cssi/SciSciGPT-SciSciNet huggingface dataset:
An information theoretic framework for active de-anonymization in social networks based on group memberships

In this paper, a new mathematical formulation for the problem of de-anonymizing social network users by actively querying their membership in social network groups is introduced. In this formulation, the attacker has access to a noisy observation of the group membership of each user in the social network. When an unidentified victim visits a malicious website, the attacker uses browser history sniffing to make queries regarding the victim's social media activity. Particularly, it can make polar queries regarding the victim's group memberships and the victim's identity. The attacker receives noisy responses to her queries. The goal is to de-anonymize the victim with the minimum number of queries. Starting with a rigorous mathematical model for this active de-anonymization problem, an upper bound on the attacker's expected query cost is derived, and new attack algorithms are proposed which achieve this bound. These algorithms vary in computational cost and performance. The results suggest that prior heuristic approaches to this problem provide sub-optimal solutions.
SIMPLE JORDAN CONFORMAL SUPERALGEBRAS

We classify simple finite Jordan conformal superalgebras and also establish preliminary results for the classification of simple finite Jordan pseudoalgebras.
Removing Malicious Nodes from Networks

A fundamental challenge in networked systems is detection and removal of suspected malicious nodes. In reality, detection is always imperfect, and the decision about which potentially malicious nodes to remove must trade off false positives (erroneously removing benign nodes) and false negatives (mistakenly failing to remove malicious nodes). However, in network settings this conventional tradeoff must now account for node connectivity. In particular, malicious nodes may exert malicious influence, so that mistakenly leaving some of these in the network may cause damage to spread. On the other hand, removing benign nodes causes direct harm to these, and indirect harm to their benign neighbors who would wish to communicate with them. We formalize the problem of removing potentially malicious nodes from a network under uncertainty through an objective that takes connectivity into account. We show that optimally solving the resulting problem is NP-Hard. We then propose a tractable solution approach based on a convex relaxation of the objective. Finally, we experimentally demonstrate that our approach significantly outperforms both a simple baseline that ignores network structure, as well as a state-of-the-art approach for a related problem, on both synthetic and real-world datasets.
Smoothness to the boundary of biholomorphic mappings

Under a plausible geometric hypothesis, we show that a biholomorphic mappingof smoothly bounded, pseudoconvex domains extends to a diffeomorphism of the closures.
Influence of subband mixing due to spin-orbit interaction on the transmission through periodically modulated waveguides

Ballistic spin transport, through periodically stubbed waveguides, is studied in the presence of a weak spin-orbit interaction (SOI) and the resulting subband mixing. By an appropriate choice of the waveguide length and of the stub parameters, injected spin-polarized electrons can be blocked completely and the transmission shows a periodic and nearly square-wave pattern with wide gaps when only one mode is allowed to propagate in the waveguide. Relative to the case when subband mixing is neglected, the transmission changes drastically as a function of the incident electron energy or of the stub height, as it exhibits new peaks or dips, but remains robust as a function of the stubs' degree of asymmetry. Varying the strength of the SOI parameter changes the relative contribution to the total transmission or conductance of the spin-up and spin-down states. The structure considered is a reasonable candidate for establishing a spin transistor.
The Goodwillie Tower and the EHP Sequence

The author studies the interaction between the EHP sequence and the Goodwillie tower of the identity evaluated at spheres at the prime $2$. Both give rise to spectral sequences (the EHP spectral sequence and the Goodwillie spectral sequence, respectively) which compute the unstable homotopy groups of spheres. He relates the Goodwillie filtration to the $P$ map, and the Goodwillie differentials to the $H$ map. Furthermore, he studies an iterated Atiyah-Hirzebruch spectral sequence approach to the homotopy of the layers of the Goodwillie tower of the identity on spheres. He shows that differentials in these spectral sequences give rise to differentials in the EHP spectral sequence. He uses his theory to recompute the $2$-primary unstable stems through the Toda range (up to the $19$-stem). He also studies the homological behavior of the interaction between the EHP sequence and the Goodwillie tower of the identity. This homological analysis involves the introduction of Dyer-Lashof-like operations associated to M. Ching's operad structure on the derivatives of the identity. These operations act on the mod $2$ stable homology of the Goodwillie layers of any functor from spaces to spaces.
The Vojta conjecture implies Galois rigidity in dynamical families

We show that the Vojta (or Hall-Lang) conjecture implies that the arboreal Galois representations in a 1-parameter family of quadratic polynomials are surjective if and only if they surject onto some finite and uniform quotient. As an application, we use the Vojta conjecture, our uniformity theorem over $\mathbb{Q}(t)$, and Hilbert's irreducibility theorem to prove that the prime divisors of many quadratic orbits have density zero.
Fourier representations in Bergman spaces

Abstract We consider a class of domains, generalizing the upper half-plane, and admitting rotational, translational and scaling symmetries analogous to the half-plane. We prove Paley-Wiener type representations of functions in Bergman spaces of such domains with respect to each of these three groups of symmetries. The Fourier series, Fourier integral and Mellin integral representations so obtained may be used to give representations of the Bergman kernels of these domains.
CDDT: Fast Approximate 2D Ray Casting for Accelerated Localization

Localization is an essential component for autonomous robots. A well-established localization approach combines ray casting with a particle filter, leading to a computationally expensive algorithm that is difficult to run on resource-constrained mobile robots. We present a novel data structure called the Compressed Directional Distance Transform for accelerating ray casting in two dimensional occupancy grid maps. Our approach allows online map updates, and near constant time ray casting performance for a fixed size map, in contrast with other methods exhibit poor worst case performance. Our experimental results show that the proposed algorithm approximates the performance characteristics of reading from a three dimensional lookup table of ray cast solutions while requiring two orders of magnitude less memory and precomputation. This results in a particle filter algorithm which can maintain 2500 particles with 61 ray casts per particle at 40Hz, using a single CPU thread onboard a mobile robot.
Efficiently decodable insertion/deletion codes for high-noise and high-rate regimes

This work constructs codes that are efficiently decodable from a constant fraction of worst-case insertion and deletion errors in three parameter settings: (i) Binary codes with rate approaching 1; (ii) Codes with constant rate for error fraction approaching 1 over fixed alphabet size; and (iii) Constant rate codes over an alphabet of size k for error fraction approaching (k − 1)/(k + 1). When errors are constrained to deletions alone, efficiently decodable codes in each of these regimes were constructed recently. We complete the picture by constructing similar codes that are efficiently decodable in the insertion/deletion regime.
VIBRATIONAL INFRARED LIFETIME OF THE ANESTHETIC NITROUS OXIDE GAS IN SOLUTION

The lifetime of the asymmetric fundamental stretching 2218 cm-1 vibration of the anesthetic gas nitrous oxide (N2O) dissolved in octanol and olive oil is reported. These solvents are model systems commonly used to assess anesthetic potency. Picosecond time-scale molecular dynamics simulations have suggested that protein dynamics or membrane dynamics play a role in the molecular mechanism of anesthetic action. Ultrafast infrared spectroscopy with 100 fs time resolution is an ideal tool to probe dynamics of anesthetic molecules on such timescales. Pump-probe studies centered at the peak of the vibrational band yield a lifetime of 55 ± 1 ps in olive oil and 52 ± 1 ps in octanol. The similarity of lifetimes suggests that energy relaxation of the anesthetic is determined primarily by the hydrophobic nature of the environment, consistent with models of anesthetic action. The results show that nitrous oxide is a good model system for probing anesthetic-solvent interactions using nonlinear infrared spectroscopy.
Predictive Simulations for Tuning Electronic and Optical Properties of SubPc Derivatives

Boron subphthalocyanine chloride is an electron donor material used in small-molecule organic photovoltaics with an unusually large molecular dipole moment. Using first-principles calculations, we investigate how to control the electronic and optical properties of boron subphthalocyanine chloride by substituting the boron and chlorine atoms with other trivalent and halogen atoms and thereby modifying the molecular dipole moment. Gas-phase molecular structures and properties are predicted using hybrid functionals. Using positions and orientations of the known compounds as the starting coordinates for these molecules, stable crystalline structures are derived following a procedure that involves perturbation and accurate total energy minimization. Electronic structure and photonic properties of the predicted crystals are computed using the GW method and the Bethe–Salpeter equation, respectively. Finally, a simple transport model is used to quantitatively demonstrate the effects of the strength and orientation of molecular dipole moments at interfaces on device performance.
Crowdsourcing for Participatory Democracies: Efficient Elicitation of Social Choice Functions

We present theoretical and empirical results demonstrating the usefulness of social choice functions in crowdsourcing for participatory democracies. First, we demonstrate the scalability of social choice functions by defining a natural notion of epsilon-approximation, and giving algorithms which efficiently elicit such approximations for two prominent social choice functions: the Borda rule and the Condorcet winner. This result circumvents previous prohibitive lower bounds and is surprisingly strong: even if the number of ideas is as large as the number of participants, each participant will only have to make a logarithmic number of comparisons, an exponential improvement over the linear number of comparisons previously needed. Second, we apply these ideas to Finland's recent off-road traffic law reform, an experiment on participatory democracy in real life. This allows us to verify the scaling predicted in our theory and show that the constant involved is also not large. In addition, by collecting data on the time that users take to complete rankings of varying sizes, we observe that eliciting partial rankings can further decrease elicitation time as compared to the common method of eliciting pairwise comparisons.
Global Anomalies and Effective Field Theory

We show that matching anomalies under large gauge transformations and large diffeomorphisms can explain the appearance and non-renormalization of couplings in effective field theory. We focus on thermal effective field theory, where we argue that the appearance of certain unusual Chern-Simons couplings is a consequence of global anomalies. As an example, we show that a mixed global anomaly in four dimensions fixes the chiral vortical effect coefficient (up to an overall additive factor). This is an experimentally measurable prediction from a global anomaly. For certain situations, we propose a simpler method for calculating global anomalies which uses correlation functions rather than eta invariants.
Intermediate arithmetic operations on ordinal numbers

There are two well-known ways of doing arithmetic with ordinal numbers: the “ordinary” addition, multiplication, and exponentiation, which are defined by transfinite iteration; and the “natural” (or “Hessenberg”) addition and multiplication (denoted ⊕ and ⊗), each satisfying its own set of algebraic laws. In 1909, Jacobsthal considered a third, intermediate way of multiplying ordinals (denoted × ), defined by transfinite iteration of natural addition, as well as the notion of exponentiation defined by transfinite iteration of his multiplication, which we denote α×β. (Jacobsthal's multiplication was later rediscovered by Conway.) Jacobsthal showed these operations too obeyed algebraic laws. In this paper, we pick up where Jacobsthal left off by considering the notion of exponentiation obtained by transfinitely iterating natural multiplication instead; we shall denote this α⊗β. We show that α⊗(β⊕γ)=(α⊗β)⊗(α⊗γ) and that α⊗(β×γ)=(α⊗β)⊗γ; note the use of Jacobsthal's multiplication in the latter. We also demonstrate the impossibility of defining a “natural exponentiation” satisfying reasonable algebraic laws.
Decentralized control problems with substitutable actions

We consider a decentralized system with multiple controllers and define substitutability of one controller by another in open-loop strategies. We explore the implications of this property on the optimization of closed-loop strategies. In particular, we focus on the decentralized LQG problem with substitutable actions. Even though the problem we formulate does not belong to the known classes of “simpler” decentralized problems such as partially nested or quadratically invariant problems, our results show that, under the substitutability assumption, linear strategies are optimal and we provide a complete state space characterization of optimal strategies. We also identify a family of information structures that all give the same optimal cost as the centralized information structure under the substitutability assumption. Our results suggest that open-loop substitutability can work as a counterpart of the information structure requirements that enable simplification of decentralized control problems.
Beyond the Child-Langmuir limit

This article presents a new formulation of the solution for fully nonlinear and unsteady planar flow of an electron beam in a diode. Using characteristic variables (i.e., variables that follow particle paths) the solution is expressed through an exact analytic, but implicit, formula for any choice of incoming velocity ${v}_{0}$, electric field ${E}_{0}$, and current ${J}_{0}$. For steady solutions, this approach clarifies the origin of the maximal current ${J}_{\mathrm{max}}$, derived by Child and Langmuir for ${v}_{0}=0$ and by Jaffe for ${v}_{0}g0$. The implicit formulation is used to find (1) unsteady solutions having constant incoming flux ${J}_{0}g{J}_{\mathrm{max}}$, which leads to formation of a virtual cathode, and (2) time-periodic solutions whose average flux exceeds the adiabatic average of ${J}_{\mathrm{max}}$.
Algorithm independent bounds on community detection problems and associated transitions in stochastic block model graphs

We derive rigorous bounds for well-defined community structure in complex networks for a stochastic block model (SBM) benchmark. In particular, we analyze the effect of inter-community “noise” (inter-community edges) on any “community detection” algorithm’s ability to correctly group nodes assigned to a planted partition, a problem which has been proven to be NP complete in a standard rendition. Our result does not rely on the use of any one particular algorithm nor on the analysis of the limitations of inference. Rather, we turn the problem on its head and work backwards to examine when, in the first place, well defined structure may exist in SBMs. The method that we introduce here could potentially be applied to other computational problems. The objective of community detection algorithms is to partition a given network into optimally disjoint subgraphs (or communities). Similar to k−SAT and other combinatorial optimization problems, “community detection” exhibits different phases. Networks that lie in the “unsolvable phase” lack well-defined structure and thus have no partition that is meaningful. Solvable systems splinter into two disparate phases: those in the “hard” phase and those in the “easy” phase. As befits its name, within the easy phase, a partition is easy to achieve by known algorithms. When a network lies in the hard phase, it still has an underlying structure yet finding a meaningful partition which can be checked in polynomial time requires an exhaustive computational effort that rapidly increases with the size of the graph. When taken together, (i) the rigorous results that we report here on when graphs have an underlying structure and (ii) recent results concerning the limits of rather general algorithms, suggest bounds on the hard phase.
Chromatic and flow polynomials of generalized vertex join graphs and outerplanar graphs

A generalized vertex join of a graph is obtained by joining an arbitrary multiset of its vertices to a new vertex. We present a low-order polynomial time algorithm for computing the chromatic polynomials of generalized vertex joins of trees; by duality, this algorithm can also be used to compute the flow polynomials of arbitrary outerplanar graphs. We also present closed formulas for the chromatic polynomials of generalized vertex joins of cliques, and the chromatic and flow polynomials of generalized vertex joins of cycles.
On the singular values of matrices with high displacement rank

Abstract We introduce a new ADI-based low rank solver for A X − X B = F , where F has rapidly decaying singular values. Our approach results in both theoretical and practical gains, including (1) the derivation of new bounds on singular values for classes of matrices with high displacement rank, (2) a practical algorithm for solving certain Lyapunov and Sylvester matrix equations with high rank right-hand sides, and (3) a collection of low rank Poisson solvers that achieve spectral accuracy and optimal computational complexity.
When does spatial correlation add value to delayed channel state information

Fast fading wireless networks with delayed knowledge of the channel state information have received significant attention in recent years. An exception is networks where channels are spatially correlated. This paper characterizes the capacity region of two-user erasure interference channels with delayed knowledge of the channel state information and spatially correlated channels. There are instances where spatial correlation eliminates any potential gain from delayed channel state information and instances where it enables the same performance that is possible with instantaneous knowledge of channel state. The key is an extremal entropy inequality for spatially correlated channels that separates the two types of instances. It is also shown that to achieve the capacity region, each transmitter only needs to rely on the delayed knowledge of the channels to which it is connected.
Gibbs sampling for (Coupled) infinite mixture models in the stick breaking representation

Nonparametric Bayesian approaches to clustering, information retrieval, language modeling and object recognition have recently shown great promise as a new paradigm for unsupervised data analysis. Most contributions have focused on the Dirichlet process mixture models or extensions thereof for which efficient Gibbs samplers exist. In this paper we explore Gibbs samplers for infinite complexity mixture models in the stick breaking representation. The advantage of this representation is improved modeling flexibility. For instance, one can design the prior distribution over cluster sizes or couple multiple infinite mixture models (e.g., overtime) at the level of their parameters (i.e., the dependent Dirichlet process model). However, Gibbs samplers for infinite mixture models (as recently introduced in the statistics literature) seem to mix poorly over cluster labels. Among others issues, this can have the adverse effect that labels for the same cluster in coupled mixture models are mixed up. We introduce additional moves in these samplers to improve mixing over cluster labels and to bring clusters into correspondence. An application to modeling of storm trajectories is used to illustrate these ideas.
Fast Training of Convolutional Networks through FFTs

Abstract: Convolutional networks are one of the most widely employed architectures in computer vision and machine learning. In order to leverage their ability to learn complex functions, large amounts of data are required for training. Training a large convolutional network to produce state-of-the-art results can take weeks, even when using modern GPUs. Producing labels using a trained network can also be costly when dealing with web-scale datasets. In this work, we present a simple algorithm which accelerates training and inference by a significant factor, and can yield improvements of over an order of magnitude compared to existing state-of-the-art implementations. This is done by computing convolutions as pointwise products in the Fourier domain while reusing the same transformed feature map many times. The algorithm is implemented on a GPU architecture and addresses a number of related challenges.
Minimax Redundancy for Markov Chains with Large State Space

For any Markov source, there exist universal codes whose normalized codelength approaches the Shannon limit asymptotically as the number of samples goes to infinity. This paper investigates how fast the gap between the normalized codelength of the “best” universal compressor and the Shannon limit (i.e. the compression redundancy) vanishes non-asymptotically in terms of the alphabet size and mixing time of the Markov source. We show that, for Markov sources whose relaxation time is at least $1+ \frac{(2+c)}{\sqrt{k}}$ , where $k$ is the state space size (and $c$ > 0 is a constant), the phase transition for the number of samples required to achieve vanishing compression redundancy is precisely $\Theta(k^{2})$ .
Tuning the magnetic ground state of a triangular lattice system

The anisotropic triangular lattice of the crednerite system Cu(Mn$$_{1-x}$$Cu$$_{x}$$)O$$_{2}$$ is used as a basic model for studying the influence of spin disorder on the ground state properties of a two-dimensional frustrated antiferromagnet. Neutron diffraction measurements show that the undoped phase (x=0) undergoes a transition to antiferromagnetic long-range order that is stabilized by a frustration-relieving structural distortion. Small deviation from the stoichiometric composition alters the magnetoelastic characteristics and reduces the effective dimensionality of the magnetic lattice. Upon increasing the doping level, the interlayer coupling changes from antiferromagnetic to ferromagnetic, while the structural distortion is fully suppressed. Concomitantly, the long-range magnetic order is gradually transformed into a two-dimensional order.
Accurate description of optical precursors and their relation to weak-field coherent optical transients

We study theoretically the propagation of a step-modulated optical field as it passes through a dispersive dielectric made up of a dilute collection of oscillators characterized by a single narrow-band resonance. The propagated field is given in terms of an integral of a Fourier type, which cannot be evaluated even for simple models of the dispersive dielectric. The fact that the oscillators have a low number density (dilute medium) and have a narrow-band resonance allows us to simplify the integrand. In this case, the integral can be evaluated exactly, although it is not possible using this method to separate out the transient part of the propagated field known as optical precursors. We also use an asymptotic method (saddle-point method) to evaluate the integral. The contributions to the integral related to the saddle points of the integrand give rise to the optical precursors. We obtain analytic expressions for the precursor fields and the domain over which the asymptotic method is valid. When combined to obtain the total transient field, we find that the agreement between the solutions obtained by the asymptotic and the exact methods is excellent. Our results demonstrate that precursors can persist for many nanoseconds and the chirp in the instantaneous frequency of the precursors can manifest itself in beats in the transmitted intensity. Our work strongly suggests that precursors have been observed in many previous experiments.
Recent X-ray hybrid CMOS detector developments and measurements

The Penn State X-ray detector lab, in collaboration with Teledyne Imaging Sensors (TIS), have progressed their efforts to improve soft X-ray Hybrid CMOS detector (HCD) technology on multiple fronts. Having newly acquired a Teledyne cryogenic SIDECARTM ASIC for use with HxRG devices, measurements were performed with an H2RG HCD and the cooled SIDECARTM. We report new energy resolution and read noise measurements, which show a significant improvement over room temperature SIDECARTM operation. Further, in order to meet the demands of future high-throughput and high spatial resolution X-ray observatories, detectors with fast readout and small pixel sizes are being developed. We report on characteristics of new X-ray HCDs with 12.5 micron pitch that include in-pixel CDS circuitry and crosstalk-eliminating CTIA amplifiers. In addition, PSU and TIS are developing a new large-scale array Speedster-EXD device. The original 64 × 64 pixel Speedster-EXD prototype used comparators in each pixel to enable event driven readout with order of magnitude higher effective readout rates, which will now be implemented in a 550 × 550 pixel device. Finally, the detector lab is involved in a sounding rocket mission that is slated to fly in 2018 with an off-plane reflection grating array and an H2RG X-ray HCD. We report on the planned detector configuration for this mission, which will increase the NASA technology readiness level of X-ray HCDs to TRL 9.
Full-f gyrokinetic simulation of turbulence in a helical open-field-line plasma

Curvature-driven turbulence in a helical open-field-line plasma is investigated using electrostatic five-dimensional gyrokinetic continuum simulations in an all-bad-curvature helical-slab geometry. Parameters for a National Spherical Torus Experiment scrape-off-layer plasma are used in the model. The formation and convective radial transport of plasma blobs is observed, and it is shown that the radial particle-transport levels are several times higher than diffusive Bohm-transport estimates. By reducing the strength of the poloidal magnetic field, the profile of the heat flux to the divertor plate is observed to broaden.
Dimension Detection with Local Homology.

Detecting the dimension of a hidden manifold from a point sample has become an important problem in the current data-driven era. Indeed, estimating the shape dimension is often the first step in studying the processes or phenomena associated to the data. Among the many dimension detection algorithms proposed in various fields, a few can provide theoretical guarantee on the correctness of the estimated dimension. However, the correctness usually requires certain regularity of the input: the input points are either uniformly randomly sampled in a statistical setting, or they form the so-called $(\varepsilon,\delta)$-sample which can be neither too dense nor too sparse. Here, we propose a purely topological technique to detect dimensions. Our algorithm is provably correct and works under a more relaxed sampling condition: we do not require uniformity, and we also allow Hausdorff noise. Our approach detects dimension by determining local homology. The computation of this topological structure is much less sensitive to the local distribution of points, which leads to the relaxation of the sampling conditions. Furthermore, by leveraging various developments in computational topology, we show that this local homology at a point $z$ can be computed \emph{exactly} for manifolds using Vietoris-Rips complexes whose vertices are confined within a local neighborhood of $z$. We implement our algorithm and demonstrate the accuracy and robustness of our method using both synthetic and real data sets.
The Joseph Greenberg Problem: Combinatorics and Comparative Linguistics

In 1957, the prominent linguist Joseph H. Greenberg (1915–2001) proposed the method of mass comparison (also known as multilateral comparison) for determining genetic relatedness between languages ...
Nonstandard second-order formulation of the LWR model

In this study, we present an equivalent second-order formulation of the LWR model based on Phillips’ model, in which the acceleration rate equals a relaxation term with an infinitesimal relaxation ...
Sums of sets of lattice points and unimodular coverings of polytopes

If P is a lattice polytope (that is, the convex hull of a finite set of lattice points in \({\mathbf{R}^n}\)), then every sum of h lattice points in P is a lattice point in the h-fold sumset hP. However, a lattice point in the h-fold sumset hP is not necessarily the sum of h lattice points in P. It is proved that if the polytope P is a union of unimodular simplices, then every lattice point in the h-fold sumset hP is the sum of h lattice points in P.
Time-Fractional User Association in Millimeter Wave MIMO Networks

User association determines which base stations a user connects to, hence affecting the amount of network interference and consequently the network throughput. Conventional user association schemes, however, assume that user instantaneous rates are independent of user association. In this paper, we introduce a new load-aware user association scheme for millimeter wave (mmWave) MIMO networks which takes into account the dependency of network interference on user association. This consideration is well suited for mmWave communications, where the links are highly directional and vulnerable to small channel variations. We formulate our user association problem as a mixed integer nonlinear programming (MINLP) and solve it using the genetic algorithm. We show that the proposed method can improve network performance by moving the traffic of congested base stations to lightly-loaded base stations and adjusting the interference accordingly. Our simulations confirm that our scheme results in a higher network throughput compared to conventional user association techniques.
Elevated multiplexing and signal space partitioning in the 2 User MIMO IC with partial CSIT

The 2 user MIMO interference channel with arbitrary antenna configurations is studied under arbitrary levels of partial CSIT for each of the channels, to find the degrees of freedom (DoF) achievable by User 2 while User 1 achieves his full interference-free DoF. The goal is to gain new insights due to the inclusion of MIMO (multiple antennas at both transmitters and receivers) into the signal space partitioning schemes associated with partial CSIT. An interesting idea that emerges from this study is “elevated multiplexing” where the signals are split into streams and transmitted from separate antennas at elevated power levels, which allows these signals to be jointly decoded at one receiver which has fewer spatial dimensions with lower interference floors, while another receiver is simultaneously able to separately decode these signals with a higher interference floor but across a greater number of spatial dimensions. Through elevated multiplexing we find that there is a DoF benefit from increasing the number of antennas at a transmitter even if that transmitter already has more antennas than its desired receiver and has no CSIT.
Edges control clustering in levitated granular matter

The properties of small clusters depend dramatically on the interactions between their constituent particles. However, it remains challenging to design and tune the interactions between macroscopic particles, such as in a granular material. Here, we use acoustic levitation to trap macroscopic grains and induce forces between them. Our main results show that particles levitated in an acoustic field prefer to make contact along sharp edges. The radius of curvature of the edges directly controls the magnitude of these forces. These highly directional interactions, combined with local contact forces, give rise to a diverse array of cluster shapes. Our results open up new possibilities for the design of specific forces between macroscopic particles, directing their assembly, and actuating their motion.
"How Was Your Weekend?" A Generative Model of Phatic Conversation.

Unspoken social rules, such as those that govern choosing a proper discussion topic and when to change discussion topics, guide conversational behaviors. We propose a computational model of conversation that can follow or break such rules, with participant agents that respond accordingly. Additionally, we demonstrate an application of the model: the Experimental Social Tutor (EST), a first step toward a social skills training tool that generates human-readable conversation and a conversational guideline at each point in the dialogue. Finally, we discuss the design and results of a pilot study evaluating the EST. Results show that our model is capable of producing conversations that follow social norms.
Distributed Active State Estimation With User-Specified Accuracy

In this paper, we address the problem of controlling a network of mobile sensors so that a set of hidden states are estimated up to a user-specified accuracy. The sensors take measurements and fuse them online using an information consensus filter (ICF). At the same time, the local estimates guide the sensors to their next best configuration. This leads to an LMI-constrained optimization problem that we solve by means of a new distributed random approximate projections method. The new method is robust to the state disagreement errors that exist among the robots as the ICF fuses the collected measurements. Assuming that the noise corrupting the measurements is zero-mean and Gaussian and that the robots are self-localized in the environment, the integrated system converges to the next best positions from where new observations will be taken. This process is repeated with the robots taking a sequence of observations until the hidden states are estimated up to the desired user-specified accuracy. We present simulations of sparse landmark localization, where the robotic team achieves the desired estimation tolerances while exhibiting interesting emergent behavior.
Two Theorems in List Decoding

We prove the following results concerning the list decoding of error-correcting codes: 1. We show that for any code with a relative distance of δ (over a large enough alphabet), the following result holds for random errors: With high probability, for a ρ 6 δ− e fraction of random errors (for any e > 0), the received word will have only the transmitted codeword in a Hamming ball of radius ρ around it. Thus, for random errors, one can correct twice the number of errors uniquely correctable from worst-case errors for any code. A variant of our result also gives a simple algorithm to decode Reed-Solomon codes from random errors that, to the best of our knowledge, runs faster than known algorithms for certain ranges of parameters. 2. We show that concatenated codes can achieve the list decoding capacity for erasures. A similar result for worst-case errors was proven by Guruswami and Rudra (SODA 08), although their result does not directly imply our result. Our results show that a subset of the random ensemble of codes considered by Guruswami and Rudra also achieve the list decoding capacity for erasures. Our proofs employ simple counting and probabilistic arguments. ∗Research supported by NSF CAREER Award CCF-0844796. ISSN 1433-8092 Electronic Colloquium on Computational Complexity, Report No. 7 (2010)
On the competition complexity of dynamic mechanism design

The Competition Complexity of an auction measures how much competition is needed for the revenue of a simple auction to surpass the optimal revenue. A classic result from auction theory by Bulow and Klemperer [11], states that the Competition Complexity of VCG, in the case of n i.i.d. buyers and a single item, is 1. In other words, it is better to invest in recruiting one extra buyer and run a second price auction than to invest in learning exactly the buyers' underlying distribution and run the revenue-maximizing auction tailored to this distribution. In this paper we study the Competition Complexity of dynamic auctions. Consider the following problem: a monopolist is auctioning off m items in m consecutive stages to n interested buyers. A buyer realizes her value for item k in the beginning of stage k. How many additional buyers are necessary and sufficient for a second price auction at each stage to extract revenue at least that of the optimal dynamic auction? We prove that the Competition Complexity of dynamic auctions is at most 3n - and at least linear in n - even when the buyers' values are correlated across stages, under a monotone hazard rate assumption on the stage (marginal) distributions. This assumption can be relaxed if one settles for independent stages. We also prove results on the number of additional buyers necessary for VCG at every stage to be an α-approximation of the optimal revenue; we term this number the α-approximate Competition Complexity. For example, under the same mild assumptions on the stage distributions we prove that one extra buyer suffices for a [EQUATION]-approximation. As a corollary we provide the first results on prior-independent dynamic auctions. This is, to the best of our knowledge, the first non-trivial positive guarantees for simple ex-post IR dynamic auctions for correlated stages. A key step towards proving bounds on the Competition Complexity is getting a good benchmark/upper bound to the optimal revenue. To this end, we extend the recent duality framework of [14] to dynamic settings. As an aside to our approach we obtain a revenue non-monotonicity lemma for dynamic auctions, which may be of independent interest.
Non-abelian cohomology of extensions of Lie algebras as Deligne groupoid

Abstract In this note we show that the theory of non-abelian extensions of a Lie algebra g by a Lie algebra h can be understood in terms of a differential graded Lie algebra L. More precisely we show that the non-abelian cohomology H nab 2 ( g , h ) is MC ( L ) , the π 0 of the Deligne groupoid of L.
Thermomechanical Two-Mode Squeezing in an Ultrahigh-Q Membrane Resonator.

We realize a quantum-compatible multimode interaction in an ultrahigh $Q$ mechanical resonator via a reservoir-mediated parametric coupling. We use this interaction to demonstrate nondegenerate parametric amplification and thermomechanical noise squeezing, finding excellent agreement with a theoretical model of this interaction over a large dynamic range. This realization of strong multimode nonlinearities in a mechanical platform compatible with quantum-limited optical detection and cooling makes this a powerful system for nonlinear approaches to quantum metrology, transduction between optical and phononic fields, and the quantum manipulation of phononic degrees of freedom.
Turaev–Viro invariants, colored Jones polynomials, and volume

We obtain a formula for the Turaev-Viro invariants of a link complement in terms of values of the colored Jones polynomial of the link. As an application we give the first examples for which the volume conjecture of Chen and the third named author\,\cite{Chen-Yang} is verified. Namely, we show that the asymptotics of the Turaev-Viro invariants of the Figure-eight knot and the Borromean rings complement determine the corresponding hyperbolic volumes. Our calculations also exhibit new phenomena of asymptotic behavior of values of the colored Jones polynomials that seem not to be predicted by neither the Kashaev-Murakami-Murakami volume conjecture and various of its generalizations nor by Zagier's quantum modularity conjecture. We conjecture that the asymptotics of the Turaev-Viro invariants of any link complement determine the simplicial volume of the link, and verify it for all knots with zero simplicial volume. Finally we observe that our simplicial volume conjecture is stable under connect sum and split unions of links.
Cyber-Physical Attacks With Control Objectives

This paper studies attackers with control objectives against cyber-physical systems (CPSs). The goal of the attacker is to counteract the CPS's controller and move the system to a target state while evading detection. We formulate a cost function that reflects the attacker's goals, and, using dynamic programming, we show that the optimal attack strategy reduces to a linear feedback of the attacker's state estimate. By changing the parameters of the cost function, we show how an attacker can design optimal attacks to balance the control objective and the detection avoidance objective. Finally, we provide a numerical illustration based on a remotely controlled helicopter under attack.
THE MOSDEF SURVEY: MEASUREMENTS OF BALMER DECREMENTS AND THE DUST ATTENUATION CURVE AT REDSHIFTS z ∼ 1.4–2.6*

We present results on the dust attenuation curve of z ? 2 galaxies using early observations from the MOSFIRE Deep Evolution Field survey. Our sample consists of 224 star-forming galaxies with zspec = 1.36?2.59 and high signal-to-noise ratio measurements of H? and H? obtained with Keck/MOSFIRE. We construct composite spectral energy distributions (SEDs) of galaxies in bins of Balmer decrement to measure the attenuation curve. We find a curve that is similar to the SMC extinction curve at ? ? 2500 ?. At shorter wavelengths, the shape is identical to that of the Calzetti et al. relation, but with a lower normalization. Hence, the new attenuation curve results in star formation rates (SFRs) that are lower, and stellar masses that are dex lower, than those obtained with the Calzetti relation. We find that the difference in the total attenuation of the ionized gas and stellar continuum correlates strongly with SFR, such that for dust-corrected SFRs ? 20 M? yr?1, assuming a Chabrier initial mass function, the nebular emission lines suffer an increasing degree of obscuration relative to the continuum. A simple model that can account for these trends is one in which the UV through optical stellar continuum is dominated by a population of less-reddened stars, while the nebular line and bolometric luminosities become increasingly dominated by dustier stellar populations for galaxies with large SFRs, as a result of the increased dust enrichment that accompanies such galaxies. Consequently, UV- and SED-based SFRs may underestimate the total SFR at even modest levels of ?20 M? yr?1.
Temporal Pattern Recognition through Analog Molecular Computation.

Living cells communicate information about physiological conditions by producing signaling molecules in a specific timed manner. Different conditions can result in the same total amount of a signal...
Indium Tin Oxide/Barium Strontium Titanate THz Sensor Antenna

A Bow-Tie ITO/BST and Au/BST antennas were designed and analyzed using the finite element, Multiphysics COMSOLTM program. The study shows that the outputs peak at several modes and ITO antennas allow for the integration of the antenna with optoelectronic devices.
The Smith normal form of a specialized Jacobi–Trudi matrix

Abstract Let JT λ be the Jacobi–Trudi matrix corresponding to the partition λ , so det JT λ is the Schur function s λ in the variables x 1 , x 2 , … . Set x 1 = ⋯ = x n = 1 and all other x i = 0 . Then the entries of JT λ become polynomials in n of the form ( n + j − 1 j ) . We determine the Smith normal form over the ring Q [ n ] of this specialization of JT λ . The proof carries over to the specialization x i = q i − 1 for 1 ≤ i ≤ n and x i = 0 for i > n , where we set q n = y and work over the ring Q ( q ) [ y ] .
Learning with scope, with application to information extraction and classification

In probabilistic approaches to classification and information extraction, one typically builds a statistical model of words under the assumption that future data will exhibit the same regularities as the training data. In many data sets, however, there are scopelimited features whose predictive power is only applicable to a certain subset of the data. For example, in information extraction from web pages, word formatting may be indicative of extraction category in different ways on different web pages. The difficulty with using such features is capturing and exploiting the new regularities encountered in previously unseen data. In this paper, we propose a hierarchical probabilistic model that uses both local/scope-limited features, such as word formatting, and global features, such as word content. The local regularities are modeled as an unobserved random parameter which is drawn once for each local data set. This random parameter is estimated during the inference process and then used to perform classification with both the local and global features- a procedure which is akin to automatically retuning the classifier to the local regularities on each newly encountered web page. Exact inference is intractable and we present approximations via point estimates and variational methods. Empirical results on large collections of web data demonstrate that this method significantly improves performance from traditional models of global features alone.
Beyond Degree Choosability

Let $G$ be a connected graph with maximum degree $\Delta$. Brooks' theorem states that $G$ has a $\Delta$-coloring unless $G$ is a complete graph or an odd cycle. A graph $G$ is degree-choosable if $G$ can be properly colored from its lists whenever each vertex $v$ gets a list of $d(v)$ colors. In the context of list coloring, Brooks' theorem can be strengthened to the following. Every connected graph $G$ is degree-choosable unless each block of $G$ is a complete graph or an odd cycle; such a graph $G$ is a Gallai tree . This degree-choosability result was further strengthened to Alon—Tarsi orientations; these are orientations of $G$ in which the number of spanning Eulerian subgraphs with an even number of edges differs from the number with an odd number of edges. A graph $G$ is degree-AT if $G$ has an Alon—Tarsi orientation in which each vertex has indegree at least 1. Alon and Tarsi showed that if $G$ is degree-AT, then $G$ is also degree-choosable. Hladký, Kral', and Schauz showed that a connected graph is degree-AT if and only if it is not a Gallai tree. In this paper, we consider pairs $(G,x)$ where $G$ is a connected graph and $x$ is some specified vertex in $V(G)$. We characterize pairs such that $G$ has no Alon—Tarsi orientation in which each vertex has indegree at least 1 and $x$ has indegree at least 2. When $G$ is 2-connected, the characterization is simple to state.
Lattice Hadron Structure: Applications within and beyond QCD

Study of the hadronic matrix elements can provide not only tests of the QCD sector of the Standard Model (in comparing with existing experiments) but also reliable low-energy hadronic quantities applicable to a wide range of beyond-the-Standard Model scenarios where experiments or theoretical calculations are limited or difficult. On the QCD side, progress has been made in the notoriously difficult problem of addressing gluonic structure inside the nucleon, reaching higherQ2 region of the form factors, and providing a complete picture of the proton spin. However, even further study and improvement of systematic uncertainties are needed. There are also proposed calculations of higher-order operators in the neutron electric dipole moment Lagrangian, which would be useful when combined with effective theory to probe BSM. Lattice isovector tensor and scalar charges can be combined with upcoming neutron beta-decay measurements of the Fierz interference term and neutrino asymmetry parameter to probe new interactions in the effective theory, revealing the scale of potential new TeV particles. Finally, I revisit the systematic uncertainties in recent calculations of gA and review prospects for future calculations.