cssi/SciSciGPT-SciSciNet huggingface dataset:
The Jacobian module, the Milnor fiber, and the $D$-module generated by $f^s$

For a germ f on a complex manifold X, we introduce a complex derived from the Liouville form acting on logarithmic differential forms, and give an exactness criterion. We use this Liouville complex to connect properties of the D-module generated by \(f^s\) to homological data of the Jacobian ideal; specifically we show that for a large class of germs the annihilator of \(f^s\) is generated by derivations. Through local cohomology, we connect the cohomology of the Milnor fiber to the Jacobian module via logarithmic differentials. In particular, we consider (not necessarily reduced) hyperplane arrangements: we prove a conjecture of Terao on the annihilator of 1 / f; we confirm in many cases a corresponding conjecture on the annihilator of \(f^s\) but we disprove it in general; we show that the Bernstein–Sato polynomial of an arrangement is not determined by its intersection lattice; we prove that arrangements for which the annihilator of \(f^s\) is generated by derivations fulfill the Strong Monodromy Conjecture, and that this includes as very special cases all arrangements of Coxeter and of crystallographic type, and all multi-arrangements in dimension 3.
Out-of-time-order Operators and the Butterfly Effect

Abstract Out-of-time-order (OTO) operators have recently become popular diagnostics of quantum chaos in many-body systems. The usual way they are introduced is via a quantization of classical Lyapunov growth, which measures the divergence of classical trajectories in phase space due to the butterfly effect. However, it is not obvious how exactly they capture the sensitivity of a quantum system to its initial conditions beyond the classical limit. In this paper, we analyze sensitivity to initial conditions in the quantum regime by recasting OTO operators for many-body systems using various formulations of quantum mechanics. Notably, we utilize the Wigner phase space formulation to derive an ħ -expansion of the OTO operator for spatial degrees of freedom, and a large spin 1 ∕ s -expansion for spin degrees of freedom. We find in each case that the leading term is the Lyapunov growth for the classical limit of the system and argue that quantum corrections become dominant at around the scrambling time, which is also when we expect the OTO operator to saturate. We also express the OTO operator in terms of propagators and see from a different point of view how it is a quantum generalization of the divergence of classical trajectories.
Adaptive optics images. II. 12 kepler objects of interest and 15 confirmed transiting planets

All transiting planet observations are at risk of contamination from nearby, unresolved stars. Blends dilute the transit signal, causing the planet to appear smaller than it really is, or producing a false positive detection when the target star is blended with an eclipsing binary. High spatial resolution adaptive optics images are an effective way of resolving most blends. Here we present visual companions and detection limits for 12 Kepler planet candidate host stars, of which 4 have companions within 4''. One system (KOI 1537) consists of two similar-magnitude stars separated by 0.''1, while KOI 174 has a companion at 0.''5. In addition, observations were made of 15 transiting planets that were previously discovered by other surveys. The only companion found within 1'' of a known planet is the previously identified companion to WASP-2b. An additional four systems have companions between 1'' and 4'': HAT-P-30b (3.''7, ΔKs = 2.9), HAT-P-32b (2.''9, ΔKs = 3.4), TrES-1b (2.''3, ΔKs = 7.7), and WASP-P-33b (1.''9, ΔKs = 5.5), some of which have not been reported previously. Depending on the spatial resolution of the transit photometry for these systems, these companion stars may require a reassessment of the planetary parameters derived from transit light curves. For all systems observed, we report the limiting magnitudes beyond which additional fainter objects located 0.''1-4'' from the target may still exist.
Nonequilibrium transport and population inversion in double quantum dot systems

We present a microscopic theory for both equilibrium and nonequilibrium transport properties of coupled double quantum dots (DQD). A general formula for current tunneling through the DQD is derived by the nonequilibrium Green's function method. Using a Hartree–Fock approach, effects of multi-level coupling and nonequilibrium electron distributions in resonant tunneling are considered. We find that the peak in the resonant tunneling current through two symmetric dots will split only when the inter-dot coupling is stronger than dot-lead coupling. We predict that population inversion can be achieved in one dot in the nonequilibrium regime.
Vector Bundle Moduli and Small Instanton Transitions

We give the general presciption for calculating the moduli of irreducible, stable SU(n) holomorphic vector bundles with positive spectral covers over elliptically fibered Calabi-Yau threefolds. Explicit results are presented for Hirzebruch base surfaces B = r. The transition moduli that are produced by chirality changing small instanton phase transitions are defined and specifically enumerated. The origin of these moduli, as the deformations of the spectral cover restricted to the ``lift'' of the horizontal curve of the M5-brane, is discussed. We present an alternative description of the transition moduli as the sections of rank n holomorphic vector bundles over the M5-brane curve and give explicit examples. Vector bundle moduli appear as gauge singlet scalar fields in the effective low-energy actions of heterotic superstrings and heterotic M-theory.
The Shining Future of UV Spectral Synthesis

With the coming generation of instruments and telescopes capable of spectroscopy of high redshift galaxies, the spectral synthesis technique in the rest-frame UV and Far-UV range will become one of a few number of tools remaining to study their young stellar populations in detail. The rest-frame UV lines and continuum of high redshift galaxies, observed with visible and infrared telescopes on Earth, can be used for accurate line profile fitting such as P v λλ1118, 1128, C iii λ1176, and C iv λ1550. These lines are very precise diagnostic tools to estimate ages, metallicities, and masses of stellar populations. Here we discuss the potential for spectral synthesis of rest-frame UV spectra obtained at the Keck telescope. As an example, we study the 8 o'clock arc, a lensed galaxy at z=2.7322. We show that the poor spectral type coverage of the actual UV empirical spectral libraries limits the age and metallicity diagnostic. In order to improve our knowledge of high redshift galaxies using spectral synthesis, UV stellar libraries need to be extended to obtain accurate age, metallicity, and mass estimates likely to be occuring in young stellar populations observed in the early universe.
New sum rules from low energy Compton scattering on arbitrary spin target

We derive two sum rules by studying the low energy Compton scattering on a target of arbitrary (nonzero) spin j. In the first sum rule, we consider the possibility that the intermediate state in the scattering can have spin |j±1| and the same mass as the target. The second sum rule applies if the theory at hand possesses intermediate narrow resonances with masses different from the mass of the scatterer. These sum rules are generalizations of the Gerasimov-Drell-Hearn-Weinberg sum rule. Along with the requirement of tree level unitarity, they relate different low energy couplings in the theory. Using these sum rules, we show that in certain cases the gyromagnetic ratio can differ from the “natural” value g = 2, even at tree level, without spoiling perturbative unitarity. These sum rules can be used as constraints applicable to all supergravity and higher-spin theories that contain particles charged under some U(1) gauge field. In particular, applied to four dimensional N = 8 supergravity in a spontaneously broken phase, these sum rules suggest that for the theory to have a good ultraviolet behavior, additional massive states need to be present, such as those coming from the embedding of the N = 8 supergravity in type II superstring theory. We also discuss the possible implications of the sum rules for QCD in the large-N c limit.
Can students apply the concept of "which-path" information learned in the context of Mach-Zehnder interferometer to the double-slit experiment?

The ability to use concepts learned in one context to solve problems in a different context (i.e., transfer of learning) is often one of the major goals of physics instruction. After all, the ability to transfer learning from one context to another is a prerequisite to recognizing the applicability of compact physical laws to a multitude of contexts and is a hallmark of expertise in physics. The majority of the studies on transfer of learning in physics have focused on introductory students and it has often been found that introductory students have difficulty in effectively transferring their learning and applying what they learned in one situation to another situation. However, compared to advanced students, introductory students have significantly less prior relevant knowledge and skills, which may be crucial for promoting effective transfer. Here, we examine upper-level undergraduate and graduate students' ability to transfer their learning about the concept of “which-path” information and its relation to whether or not interference is observed from the context of the Mach Zehnder interferometer (MZI) to a new context of the double-slit experiment (DSE). Students worked through a tutorial on the MZI in which they learned to use the concept of which-path information to reason about interference of single photons when polarizers with various orientations are placed in one or both paths of the MZI. After working on the MZI tutorial, students were asked similar questions in the isomorphic context of the DSE before any instruction about the DSE and we examined the extent to which transfer of learning about which-path information occurred from the MZI to the DSE context. We find evidence suggesting that positive transfer of learning from the MZI to the DSE occurred despite the lack of an instructional intervention designed to help students recognize the similarity between the two contexts. The effectiveness of the MZI tutorial in promoting positive transfer of learning from one context to another in quantum mechanics sheds light on the expertise of the advanced physics students. Instructors of advanced physics courses can take advantage of the findings presented here, which show the difference between introductory and advanced physics students' expertise and ability to transfer from one context to another.
Whose side are ethics codes on?: power, responsibility and the social good

The moral authority of ethics codes stems from an assumption that they serve a unified society, yet this ignores the political aspects of any shared resource. The sociologist Howard S. Becker challenged researchers to clarify their power and responsibility in the classic essay: Whose Side Are We On. Building on Becker's hierarchy of credibility, we report on a critical discourse analysis of data ethics codes and emerging conceptualizations of beneficence, or the "social good", of data technology. The analysis revealed that ethics codes from corporations and professional associations conflated consumers with society and were largely silent on agency. Interviews with community organizers about social change in the digital era supplement the analysis, surfacing the limits of technical solutions to concerns of marginalized communities. Given evidence that highlights the gulf between the documents and lived experiences, we argue that ethics codes that elevate consumers may simultaneously subordinate the needs of vulnerable populations. Understanding contested digital resources is central to the emerging field of public interest technology. We introduce the concept of digital differential vulnerability to explain disproportionate exposures to harm within data technology and suggest recommendations for future ethics codes..
Separated rows structure of vortex streets behind triangular objects

We discuss two distinct spatial structures of vortex streets. The ‘conventional mushroom’ structure is commonly discussed in many experimental studies, and the exotic ‘separated rows’ structure is characterized by a thin layer of irrotational fluid between two rows of vortices. In a two-dimensional soap film channel, we generate the exotic vortex arrangement by using triangular objects. This setting allows us to vary the thickness of boundary layers and their separation distance independently. We find that the separated rows structure appears only when the boundary layer is thinner than 40 % of the separation distance. We also discuss two physical mechanisms of the breakdown of vortex structures. The conventional mushroom structure decays due to the mixing, and the separated rows structure decays because its arrangement is hydrodynamically unstable.
Critical behavior of the Marinari-Parisi model

Abstract We consider the continuum string theory corresponding to the Marinari-Parisi supersymmetric matrix model. We argue that the world-sheet physics is exotic, and different from any known supersymmetric string theory. The embedding superspace coordinates become disordered on the world-sheet, but because of the noncompactness of the embedding time the disorder becomes complete only at asymptotic world-sheet scales.
Short geodesics in hyperbolic 3–manifolds

For each g 2, we prove existence of a computable constant . g/ > 0 such that if S is a strongly irreducible Heegaard surface of genus g in a complete hyperbolic 3‐manifold M and is a simple geodesic of length less than . g/ in M , then is isotopic into S . 57M50
Circumnavigating collinear superspace

In this paper, we extend the collinear superspace formalism to include the full range of $$ \mathcal{N} $$ = 1 supersymmetric interactions. Building on the effective field theory rules developed in a companion paper — Navigating Collinear Superspace [1] — we construct collinear superspace Lagrangians for theories with non-trivial F- and D-term auxiliary fields. For (massless) Wess-Zumino models, the key ingredient is a novel type of Grassmann-valued supermultiplet whose lowest component is a (non-propagating) fermionic degree of freedom. For gauge theories coupled to charged chiral matter, the key ingredient is a novel type of vector superfield whose lowest component is a non-propagating gauge potential. This unique vector superfield is used to construct a gauge-covariant derivative; while such an object does not appear in the standard full superspace formalism, it is crucial for modeling gauge interactions when the theory is expres sed on a collinear slice. This brings us full circle, by showing that all types of $$ \mathcal{N} $$ = 1 theories in four dimensions can beconstructed in collinear superspace from purely infrared considerations. We speculate that supersymmetric theories with $$ \mathcal{N} $$ > 1 could also be implemented using similar collinear superspace constructions.
Homology cobordism invariants and the Cochran-Orr-Teichner filtration of the link concordance group

For any group G, we define a new characteristic series related to the derived series, that we call the torsion-free derived series of G. Using this series and the Cheeger‐ Gromov ‐invariant, we obtain new real-valued homology cobordism invariants n for closed .4k 1/‐dimensional manifolds. For 3‐dimensional manifolds, we show thatf njn2 Ng is a linearly independent set and for each n 0, the image of n is an infinitely generated and dense subset of R. In their seminal work on knot concordance, T Cochran, K Orr and P Teichner define a filtration F m .n/ of the m‐component (string) link concordance group, called the .n/‐solvable filtration. They also define a grope filtration G m n . We show that n vanishes for .nC1/‐solvable links. Using this, and the nontriviality of n , we show that for each m 2, the successive quotients of the .n/‐solvable filtration of the link concordance group contain an infinitely generated subgroup. We also establish a similar result for the grope filtration. We remark that for knots (mD1), the successive quotients of the .n/‐solvable filtration are known to be infinite. However, for knots, it is unknown if these quotients have infinite rank when n 3. 57M27; 20F14
Asymptotics of a Discrete-Time Particle System Near a Reflecting Boundary

We examine a discrete-time Markovian particle system on ℕ×ℤ+ introduced in Defosseux (arXiv: 1012.0117v1 ). The boundary {0}×ℤ+ acts as a reflecting wall. The particle system lies in the Anisotropic Kardar-Parisi-Zhang with a wall universality class. After projecting to a single horizontal level, we take the long-time asymptotics and obtain the discrete Jacobi and symmetric Pearcey kernels. This is achieved by showing that the particle system is identical to a Markov chain arising from representations of O(∞) (introduced in Borodin and Kuan (Commun. Pure. Appl. Math. 63(7):831–894, 2010, arXiv: 0904.2607 )). The fixed-time marginals of this Markov chain are known to be determinantal point processes, allowing us to take the limit of the correlation kernel. We also give a simple example which shows that in the multi-level case, the particle system and the Markov chain evolve differently.
The Team Keck Redshift Survey 2: MOSFIRE Spectroscopy of the GOODS-North Field

We present the Team Keck Redshift Survey 2 (TKRS2), a near-infrared spectral observing program targeting selected galaxies within the CANDELS subsection of the GOODS-North Field. The TKRS2 program exploits the unique capabilities of MOSFIRE, an infrared multi-object spectrometer which entered service on the Keck I telescope in 2012 and contributes substantially to the study of galaxy spectral features at redshifts inaccessible to optical spectrographs. The TKRS2 project targets 97 galaxies drawn from samples that include z~2 emission-line galaxies with features observable in the JHK bands as well as lower-redshift targets with features in the Y band. We present a detailed measurement of MOSFIRE's sensitivity as a function of wavelength, including the effects of telluric features across the YJHK filters. The largest utility of our survey is in providing rest-frame-optical emission lines for z>1 galaxies, and we demonstrate that the ratios of strong, optical emission lines of z~2 galaxies suggest the presence of either higher N/O abundances than are found in z~0 galaxies or low-metallicity gas ionized by an active galactic nucleus. We have released all TKRS2 data products into the public domain to allow researchers access to representative raw and reduced MOSFIRE spectra.
Photometric redshift estimation using spectral connectivity analysis

The development of fast and accurate methods of photometric redshift estimation is a vital step towards being able to fully utilize the data of next-generation surveys within precision cosmology. In this paper, we apply a specific approach to spectral connectivity analysis (SCA) called diffusion map. SCA is a class of non-linear techniques for transforming observed data (e.g. photometric colours for each galaxy, where the data lie on a complex subset of p-dimensional space) to a simpler, more natural coordinate system wherein we apply regression to make redshift predictions. In previous applications of SCA to other astronomical problems, we demonstrate its superiority vis-a-vis the principal components analysis, a standard linear technique for transforming data. As SCA relies upon eigen-decomposition, our training set size is limited to ≤ 10 4 galaxies; we use the Nystrom extension to quickly estimate diffusion coordinates for objects not in the training set. We apply our method to 350 738 Sloan Digital Sky Survey (SDSS) main sample galaxies, 29 816 SDSS luminous red galaxies and 5223 galaxies from DEEP2 with Canada-France-Hawaii Telescope Legacy Survey ugriz photometry. For all three data sets, we achieve prediction accuracies at par with previous analyses, and find that the use of the Nystrom extension leads to a negligible loss of prediction accuracy relative to that achieved with the training sets. As in some previous analyses, we observe that our predictions are generally too high (low) in the low (high) redshift regimes. We demonstrate that this is a manifestation of attenuation bias, wherein measurement error (i.e. uncertainty in diffusion coordinates due to uncertainty in the measured fluxes/magnitudes) reduces the slope of the best-fitting regression line. Mitigation of this bias is necessary if we are to use photometric redshift estimates produced by computationally efficient empirical methods in precision cosmology.
Point X-ray sources in elliptical galaxies

We analyze the upper-end X-ray luminosity function (XLF) observed in elliptical galaxies for point sources. We propose that the observed XLF is dominated by transient BH systems in outburst and the XLF shape reflects the black hole (BH) mass spectrum among old X-ray transients. The BH mass spectrum – XLF connection depends on a weighting factor that is related to the transient duty cycle and depends on the host-galaxy age, the BH mass and the donor type (main sequence, red giant, or white dwarf). We argue that the assumption of a constant duty cycle for all systems leads to results inconsistent with current observations. The type of dominant donors in the upper-end XLF depends on what type of magnetic braking operates: in the case of “standard” magnetic braking, BH X-ray binaries with red-giant donors dominate, and in the case of weaker magnetic braking prescriptions main sequence donors dominate.
Novel Phenomena in Particle Correlations in Relativistic Heavy-Ion Collisions

Abstract The novel phenomena observed in particle angular correlations are reviewed. They include the double-peak away-side azimuthal correlations in relativistic heavy-ion collisions and the long-range pseudorapidity near-side (ridge) correlations in heavy-ion as well as in proton-induced collisions. The collision system and energy dependence of these phenomena are examined, wherever possible and most abundantly for the ridge correlations. Their possible theoretical interpretations and what might be learned about the properties of the collision systems from theoretical comparisons are discussed. Prospective future measurements and theoretical undertakings are outlined that might help further the understanding of the physics mechanisms underlying these phenomena.
Estimating a common period for a set of irregularly sampled functions with applications to periodic variable star data

We consider the problem of estimating a common period for a set of functions sampled at irregular intervals. The motivating problem arises in astronomy, where the functions represent a star’s observed brightness over time through different photometric filters. While current methods perform well when the brightness is sampled densely enough in at least one filter, they break down when no brightness function is densely sampled. In this paper we introduce two new methods for period estimation in this important latter case. The first, multiband generalized Lomb–Scargle (MGLS), extends the frequently used Lomb–Scargle method to naively combine information across filters. The second, penalized generalized Lomb–Scargle (PGLS), builds on MGLS by more intelligently borrowing strength across filters. Specifically, we incorporate constraints on the phases and amplitudes across the different functions using a nonconvex penalized likelihood function. We develop a fast algorithm to optimize the penalized likelihood that combines block coordinate descent with the majorization–minimization (MM) principle. We test and validate our methods on synthetic and real astronomy data. Both PGLS and MGLS improve period estimation accuracy over current methods based on using a single function; moreover, PGLS outperforms MGLS and other leading methods when the functions are sparsely sampled.
Tunable THz generation and enhanced nonlinear effects with active and passive graphene hyperbolic metamaterials

The active and nonlinear graphene properties are limited due to weak light-matter interaction between the ultrathin graphene and the incident light. In this work, we present enhanced nonlinear effects at the low terahertz (THz) range by designing a new patterned graphene hyperbolic metamaterial (GHMM). More specifically, it is demonstrated that the third harmonic generation (THG) can be significantly enhanced by the proposed GHMM due to the field enhancement at the resonance as well as the supported slow-light response that fosters strong light–matter interaction.
Higher Criticism for Large-Scale Inference, Especially for Rare and Weak Effects

In modern high-throughput data analysis, researchers perform a large number of statistical tests, expecting to nd perhaps a small fraction of signicant eects against a predominantly null background. Higher Criticism (HC) was introduced to determine whether there are any non-zero eects; more recently, it was applied to feature selection, where it provides a method for selecting useful predictive features from a large body of potentially useful features, among which only a rare few will prove truly useful. In this article, we review the basics of HC in both the testing and feature selection settings. HC is a exible idea, which adapts easily to new situations; we point out how it adapts to clique detection and bivariate outlier detection. HC, although still early in its development, is seeing increasing interest from practitioners; we illustrate this with worked examples. HC is computationally eective, which gives it a nice leverage in the increasingly more relevant \Big Data" settings we see today. We also review the underlying theoretical \ideology" behind HC. The Rare/Weak (RW) model is a theoretical framework simultaneously controlling the size and prevalence of useful/signicant items among the useless/null bulk. The RW model shows that HC has important advantages over better known procedures such as False Discovery Rate (FDR) control and Family-wise Error control (FwER), in particular, certain optimality properties. We discuss the rare/weak phase diagram, a way to visualize clearly the class of RW settings where the true signals are so rare or so weak that detection and feature selection are simply impossible, and a way to understand the known optimality properties of HC. Dedications. To the memory of John W. Tukey 1915{2000 and of Yuri I. Ingster 1946{2012,
Near-symplectic 2n–manifolds

We give a generalization of the concept of near-symplectic structures to 2n dimensions. According to our definition, a closed 2-form on a 2n-manifold M is near-symplectic, if it is symplectic outside a submanifold Z of codimension 3, where the (n-1)-th power of the 2-form vanishes. We depict how this notion relates to near-symplectic 4-manifolds and broken Lefschetz fibrations via some examples. We define a generalized broken Lefschetz fibration, or BLF, as a singular map with indefinite folds and Lefschetz-type singularities. We show that given such a map on a 2n-manifold over a symplectic base of codimension 2, then the total space carries such a near-symplectic structure, whose singular locus corresponds precisely to the singularity set of the fibration. A second part studies the geometry around the codimension--3 singular locus Z . We describe a splitting property of the normal bundle N_Z that is also present in dimension four. A tubular neighbourhood for Z is provided, which has as a corollary a Darboux-type theorem for near-symplectic forms.
An analytic approximation to the Bayesian detection statistic for continuous gravitational waves

We consider the Bayesian detection statistic for a targeted search for continuous gravitational waves, known as the $\mathcal{B}$-statistic. This is a Bayes factor between signal and noise hypotheses, produced by marginalizing over the four amplitude parameters of the signal. We show that by Taylor-expanding to first order in certain averaged combinations of antenna patterns (elements of the parameter space metric), the marginalization integral can be performed analytically, producing a closed-form approximation in terms of confluent hypergeometric functions. We demonstrate using Monte Carlo simulations that this approximation is as powerful as the full $\mathcal{B}$-statistic, and outperforms the traditional maximum-likelihood $\mathcal{F}$-statistic, for several observing scenarios which involve an average over sidereal times. We also show that the approximation does not perform well for a near-instantaneous observation, so the approximation is suited to long-time continuous wave observations rather than transient modelled signals such as compact binary inspiral.
Reinforcement Learning for IoT Security: A Comprehensive Survey

The number of connected smart devices has been increasing exponentially for different Internet-of-Things (IoT) applications. Security has been a long run challenge in the IoT systems which has many attack vectors, security flaws and vulnerabilities. Securing billions of connected devices in IoT is a must task to realize the full potential of IoT applications. Recently, researchers have proposed many security solutions for IoT. Machine learning has been proposed as one of the emerging solutions for IoT security and reinforcement learning (RL) is gaining more popularity for securing IoT systems. RL, unlike other machine learning techniques, can learn the environment by having minimum information about the parameters to be learned. It solves the optimization problem by interacting with the environment adapting the parameters on the fly. In this article, we present an comprehensive survey of different types of cyberattacks against different IoT systems and then we present RL and deep RL-based security solutions to combat those different types of attacks in different IoT systems. Furthermore, we present the RL for securing CPS systems (i.e., IoT with feedback and control), such as smart grid and smart transportation system. The recent important attacks and countermeasures using RL in IoT are also summarized in the form of tables. With this article, readers can have a more thorough understanding of IoT security attacks and countermeasures using RL, as well as research trends in this area.
Directional temporal modeling for action recognition

Many current activity recognition models use 3D convolutional neural networks (e.g. I3D, I3D-NL) to generate local spatial-temporal features. However, such features do not encode clip-level ordered temporal information. In this paper, we introduce a channel independent directional convolution (CIDC) operation, which learns to model the temporal evolution among local features. By applying multiple CIDC units we construct a light-weight network that models the clip-level temporal evolution across multiple spatial scales. Our CIDC network can be attached to any activity recognition backbone network. We evaluate our method on four popular activity recognition datasets and consistently improve upon state-of-the-art techniques. We further visualize the activation map of our CIDC network and show that it is able to focus on more meaningful, action related parts of the frame.
Biomarkers of memory variability in traumatic brain injury.

Traumatic brain injury is a leading cause of cognitive disability and is often associated with significant impairment in episodic memory. In traumatic brain injury survivors, as in healthy controls, there is marked variability between individuals in memory ability. Using recordings from indwelling electrodes, we characterized and compared the oscillatory biomarkers of mnemonic variability in two cohorts of epilepsy patients: a group with a history of moderate-to-severe traumatic brain injury (n = 37) and a group of controls without traumatic brain injury (n = 111) closely matched for demographics and electrode coverage. Analysis of these recordings demonstrated that increased high-frequency power and decreased theta power across a broad set of brain regions mark periods of successful memory formation in both groups. As features in a logistic-regression classifier, spectral power biomarkers effectively predicted recall probability, with little difference between traumatic brain injury patients and controls. The two groups also displayed similar patterns of theta-frequency connectivity during successful encoding periods. These biomarkers of successful memory, highly conserved between traumatic brain injury patients and controls, could serve as the basis for novel therapies that target disordered memory across diverse forms of neurological disease.
A composite Higgs with a heavy composite axion

We consider the strong dynamics associated with a composite Higgs model that simultaneously produces dynamical axions and solves the strong CP problem. The strong dynamics arises from a new Sp or SU(4) hypercolor gauge group containing QCD colored hyperfermions that confines at a high scale. The hypercolor global symmetry is weakly gauged by the Standard Model electroweak gauge group and an enlarged color group, SU(N + 3) × SU(N)′. When hyperfermion condensates form, they not only lead to an SU(5)/SO(5) composite Higgs model but also spontaneously break the enlarged color group to SU(3)c × SU(N)D. At lower energies, the SU(N)D group confines, producing two dynamical axions that eliminates all CP violation. Furthermore, small instantons from the SU(N)′ group can enhance the axion mass, giving rise to TeV scale axion masses that can be detected at collider experiments. Our model provides a way to unify the composite Higgs with dynamical axions, without introducing new elementary scalar fields, while also extending the range of axion masses that addresses the strong CP problem.
n-Regular functions in quaternionic analysis

In this paper, we study left and right n-regular functions that originally were introduced in [I. Frenkel and M. Libine, Quaternionic analysis, representation theory and physics II, accepted in Adv...
Distributed Non-Negative Tensor Train Decomposition

The era of exascale computing opens new venues for innovations and discoveries in many scientific, engineering, and commercial fields. However, with the exaflops also come the extra-large high-dimensional data generated by highperformance computing. High-dimensional data is presented as multidimensional arrays, aka tensors. The presence of latent (not directly observable) structures in the tensor allows a unique representation and compression of the data by classical tensor factorization techniques. However, the classical tensor methods are not always stable or they can be exponential in their memory requirements, which makes them not suitable for high-dimensional tensors. Tensor train (TT) is a state-of-the-art tensor network introduced for factorization of high-dimensional tensors. TT transforms the initial high-dimensional tensor in a network of three-dimensional tensors that requires only a linear storage. Many real-world data, such as, density, temperature, population, probability, etc., are non-negative and for an easy interpretation, the algorithms preserving non-negativity are preferred. Here, we introduce a distributed non-negative tensor-train and demonstrate its scalability and the compression on synthetic and realworld big datasets.
Optimal Control-Based Eco-Ramp Merging System for Connected and Automated Vehicles

Our current transportation system suffers from a number of problems in terms of safety, mobility, and environmental sustainability. The emergence of innovative intelligent transportation systems (ITS) technologies, and in particular connected and automated vehicles (CAVs), provides many opportunities to address the aforementioned issues. In this paper, we propose a hierarchical ramp merging system that not only generates microscopic cooperative maneuvers for CAVs on the ramp to merge into the mainline traffic flow, but also provides controllability of the ramp inflow rate, thereby enabling macroscopic traffic flow control. A centralized optimal control-based approach is proposed to smooth the merging flow, improve the system-wide mobility, and decrease the overall fuel consumption of the network. Linear quadratic trackers in both finite horizon and receding horizon forms are developed to solve the optimization problem in terms of path planning and sequence determination, where a microscopic vehicle fuel consumption model is applied. Extensive traffic simulation runs have been conducted using PTV VISSIM to evaluate the impact of the proposed system on a segment of SR-91 E in Corona, California. The results confirm that under the regulated inflow rate, the proposed system can avoid potential traffic congestion and improve mobility (e.g., VMT/VHT) up to 147%, with a 47% fuel savings compared to the conventional ramp metering and the ramp without any control approach.
Non-asymptotic Closed-Loop System Identification using Autoregressive Processes and Hankel Model Reduction

One of the primary challenges of system identification is determining how much data is necessary to adequately fit a model. Non-asymptotic characterizations of the performance of system identification methods provide this knowledge. Such characterizations are available for several algorithms performing open-loop identification. Often times, however, data is collected in closed-loop. Application of open-loop identification methods to closed-loop data can result in biased estimates. One method to eliminate these biases involves first fitting a long-horizon autoregressive model and then performing model reduction. The asymptotic behavior of such algorithms is well characterized, but the non-asymptotic behavior is not. This work provides a non-asymptotic characterization of one particular variant of these algorithms. More specifically, we provide non-asymptotic upper bounds on the generalization error of the produced model, as well as high probability bounds on the difference between the produced model and the finite horizon Kalman Filter.
Two-Level Lattice Neural Network Architectures for Control of Nonlinear Systems

In this paper, we consider the problem of automatically designing a Rectified Linear Unit (ReLU) Neural Network (NN) architecture (number of layers and number of neurons per layer) with the guarantee that it is sufficiently parametrized to control a nonlinear system. Whereas current state-of-the-art techniques are based on hand-picked architectures or heuristic-based search to find such NN architectures, our approach exploits a given model of the system to design an architecture; as a result, we provide a guarantee that the resulting NN architecture is sufficient to implement a controller that satisfies an achievable specification. Our approach exploits two basic ideas. First, we assume that the system can be controlled by a Lipschitz-continuous state-feedback controller that is unknown but whose Lipschitz constant is upper-bounded by a known constant; then using this assumption, we bound the number of affine functions needed to construct a Continuous Piecewise Affine (CPWA) function that can approximate the unknown Lipschitz-continuous controller. Second, we utilize the authors’ recent results on the Two-Level Lattice (TLL) NN architecture, a novel NN architecture that was shown to be parameterized directly by the number of affine functions that comprise the CPWA function it realizes. We also evaluate our method by designing a NN architecture to control an inverted pendulum.
Use and interpretation of spatial autoregressive probit models

Applications of spatial probit regression models that have appeared in the literature have incorrectly interpreted estimates from these models. Spatially dependent choices frequently arise in various modeling scenarios, including situations involving analysis of regional voting behavior, decisions by states or cities to change tax rates relative to neighboring jurisdictions, decisions by households to move or stay in a particular location. We use county-level voting results from the 2004 presidential election as an illustrative example of some issues that arise when drawing inferences from spatial probit model estimates. Although the voting example holds particular intuitive appeal that allows us to focus on interpretive issues, there are numerous other situations where these same considerations come into play. Past work regarding Bayesian Markov Chain Monte Carlo estimation of spatial probit models from LeSage and Pace (Introduction to spatial econometrics. Taylor and Francis, New York, 2009) is used, as well as derivations from LeSage et al. (J R Stat Soc Ser A Stat Soc 174(4):1007–1027, 2011) regarding proper interpretation of the partial derivative impacts from changes in the explanatory variables on the probability of voting for a candidate. As in the case of conventional probit models, the effects arising from changes in the explanatory variables depend in a nonlinear way on the levels of these variables. In non-spatial probit regressions, a common way to explore the nonlinearity in this relationship is to calculate “marginal effects” estimates using particular values of the explanatory variables (e.g., mean values or quintile intervals). The motivation for this practice is consideration of how the impact of changing explanatory variable values varies across the range of values encompassed by the sample data. Given the nonlinear nature of the normal cumulative density function transform on which the (non-spatial) probit model relies, we know that changes in explanatory variable values near the mean may have a very different impact on decision probabilities than changes in very low or high values. For spatial probit regression models, the effects or impacts from changes in the explanatory variables are more highly nonlinear. In addition, since spatial models rely on observations that each represent a location or region located on a map, the levels of the explanatory variables can be viewed as varying over space. We discuss important implications of this for proper interpretation of spatial probit regression models in the context of our election application.
Transcending the Tacit Dimension: Patents, Relationships, and Organizational Integration in Technology Transfer

As a key driver of innovation and economic growth, university-industry technology transfer has attracted significant attention. Formal technology transfer, which encompasses patenting and licensing university inventions, is often characterized as proceeding according to market principles. According to this dominant conception, patents help commodify academic inventions, which universities then advertise and transfer to private firms in licensing markets. This Article challenges and refines this market-oriented view of technology transfer. Drawing from empirical studies, it shows that effective technology transfer often involves long-term personal relationships rather than discrete market exchanges. In particular, it explores the significant role of tacit, uncodified knowledge in effectively exploiting patented academic inventions. Markets, patents, and licenses are ill-suited to transferring such tacit knowledge, leading licensees to seek direct relationships with academic inventors themselves. Drawing on the theory of the firm, this Article then explores the role of organizational integration in transferring patented technologies and associated tacit knowledge to private companies. Presenting a descriptive theory of university-industry technology transfer, it argues that the difficulties of conveying tacit knowledge encourage various forms of organizational integration by which licensees directly absorb academic human capital. From consulting arrangements to seats on boards of directors, licensees are bringing faculty inventors (and their tacit knowledge) “in house” to aid in commercialization. Turning from the descriptive to the normative, this Article provides prescriptions for enhancing tacit knowledge transmission and technology transfer. It concludes by exploring the implications of tacit knowledge for patent theory and the organization of technology commercialization efforts.
Actively managed mutual funds holding passive investments: What do ETF positions tell us about mutual fund ability?

Abstract We provide the first in-depth examination of exchange-traded funds (ETFs) within actively managed mutual fund (AMMF) portfolios to better understand why AMMFs make substantial investments in passive ETFs. We examine the association between holding ETF positions and AMMF performance, as well as indirect measures of performance, including market timing, flow management, and cash holdings. We find that over one-third of AMMFs take an ETF position between 2004 and 2015. Our results indicate that AMMFs allocating large portions of their portfolio to ETFs perform worse, by between 0.41% and 1.63% annually using various performance measures. These AMMFs also exhibit worse market timing and hold more cash. In contrast, AMMFs that hold ETFs in small amounts have similar characteristics to non-user AMMFs. Therefore, the act of holding an ETF does not signal inferior ability, however, taking large ETF positions does.
Market efficiency, asset returns, and the size of the risk premium in global equity markets

Abstract An important economic insight is that observed equity prices must equal the present value of the cash flows associated with the equity claim. An implication of this insight is that present values of cash flows must also quantitatively justify the observed volatility and cross-correlations of asset returns. In this paper, we show that parametric economic models for present values can indeed account for the observed high ex post return volatility and cross-correlation observed across five major equity markets—the U.S., the U.K., France, Germany, and Japan. We present evidence that cash flow growth rates contain a small predictable long-run component; this feature, in conjunction with time-varying systematic risk, can justify key empirical characteristics of observed equity prices. Our model also has direct implications for the level of equity prices and specific versions of the model can, in many cases, capture observed price levels. Our evidence suggests that the ex ante risk premium on the global market portfolio has dropped considerably—we show that this fall in the risk premium is related to a decline in the conditional variance of global real cash flow growth rates.
The hollow corporation revisited: Can governance mechanisms substitute for technical expertise in managing buyer‐supplier relationships?

This article considers how a firm's system of exchange skills including internal technical expertise and supplier governance mechanisms influence supplier performance, both independently and jointly. The core question is whether inter-firm governance mechanisms, including both relational and contractual mechanisms, can substitute for a firm's internal technical skills in maintaining supplier performance or, alternatively, whether a firm risks hollowing itself out by de-emphasizing internal expertise when it outsources. The arguments build on the capabilities, inter-organizational governance, and supply management literatures. We find that internal technical expertise influences multiple dimensions of supplier performance, including cooperation, price, quality, delivery, and communication, while relational governance also affects supplier performance though in a more focused way. In turn, combinations of technical expertise, relational governance, and contractual agreements jointly affect supplier performance. Thus, firms generate superior supplier performance if they retain internal technical skills as well as increase their use of external governance mechanisms to manage buyer-supplier relationships.