Miracle-dz/UniFAR-FTUs huggingface dataset:
Scaling of population resilience with dispersal length and habitat size

Environmental fluctuations can create population-depleted areas and even extinct areas for the population. This effect is more severe in the presence of the Allee effect (decreasing growth rate at low population densities). Dispersal inside the habitat provides a rescue effect on population-depleted areas, enhancing the population resilience to environmental fluctuations. Habitat reduction decreases the effectiveness of the dispersal rescue mechanism. We report here how the population resilience to environmental fluctuations decreases when the dispersal length or the habitat size are reduced. The resilience reduction is characterized by a decrease of the extinction threshold for environmental fluctuations. The extinction threshold is shown to scale with the ratio between the dispersal length and the scale of environmental synchrony, i.e. it is the dispersal connection between non-environmentally-correlated regions that provides resilience to environmental fluctuations. Habitat reduction also decreases the resilience to environmental fluctuations, when the habitat size is similar to or smaller than the characteristic dispersal distances. The power laws of these scaling behaviors are characterized here. Alternative scaling functions with spatial scales of population synchrony are found to fit the simulations worse. These results support the dispersal length as the critical scale for extinction induced by habitat reduction.
A Note on Simulation-Based Inference by Matching Random Features

We can, and should, do statistical inference on simulation models by adjusting the parameters in the simulation so that the values of {\em randomly chosen} functions of the simulation output match the values of those same functions calculated on the data. Results from the"state-space reconstruction"or"geometry from a time series'' literature in nonlinear dynamics indicate that just $2d+1$ such functions will typically suffice to identify a model with a $d$-dimensional parameter space. Results from the"random features"literature in machine learning suggest that using random functions of the data can be an efficient replacement for using optimal functions. In this preliminary, proof-of-concept note, I sketch some of the key results, and present numerical evidence about the new method's properties. A separate, forthcoming manuscript will elaborate on theoretical and numerical details.
Subject Enveloped Deep Sample Fuzzy Ensemble Learning Algorithm of Parkinson's Speech Data

Parkinson disease (PD)'s speech recognition is an effective way for its diagnosis, which has become a hot and difficult research area in recent years. As we know, there are large corpuses (segments) within one subject. However, too large segments will increase the complexity of the classification model. Besides, the clinicians interested in finding diagnostic speech markers that reflect the pathology of the whole subject. Since the optimal relevant features of each speech sample segment are different, it is difficult to find the uniform diagnostic speech markers. Therefore, it is necessary to reconstruct the existing large segments within one subject into few segments even one segment within one subject, which can facilitate the extraction of relevant speech features to characterize diagnostic markers for the whole subject. To address this problem, an enveloped deep speech sample learning algorithm for Parkinson's subjects based on multilayer fuzzy c-mean (MlFCM) clustering and interlayer consistency preservation is proposed in this paper. The algorithm can be used to achieve intra-subject sample reconstruction for Parkinson's disease (PD) to obtain a small number of high-quality prototype sample segments. At the end of the paper, several representative PD speech datasets are selected and compared with the state-of-the-art related methods, respectively. The experimental results show that the proposed algorithm is effective signifcantly.
Random Graph-Based Neuromorphic Learning with a Layer-Weaken Structure

Unified understanding of neuro networks (NNs) gets the users into great trouble because they have been puzzled by what kind of rules should be obeyed to optimize the internal structure of NNs. Considering the potential capability of random graphs to alter how computation is performed, we demonstrate that they can serve as architecture generators to optimize the internal structure of NNs. To transform the random graph theory into an NN model with practical meaning and based on clarifying the input-output relationship of each neuron, we complete data feature mapping by calculating Fourier Random Features (FRFs). Under the usage of this low-operation cost approach, neurons are assigned to several groups of which connection relationships can be regarded as uniform representations of random graphs they belong to, and random arrangement fuses those neurons to establish the pattern matrix, markedly reducing manual participation and computational cost without the fixed and deep architecture. Leveraging this single neuromorphic learning model termed random graph-based neuro network (RGNN) we develop a joint classification mechanism involving information interaction between multiple RGNNs and realize significant performance improvements in supervised learning for three benchmark tasks, whereby they effectively avoid the adverse impact of the interpretability of NNs on the structure design and engineering practice.
Parameter sensitivity analysis of non-dimensional models of quayside container cranes

This paper gives an analysis of dynamic behaviour of the waterside boom, previously identified as the most important structural part of the mega quayside container cranes (QCC) under moving concentrated mass. The non-dimensional mathematical model used in this paper presents a conceptual substitution of the real system of the mega container crane boom and provides understanding and prediction of its dynamic behaviour under the action of a moving trolley. The paper discusses the procedure for setting up the nondimensional mathematical model of the crane boom as a necessary condition for an estimation of structural and trolley drive parameters, such as the effects of stiffness, mass, spatial position of structural segments as well as trolley velocity and acceleration, on the dynamic values of deflection under the moving mass. The results obtained by the simulation of the trolley motion alongside the QCC boom during container transfer from quay-to-ship are implemented in the parameter sensitivity analysis in order to obtain mutual influences of the dynamic parameters.
Neutrino meets ultralight dark matter: 0νββ decay and cosmology

We explore the neutrinoless double beta (0νββ) decay induced by an ultralight dark matter field coupled to neutrinos. The effect on 0νββ decay is significant if the coupling violates the lepton number, for which the ΔL = 2 transition is directly driven by the dark matter field without further suppression of small neutrino masses. As the ultralight dark matter can be well described by a classical field, the effect features a periodic modulation pattern in decay events. However, we find that in the early Universe such coupling will be very likely to alter the standard cosmological results. In particular, the requirement of neutrino free-streaming before the matter-radiation equality severely constrains the parameter space, such that the future 0νββ decay experiments can hardly see any signal even with a meV sensitivity to the effective neutrino mass.
Improved Pan-Private Stream Density Estimation

Differential privacy is a rigorous definition for privacy that guarantees that any analysis performed on a sensitive dataset leaks no information about the individuals whose data are contained therein. In this work, we develop new differentially private algorithms to analyze streaming data. Specifically, we consider the problem of estimating the density of a stream of users (or, more generally, elements), which expresses the fraction of all users that actually appear in the stream. We focus on one of the strongest privacy guarantees for the streaming model, namely user-level pan-privacy, which ensures that the privacy of any user is protected, even against an adversary that observes, on rare occasions, the internal state of the algorithm. Our proposed algorithms employ optimally all the allocated privacy budget, are specially tailored for the streaming model, and, hence, outperform both theoretically and experimentally the conventional sampling-based approach.
A Normative and Biologically Plausible Algorithm for Independent Component Analysis

The brain effortlessly solves blind source separation (BSS) problems, but the algorithm it uses remains elusive. In signal processing, linear BSS problems are often solved by Independent Component Analysis (ICA). To serve as a model of a biological circuit, the ICA neural network (NN) must satisfy at least the following requirements: 1. The algorithm must operate in the online setting where data samples are streamed one at a time, and the NN computes the sources on the fly without storing any significant fraction of the data in memory. 2. The synaptic weight update is local, i.e., it depends only on the biophysical variables present in the vicinity of a synapse. Here, we propose a novel objective function for ICA from which we derive a biologically plausible NN, including both the neural architecture and the synaptic learning rules. Interestingly, our algorithm relies on modulating synaptic plasticity by the total activity of the output neurons. In the brain, this could be accomplished by neuromodulators, extracellular calcium, local field potential, or nitric oxide.
EnviroMic: TowardsCooperativeStorageandRetrievalinAudioSensor Networks

This paperpresents EnviroMic,a novel distributed acoustic monitoring, storage, and trace retrieval system. Audio represents one of the least exploited modalities in sensor networks to date. The relatively high frequency and large size of audio traces motivate distributed algorithms for coordinating recording tasks, reducing redundancy of data stored by nearby sensors, filtering out silence, and balancing storage utilization in the network. Applications of acoustic monitoring with EnviroMic range from the study of mating rituals and social behavior of animals in the wild to audio surveillance of military targets. EnviroMic is designed for disconnected operation, where the luxury of having a basestation cannot be assumed. We implement the system on a TinyOS-based platform and systematically evaluate its performance through both indoor testbed experiments and a preliminary outdoor deployment. Results demonstrate up to a 4-fold improvement in effective storage capacity of the network compared to uncoordinated recording.
Understanding Security Issues in the NFT Ecosystem

Non-Fungible Tokens (NFTs) have emerged as a way to collect digital art as well as an investment vehicle. Despite having been popularized only recently, NFT markets have witnessed several high-profile (and high-value) asset sales and a tremendous growth in trading volumes over the last year. Unfortunately, these marketplaces have not yet received much security scrutiny. Instead, most academic research has focused on attacks against decentralized finance (DeFi) protocols and automated techniques to detect smart-contract vulnerabilities. To the best of our knowledge, we are the first to study the market dynamics and security issues of the multi-billion dollar NFT ecosystem. In this paper, we first present a systematic overview of how the NFT ecosystem works, and we identify three major actors: marketplaces, external entities, and users. We then perform an in-depth analysis of the top 8 marketplaces (ranked by transaction volume) to discover potential issues, many of which can lead to substantial financial losses. We also collected a large amount of asset and event data pertaining to the NFTs being traded in the examined marketplaces. We automatically analyze this data to understand how the entities external to the blockchain are able to interfere with NFT markets, leading to serious consequences, and quantify the malicious trading behaviors carried out by users under the cloak of anonymity.
Fast Yet Effective Machine Unlearning

Unlearning the data observed during the training of a machine learning (ML) model is an important task that can play a pivotal role in fortifying the privacy and security of ML-based applications. This article raises the following questions: 1) can we unlearn a single or multiple class(es) of data from an ML model without looking at the full training data even once? and 2) can we make the process of unlearning fast and scalable to large datasets, and generalize it to different deep networks? We introduce a novel machine unlearning framework with error-maximizing noise generation and impair-repair based weight manipulation that offers an efficient solution to the above questions. An error-maximizing noise matrix is learned for the class to be unlearned using the original model. The noise matrix is used to manipulate the model weights to unlearn the targeted class of data. We introduce impair and repair steps for a controlled manipulation of the network weights. In the impair step, the noise matrix along with a very high learning rate is used to induce sharp unlearning in the model. Thereafter, the repair step is used to regain the overall performance. With very few update steps, we show excellent unlearning while substantially retaining the overall model accuracy. Unlearning multiple classes requires a similar number of update steps as for a single class, making our approach scalable to large problems. Our method is quite efficient in comparison to the existing methods, works for multiclass unlearning, does not put any constraints on the original optimization mechanism or network design, and works well in both small and large-scale vision tasks. This work is an important step toward fast and easy implementation of unlearning in deep networks. Source code: https://github.com/vikram2000b/Fast-Machine-Unlearning.
Blandford-Znajek process in quadratic gravity

Jameson Dong ,1 Nicolás Patiño ,1 Yiqi Xie ,1, 2 Alejandro Cárdenas-Avendaño ,2, 3, 4 Charles F. Gammie ,1, 2, 5, 6 and Nicolás Yunes 1, 2 Department of Physics, University of Illinois at Urbana-Champaign, 1110 West Green St, Urbana, IL 61801, USA Illinois Center for Advanced Studies of the Universe, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA Programa de Matemática, Fundación Universitaria Konrad Lorenz, 110231 Bogotá, Colombia. Department of Physics, Princeton University, Princeton, NJ, 08544, USA Department of Astronomy, University of Illinois at Urbana-Champaign, 1002 West Green Street, Urbana, IL 61801, USA National Center for Supercomputing Applications, 1205 W Clark St, Urbana, IL 61801, USA (Dated: February 5, 2022)
Kinematic Fundamentals of Planar Harmonic Drives

This paper presents the kinematic model and offers a rigorous analysis and description of the kinematics of planar harmonic drives. In order to reflect the fundamental kinematic principle of harmonic drives, the flexspline of a harmonic drive is assumed to be a ring without a cup. A tooth on the flexspline is a rigid body, and the motion of the tooth is fully governed by the wave generator and the nominal transmission ratio of the harmonic drive. The proposed model depicts the flexspline tooth and the wave generator as a cam-follower mechanism, with the follower executing a combined translating and oscillating motion. With the rigid tooth motion obtained, the conjugate condition between the flexspline and the circular spline is determined, from which the conjugate tooth profile can be derived. In this paper, the motion is governed by geometry, and the flexibility of the flexspline only serves as a spring to maintain the contact between the cam and the follower. For any wave generator and any transmission ratio, the explicit expression of the conjugate condition is presented. For a given circular or flexspline tooth profile, the exact conjugate tooth profile can be obtained. The phenomenon of twice engagement is discussed for the first time.
ToM2C: Target-oriented Multi-agent Communication and Cooperation with Theory of Mind

Being able to predict the mental states of others is a key factor to effective social interaction. It is also crucial for distributed multi-agent systems, where agents are required to communicate and cooperate. In this paper, we introduce such an important social-cognitive skill, i.e. Theory of Mind (ToM), to build socially intelligent agents who are able to communicate and cooperate effectively to accomplish challenging tasks. With ToM, each agent is capable of inferring the mental states and intentions of others according to its (local) observation. Based on the inferred states, the agents decide"when"and with"whom"to share their intentions. With the information observed, inferred, and received, the agents decide their sub-goals and reach a consensus among the team. In the end, the low-level executors independently take primitive actions to accomplish the sub-goals. We demonstrate the idea in two typical target-oriented multi-agent tasks: cooperative navigation and multi-sensor target coverage. The experiments show that the proposed model not only outperforms the state-of-the-art methods on reward and communication efficiency, but also shows good generalization across different scales of the environment.
A multiple-degree-of-freedom piezoelectric energy harvesting model

Conventional vibration energy harvesters have been usually studied as single-degree-of-freedom models. The fact that such harvesters are only efficient near sole resonance limits their applicability in frequency-variant or random vibration scenarios. In this article, a novel multiple-degree-of-freedom piezoelectric energy harvesting model is presented. First, a two-degree-of-freedom model is analyzed, and its two configurations are characterized. In the first configuration, the piezoelectric element is placed between one mass and the base, and in the second configuration, it is placed between the two masses. It is shown that the former is advantageous over the latter since with a slight increase of overall weight to the single-degree-of-freedom model, we can achieve two close and effective peaks in power response or one effective peak with significantly enhanced magnitude. The first configuration is then generalized to an n-degree-of-freedom model, and its analytical solution is derived. This solution provides a convenient tool for parametric study and design of a multiple-degree-of-freedom piezoelectric energy harvesting model. Finally, the equivalent circuit model of the proposed n-degree-of-freedom piezoelectric energy harvesting model is developed via the analogy between the mechanical and electric domains. With the equivalent circuit model, system-level electric simulation can be performed to evaluate the system performance when sophisticated interface circuits are attached.
Combined Effect of Slot Injection, Effusion Array and Dilution Hole on the Cooling Performance of a Real Combustor Liner

Due to the higher cooling requirements of novel combustor liners a comprehensive understanding of the phenomena concerning the interaction of hot gases with different coolant flows plays a major role in the definition of a well performing liner. An experimental analysis of a real engine cooling scheme was performed on a test article replicating a slot injection and an effusion array with a central large dilution hole. Test section consists of a rectangular cross-section duct and a flat perforated plate with 272 holes arranged in 29 staggered rows (d = 1.65 mm, Sx /d = 7.6, Sy /d = 6, L/d = 5.5, α = 30 deg); a dilution hole (D = 18.75 mm) is located at the 14th row. Both effusion and dilution holes are fed by a channel replicating combustor annulus, that allows to control cold gas side cross-flow parameters. Upstream the first effusion row, a 6.0 mm high slot ensure the protection of the very first region of the liner. Final aim was the measurement of adiabatic effectiveness of the cooling scheme by means of a steady-state Thermochromic Liquid Crystals (TLC) technique, considering the combined effects of slot, effusion and dilution holes. Experiments were carried out imposing three different effusion velocity ratios typical of modern engine working conditions (VReff = 3, 5, 7) and keeping constant slot flow parameters (VRsl = 1.1). CFD RANS calculations were also performed with the aim of better understanding interactions between coolant exiting from the slot and injected by effusion cooling rows. Numerical analysis revealed a large dependency on effusion velocity ratio. An in-house one-dimensional fluid network solver was finally used to compare experimental and numerical results with the ones predicted by correlations and then quantify the possibility of giving predictions. Both CFD and experimental results reveal that slot protection is reduced in the first rows by coolant injected with such high velocity ratios; nevertheless effusion, though in penetration regime, guarantees a significant effectiveness level in the more downstream region. Dilution hole alters the effectiveness growth rate, moreover leading to local protection lowering just after its injection.Copyright © 2009 by ASME
Systematic exploration of Escherichia coli phage–host interactions with the BASEL phage collection

Bacteriophages, the viruses infecting bacteria, hold great potential for the treatment of multidrug-resistant bacterial infections and other applications due to their unparalleled diversity and recent breakthroughs in their genetic engineering. However, fundamental knowledge of molecular mechanisms underlying phage-host interactions is mostly confined to a few traditional model systems and did not keep pace with the recent massive expansion of the field. The true potential of molecular biology encoded by these viruses has therefore remained largely untapped, and phages for therapy or other applications are often still selected empirically. We therefore sought to promote a systematic exploration of phage-host interactions by composing a well-assorted library of 66 newly isolated phages infecting the model organism Escherichia coli that we share with the community as the BASEL collection (BActeriophage SElection for your Laboratory). This collection is largely representative of natural E. coli phage diversity and was intensively characterized phenotypically and genomically alongside ten well-studied traditional model phages. We experimentally determined essential host receptors of all phages, quantified their sensitivity to eleven defense systems across different layers of bacterial immunity, and matched these results to the phages’ host range across a panel of pathogenic enterobacterial strains. Our results reveal clear patterns in the distribution of phage phenotypes and genomic features that highlight systematic differences in the potency of different immunity systems and point towards the molecular basis of receptor specificity in several phage groups. Strong trade-offs were detected between fitness traits like broad host recognition and resistance to bacterial immunity that might drive the divergent adaptation of different phage groups to specific niches. We envision that the BASEL collection will inspire future work exploring the biology of bacteriophages and their hosts by facilitating the discovery of underlying molecular mechanisms as the basis for an effective translation into biotechnology or therapeutic applications.