cssi/SciSciGPT-SciSciNet huggingface dataset:
THE EFFECT OF RISING INCOME INEQUALITY ON TAXATION AND PUBLIC EXPENDITURES: EVIDENCE FROM U.S. MUNICIPALITIES AND SCHOOL DISTRICTS, 1970-2000

The income distribution in many developed countries widened dramatically from 1970 to 2000. Some scholars argue that income inequality contributes to a host of social ills by undermining voters' will- ingness to support public expenditures. In contrast, we find that growing income inequality is associated with an expansion in government reven- ues and expenditures on a wide range of services in U.S. municipalities and school districts. Results are robust to a number of model specifica- tions, including instrumental variables that address the endogeneity of the local income distribution. Our results are inconsistent with models pre- dicting that heterogeneous societies provide lower levels of public goods. public sector. 3 Two types of identification problems com- plicate estimation of the empirical relationship between income inequality and public goods provision. The cross- country variation often used for this exercise suffers from omitted variable bias; that is, countries with high income inequality may also have other characteristics that could limit the size of the public sector. Cross-state comparisons also suffer from endogenous household sorting. If low- or high-income families migrate to states with high public expenditures, the positive association observed in the litera- ture between state public expenditures and income inequal- ity may be spurious. In this paper, we examine the relationship between income inequality and government finances in municipali- ties and school districts in the United States from 1970 to 2000. Local government represents a large segment of the economy; in fiscal year 2009, local governments disbursed more than $3 trillion for such important services as educa- tion and public safety. 4 Our study has several advantages over existing empirical work. First, the large number of local governments in our data exhibit much greater variation in income inequality over time than do the small number of countries or states used in previous studies. A large sample size allows us to separately control for changes in the top end or bottom end of the income distribution and for initial levels of inequal- ity, thus ruling out a mechanical association between tax revenues and rising income for the affluent. Second, we develop an instrumental variable strategy to mitigate con- cerns about potential reverse causality from the endogenous
Taking Burlington Northern Railroad Private

Berkshire Hathaway took Burlington Northern Santa Fe Railroad private in November 2009 in a deal valued at $100 per share, which was approximately 31% higher than the railroad’s share price at the time. Surprisingly, the deal came under immediate criticism from sources relatively close to Warren Buffett, including his official biographer and the value investing professor at his alma mater. The criticism centered on the deal’s price and claimed, essentially, that Buffett uncharacteristically paid too much for Burlington. The author analyzes this deal by valuing Burlington at the time of its acquisition using the modern Graham and Dodd approach. In doing so, he found that it involves a situation that many private equity acquirers could face—namely, evaluating the purchase of a profitable and stable firm with seemingly low-risk expected improvements, which command a higher multiple. In the author’s valuation, he identifies Burlington’s expected improvements and assesses the risks generated by them in a way that could facilitate post-acquisition value realization, which may prove useful to private equity investors.
Housing, Health, and Annuities

Annuities, long-term care insurance (LTCI), and reverse mortgages appear to offer important consumption smoothing benefits to the elderly, yet private markets for these products are small. A prominent idea is to combine LTCI and annuities to alleviate both supply (selection) and demand (liquidity) problems in these markets. This article shows that if consumers typically liquidate home equity only in the event of illness or very old age, then LTCI and annuities become less attractive and may become substitutes rather than complements. The reason is that the marginal utility of wealth drops when an otherwise illiquid home is sold, an event correlated with the payouts of both annuities and LTCI. Simulations confirm that demand for LTCI and
Market–adjusted options for executive compensation

Compensation theory implies that managers should not be rewarded or penalised for factors outside their control. However, firms do not adjust the exercise prices of executive stock options to reflect overall stock market movements that are outside the control of the manager. This results in an option much more expensive than necessary to reward a particular level of relative performance. Current accounting rules give firms a strong incentive not to adjust the prices of the options, since to do so would result in a higher reported expense despite the lower economic cost.
Rebalancing versus buy and hold: theory, simulation and empirical analysis

We consider returns from rebalanced and buy and hold portfolios consisting of the same stocks. Theoretical properties are derived using Jensen’s inequality and the Holder’s Defect Formula. Simulations are used to confirm theory and to investigate ambiguous cases where theory is silent. Rebalancing decreases total return volatility, while buy and hold produces greater expected return. Results are more opaque with respect to Sharpe Ratios and expected geometric means. Our empirical tests are based on portfolios composed of the risk-free asset, CRSP market value returns and returns from five Fama–French industries. While rebalancing reduces volatility and momentum effect, our tests largely favor the buy and hold strategy due to the high relative returns enjoyed by stocks vis-a-vis the risk-free asset. Transactions cost for rebalancing the portfolio are economically negligible.
Property as Control: The Case of Information

Before long, the fragmented, uncoordinated, and geographically dispersed paper records in which our medical information is currently recorded will be replaced by integrated, longitudinal, networked electronic health records (“EHRs”). Though nominally confidential, the information in EHRs, like other information collected about individuals in cyberspace, is as vulnerable as it is valuable. Health law, privacy, and intellectual property scholars have all suggested that the river of information created by EHRs and other data systems present a problem of “control,” and many of these scholars have proposed that “property” might provide the control individuals want and need. These arguments for control rights in personal information test contemporary understandings of what property is and reveal fault lines in modern property theory. If property rights exist at all in dephysicalized, digitalized information, those rights are unlikely to be consolidated in a single person, to operate in rem, to grant owners significant powers to exclude, or to be standardized - all qualities that, in the eyes of some, are required of true “property” interests. Moreover, it is not clear that the rhetoric of property properly addresses the values at play with respect to medical and personal information, where the asset in question is some aspect of our very selves. Finally, if there is “property” in information, it may be a very unappealing kind of property - a claim derived from envy of the value that others have found in what we had ignored or thought worthless. The question of how power and control over information will be shared between individuals and others involves hard public policy choices. But because property theory is itself deeply divided over the extent to which property provides control, “property” itself cannot determine how these choices should be made.
Competing for the Public Through the News Media

Interest groups seek to influence economic activity through public and private politics. Public politics takes place in the arena of public institutions, whereas private politics takes place outside public institutions often in the arena of public sentiment. Private politics refers to action by interest groups directed at private parties, as in the case of an activist group launching a campaign against a firm. This paper presents a model of informational competition between an activist and an industry, where both interest groups seek to influence public sentiment and do so by advocating their positions through the news media. Citizen consumers make both a private consumption decision and a collective choice on the regulation of a product that has an externality associated with it. In the absence of the news organization, the collective choice is not to regulate. The activist and the industry obtain private, hard information on the seriousness of the externality and provide favorable information to the news media and may conceal unfavorable information. The news media can conduct investigative journalism to obtain its own information, and based on that information and the information it has received from its sources, provides a news report to the public. Because of its role in society, the media has an incentive to bias its report, and the direction of bias is toward regulation. Its bias serves to mitigate both market failure by decreasing demand and a government failure by shifting votes in favor of regulation. The activist then has incentive to conceal information unfavorable to its interests, whereas the industry fully reveals its information.
Carleson perturbations of elliptic operators on domains with low dimensional boundaries

Abstract We prove an analogue of a perturbation result for the Dirichlet problem of divergence form elliptic operators by Fefferman, Kenig and Pipher, for the degenerate elliptic operators of David, Feneuil and Mayboroda, which were developed to study geometric and analytic properties of sets with boundaries whose co-dimension is higher than 1. These operators are of the form − div A ∇ , where A is a weighted elliptic matrix crafted to weigh the distance to the high co-dimension boundary in a way that allows for the nourishment of an elliptic PDE theory. When this boundary is a d-Alhfors-David regular set in R n with d ∈ [ 1 , n − 1 ) and n ≥ 3 , we prove that the membership of the harmonic measure in A ∞ is preserved under Carleson measure perturbations of the matrix of coefficients, yielding in turn that the L p -solvability of the Dirichlet problem is also stable under these perturbations (with possibly different p). If the Carleson measure perturbations are suitably small, we establish solvability of the Dirichlet problem in the same L p space. One of the corollaries of our results together with a previous result of David, Engelstein and Mayboroda, is that, given any d-ADR boundary Γ with d ∈ [ 1 , n − 2 ) , n ≥ 3 , there is a family of degenerate operators of the form described above whose harmonic measure is absolutely continuous with respect to the d-dimensional Hausdorff measure on Γ.
AFTA after NAFTA: Regional Trade Blocs and the Propagation of Environmental and Labor Standards

Acknowledging the special difficulty of managing the politically critical linkage of free trade with labor welfare and environmental protection across the Pacific Basin, this Article argues that, despite the differences between various regional free trade groupings, regional development of this linkage presents distinct advantages over the GATT model of global, multilateral standards. It examines the regional model that evolved as a product of controversies over NAFTA (and particularly the dispute resolution model originated in the NAFTA Side Agreements). The Article demonstrates how this model can be drawn upon to facilitate constructive linkage of trade liberalization with environmental and labor welfare, to meet the challenges for developing a Pacific Basin free trade area.
When Machine Learning Meets Quantum Computers: A Case Study

Along with the development of AI democratization, the machine learning approach, in particular neural networks, has been applied to wide-range applications. In different application scenarios, the neural network will be accelerated on the tailored computing platform. The acceleration of neural networks on classical computing platforms, such as CPU, GPU, FPGA, ASIC, has been widely studied; however, when the scale of the application consistently grows up, the memory bottleneck becomes obvious, widely known as memory-wall. In response to such a challenge, advanced quantum computing, which can represent 2N states with N quantum bits (qubits), is regarded as a promising solution. It is imminent to know how to design the quantum circuit for accelerating neural networks. Most recently, there are initial works studying how to map neural networks to actual quantum processors. To better understand the state-of-the-art design and inspire new design methodology, this paper carries out a case study to demonstrate an end-to-end implementation. On the neural network side, we employ the multilayer perceptron to complete image classification tasks using the standard and widely used MNIST dataset. On the quantum computing side, we target IBM Quantum processors, which can be programmed and simulated by using IBM Qiskit. This work targets the acceleration of the inference phase of a trained neural network on the quantum processor. Along with the case study, we will demonstrate the typical procedure for mapping neural networks to quantum circuits.
Multi-user Capacity of Cyclic Prefix Direct Sequence Spread Spectrum with Linear Detection and Precoding

Cyclic Prefix Direct Sequence Spread Spectrum (CP-DSSS) is a promising solution to increase user density for massive machine type communication (mMTC) applications. We propose that in such applications, the CP-DSSS waveform would operate as a secondary network, using the same spectrum as the primary network but at much lower signal to noise ratio (SNR). In this paper, we evaluate per-user capacity of CP-DSSS when simple matched filtering (MF) is performed on the uplink and time-reversal (TR) precoding is used on the downlink. In this setting and when operating in the low SNR regime, CP-DSSS achieves a per-user capacity that is near the optimum single-user capacity. TR precoding converges to the optimal capacity as the number of antennas at the hub/gateway increases. Using the estimated channel impulse response for MF and TR introduces little to no capacity loss. Given the near-optimal performance of MF detection and TR precoding for each of the users, CP-DSSS can be implemented with simple device transceiver structures, reducing per-unit cost for massively deployed 5G and beyond networks.
Module categories over affine supergroup schemes

Abstract Let k be an algebraically closed field of characteristic 0 or p > 2 . Let G be an affine supergroup scheme over k. We classify the indecomposable exact module categories over the tensor category sCoh f ( G ) of (coherent sheaves of) finite dimensional O ( G ) -supermodules in terms of ( H , Ψ ) -equivariant coherent sheaves on G . We deduce from it the classification of indecomposable geometrical module categories over sRep ( G ) . When G is finite, this yields the classification of all indecomposable exact module categories over the finite tensor category sRep ( G ) . In particular, we obtain a classification of twists for the supergroup algebra k G of a finite supergroup scheme G , and then combine it with [7, Corollary 4.1] to classify finite dimensional triangular Hopf algebras with the Chevalley property over k.
Directed long-range transport of a nearly pure component atom clusters by the electromigration of a binary surface alloy

Assuming a vacancy-mediated diffusion, a continuum model for electromigration-driven transport of an embedded atom cluster across a surface terrace of a phase-separating ${\mathrm{A}}_{x}{\mathrm{B}}_{1\ensuremath{-}x}$ surface alloy, such as fcc AgPt(111), is presented. Computations show that the electron wind carries the cluster over hundreds of lattice spacings and in the set direction, while the cluster grows and its purity improves during the drift. Impacts of the current density, the diffusion anisotropy, the magnitude, and sign of the ratio of the effective charges ${q}_{A}/{q}_{B}$, and the jump frequencies ratio ${\mathrm{\ensuremath{\Gamma}}}_{A}/{\mathrm{\ensuremath{\Gamma}}}_{B}$ on the cluster's drift speed, drift direction, purity, and shape are demonstrated.
A superconducting switch actuated by injection of high-energy electrons.

Recent experiments with metallic nanowires devices seem to indicate that superconductivity can be controlled by the application of electric fields. In such experiments, critical currents are tuned and eventually suppressed by relatively small voltages applied to nearby gate electrodes, at odds with current understanding of electrostatic screening in metals. We investigate the impact of gate voltages on superconductivity in similar metal nanowires. Varying materials and device geometries, we study the physical mechanism behind the quench of superconductivity. We demonstrate that the transition from superconducting to resistive state can be understood in detail by tunneling of high-energy electrons from the gate contact to the nanowire, resulting in quasiparticle generation and, at sufficiently large currents, heating. Onset of critical current suppression occurs below gate currents of 100fA, which are challenging to detect in typical experiments. A recent finding of tuning critical current in metallic nanowires by application of small gate voltages seems at odds with general understanding. Here, Ritter et al. study similar nanowires and link the origin of the critical current suppression to tunneling of few high-energy electrons between gate and nanowire, ruling out direct tuning by electric fields.
Stretch-activated ion channels identified in the touch-sensitive structures of carnivorous Droseraceae plants.

In response to touch, some carnivorous plants such as the Venus flytrap have evolved spectacular movements to capture animals for nutrient acquisition. However, the molecules that confer this sensitivity remain unknown. We used comparative transcriptomics to show that expression of three genes encoding homologs of the MscS-Like (MSL) and OSCA/TMEM63 family of mechanosensitive ion channels are localized to touch-sensitive trigger hairs of Venus flytrap. We focus here on the candidate with the most enriched expression in trigger hairs, the MSL homolog FLYCATCHER1 (FLYC1). We show that FLYC1 transcripts are localized to mechanosensory cells within the trigger hair, transfecting FLYC1 induces chloride-permeable stretch-activated currents in naive cells, and transcripts coding for FLYC1 homologs are expressed in touch-sensing cells of Cape sundew, a related carnivorous plant of the Droseraceae family. Our data suggest that the mechanism of prey recognition in carnivorous Droseraceae evolved by co-opting ancestral mechanosensitive ion channels to sense touch.
Face Off: Polarized Public Opinions on Personal Face Mask Usage during the COVID-19 Pandemic

In spite of a growing body of scientific evidence on the effectiveness of individual face mask usage for reducing transmission rates [1], individual face mask usage has become a highly polarized topic within the United States. A series of policy shifts by various governmental bodies have been speculated to have contributed to the polarization of face masks. A typical method to investigate the effects of these policy shifts is to use surveys. However, survey-based approaches have multiple limitations: biased responses, limited sample size, badly crafted questions may skew responses and inhibit insight, and responses may prove quickly irrelevant as opinions change in response to a dynamic topic. We propose a novel approach to 1) accurately gauge public sentiment towards face masks in the United States during COVID-19 using a multi-modal demographic inference framework with topic modeling and 2) determine whether face mask policy shifts contributed to polarization towards face masks using offline change point analysis on Twitter data. First, we infer several key demographics of individual Twitter users such as their age, gender, and whether they are a college student using a multi-modal demographic prediction framework and analyze the average sentiment for each respective demographic. Next, we conduct topic analysis using latent Dirichlet allocation (LDA). Finally, we conduct offline change point discovery on our sentiment time series data using the Pruned Exact Linear Time (PELT) search algorithm. Experimental results on a large corpus of Twitter data reveal multiple insights regarding demographic sentiment towards face masks that agree with existing surveys. Furthermore, we find two key policy-shift events contributed to statistically significant changes in sentiment for both Republicans and Democrats.
Road Damage Detection using Deep Ensemble Learning

Road damage detection is critical for the maintenance of a road, which traditionally has been performed using expensive high-performance sensors. With the recent advances in technology, especially in computer vision, it is now possible to detect and categorize different types of road damages, which can facilitate efficient maintenance and resource management. In this work, an ensemble model for efficient detection and classification of road damages, which has been submitted to the IEEE BigData Cup Challenge 2020. The solution utilizes a state-of-the-art object detector known as You Only Look Once (YOLO-v4), which is trained on images of various types of road damages from Czech, Japan and India. The ensemble approach was extensively tested with several different model versions and it was able to achieve an F1 score of 0.628 on the test 1 dataset and 0.6358 on the test 2 dataset.
The existence of minimizers for an isoperimetric problem with Wasserstein penalty term in unbounded domains

In this article, we consider the (double) minimization problem $$\min\left\{P(E;\Omega)+\lambda W_p(E,F):~E\subseteq\Omega,~F\subseteq \mathbb{R}^d,~\lvert E\cap F\rvert=0,~ \lvert E\rvert=\lvert F\rvert=1\right\},$$ where $p\geqslant 1$, $\Omega$ is a (possibly unbounded) domain in $\mathbb{R}^d$, $P(E;\Omega)$ denotes the relative perimeter of $E$ in $\Omega$ and $W_p$ denotes the $p$-Wasserstein distance. When $\Omega$ is unbounded and $d\geqslant 3$, it is an open problem proposed by Buttazzo, Carlier and Laborde in the paper ON THE WASSERSTEIN DISTANCE BETWEEN MUTUALLY SINGULAR MEASURES. We prove the existence of minimizers to this problem when $\frac{1}{p}+\frac{2}{d}>1$, $\Omega=\mathbb{R}^d$ and $\lambda$ is sufficiently small.
Young Adults’ Physical Distancing Behaviors During the Initial Months of the COVID-19 Pandemic: Adherence to Guidelines and Associations With Alcohol Use Behavior

Throughout the COVID-19 pandemic, the scientific and public health communities have become increasingly focused on understanding young adults’ physical distancing behaviors and the role that young ...
On the Complexity of Finding Tensor Ranks

The purpose of this note is to give a linear algebra algorithm to find out if a rank of a given tensor over a field $$\mathbb {F}$$ is at most k over the algebraic closure of $$\mathbb {F}$$ , where k is a given positive integer. We estimate the arithmetic complexity of our algorithm.
A Heuristic Independent Particle Approximation to Determinantal Point Processes

A determinantal point process is a stochastic point process that is commonly used to capture negative correlations. It has become increasingly popular in machine learning in recent years. Sampling a determinantal point process however remains a computationally intensive task. This note introduces a heuristic independent particle approximation to determinantal point processes. The approximation is based on the physical intuition of fermions and is implemented using standard numerical linear algebra routines. Sampling from this independent particle approximation can be performed at a negligible cost. Numerical results are provided to demonstrate the performance of the proposed algorithm.
Inverse design and flexible parameterization of meta-optics using algorithmic differentiation

Ultrathin meta-optics offer unmatched, multifunctional control of light. Next-generation optical technologies, however, demand unprecedented performance. This will likely require design algorithms surpassing the capability of human intuition. For the adjoint method, this requires explicitly deriving gradients, which is sometimes challenging for certain photonics problems. Existing techniques also comprise a patchwork of application-specific algorithms, each focused in scope and scatterer type. Here, we leverage algorithmic differentiation as used in artificial neural networks, treating photonic design parameters as trainable weights, optical sources as inputs, and encapsulating device performance in the loss function. By solving a complex, degenerate eigenproblem and formulating rigorous coupled-wave analysis as a computational graph, we support both arbitrary, parameterized scatterers and topology optimization. With iteration times below the cost of two forward simulations typical of adjoint methods, we generate multilayer, multifunctional, and aperiodic meta-optics. As an open-source platform adaptable to other algorithms and problems, we enable fast and flexible meta-optical design. The design of meta-optics and photonics components poses a number of high-dimensional, fabrication-constrained, computationally expensive optimization problems. Here, the authors propose a framework based on algorithmic differentiation to optimize arbitrary meta-optical devices with any shape or topology, enabling rapid inverse design of photonics and complex meta-optical devices.
Earth’s carbon deficit caused by early loss through irreversible sublimation

Carbon is an essential element for life, but its behavior during Earth’s accretion is not well understood. Carbonaceous grains in meteoritic and cometary materials suggest that irreversible sublimation, and not condensation, governs carbon acquisition by terrestrial worlds. Through astronomical observations and modeling, we show that the sublimation front of carbon carriers in the solar nebula, or the soot line, moved inward quickly so that carbon-rich ingredients would be available for accretion at 1 astronomical unit after the first million years. On the other hand, geological constraints firmly establish a severe carbon deficit in Earth, requiring the destruction of inherited carbonaceous organics in the majority of its building blocks. The carbon-poor nature of Earth thus implies carbon loss in its precursor material through sublimation within the first million years.
Widening the Range of Trackable Environmental and Health Pollutants for Li-Garnet-Based Sensors

Classic chemical sensors integrated in phones, vehicles, and industrial plants monitor the levels of humidity or carbonaceous/oxygen species to track environmental changes. Current projections for the next two decades indicate the strong need to increase the ability of sensors to sense a wider range of chemicals for future electronics not only to continue monitoring environmental changes but also to ensure the health and safety of humans. To achieve this goal, more chemical sensing principles and hardware must be developed. Here, a proof-of-principle for the specific electrochemistry, material selection, and design of a Li-garnet Li7 La3 Zr2 O12 (LLZO)-based electrochemical sensor is provided, targeting the highly corrosive environmental pollutant sulfur dioxide (SO2 ). This work extends the prime use of LLZO as a battery component as well as the range of trackable pollutants for potential future sensor-noses. Novel composite sensing-electrode designs using LLZO-based porous scaffolds are employed to define a high number of reaction sites, and successfully track SO2 at the dangerous levels of 0-10 ppm with close-to-theoretical SO2 sensitivity. The insights on the sensing electrochemistry, phase stability and sensing electrode/Li+ electrolyte structures provide first guidelines for future Li-garnet sensors to monitor a wider range of environmental pollutants and toxins.
Investigation of patient‐sharing networks using a Bayesian network model selection approach for congruence class models

A Bayesian approach to conduct network model selection is presented for a general class of network models referred to as the congruence class models (CCMs). CCMs form a broad class that includes as special cases several common network models, such as the Erdős-Renyi-Gilbert model, stochastic block model, and many exponential random graph models. Due to the range of models that can be specified as CCMs, our proposed method is better able to select models consistent with generative mechanisms associated with observed networks than are current approaches. In addition, our approach allows for incorporation of prior information. We illustrate the use of this approach to select among several different proposed mechanisms for the structure of patient-sharing networks; such networks have been found to be associated with the cost and quality of medical care. We found evidence in support of heterogeneity in sociality but not selective mixing by provider type or degree.
Real-time PCR assay for detection and differentiation of Coccidioides immitis and Coccidioides posadasii from culture and clinical specimens.

Coccidioidomycosis (Valley Fever) is a pulmonary and systemic fungal disease with increasing incidence and expanding endemic areas. The differentiation of etiologic agents Coccidioides immitis and C. posadasii remains problematic in the clinical laboratories as conventional PCR and satellite typing schemes are not facile. Therefore, we developed Cy5- and FAM-labeled TaqMan-probes for duplex real-time PCR assay for rapid differentiation of C. immitis and C. posadasii from culture and clinical specimens. The RRA2 gene encoding proline-rich antigen 2, specific for Coccidioides genus, was the source for the first set of primers and probe. Coccidioides immitis contig 2.2 (GenBank: AAEC02000002.1) was used to design the second set of primers and probe. The second primers/probe did not amplify the corresponding C. posadasii DNA, because of an 86-bp deletion in the contig. The assay was highly sensitive with limit of detection of 0.1 pg gDNA/PCR reaction, which was equivalent to approximately ten genome copies of C. immitis or C. posadasii. The assay was highly specific with no cross-reactivity to the wide range of fungal and bacterial pathogens. Retrospective analysis of fungal isolates and primary specimens submitted from 1995 to 2020 confirmed 168 isolates and four primary specimens as C. posadasii and 30 isolates as C. immitis from human coccidioidomycosis cases, while all eight primary samples from two animals (rhesus monkey and rhinoceros) were confirmed as C. posadasii. A preliminary analysis of cerebrospinal fluid (CSF) and pleural fluid samples showed positive correlation between serology tests and real-time PCR for two of the 15 samples. The Coccidioides spp. duplex real-time PCR will allow rapid differentiation of C. immitis and C. posadasii from clinical specimens and further augment the treatment and surveillance of coccidioidomycosis.
SoWaF: Shuffling of Weights and Feature Maps: A Novel Hardware Intrinsic Attack (HIA) on Convolutional Neural Network (CNN)

Security of inference phase deployment of Convolutional neural network (CNN) into resource constrained embedded systems (e.g. low end FPGAs) is a growing research area. Using secure practices, third party FPGA designers can be provided with no knowledge of initial and final classification layers. In this work, we demonstrate that hardware intrinsic attack (HIA) in such a "secure" design is still possible. Proposed HIA is inserted inside mathematical operations of individual layers of CNN, which propagates erroneous operations in all the subsequent CNN layers that leads to misclassification. The attack is non-periodic and completely random, hence it becomes difficult to detect. Five different attack scenarios with respect to each CNN layer are designed and evaluated based on the overhead resources and the rate of triggering in comparison to the original implementation. Our results for two CNN architectures show that in all the attack scenarios, additional latency is negligible (< 0.61%), increment in DSP, LUT, FF is also less than 2.36%. Three attack scenarios does not require any additional BRAM resources, while in two scenarios BRAM increases, which compensates with the corresponding decrease in FF and LUTs. To the authors' best knowledge this work is the first to address the hardware intrinsic CNN attack with attacker does not have knowledge of the full CNN.
Regenerated interneurons integrate into locomotor circuitry following spinal cord injury.

Abstract Whereas humans and other adult mammals lack the ability to regain locomotor function after spinal cord injury, zebrafish are able to recover swimming behavior even after complete spinal cord transection. We have previously shown that zebrafish larvae regenerate lost spinal cord neurons within 9 days post-injury (dpi), but it is unknown whether these neurons are physiologically active or integrate into functional circuitry. Here we show that genetically defined premotor interneurons are regenerated in injured spinal cord segments as functional recovery begins. Further, we show that these newly-generated interneurons receive excitatory input and fire synchronously with motor output by 9 dpi. Taken together, our data indicate that regenerative neurogenesis in the zebrafish spinal cord produces interneurons with the ability to integrate into existing locomotor circuitry.
Fractional moments of the stochastic heat equation

Consider the solution Z(t,x) of the one-dimensional stochastic heat equation, with a multiplicative spacetime white noise, and with the delta initial data Z(0,x)=δ(x). For any real p>0, we obtained detailed estimates of the pth moment of et/12Z(2t,0), as t→∞, and from these estimates establish the one-point upper-tail large deviation principle of the Kardar–Parisi–Zhang equation. The deviations have speed t and rate function Φ+(y)=43y3/2. Our result confirms the existing physics predictions [Europhys. Lett. 113 (2016) 60004] and also [Phys. Rev. E 94 (2016) 032108].
Dopamine differentially modulates the size of projection neuron ensembles in the intact and dopamine-depleted striatum

Dopamine (DA) is a critical modulator of brain circuits that control voluntary movements, but our understanding of its influence on the activity of target neurons in vivo remains limited. Here, we use two-photon Ca2+ imaging to monitor the activity of direct and indirect-pathway spiny projection neurons (SPNs) simultaneously in the striatum of behaving mice during acute and prolonged manipulations of DA signaling. We find that increasing and decreasing DA biases striatal activity towards the direct and indirect pathways, respectively, by changing the overall number of SPNs recruited during behavior in a manner not predicted by existing models of DA function. This modulation is drastically altered in a model of Parkinson's disease. Our results reveal a previously unappreciated population-level influence of DA on striatal output and provide novel insights into the pathophysiology of Parkinson's disease.
Chasm in Hegemony: Explaining and Reproducing Disparities in Homophilous Networks

In networks with a minority and a majority community, it is well-studied that minorities are under-represented at the top of the social hierarchy. However, researchers are less clear about the representation of minorities from the lower levels of the hierarchy, where other disadvantages or vulnerabilities may exist. We offer a more complete picture of social disparities at each social level with empirical evidence that the minority representation exhibits two opposite phases: at the higher rungs of the social ladder, the representation of the minority community decreases; but, lower in the ladder, which is more populous, as you ascend, the representation of the minority community improves. We refer to this opposing phenomenon between the upper-level and lower-level as the chasm effect. Previous models of network growth with homophily fail to detect and explain the presence of this chasm effect. We analyze the interactions among a few well-observed network-growing mechanisms with a simple model to reveal the sufficient and necessary conditions for both phases in the chasm effect to occur. By generalizing the simple model naturally, we present a complete bi-affiliation bipartite network-growth model that could successfully capture disparities at all social levels and reproduce real social networks. Finally, we illustrate that addressing the chasm effect can create fairer systems with two applications in advertisement and fact-checks, thereby demonstrating the potential impact of the chasm effect on the future research of minority-majority disparities and fair algorithms.
BluesMPI: Efficient MPI Non-blocking Alltoall Offloading Designs on Modern BlueField Smart NICs

In the state-of-the-art production quality MPI (Message Passing Interface) libraries, communication progress is either performed by the main thread or a separate communication progress thread. Taking advantage of separate communication threads can lead to a higher overlap of communication and computation as well as reduced total application execution time. However, such an approach can also lead to contention for CPU resources leading to sub-par application performance as the application itself has less number of available cores for computation. Recently, Mellanox has introduced the BlueField series of adapters which combine the advanced capabilities of traditional ASIC based network adapters with an array of ARM processors. In this paper, we propose BluesMPI, a high performance MPI non-blocking Alltoall design that can be used to offload MPI_Ialltoall collective operations from the host CPU to the Smart NIC. BluesMPI guarantees the full overlap of communication and computation for Alltoall collective operations while providing on-par pure communication latency to CPU based on-loading designs. We explore several designs to achieve the best pure communication latency for MPI_Ialltoall. Our experiments show that BluesMPI can improve the total execution time of the OSU Micro Benchmark for MPI_Ialltoall and P3DFFT application up to 44% and 30%, respectively. To the best of our knowledge, this is the first design that efficiently takes advantage of modern BlueField Smart NICs in deriving the MPI Alltoall collective operation to get peak overlap of communication and computation.
A System for Automated Open-Source Threat Intelligence Gathering and Management

To remain aware of the fast-evolving cyber threat landscape, open-source Cyber Threat Intelligence (OSCTI) has received growing attention from the community. Commonly, knowledge about threats is presented in a vast number of OSCTI reports. Despite the pressing need for high-quality OSCTI, existing OSCTI gathering and management platforms, however, have primarily focused on isolated, low-level Indicators of Compromise. On the other hand, higher-level concepts (e.g., adversary tactics, techniques, and procedures) and their relationships have been overlooked, which contain essential knowledge about threat behaviors that is critical to uncovering the complete threat scenario. To bridge the gap, we propose SecurityKG, a system for automated OSCTI gathering and management. SecurityKG collects OSCTI reports from various sources, uses a combination of AI and NLP techniques to extract high-fidelity knowledge about threat behaviors, and constructs a security knowledge graph. SecurityKG also provides a UI that supports various types of interactivity to facilitate knowledge graph exploration.
Plume and discharge plasma measurements of an NSTAR-type Ion thruster

The success of the NASA Deep Space 1 spacecraft has demonstrated that ion propulsion is a viable option for deep space science missions. More aggressive missions such as Comet Nuclear Sample Return and Europa lander will require higher power, higher propellant throughput and longer thruster lifetime than the NASA Solar Electric Propulsion Technology Application Readiness (NSTAR) engine. Presented here are thruster plume and discharge plasma measurements of an NSTAR-type thruster operated from 0.5 kW to 5 kW. From Faraday plume sweeps, beam divergence was determined. From Langmuir probe plume measurements on centerline, low energy ion production on axis due to charge-exchange and direct ionization was assessed. Additionally, plume plasma potential measurements made on axis were used to determine the upper energy limits at which ions created on centerline could be radially accelerated. Wall probes flush-mounted to the thruster discharge chamber anode were used to assess plasma conditions. Langmuir probe measurements at the wall indicated significant differences in the electron temperature in the cylindrical and conical sections of the discharge chamber.
Central limit theorem for toric Kähler manifolds

Associated to the Bergman kernels of a polarized toric \kahler manifold $(M, \omega, L, h)$ are sequences of measures $\{\mu_k^z\}_{k=1}^{\infty}$ parametrized by the points $z \in M$. For each $z$ in the open orbit, we prove a central limit theorem for $\mu_k^z$. The center of mass of $\mu_k^z$ is the image of $z$ under the moment map; after re-centering at $0$ and dilating by $\sqrt{k}$, the re-normalized measure tends to a centered Gaussian whose variance is the Hessian of the \kahler potential at $z$. We further give a remainder estimate of Berry-Esseen type. The sequence $\{\mu_k^z\}$ is generally not a sequence of convolution powers and the proofs only involve \kahler analysis.
Adversarial Threats to DeepFake Detection: A Practical Perspective

Facially manipulated images and videos or DeepFakes can be used maliciously to fuel misinformation or defame individuals. Therefore, detecting DeepFakes is crucial to increase the credibility of social media platforms and other media sharing web sites. State-of-the art DeepFake detection techniques rely on neural network based classification models which are known to be vulnerable to adversarial examples. In this work, we study the vulnerabilities of state-of-the-art DeepFake detection methods from a practical stand point. We perform adversarial attacks on DeepFake detectors in a black box setting where the adversary does not have complete knowledge of the classification models. We study the extent to which adversarial perturbations transfer across different models and propose techniques to improve the transferability of adversarial examples. We also create more accessible attacks using Universal Adversarial Perturbations which pose a very feasible attack scenario since they can be easily shared amongst attackers. We perform our evaluations on the winning entries of the DeepFake Detection Challenge (DFDC) and demonstrate that they can be easily bypassed in a practical attack scenario by designing transferable and accessible adversarial attacks.1
Supercirculation Effects Induced by Vectoring a Partial-Span Rectangular Jet

Thrust-induced supercirculation effects from thrust vectoring have indicated a potential for not only increasing maneuverability of fighter aircraft but also as a means of improving cruise performance. The current study investigated a partial-span rectangular jet-exhaust nozzle located at the wing trailing edge that acts similar to a jet flap by increasing lift due to supercirculation. This paper summarizes experimental studies including the effects of nozzle deflection angle, wing camber, and nozzle shape and exit location on lift, drag and load distributions. The results indicate that significant increases in thrust-induced lift along with substantial decreases in drag are possible.
Light-Induced Quantum Anomalous Hall Effect on the 2D Surfaces of 3D Topological Insulators

Quantum anomalous Hall (QAH) effect generates quantized electric charge Hall conductance without external magnetic field. It requires both nontrivial band topology and time-reversal symmetry (TRS) breaking. In most cases, one can break the TRS of time-reversal invariant topological materials to yield QAH effect, which is essentially a topological phase transition. However, conventional topological phase transition induced by external field/stimulus usually needs a route along which the bandgap closes and reopens. Hence, the transition occurs only when the magnitude of field/stimulus is larger than a critical value. In this work the authors propose that using gapless systems, the transition can happen at an arbitrarily weak (but finite) external field strength. For such an unconventional topological phase transition, the bandgap closing is guaranteed by bulk-edge correspondence and symmetries, while the bandgap reopening is induced by external fields. This concept is demonstrated on the 2D surface states of 3D topological insulators like Bi2 Se3 , which become 2D QAH insulators once a circularly polarized light is turned on, according to the Floquet time crystal theory. The sign of quantized Chern number can be controlled via the chirality of the light. This provides a convenient and dynamic approach to trigger topological phase transitions and create QAH insulators.
A mathematical model of pH i regulation in central CO 2 chemoreception

In CO2 chemosensitive neurons, an increase in CO2 (hypercapnia) leads to a maintained reduction in intracellular pH (pHi) while in non-chemosensitive neurons pHi recovery is observed. The precise mechanisms for the differential regulation of pHi recovery between these cell populations remain to be identified; however, studies have begun to explore the role of Na+/H+ exchange (NHE). Here we present the results of a mathematical model that explores pHi regulation in central CO, chemoreception, and in particular the role of the isoform NHE-5 (present in brainstem neurons). We found that NHE-5 kinetics leads to pHi recovery in response to simulated hypercapnia as we already reported for the isoforms NHE-1 and NHE-3. Further, we wanted to test whether our model can reproduce common experimental protocols like NH4 + pre-pulse experiments designed to alter pHi.
House Price Growth Synchronization and Business Cycle Alignment

One of the most notable trends in the U.S. housing market in the recent decades is the increasing house price growth (HPG) synchronization across states. Using four decades of data, we provide novel evidence that the increasing HPG synchronization leads to higher business cycle alignment across U.S. states. One standard deviation increase in HPG synchronization is associated with a 15%, 12%, and 10% increase in the alignment of the states’ gross state product, employment, and income growth, respectively. The relation is stronger between states with similar banking development and in non-tradable sectors. Supporting both the collateral and direct wealth effect channels, we find more aligned house-secured borrowing activities and consumption growth between states with more synchronized house price growth. Results also hold at the MSA level and are robust to various endogeneity controls, including a Bartik-type instrument. Overall, our findings suggest that the housing market integration can lead to amplified business cycles associated with an increased systemic economic risk at the country level.