cssi/SciSciGPT-SciSciNet huggingface dataset:
Network inference from consensus dynamics

We consider the problem of identifying the topology of a weighted, undirected network G from observing snapshots of multiple independent consensus dynamics. Specifically, we observe the opinion profiles of a group of agents for a set of M independent topics and our goal is to recover the precise relationships between the agents, as specified by the unknown network G. In order to overcome the under-determinacy of the problem at hand, we leverage concepts from spectral graph theory and convex optimization to unveil the underlying network structure. More precisely, we formulate the network inference problem as a convex optimization that seeks to endow the network with certain desired properties — such as sparsity — while being consistent with the spectral information extracted from the observed opinions. This is complemented with theoretical results proving consistency as the number M of topics grows large. We further illustrate our method by numerical experiments, which showcase the effectiveness of the technique in recovering synthetic and real-world networks.
Approximate partition selection for big-data workloads using summary statistics

Many big-data clusters store data in large partitions that support access at a coarse, partition-level granularity. As a result, approximate query processing via row-level sampling is inefficient, often requiring reads of many partitions. In this work, we seek to answer queries quickly and approximately by reading a subset of the data partitions and combining partial answers in a weighted manner without modifying the data layout. We illustrate how to efficiently perform this query processing using a set of pre-computed summary statistics, which inform the choice of partitions and weights. We develop novel means of using the statistics to assess the similarity and importance of partitions. Our experiments on several datasets and data layouts demonstrate that to achieve the same relative error compared to uniform partition sampling, our techniques offer from 2.7x to 70x reduction in the number of partitions read, and the statistics stored per partition require fewer than 100KB.
Harnessing the Power of Deep Learning Methods in Healthcare: Neonatal Pain Assessment from Crying Sound

Neonatal pain assessment in clinical environments is challenging as it is discontinuous and biased. Facial/body occlusion can occur in such settings due to clinical condition, developmental delays, prone position, or other external factors. In such cases, crying sound can be used to effectively assess neonatal pain. In this paper, we investigate the use of a novel CNN architecture (N-CNN) along with other CNN architectures (VGG16 and ResNet50) for assessing pain from crying sounds of neonates. The experimental results demonstrate that using our novel N-CNN for assessing pain from the sounds of neonates has a strong clinical potential and provides a viable alternative to the current assessment practice.
Stellar dynamics and black holes

Chandrasekhar’s most important contribution to stellar dynamics was the concept of dynamical friction. I briefly review that work, then discuss some implications of Chandrasekhar’s theory of gravitational encounters for motion in galactic nuclei.
Spin- and Pair-Density-Wave Glasses

Researchers show that electronic systems with a common type of magnetic order form a new glassy state of matter due to imperfections. In this phase, the spins of the electrons are randomly aligned or anti-aligned with a spontaneously chosen axis.
Wave train selection by invasion fronts in the FitzHugh-Nagumo equation

We study invasion fronts in the FitzHugh?Nagumo equation in the oscillatory regime using singular perturbation techniques. Phenomenologically, localized perturbations of the unstable steady-state grow and spread, creating temporal oscillations whose phase is modulated spatially. The phase modulation appears to be selected by an invasion front that describes the behavior in the leading edge of the spreading process. We construct these invasion fronts for large regions of parameter space using singular perturbation techniques. Key ingredients are the construction of periodic orbits, their unstable manifolds, and the analysis of pushed and pulled fronts in the fast system. Our results predict the wavenumbers and frequencies of oscillations in the wake of the front through a phase locking mechanism. We also identify a parameter regime where nonlinear phase locked fronts are inaccessible in the singularly perturbed geometry of the traveling-wave equation. Direct simulations confirm our predictions and point to interesting phase slip dynamics.
Syzygies of the secant variety of a curve

We show the secant variety of a linearly normal smooth curve of degree at least 2g+3 is arithmetically Cohen–Macaulay, and we use this information to study the graded Betti numbers of the secant variety.
FORWARD AND INVERSE MODELING OF THE EMISSION AND TRANSMISSION SPECTRUM OF GJ 436B: INVESTIGATING METAL ENRICHMENT, TIDAL HEATING, AND CLOUDS

The Neptune-mass GJ 436b is one of the most studied transiting exoplanets with repeated measurements of its thermal emission and transmission spectra. We build on previous studies to answer outstanding questions about this planet, including its potentially high metallicity and tidal heating of its interior. We present new observations of GJ 436b's thermal emission at 3.6 and 4.5 μm, which reduce uncertainties in estimates of GJ 436b's flux at those wavelengths and demonstrate consistency between Spitzer observations spanning more than 7 yr. We analyze the Spitzer thermal emission photometry and Hubble WFC3 transmission spectrum. We use a dual-pronged modeling approach of both self-consistent and retrieval models. We vary the metallicity, intrinsic luminosity from tidal heating, disequilibrium chemistry, and heat redistribution. We also study clouds and photochemical hazes, but do not find strong evidence for either. The self-consistent and retrieval models combine to suggest that GJ 436b has a high atmospheric metallicity, with best fits at or above several hundred times solar metallicity, tidal heating warming its interior with best-fit intrinsic effective temperatures around 300–350 K, and disequilibrium chemistry. High metal enrichments (>600× solar) occur from the accretion of rocky, rather than icy, material. Assuming the interior temperature T int ~ 300–350 K, we find a dissipation factor Q' ~ 2 × 10^5–10^6, larger than Neptune's Q', implying a long tidal circularization timescale for the orbit. We suggest that Neptune-mass planets may be more diverse than imagined, with metal enhancements spanning several orders of magnitude, to perhaps over 1000× solar metallicity. High-fidelity observations with instruments like the James Webb Space Telescope will be critical for characterizing this diversity.
Parafermions in hierarchical fractional quantum Hall states

This work establishes the nature of non-Abelian parafermion defects, which are localized on domain walls at the interface of hierarchical Abelian fractional quantum Hall states. The author uncovers a sequence of parafermions and show that their quantum dimensions are directly related to the topological properties of the bulk Abelian phase, thus revealing hierarchical fractional quantum Hall states as a setting for the realization of non-Abelian defects.
The genus 0 Gromov–Witten invariants of projective complete intersections

We describe the structure of mirror formulas for genus 0 Gromov‐Witten invariants of projective complete intersections with any number of marked points and provide an explicit algorithm for obtaining the relevant structure coefficients. As an application, we give explicit closed formulas for the genus 0 Gromov‐Witten invariants of Calabi‐ Yau complete intersections with 3 and 4 constraints. The structural description alone suffices for some qualitative applications, such as vanishing results and the bounds on the growth of these invariants predicted by R Pandharipande. The resulting theorems suggest intriguing conjectures relating GW‐invariants to the energy of pseudoholomorphic maps and the expected dimensions of their moduli spaces. 14N35; 53D45
Applications of the duality method to generalizations of the Jordan canonical form

Abstract The Jordan normal form for a matrix over an arbitrary field and the canonical form for a pair of matrices under contragredient equivalence are derived using Ptak's duality method.
On Quantum Microstates in the Near Extremal, Near Horizon Kerr Geometry

We study the thermodynamics of near horizon near extremal Kerr (NHNEK) geometry within the framework of AdS2/CFT1 correspondence. We start by shifting the horizon of near horizon extremal Kerr (NHEK) geometry by a general finite mass. While this shift does not alter the geometry in that the resulting classical solution is still diffeomorphic to the NHEK solution, it does lead to a quantum theory different from that of NHEK. We obtain this quantum theory by means of a Robinson-Wilczek two-dimensional Kaluza-Klein reduction which enables us to introduce a finite regulator on the AdS2 boundary and compute the full asymptotic symmetry group of the two-dimensional quantum conformal field theory on the respective AdS2 boundary. The s-wave contribution of the energy-momentum-tensor of this conformal field theory, together with the asymptotic symmetries, generate a Virasoro algebra with a calculable center, which agrees with the standard Kerr/CFT result, and a non-vanishing lowest Virasoro eigenmode. The central charge and lowest eigenmode produce the Bekenstein-Hawking entropy and Hawking temperature for NHNEK.
Non‐linear conductivity of charge‐density‐wave systems

We consider the problem of sliding motion of a charge-density-wave subject to static disorder within an elastic medium model. Starting with a field-theoretical formulation, which allows exact disorder averaging, we propose a self-consistent approximation scheme to obtain results beyond the standard large-velocity expansion. Explicit calculations are carried out in three spatial dimensions. For the conductivity, we find a strong-coupling regime at electrical fields just above the pinning threshold. Phase and velocity correlation functions scale differently from the high-field regime, and static phase correlations converge to the pinned-phase behaviour. The sliding charge-density-wave is accompanied by narrow-band noise.
Jitter Characterization of a Dual-Readout SNSPD

To better understand the origins of the timing resolution, also known as jitter, of superconducting nanowire single-photon detectors (SNSPDs), we have performed timing characterizations of a niobium nitride SNSPD with a dual-ended readout. By simultaneously measuring both readout pulses along with an optical timing reference signal, we are able to quantify each independent contribution to the total measured jitter. In particular, we are able to determine values for the jitter due to the stochastic nature of hotspot formation and the jitter due to the variation of the photon detection location along the length of the nanowire. We compare the results of this analysis for measurements at temperatures of 1.5 and 4.5 K.
HLVU: A New Challenge to Test Deep Understanding of Movies the Way Humans do

In this paper we propose a new evaluation challenge and direction in the area of High-level Video Understanding. The challenge we are proposing is designed to test automatic video analysis and understanding, and how accurately systems can comprehend a movie in terms of actors, entities, events and their relationship to each other. A pilot High-Level Video Understanding (HLVU) dataset of open source movies were collected for human assessors to build a knowledge graph representing each of them. A set of queries will be derived from the knowledge graph to test systems on retrieving relationships among actors, as well as reasoning and retrieving non-visual concepts. The objective is to benchmark if a computer system can "understand" non-explicit but obvious relationships the same way humans do when they watch the same movies. This is long-standing problem that is being addressed in the text domain and this project moves similar research to the video domain. Work of this nature is foundational to future video analytics and video understanding technologies. This work can be of interest to streaming services and broadcasters hoping to provide more intuitive ways for their customers to interact with and consume video content.
Real Time Data Analysis With the ATLAS Trigger at the LHC in Run-2

The trigger selection capabilities of the ATLAS detector have been significantly enhanced for the Large Hadron Collider (LHC) Run-2 in order to cope with the higher event rates and with a large number of simultaneous interactions (pile-up) per proton–proton bunch crossing. A new hardware system, designed to analyze real-time event-topologies at level-1, came to full use in 2017. A hardware-based track reconstruction system, expected to be used real time in run-3, is designed to provide track information to the high-level software trigger at its full input rate. The high-level trigger (HLT) selections largely rely on off-line-like reconstruction techniques and in some cases multivariate analysis methods. Despite the sudden change in LHC operations during the second half of 2017, which caused an increase in pile-up and, therefore, also in CPU usage of the trigger algorithms, the set of triggers (so-called trigger menu) running online has undergone only minor modifications thanks to the robustness and redundancy of the trigger system and the use of a leveling luminosity scheme in agreement with LHC and other experiments. This article gives a brief yet comprehensive review of the real-time performance of the ATLAS trigger system in 2017. Considerations will be presented on the most relevant parameters of the trigger (efficiency to collect signal and output data rate) and details on some aspects of the algorithms which are run real time on the HLT CPU farm will be presented.
WISEP J180026.60+013453.1: A nearby late-L Dwarf near the Galactic Plane

We report a nearby L7.5 dwarf discovered using the Preliminary Data Release of the Wide-field Infrared Survey Explorer (WISE) and the Two Micron All Sky Survey. WISEP J180026.60+013453.1 has a motion of 0.42 arcsec yr{sup -1} and an estimated distance of 8.8 {+-} 1.0 pc. With this distance, it currently ranks as the sixth closest known L dwarf, although a trigonometric parallax is needed to confirm this distance. It was previously overlooked because it lies near the Galactic plane (b = 12 Degree-Sign ). As a relatively bright and nearby late L dwarf with normal near-infrared colors, W1800+0134 will serve as a benchmark for studies of cloud-related phenomena in cool substellar atmospheres.
A Two-Layer Decentralized Control Architecture for DER Coordination

This paper presents a two-layer distributed energy resource (DER) coordination architecture that allows for separate ownership of data, operates with data subjected to a large buffering delay, and employs a new measure of power quality. The two-layer architecture comprises a centralized model predictive controller (MPC) and several decentralized MPCs each operating independently with no direct communication between them and with infrequent communication with the centralized controller. The goal is to minimize a combination of total energy cost and a measure of power quality while obeying cyber-physical constraints. The global controller utilizes a fast optimal power flow (OPF) solver and extensive parallelization to scale the solution to large networks. Each local controller attempts to maximize arbitrage profit while following the load profile and constraints dictated by the global controller. Extensive simulations are performed for two distribution networks under a wide variety of possible storage and solar penetrations enabled by the controller speed. The simulations show that (i) the two-layer architecture can achieve tenfold improvement in power quality relative to no coordination, while capturing nearly all of the available arbitrage profit for a moderate amount of storage penetration, and (ii) both power quality and arbitrage profits are optimized when the solar and storage are distributed more widely over the network, hence it is more effective to install storage closer to the consumer.
Coherent states for a 2-sphere with a magnetic field

We consider a particle moving on a 2-sphere in the presence of a constant magnetic field. Building on our earlier work in the nonmagnetic case we construct coherent states for this system. The coherent states are labeled by points in the associated phase space, the (co)tangent bundle of S2. They are constructed as eigenvectors for certain annihilation operators and expressed in terms of a certain heat kernel. These coherent states are not of Perelomov type but rather are constructed according to the ?complexifier? approach of Thiemann. We describe the Segal?Bargmann representation associated with the coherent states which is equivalent to a resolution of the identity.This article is part of a special issue of Journal of Physics A: Mathematical and Theoretical devoted to ?Coherent states: mathematical and physical aspects?.
Conceptual design studies for a CEPC detector

The physics potential of the Circular Electron Positron Collider (CEPC) can be significantly strengthened by two detectors with complementary designs. A promising detector approach based on the Silicon Detector (SiD) designed for the International Linear Collider (ILC) is presented. Several simplifications of this detector for the lower energies expected at the CEPC are proposed. A number of cost optimizations of this detector are illustrated using full detector simulations. We show that the proposed changes will enable one to reach the physics goals at the CEPC.
Vibrational modes of circular free plates under tension

The vibrational frequencies of a plate under tension are given by the eigenvalues ω of the equation Δ2 u − τΔu = ωu. This article determines the eigenfunctions and eigenvalues of this bi-Laplace problem on the ball under natural (free) boundary conditions. In particular, the fundamental modes – the eigenfunctions of the lowest nonzero eigenvalue – are identified and found to have simple angular dependence.
Quantum phases of the frustrated XY models on the honeycomb lattice

Searching for spin-liquid states has long been attracting both experimentalists and theorists. In this paper, we review recent density matrix renormalization group studies of the spin-½ XY model on the honeycomb lattice, with first-neighbor (J1 = 1) and frustrating second-neighbor (J2 > 0) interactions. For the intermediate frustration regime 0.22 ≲ J2 ≲ 0.36, there exists a surprising antiferromagnetic Ising phase, with ordered moments pointing along the z-axis, despite the absence of any SzSz interactions in the Hamiltonian. Surrounding this phase as a function of J2 are antiferromagnetic phases with the moments pointing in the xy-plane for small J2 and a close competition between an xy-plane magnetic collinear phase and a dimer phase for large values of J2. No spin-liquid phases was found in the XY model even with the third-neighbor (J3 > 0) interactions.
Behavior of compressed plasmas in magnetic fields

Plasma in the earth’s magnetosphere is subjected to compression during geomagnetically active periods and relaxation in subsequent quiet times. Repeated compression and relaxation is the origin of much of the plasma dynamics and intermittency in the near-earth environment. An observable manifestation of compression is the thinning of the plasma sheet resulting in magnetic reconnection when the solar wind mass, energy, and momentum floods into the magnetosphere culminating in the spectacular auroral display. This phenomenon is rich in physics at all scale sizes, which are causally interconnected. This poses a formidable challenge in accurately modeling the physics. The large-scale processes are fluid-like and are reasonably well captured in the global magnetohydrodynamic (MHD) models, but those in the smaller scales responsible for dissipation and relaxation that feed back to the larger scale dynamics are often in the kinetic regime. The self-consistent generation of the small-scale processes and their feedback to the global plasma dynamics remains to be fully explored. Plasma compression can lead to the generation of electromagnetic fields that distort the particle orbits and introduce new features beyond the purview of the MHD framework, such as ambipolar electric fields, unequal plasma drifts and currents among species, strong spatial and velocity gradients in gyroscale layers separating plasmas of different characteristics, etc. These boundary layers are regions of intense activity characterized by emissions that are measurable. We study the behavior of such compressed plasmas and discuss the relaxation mechanisms to understand their measurable signatures as well as their feedback to influence the global scale plasma evolution.
A Comprehensive Dictionary and Term Variation Analysis for COVID-19 and SARS-CoV-2.

The number of unique terms in the scientific literature used to refer to either SARS-CoV-2 or COVID-19 is remarkably large and has continued to increase rapidly despite well-established standardized terms. This high degree of term variation makes high recall identification of these important entities difficult. In this manuscript we present an extensive dictionary of terms used in the literature to refer to SARS-CoV-2 and COVID-19. We use a rule-based approach to iteratively generate new term variants, then locate these variants in a large text corpus. We compare our dictionary to an extensive collection of terminological resources, demonstrating that our resource provides a substantial number of additional terms. We use our dictionary to analyze the usage of SARS-CoV-2 and COVID-19 terms over time and show that the number of unique terms continues to grow rapidly. Our dictionary is freely available at this https URL.
Selection rule for topological amplifiers in Bogoliubov–de Gennes systems

Dynamical instability is an inherent feature of bosonic systems described by the Bogoliubov--de Gennes (BdG) Hamiltonian. Since it causes the BdG system to collapse, it is generally thought that it should be avoided. Recently there has been much effort to harness this instability for the benefit of creating a topological amplifier with stable bulk bands but unstable edge modes which can be populated at an exponentially fast rate. We present a theorem for determining the stability of states with energies sufficiently away from zero, in terms of an unconventional commutator between the number-conserving part and number-nonconserving part of the BdG Hamiltonian. We apply the theorem to a generalization of a model from Galilo et al. [Phys. Rev. Lett. 115, 245302 (2015)] for creating a topological amplifier in an interacting spin-1 atom system in a honeycomb lattice through a quench process. We use this model to illustrate how the vanishing of the unconventional commutator selects the symmetries for a system so that its bulk states are stable against (weak) pairing interactions. We find that as long as time-reversal symmetry is preserved, our system can act like a topological amplifier, even in the presence of an onsite staggered potential which breaks the inversion symmetry.
BigNAS: Scaling Up Neural Architecture Search with Big Single-Stage Models

Neural architecture search (NAS) has shown promising results discovering models that are both accurate and fast. For NAS, training a one-shot model has become a popular strategy to rank the relative quality of different architectures (child models) using a single set of shared weights. However, while one-shot model weights can effectively rank different network architectures, the absolute accuracies from these shared weights are typically far below those obtained from stand-alone training. To compensate, existing methods assume that the weights must be retrained, finetuned, or otherwise post-processed after the search is completed. These steps significantly increase the compute requirements and complexity of the architecture search and model deployment. In this work, we propose BigNAS, an approach that challenges the conventional wisdom that post-processing of the weights is necessary to get good prediction accuracies. Without extra retraining or post-processing steps, we are able to train a single set of shared weights on ImageNet and use these weights to obtain child models whose sizes range from 200 to 1000 MFLOPs. Our discovered model family, BigNASModels, achieve top-1 accuracies ranging from 76.5% to 80.9%, surpassing state-of-the-art models in this range including EfficientNets and Once-for-All networks without extra retraining or post-processing. We present ablative study and analysis to further understand the proposed BigNASModels.
The golden hedge: From global financial crisis to global pandemic

Abstract This paper examines whether gold, and gold mining stocks, were an effective hedge during the 2020 global pandemic and 2008–2009 global financial crisis. Prior research suggests that gold’s hedging value is most evident during crisis periods, but none has compared the 2008–2009 and 2020 episodes directly. Dynamic conditional correlations and hedge ratios are estimated to determine the impact of rising market volatility on the hedging properties of physical gold and gold mining stocks. The results suggest that gold provided strong hedging value during the global financial crisis but did not consistently exhibit this property in 2020. There was less scope for hedging against losses in 2020 because the market recovered so quickly from the March 2020 lows. This contrasts with the extended stock market weakness following the onset of the global financial crisis.
Atypical Neocortical Development in the Cited2 Conditional Knockout Leads to Behavioral Deficits Associated with Neurodevelopmental Disorders

The mammalian neocortex develops from a single layer of neuroepithelial cells to form a six-layer heterogeneous mosaic of differentiated neurons and glial cells. This process requires a complex choreography of temporally and spatially restricted transcription factors and epigenetic regulators. Even subtle disruptions in this regulation can alter the way the neocortex forms and functions, leading to a neurodevelopmental disorder. One epigenetic regulator that is essential for the precise development of the neocortex is CITED2 (CBP/p300 Interacting Transactivator with ED-rich termini). Cited2 is highly expressed by intermediate progenitor cells in the subventricular zone during the generation of the superficial layers of the neocortex. A forebrain-specific conditional knockout of Cited2 (cKO) exhibits reduced proliferation of intermediate progenitor cells embryonically, leading to reduced thickness of the superficial layers and reduced corpus callosum (CC) volume postnatally. Further, the Cited2 cKO display disruptions in balanced neocortical arealization, with a specific reduction in the somatosensory neocortical length, and dysregulation of precise, area-specific neuronal connectivity. Here, we explore the behavioral consequences resulting from this aberrant neocortical development. We demonstrate that Cited2 cKO mice display decreased maternal separation-induced ultrasonic vocalizations (USVs) as neonates, and an increase in rearing behavior and lack of habituation following repeated acoustic startle as adults. They do not display alterations in anxiety-like behavior, overall locomotor activity, or social interactions. Together with the morphological, molecular, and connectivity disruptions, these results identify the Cited2 cKO neocortex as an ideal system to study mechanisms underlying neurodevelopmental and neuroanatomical disruptions with relevance to human neurodevelopmental disorders.
Solute softening and vacancy generation by diffusion-less dislocation climb in magnesium alloys

Abstract Active room temperature solute induced diffusion-less climb of the edge dislocations in model Mg-Al alloys was observed using molecular dynamics simulations. Dislocations on prismatic and pyramidal I planes climb through the basal plane to overcome solute obstacles. This out-of-plane dislocation motion softens the high resistance pyramidal I glide and significantly reduces the anisotropy of dislocation mobility, and could help improve the ductility of Mg. The flow stress scales linearly with solute concentration, cAl. Dislocations climb predominantly in the negative direction, with climb angle on the order of 0.01cAl, producing very high vacancy concentration on the order of 10−4. This climb behavior was rationalized using an energy analysis by comparing the in-plane and out-of-plane motions between different Mg slip planes. The ease of climb depends on the strength of the solute-dislocation interaction and the compactness of the dislocation core.
Query strategies for priced information revisited

We consider the problem of designing query strategies for priced information, introduced by Charikar et al. In this problem the algorithm designer is given a function $f : \{0,1\}^n \to \{-1,1\}$ and a price associated with each of the $n$ coordinates. The goal is to design a query strategy for determining $f$'s value on unknown inputs for minimum cost. Prior works on this problem have focused on specific classes of functions. We analyze a simple and natural strategy that applies to all functions $f$, and show that its performance relative to the optimal strategy can be expressed in terms of a basic complexity measure of $f$, its influence. For $\varepsilon \in (0,\frac1{2})$, writing $\mathsf{opt}$ to denote the expected cost of the optimal strategy that errs on at most an $\varepsilon$-fraction of inputs, our strategy has expected cost $\mathsf{opt} \cdot \mathrm{Inf}(f)/\varepsilon^2$ and also errs on at most an $O(\varepsilon)$-fraction of inputs. This connection yields new guarantees that complement existing ones for a number of function classes that have been studied in this context, as well as new guarantees for new classes. Finally, we show that improving on the parameters that we achieve will require making progress on the longstanding open problem of properly learning decision trees.
Diverse and complex muscle spindle afferent firing properties emerge from multiscale muscle mechanics

Despite decades of research, we lack a mechanistic framework capable of predicting how movement-related signals are transformed into the diversity of muscle spindle afferent firing patterns observed experimentally, particularly in naturalistic behaviors. Here, a biophysical model demonstrates that well-known firing characteristics of mammalian muscle spindle Ia afferents - including movement history dependence, and nonlinear scaling with muscle stretch velocity - emerge from first principles of muscle contractile mechanics. Further, mechanical interactions of the muscle spindle with muscle-tendon dynamics reveal how motor commands to the muscle (alpha drive) versus muscle spindle (gamma drive) can cause highly variable and complex activity during active muscle contraction and muscle stretch that defy simple explanation. Depending on the neuromechanical conditions, the muscle spindle model output appears to 'encode' aspects of muscle force, yank, length, stiffness, velocity, and/or acceleration, providing an extendable, multiscale, biophysical framework for understanding and predicting proprioceptive sensory signals in health and disease.
Inhibitory signaling in mammalian olfactory transduction potentially mediated by Gαo

Olfactory GPCRs (ORs) in mammalian olfactory receptor neurons (ORNs) mediate excitation through the Gαs family member Gαolf. Here we tentatively associate a second G protein, Gαo, with inhibitory signaling in mammalian olfactory transduction by first showing that odor evoked phosphoinositide 3-kinase (PI3K)-dependent inhibition of signal transduction is absent in the native ORNs of mice carrying a conditional OMP-Cre based knockout of Gαo. We then identify an OR from native rat ORNs that are activated by octanol through cyclic nucleotide signaling and inhibited by citral in a PI3K-dependent manner. We show that the OR activates cyclic nucleotide signaling and PI3K signaling in a manner that reflects its functionality in native ORNs. Our findings lay the groundwork to explore the interesting possibility that ORs can interact with two different G proteins in a functionally identified, ligand-dependent manner to mediate opponent signaling in mature mammalian ORNs.
Interpretable and Trustworthy Deepfake Detection via Dynamic Prototypes

In this paper we propose a novel human-centered approach for detecting forgery in face images, using dynamic prototypes as a form of visual explanations. Currently, most state-of-the-art deepfake detections are based on black-box models that process videos frame-by-frame for inference, and few closely examine their temporal inconsistencies. However, the existence of such temporal artifacts within deepfake videos is key in detecting and explaining deepfakes to a supervising human. To this end, we propose Dynamic Prototype Network (DPNet) – an interpretable and effective solution that utilizes dynamic representations (i.e., prototypes) to explain deepfake temporal artifacts. Extensive experimental results show that DPNet achieves competitive predictive performance, even on unseen testing datasets such as Google’s DeepFakeDetection, DeeperForensics, and Celeb-DF, while providing easy referential explanations of deepfake dynamics. On top of DPNet’s prototypical framework, we further formulate temporal logic specifications based on these dynamics to check our model’s compliance to desired temporal behaviors, hence providing trustworthiness for such critical detection systems.
Giant energy storage effect in nanolayer capacitors charged by the field emission tunneling

We fabricate nanolayer alumina capacitor and apply high electric fields, close to 1 GV m-1, to inject charges in the dielectric. Asymmetric charge distributions have been achieved due to the selectivity of the quantum tunneling process. Namely, the electrons near the Fermi level cannot tunnel intoregions near the cathode, where the total energy would be less than the potential energy. This mechanism exhibits a strong tendency to populate charge traps located near the anode, i.e. the regions where their potential energy is the lowest. Such spatially selective charging of the dielectric allows a permanent bulk charge storage in the dielectric layer, even if the capacitor plates are short-circuited, provided that the temperature is sufficiently low so that the conductivity of the dielectric is negligible. The stored charge can be recovered if the temperature is increased above ~250 K for the dielectric tested, i.e. Al2O3. In our experiments, the total charge stored in the dielectric was up to seven and a half times higher than the charge stored on the capacitor plates. Also, measurements of the breakdown voltage show that the breakdown electric field, i.e. the dielectric strength, is independent of the thickness of the dielectric.
The COVID-19 Pandemic Vulnerability Index (PVI) Dashboard: Monitoring County-Level Vulnerability Using Visualization, Statistical Modeling, and Machine Learning.

While the COVID-19 pandemic presents a global challenge, the U.S. response places substantial responsibility for both decision-making and communication on local health authorities. To better support counties and municipalities, we integrated baseline data on relevant community vulnerabilities with dynamic data on local infection rates and interventions into a Pandemic Vulnerability Index (PVI). The PVI presents a visual synthesis of county-level vulnerability indicators that can be compared in a regional, state, or nationwide context. We describe use of the PVI, supporting epidemiological modeling and machine-learning forecasts, and deployment of an interactive, web Dashboard. The Dashboard facilitates decision-making and communication among government officials, scientists, community leaders, and the public to enable more effective and coordinated action to combat the pandemic.
Slow Propagation in Some Disordered Quantum Spin Chains

We introduce the notion of transmission time to study the dynamics of disordered quantum spin chains and prove results relating its behavior to many-body localization properties. We also study two versions of the so-called Local Integrals of Motion (LIOM) representation of spin chain Hamiltonians and their relation to dynamical many-body localization. We prove that uniform-in-time dynamical localization expressed by a zero-velocity Lieb–Robinson bound implies the existence of a LIOM representation of the dynamics as well as a weak converse of this statement. We also prove that for a class of spin chains satisfying a form of exponential dynamical localization, sparse perturbations result in a dynamics in which transmission times diverge at least as a power law of distance, with a power for which we provide lower bound that diverges with increasing sparseness of the perturbation.
Understanding the Ethics of Natural Experiments in a Pandemic.

Pandemics invite natural experiments: testing hypotheses through observing the effects of interventions without manipulating exposure to the intervention. With novel infections like COVID-19 that spread rapidly and widely, knowledge gaps may be extensive. Quick action may be necessary and randomized trials impracticable if not downright impossible. But what can justify such interventions ethically, given that they are in some sense widescale experiments upon unaware members of the public who have no opportunity to choose not to participate? And what ethical limits to them should there be?
A New Perspective on Gaussian Dynamic Term Structure Models

In any canonical Gaussian dynamic term structure model (GDTSM), the conditional forecasts of the pricing factors are invariant to the imposition of no-arbitrage restrictions. This invariance is maintained even in the presence of a variety of restrictions on the factor structure of bond yields. To establish these results, we develop a novel canonical GDTSM in which the pricing factors are observable portfolios of yields. For our normalization, standard maximum likelihood algorithms converge to the global optimum almost instantaneously. We present empirical estimates and out-of-sample forecasts for several GDTSMs using data on U.S. Treasury bond yields. (JEL E43, G12, C13) Dynamic models of the term structure often posit a linear factor structure for a collection of yields, with these yields related to underlying factors P through a no-arbitrage relationship. Does the imposition of no-arbitrage in a Gaussian dynamic term structure model (GDTSM) improve the out-of-sample forecasts of yields relative to those from the unconstrained factor model, or sharpen model-implied estimates of expected excess returns? In practice, the answers to these questions are obscured by the imposition of over-identifying restrictions on the risk-neutral (Q) or historical (P) distributions of the risk factors, or on their market prices of risk, in addition to the cross-maturity restrictions implied by no-arbitrage. 1
Transgenic organisms and the failure of a free market argument

There are countries that will not buy our GMOwheat, if we had GMO wheat ... If you don’t have aplace to sell what you raise, what good does it do toraise it?Neutral-Transgenic Organisms LegislatorIf you’re going to buy the organic product from theUS, but you could buy it from another country thatdoesn’t have GMOs and they don’t allow GMOs tobe grown, and they don’t have threats of contamina-tion, well, where would you buy from?Anti-Transgenic Organisms Organic FarmerIf [farmers] are really concerned about thesenational markets, we shouldn’t have to pass a lawthat makes it illegal for them to use gene-splicedmaterial. It’s their business.Pro-Transgenic Organisms Legislator
An Efficient Algorithm to Compute Norms for Finite Horizon, Linear Time-Varying Systems

We present an efficient algorithm to compute the induced norms of finite-horizon Linear Time-Varying (LTV) systems. The formulation includes both induced $\mathcal {L}_{2}$ and terminal Euclidean norm penalties on outputs. Existing computational approaches include the power iteration and the bisection of a Riccati Differential Equation (RDE). The power iteration has low computation time per iteration but overall convergence can be slow. In contrast, the RDE condition provides guaranteed bounds on the induced gain but single RDE integration can be slow. The complementary features of these two algorithms are combined to develop a new algorithm that is both fast and provides provable upper and lower bounds on the induced norm within the desired tolerance. The algorithm also provides a worst-case disturbance input that achieves the lower bound on the norm. We also present a new proof which shows that the power iteration for this problem converges monotonically. Finally, we show a controllability Gramian based simpler computational method for induced $\mathcal {L}_{2}$ -to-Euclidean norm. This can be used to compute the reachable set at any time on the horizon. Numerical examples are provided to demonstrate the proposed algorithm.
Transforming Domestic Violence Representation

The dominant theories used in the law to explain domestic violence, namely, the Power and Control Wheel and the Cycle of Violence, provide only limited insight into intimate partner abuse. Both theories focus exclusively on the abusive partner' wrongful actions, consistent with recent decades' concentration on criminalization, but fail to educate about the survivor's needs and efforts to end violence. The Stages of Change Model, conversely, reveals that domestic abuse survivors seek an end to relationship violence through a five-stage cyclical sequence and identifies the survivor's needs and actions at each stage. This critical information should inform the representation of abuse survivors; however, this model remains unknown in the legal profession, and this article is the first scholarship to apply this model to lawyering. This article evaluates the contributions andshortcomings of the dominant models. It examines how the Stages of Change Modelfills a significantvoid and how insights from the Stages of Change Model can transform the representation of abuse survivors. Domestic violence representation presents unique challenges to lawyers as they struggle with limited conceptions of their role, assumptions about abuse victims and how they should respond to violence, andfeelings offear and frustration when clients return to abusive partners. The Stages of Change Model can concretely illuminate the general client-centered model of legal representation and suggest multiple lawyering lessons for representing domestic violence survivors. The Stages of Change Model is widely accepted in the field of psychology, has been validated by numerous social science studies, and has the potential to achieve more clientcentered legal representation with greater safety outcomes. As I explore the enormous implications this model has for client representation in domestic violence law, I draw on my experience teaching domestic violence clinics and the transformation my students report when they learn and apply this model. t Assistant Professor of Law and Director, Domestic Violence Clinic, Seattle University School of Law. LL.M., Georgetown University Law Center; J.D., Harvard Law School; B.A., University of Kansas. I am grateful to Ann Cammett, Lauren Bennett Cattaneo, Robert Chang, Margaret Drew, Theresa Glennon, Phyllis Goldfarb, Laurie Kohn,Tamara Kuennen, Elizabeth MacDowell, Natasha Martin, Binny Miller, David Min, Ann Shalleck, Jane P. Stoever, and David Ziff for their valuable comments. I wish to thank Michael Callahan, Tierney Edwards, Rebecca Rook, and Dominique Scalia for their research assistance. I am indebted to Lauren Bennett Cattaneo, who first introduced me to the Stages of Change Model, and to my early clinical teaching mentors, Deborah Epstein, Laurie Kohn, and Ann Shalleck. KENTUCKY LAW JOURNAL
Homeownership 2.0

INTRODUCTION 1048 I. THE ELEMENTS OF HOMEOWNERSHIP 1053 A. Homeownership as Consumption 1054 B. Homeownership as Investment 1059 C. Decoupling Initiatives, Past and Present 1063 II. INTRODUCING H2.0 1070 A. Configuring H2.0 1071 B. Disaggregating Onsite and Offsite Influences 1073 C. Advantages for Homeowners 1078 D. Off the Rack or Build from Scratch? 1082 III. COGNITIVE IMPLICATIONS 1088 A. Barriers to Acceptance 1088 B. Potential for Misuse 1095 IV. LOCAL PARTICIPATION AND POLITICS 1098 A. Homeowner Collective Control 1098 B. Investors and Local Governance 1103 V. OTHER SOCIETAL IMPACTS 1109 A. Stability andStickiness in Housing Markets 1109 B. Competitive Consumption 1111 C. Autonomy and Conformity 1113 VI. AVENUES FOR FURTHER RESEARCH 1114 A. Tailored Risk-Bearing: H3.0 and Beyond 1114 B. Government's Role 1115 C. Beyond Housing 1117 CONCLUSION 1118 INTRODUCTION Current legal arrangements make homeowners high-stakes gamblers.1 Homebuyers routinely take on crushing debt loads to put large sums of money into risky, undiversified ventures that are utterly out of their personal control-local housing markets.2 That these markets typically post positive returns over time offers little comfort to those caught on the downside of housing market volatility. Moreover, because rights to these expected gains are priced into the home, many would-be buyers are priced out of the market.3 The shortcomings of the homeowner's investment package have not escaped notice, and for decades scholars and innovators have tried to devise better ways to manage the upside and downside risks of owning a home.4 Derivatives markets for such risk have recently begun to emerge, due in large part to the collaborative efforts of Karl case, Robert Shiller, and Allan Weiss.5 As the technical capacity to slice, dice, and trade homeownership risk advances, this Article steps back to examine how a reduced-risk version of homeownership fits together with property theory, human cognition, and the social dynamics of neighborhoods and metropolitan areas. …
Dynamic interventions and informational linkages

Abstract We model a dynamic economy with strategic complementarity among investors and study how endogenous government interventions mitigate coordination failures. We establish equilibrium existence and uniqueness, and we show that one intervention can affect another through altering the public information structure. A stronger initial intervention helps subsequent interventions through increasing the likelihood of positive news, but also leads to negative conditional updates. Our results suggest optimal policy should emphasize initial interventions when coordination outcomes tend to correlate. Neglecting informational externalities of initial interventions results in over- or under-interventions. Moreover, saving smaller funds disproportionally more can generate greater informational benefits at smaller costs.