3N2 spreads most effectively, consistent with its epidemic success relative to other seasonal influenza subtypes. Finally, we provide MassiveMDS, an open-source, stand-alone C++ library and rudimentary R package, and discuss program design and high-level implementation with an emphasis on important aspects of computing architecture that become relevant at scale.
Linear Range in Gradient Descent

This paper defines linear range as the range of parameter perturbations which lead to approximately linear perturbations in the states of a network. We compute linear range from the difference between actual perturbations in states and the tangent solution. Linear range is a new criterion for estimating the effectivenss of gradients and thus having many possible applications. In particular, we propose that the optimal learning rate at the initial stages of training is such that parameter changes on all minibatches are within linear range. We demonstrate our algorithm on two shallow neural networks and a ResNet.
Combining progression and regression in state-space heuris-tic planning

One of the most promising trends in Domain Independent AI Planning, nowadays, is state-space heuristic planning. The planners of this category construct general but efficient heuristic functions, which are used as a guide to traverse the state space either in a forward or a in backward direction. Although specific problems may favor one or the other direction, there is no clear evidence why any of them should be generally preferred. This paper proposes a hybrid search strategy that combines search in both directions. The search begins from the Initial State in a forward direction and proceeds with a weighted A* search until no further improving states can be found. At that point, the algorithm changes direction and starts regressing the Goals trying to reach the best state found at the previous step. The direction of the search may change several times before a solution can be found. Two domain-independent heuristic functions based on ASP/HSP planners enhanced with a Goal Ordering technique have been implemented. The whole bi-directional planning system, named BP, was tested on a variety of problems adopted from the recent AIPS-00 planning competition with quite promising results. The paper also discusses the subject of domain analysis for state-space planning and proposes two methods for the elimination of redundant information from the problem definition and for the identification of independent sub-problems.
Microscopic theory for the light-induced anomalous Hall effect in graphene

We employ a quantum Liouville equation with relaxation to model the recently observed anomalous Hall effect in graphene irradiated by an ultrafast pulse of circularly polarized light. In the weak-field regime, we demonstrate that the Hall effect originates from an asymmetric population of photocarriers in the Dirac bands. By contrast, in the strong-field regime, the system is driven into a nonequilibrium steady state that is well described by topologically nontrivial Floquet-Bloch bands. Here, the anomalous Hall current originates from the combination of a population imbalance in these dressed bands together with a smaller anomalous velocity contribution arising from their Berry curvature. This robust and general finding enables the simulation of electrical transport from light-induced Floquet-Bloch bands in an experimentally relevant parameter regime and creates a pathway to designing ultrafast quantum devices with Floquet-engineered transport properties.
An improved algorithm for the submodular secretary problem with a cardinality constraint

We study the submodular secretary problem with a cardinality constraint. In this problem, $n$ candidates for secretaries appear sequentially in random order. At the arrival of each candidate, a decision maker must irrevocably decide whether to hire him. The decision maker aims to hire at most $k$ candidates that maximize a non-negative submodular set function. We propose an $(\mathrm{e} - 1)^2 / (\mathrm{e}^2 (1 + \mathrm{e}))$-competitive algorithm for this problem, which improves the best one known so far.
Privacy-Preserving and Collusion-Resistant Charging Coordination Schemes for Smart Grids

Charging coordination is necessary for the successful integration of the Energy Storage Units (ESUs), including electric vehicles and home batteries, into the smart grid. To coordinate charging, the ESUs should send charging requests including time-to-complete-charging (TCC) and battery state-of-charge (SoC) to the charging controller (CC) for scheduling charging, but these data can reveal sensitive information on the ESUs’ owners such as their locations, when they return home and whether they are on travel. In this article, we propose centralized and decentralized privacy-preserving and collusion-resistant charging coordination schemes for ESUs. In the centralized scheme, ESUs authenticate their requests using anonymous tokens. To thwart linkability attacks where the CC uses TCC and SoC to link requests sent from the same ESU at consecutive time slots, an ESU needs to send multiple charging requests with different TCC and SoC values instead of only one request. In the decentralized scheme, charging is coordinated in a distributed way using a privacy-preserving data aggregation technique. The idea is that each ESU selects some ESUs to act as proxies, and shares a secret mask with each proxy. Then, each ESU adds a mask to its charging request and encrypts it so that by aggregating all requests, all masks are nullified and the total charging demand is known, and then it is used to compute the charging schedules. Due to using masking technique, the scheme is secure against collusion attacks. The results of extensive experiments and simulations confirm that our schemes are efficient and secure, and can preserve ESU owners’ privacy and thwart linkability attacks.
Automatic Calibration of Multiple 3D LiDARs in Urban Environments

Multiple LiDARs have progressively emerged on autonomous vehicles for rendering a rich view and dense measurements. However, the lack of precise calibration negatively affects their potential applications. In this paper, we propose a novel system that enables automatic multi-LiDAR calibration method without any calibration target, prior environment information, and manual initialization. Our approach starts with a hand-eye calibration by aligning the motion of each sensor. The initial results are then refined by an appearance-based method by minimizing a cost function constructed by point-plane distance. Experimental results on simulated and real-world data demonstrate the reliability and accuracy of our calibration approach. The proposed approach can calibrate a multi-LiDAR system with the rotation and translation errors less than 0. 04rad and 0. 1m respectively for a mobile platform.
Nonequilibrium evolution of volatility in origination and extinction explains fat-tailed fluctuations in Phanerozoic biodiversity

Phanerozoic marine invertebrate richness fluctuates out of equilibrium due to pulsed adaptive evolution. Fluctuations in biodiversity, large and small, pervade the fossil record, yet we do not understand the processes generating them. Here, we extend theory from nonequilibrium statistical physics to describe the fat-tailed form of fluctuations in Phanerozoic marine invertebrate richness. Using this theory, known as superstatistics, we show that heterogeneous rates of origination and extinction between clades and conserved rates within clades account for this fat-tailed form. We identify orders and families as the taxonomic levels at which clades experience interclade heterogeneity and within-clade homogeneity of rates, indicating that families are subsystems in local statistical equilibrium, while the entire system is not. The separation of timescales between within-clade background rates and the origin of major innovations producing new orders and families allows within-clade dynamics to reach equilibrium, while between-clade dynamics do not. The distribution of different dynamics across clades is consistent with niche conservatism and pulsed exploration of adaptive landscapes.
Learning to Search Efficiently Using Comparisons

We consider the problem of searching in a set of items by using pairwise comparisons. We aim to locate a target item $t$ by asking an oracle questions of the form "Which item from the pair $(i,j)$ is more similar to t? ". We assume a blind setting, where no item features are available to guide the search process; only the oracle sees the features in order to generate an answer. Previous approaches for this problem either assume noiseless answers, or they scale poorly in the number of items, both of which preclude practical applications. In this paper, we present a new scalable learning framework called learn2search that performs efficient comparison-based search on a set of items despite the presence of noise in the answers. Items live in a space of latent features, and we posit a probabilistic model for the oracle comparing two items $i$ and $j$ with respect to a target $t$. Our algorithm maintains its own representation of the space of items, which it learns incrementally based on past searches. We evaluate the performance of learn2search on both synthetic and real-world data, and show that it learns to search more and more efficiently, over time matching the performance of a scheme with access to the item features.
Constant roll warm inflation in high dissipative regime

Constant-roll warm inflation is introduced in this work. A novel approach to finding an exact solution for Friedman equations in the constant-roll framework is presented for cold inflation and is extended to warm inflation with the constant dissipative parameter Q=Γ/3H. The evolution of the primordial inhomogeneities of a scalar field in a thermal bath is also studied. The 1σ consistency between the theoretical predictions of the model and observational constraints has been proven for a range of Q and β=−ϕ̈/(3Hϕ) (constant rate of inflaton roll). In addition, we briefly investigate the possible enhancement of super-horizon perturbations beyond the slow-roll approximation.