Miracle-dz/UniFAR-FTUs huggingface dataset:
On Monotone Inclusions, Acceleration and Closed-Loop Control

We propose and analyze a new dynamical system with a closed-loop control law in a Hilbert space [Formula: see text], aiming to shed light on the acceleration phenomenon for monotone inclusion problems, which unifies a broad class of optimization, saddle point, and variational inequality (VI) problems under a single framework. Given an operator [Formula: see text] that is maximal monotone, we propose a closed-loop control system that is governed by the operator [Formula: see text], where a feedback law [Formula: see text] is tuned by the resolution of the algebraic equation [Formula: see text] for some [Formula: see text]. Our first contribution is to prove the existence and uniqueness of a global solution via the Cauchy–Lipschitz theorem. We present a simple Lyapunov function for establishing the weak convergence of trajectories via the Opial lemma and strong convergence results under additional conditions. We then prove a global ergodic convergence rate of [Formula: see text] in terms of a gap function and a global pointwise convergence rate of [Formula: see text] in terms of a residue function. Local linear convergence is established in terms of a distance function under an error bound condition. Further, we provide an algorithmic framework based on the implicit discretization of our system in a Euclidean setting, generalizing the large-step hybrid proximal extragradient framework. Even though the discrete-time analysis is a simplification and generalization of existing analyses for a bounded domain, it is largely motivated by the aforementioned continuous-time analysis, illustrating the fundamental role that the closed-loop control plays in acceleration in monotone inclusion. A highlight of our analysis is a new result concerning [Formula: see text]-order tensor algorithms for monotone inclusion problems, complementing the recent analysis for saddle point and VI problems. Funding: This work was supported in part by the Mathematical Data Science Program of the Office of Naval Research [Grant N00014-18-1-2764] and by the Vannevar Bush Faculty Fellowship Program [Grant N00014-21-1-2941].
Random walks on stochastic uniform growth trees: Analytical formula for mean first-passage time

As known, the commonly-utilized ways to determine mean first-passage time $\overline{\mathcal{F}}$ for random walk on networks are mainly based on Laplacian spectra. However, methods of this type can become prohibitively complicated and even fail to work when the Laplacian matrix of network under consideration is difficult to describe in the first place. In this paper, we propose an effective approach to determining quantity $\overline{\mathcal{F}}$ on some widely-studied tree networks. To this end, we first build up a general formula between Wiener index $\mathcal{W}$ and $\overline{\mathcal{F}}$ on a tree. This enables us to convert issues to answer into calculation of $\mathcal{W}$ on networks in question. As opposed to most of previous work focusing on deterministic growth trees, our goal is to consider stochastic case. Towards this end, we establish a principled framework where randomness is introduced into the process of growing trees. As an immediate consequence, the previously published results upon deterministic cases are thoroughly covered by formulas established in this paper. Additionally, it is also straightforward to obtain Kirchhoff index on our tree networks using the proposed approach. Most importantly, our approach is more manageable than many other methods including spectral technique in situations considered herein.
Sequential Estimation of Convex Functionals and Divergences

We present a unified technique for sequential estimation of convex divergences between distributions, including integral probability metrics like the kernel maximum mean discrepancy, φ-divergences like the Kullback-Leibler divergence, and optimal transport costs, such as powers of Wasserstein distances. This is achieved by observing that empirical convex divergences are (partially ordered) reverse submartingales with respect to the exchangeable filtration, coupled with maximal inequalities for such processes. These techniques appear to be complementary and powerful additions to the existing literature on both confidence sequences and convex divergences. We construct an offline-to-sequential device that converts a wide array of existing offline concentration inequalities into time-uniform confidence sequences that can be continuously monitored, providing valid tests or confidence intervals at arbitrary stopping times. The resulting sequential bounds pay only an iterated logarithmic price over the corresponding fixedtime bounds, retaining the same dependence on problem parameters (like dimension or alphabet size if applicable). These results are also applicable to more general convex functionals, like the negative differential entropy, suprema of empirical processes, and V-Statistics.
Mixture Outlier Exposure for Out-of-Distribution Detection in Fine-Grained Settings

Enabling out-of-distribution (OOD) detection for DNNs is critical for their safe and reliable operation in the open world. Despite recent progress, current works often consider a coarse level of granularity in the OOD problem, which fail to approximate many real-world ﬁne-grained tasks where high granularity may be expected between the in-distribution (ID) data and the OOD data ( e.g ., identifying novel bird species for a bird classiﬁcation system in the wild). In this work, we start by carefully constructing four large-scale ﬁne-grained test environments in which existing methods are shown to have difﬁculties. We ﬁnd that current methods, including ones that include a large/diverse set of outliers during DNN training, have poor coverage over the broad region where ﬁne-grained OOD samples locate. We then pro-pose Mixture Outlier Exposure (MixOE), which effectively expands the covered OOD region by mixing ID data and training outliers, and regularizes the model behaviour by linearly decaying the prediction conﬁdence as the input transitions from ID to OOD. Extensive experiments and analyses demonstrate the effectiveness of MixOE for improving OOD detection in ﬁne-grained settings.
New finite-element approach for reinforced concrete beams

Repair and strengthening of existing constructions are essential in many cases and may be the only option. A computer program based on the finite-element method has been developed and a new compound element was used to simulate reinforced concrete (RC) members strengthened with external layers on the different sides. Each compound element consists of four sub-elements. The sub-elements are two-dimensional isoparametric degenerated elements with eight nodes and five degrees of freedom. Each sub-element consists of different concrete, steel and strengthening laminates. Seven models from an experimental test programme on RC beams strengthened with external layers in different sides were analysed using finite elements. The newly developed compound element has been shown to be able to solve any RC member strengthened with additional external layers on different sides with accurate representations and acceptable results.
Acoustical imaging with compact sensors for mine countermeasures applications

Navy divers are often called upon to identify contacts that have been classified as mine-like by airborne or shipboard MCM systems. Identification relies upon a visual inspection of the contact, but the limitations in contact location accuracy and optical clarity dictate the need for sonar systems to reacquire contacts. The Navy currently relies upon the diver-held aural AN-PQS/2A for this purpose. The Navy has invested heavily in visual imaging sonars to improve reacquisition performance. In the latter half of the 1990s, as small autonomous underwater vehicle (AUV) technology has emerged, multi-beam imagers have been developed for these platforms. An overview of the Navy's past, present, and planned efforts in diver and small AUV platform-based multi-beam visual imaging systems is presented. Particular attention is paid to those systems which are nearing transition to fleet systems.
Errata: Digital Beamforming on Receive: Techniques and Optimization Strategies for High-Resolution Wide-Swath SAR Imaging

Synthetic Aperture Radar (SAR) is a well-proven imaging technique for remote sensing of the Earth. However, conventional SAR systems are not capable of fulfilling the increasing demands for improved spatial resolution and wider swath coverage. To overcome these inherent limitations, several innovative techniques have been suggested which employ multiple receive-apertures to gather additional information along the synthetic aperture. These digital beamforming (DBF) on receive techniques are reviewed with particular emphasis on the multi-aperture signal processing in azimuth and a multi-aperture reconstruction algorithm is presented that allows for the unambiguous recovery of the Doppler spectrum. The impact of Doppler aliasing is investigated and an analytic expression for the residual azimuth ambiguities is derived. Further, the influence of the processing on the signal-to-noise ratio (SNR) is analyzed, resulting in a pulse repetition frequency (PRF) dependent factor describing the SNR scaling of the multi-aperture beamforming network. The focus is then turned to a complete high-resolution wide-swath SAR system design example which demonstrates the intricate connection between multi-aperture azimuth processing and the system architecture. In this regard, alternative processing approaches are compared with the multi-aperture reconstruction algorithm. In a next step, optimization strategies are discussed as pattern tapering, prebeamshaping-on-receive, and modified processing algorithms. In this context, the analytic expressions for both the residual ambiguities and the SNR scaling factor are generalized to cascaded beamforming networks. The suggested techniques can moreover be extended in many ways. Examples discussed are a combination with ScanSAR burst mode operation and the transfer to multistatic sparse array configurations.
Intrinsic functional connectivity in the default mode network predicts mnemonic discrimination: A connectome‐based modeling approach

The ability to distinguish existing memories from similar perceptual experiences is a core feature of episodic memory. This ability is often examined using the mnemonic similarity task in which people discriminate memories of studied objects from perceptually similar lures. Studies of the neural basis of such mnemonic discrimination have mostly focused on hippocampal function and connectivity. However, default mode network (DMN) connectivity may also support such discrimination, given that the DMN includes the hippocampus, and its connectivity supports many aspects of episodic memory. Here, we used connectome‐based predictive modeling to identify associations between intrinsic DMN connectivity and mnemonic discrimination. We leveraged a wide range of abilities across healthy younger and older adults to facilitate this predictive approach. Resting‐state functional connectivity in the DMN predicted mnemonic discrimination outside the MRI scanner, especially among prefrontal and temporal regions and including several hippocampal regions. This predictive relationship was stronger for younger than older adults, primarily for temporal–prefrontal connectivity. The novel associations established here are consistent with mounting evidence that broader cortical networks including the hippocampus support mnemonic discrimination. They also suggest that age‐related network disruptions undermine the extent that the DMN supports this ability. This study provides the first indication of how intrinsic functional properties of the DMN support mnemonic discrimination.
Dilute phase oligomerization can oppose phase separation and modulate material properties of a ribonucleoprotein condensate

Ribonucleoprotein bodies are exemplars of membraneless biomolecular condensates that can form via spontaneous or driven phase transitions. The fungal protein Whi3 forms ribonucleoprotein condensates with different RNA molecules, and these condensates are implicated in key processes such as cell-cycle control and generating cell polarity. Whi3 has a modular architecture that includes a Q-rich intrinsically disordered region (IDR) and a tandem RNA recognition module. Here, we demonstrate that a 21-residue stretch within the Q-rich IDR has a weak intrinsic preference for forming alpha-helical conformations. Through mutagenesis, we find that increased alpha helicity enhances oligomerization in the dilute phase. One consequence of enhanced oligomerization is a dilution of Whi3 in the dense phase. The opposite behavior is observed when helicity within the 21-residue stretch of the Q-rich region is abrogated. Thus, the formation of dilute phase oligomers, driven by a specific sequence motif and potential synergies with the rest of the IDR, opposes incorporation of the Whi3 protein into the dense phase, thereby altering the dense phase stoichiometry of protein to RNA. Our findings, which stand in contrast to other systems where oligomerization has been shown to enhance the drive for phase separation, point to a novel mechanism that might be operative for influencing compositions of condensates. Our work also points to routes for designing synthetic ribonucleoprotein condensates whereby modulation of protein oligomerization via homotypic interactions can impact dense phase concentrations, stoichiometries, and material properties. Significance A large sub-class of biomolecular condensates are linked to RNA regulation and are known as ribonucleoprotein (RNP) bodies. While extensive work has identified driving forces for biomolecular condensate formation, relatively little is known about forces that oppose assembly. Here, using a fungal RNP protein, Whi3, we show that a portion of its intrinsically disordered, glutamine-rich region modulates phase separation by forming transient alpha helical structures that promote the assembly of dilute phase oligomers. These oligomers detour Whi3 proteins from condensates, thereby impacting the driving forces for phase separation, the protein-to-RNA ratio in condensates, and the material properties of condensates. Our findings show how nanoscale conformational and oligomerization equilibria can influence mesoscale phase equilibria.
EM fields generated by lightning channels with arbitrary location and slope

It is well known that lightning discharges follow a tortuous path; therefore, a general technique able to evaluate the electromagnetic (EM) fields associated with discharge currents flowing into tortuous channels seems to be worthy of consideration. Two techniques have been adopted to find the EM field generated by a current pulse traveling along a single line radiator with arbitrary slope and location above the ground. The first one employs the Fraunhofer approximation, which can provide useful information only on distant radiated fields. The second technique is exact, but applies only to the case of a velocity of propagation v of the current pulse equal to c (velocity of light). Even this solution is indeed inadequate for our purposes since v<c, as evidenced in the literature. In this paper, we evaluate the EM field associated to an arbitrarily oriented radiator without making any mathematical approximation in order to obtain closed-form solutions for the fields. A schematic square-pulse representation of the current and charge distributions along the discharge channels are adopted. The fields due to arbitrary time-varying sources are obtained adopting a suitable convolution integration.
An RNA-based feed-forward mechanism ensures motor switching in oskar mRNA transport

Regulated recruitment and activity of motor proteins is essential for intracellular transport of cargoes, including messenger ribonucleoprotein complexes (RNPs). Here we show that orchestration of oskar RNP transport in the Drosophila germline relies on the interplay of two double-stranded RNA binding proteins, Staufen and the dynein adaptor Egalitarian (Egl). We find that Staufen antagonizes Egl-mediated transport of oskar mRNA by dynein both in vitro and in vivo. Following delivery of nurse cell-synthesized oskar mRNA into the oocyte by dynein, recruitment of Staufen to the RNPs results in dissociation of Egl and a switch to kinesin-1-mediated translocation of the mRNA to its final destination at the posterior pole of the oocyte. We additionally show that Egl associates with staufen (stau) mRNA in the nurse cells, mediating its enrichment and translation in the ooplasm. Our observations identify a novel feed-forward mechanism, whereby dynein-dependent accumulation of stau mRNA, and thus protein, in the oocyte enables motor switching on oskar RNPs by downregulating dynein activity.
Brain simulation augments machine‐learning–based classification of dementia

INTRODUCTION Computational brain network modeling using The Virtual Brain (TVB) simulation platform acts synergistically with machine learning and multi-modal neuroimaging to reveal mechanisms and improve diagnostics in Alzheimer’s disease. METHODS We enhance large-scale whole-brain simulation in TVB with a cause-and-effect model linking local Amyloid β PET with altered excitability. We use PET and MRI data from 33 participants of Alzheimer’s Disease Neuroimaging Initiative (ADNI3) combined with frequency compositions of TVB-simulated local field potentials (LFP) for machine-learning classification. RESULTS The combination of empirical neuroimaging features and simulated LFPs significantly outperformed the classification accuracy of empirical data alone by about 10% (weighted F1-score empirical 64.34% vs. combined 74.28%). Informative features showed high biological plausibility regarding the Alzheimer’s-typical spatial distribution. DISCUSSION The cause-and-effect implementation of local hyperexcitation caused by Amyloid β can improve the machine-learning-driven classification of Alzheimer’s and demonstrates TVB’s ability to decode information in empirical data employing connectivity-based brain simulation. RESEARCH IN CONTEXT SYSTEMATIC REVIEW. Machine-learning has been proven to augment diagnostics of dementia in several ways. Imaging-based approaches enable early diagnostic predictions. However, individual projections of long-term outcome as well as differential diagnosis remain difficult, as the mechanisms behind the used classifying features often remain unclear. Mechanistic whole-brain models in synergy with powerful machine learning aim to close this gap. INTERPRETATION. Our work demonstrates that multi-scale brain simulations considering Amyloid β distributions and cause-and-effect regulatory cascades reveal hidden electrophysiological processes that are not readily accessible through measurements in humans. We demonstrate that these simulation-inferred features hold the potential to improve diagnostic classification of Alzheimer’s disease. FUTURE DIRECTIONS. The simulation-based classification model needs to be tested for clinical usability in a larger cohort with an independent test set, either with another imaging database or a prospective study to assess its capability for long-term disease trajectories.
A plastic-damage concrete model for earthquake analysis of dams

A new plastic-damage constitutive model for cyclic loading of concrete has been developed for the earthquake analysis of concrete dams. The rate-independent model consistently includes the effects of strain softening, represented by separate damage variables for tension and compression. A simple scalar degradation model simulates the effects of damage on the elastic stiffness and the recovery of stiffness after cracks close. To simulate large crack opening displacements, the evolution of inelastic strain is stopped beyond a critical value for the tensile damage variable. Subsequent deformation can be recovered upon crack closing. The rate-independent plastic-damage model forms the backbone model for a rate-dependent viscoplastic extension. The rate-dependent regularization is necessary to obtain a unique and mesh objective numerical solution. Damping is represented as a linear viscoelastic behaviour proportional to the elastic stiffness including the degradation damage. The plastic-damage constitutive model is used to evaluate the response of Koyna dam in the 1967 Koyna earthquake. The analysis shows two localized cracks forming and then joining at the change in geometry of the upper part of the dam. The upper portion of the dam vibrates essentially as rigid-body rocking motion after the upper cracks form, but the dam remains stable. The vertical component of ground motion influences the post-cracking response. © 1998 John Wiley & Sons, Ltd.
Human survivability in motor vehicle fires

Automobile fires are consistently among the largest causes of fire death in the United States (about 500 annually) and the U.S. motor vehicle industry and others have spent a significant amount of money in recent years studying this problem. The authors of this review have analyzed the auto industry reports, the scientific literature, and statistical data, and conclude that measures should be taken to improve survivability in automobile fires. The U.S. Federal Motor Vehicle Safety Standard 302 (FMVSS 302) was introduced almost 40 years ago to measure the flammability of interior materials, but improvements in the crashworthiness of automobiles and their fuel tanks and the increased use of combustible materials have changed the motor vehicle fire scenario significantly. In particular, the primary threat has changed from ignition of a small quantity of combustible interior materials by a lit cigarette, in 1960, to ignition of a large quantity of combustible interior and exterior materials by an impact‐induced fire, at present. The authors therefore suggest that FMVSS 302 is no longer relevant to automobile fire safety and recommend improved standards based on objective criteria for fire safety performance (fireworthiness) at the system/vehicle level as is routinely done for crashworthiness. Copyright © 2008 John Wiley & Sons, Ltd.
Multiobjective optimization of component placement on planar printed wiring boards

This paper presents a solution methodology for the optimal placement of convectively and conductively air-cooled electronic components on planar printed wiring boards considering thermal and electrical/cost design objectives. The methodology combines the use of a heat transfer solver for the prediction of the temperature distribution among the electronic components and a genetic algorithm for the adaptive search of optimal or near optimal solutions and a multiobjective optimization strategy (Pareto optimization and Multiattribute utility analysis). After proper validation of the elements of the solution methodology (heat transfer solver/genetic algorithm) in isolation, the methodology under consideration is tested using a placement problem (case study) that considers as optimization criteria the minimization of an estimate of the failure rate of the system of components due to thermal overheating (via an Arrhenius relation) and the minimization of the total wiring length (given some interconnectivity requirements). Results corresponding to the case study are presented and discussed for both Pareto optimization and Multiattribute utility analysis.