cssi/SciSciGPT-SciSciNet huggingface dataset:
Scalable Generalized Linear Bandits: Online Computation and Hashing

Generalized Linear Bandits (GLBs), a natural extension of the stochastic linear bandits, has been popular and successful in recent years. However, existing GLBs scale poorly with the number of rounds and the number of arms, limiting their utility in practice. This paper proposes new, scalable solutions to the GLB problem in two respects. First, unlike existing GLBs, whose per-time-step space and time complexity grow at least linearly with time $t$, we propose a new algorithm that performs online computations to enjoy a constant space and time complexity. At its heart is a novel Generalized Linear extension of the Online-to-confidence-set Conversion (GLOC method) that takes \emph{any} online learning algorithm and turns it into a GLB algorithm. As a special case, we apply GLOC to the online Newton step algorithm, which results in a low-regret GLB algorithm with much lower time and memory complexity than prior work. Second, for the case where the number $N$ of arms is very large, we propose new algorithms in which each next arm is selected via an inner product search. Such methods can be implemented via hashing algorithms (i.e., ``hash-amenable'') and result in a time complexity sublinear in $N$. While a Thompson sampling extension of GLOC is hash-amenable, its regret bound for $d$-dimensional arm sets scales with $d^{3/2}$, whereas GLOC's regret bound scales with $d$. Towards closing this gap, we propose a new hash-amenable algorithm whose regret bound scales with $d^{5/4}$. Finally, we propose a fast approximate hash-key computation (inner product) with a better accuracy than the state-of-the-art, which can be of independent interest. We conclude the paper with preliminary experimental results confirming the merits of our methods.
Constraints on dark matter protohalos in effective theories and neutrinophilic dark matter

Abstract The mass of primordial dark matter (DM) protohalos remains unknown. However, the missing satellites problem may be an indication that they are quite large. In this paper, we use effective field theory to map constraints on dark matter-SM interactions into limits on the mass of DM protohalos. Given that leptons remain in the thermal bath until late times, we focus on their interactions with DM. To illustrate the method, we use the null results of LEP missing energy searches along with Fermi-LAT searches for DM annihilation in nearby dwarf galaxies, to derive limits on the protohalo mass, ≲ (10−6 to 10−1) M⊙, with the range depending on the DM mass and the operator. Thus, if DM is to remain thermally coupled until late times and account for the missing satellites, charged lepton interactions are insufficient. This motivates neutrinophilic DM, which can have protohalo masses orders of magnitude larger, with constraints arising from Planck, IceCube and unpublished Super-K data. We show that effective neutrinophilic models offer a viable solution to the missing satellites problem for sub-GeV DM masses with larger than WIMP-sized annihilation cross sections.
Lehmer Pairs Revisited

ABSTRACTWe seek to understand how the technical definition of a Lehmer pair can be related to more analytic properties of the Riemann zeta function, particularly the location of the zeros of ζ′(s). Because we are interested in the connection [Csordas et al. 94] between Lehmer pairs and the de Bruijn–Newman constant Λ, we assume the Riemann hypothesis throughout. We define strong Lehmer pairs via an inequality on the derivative of the pre-Schwarzian of Riemann’s function Ξ(t), evaluated at consecutive zeros: Theorem 1 shows that strong Lehmer pairs are Lehmer pairs. Theorem 2 describes PΞ′(γ) in terms of ζ′(ρ) where ρ = 1/2 + iγ. Theorem 3 expresses PΞ′(γ+) + PΞ′(γ−) in terms of nearby zeros ρ′ of ζ′(s). We examine 114, 661 pairs of zeros of ζ(s) around height t = 106, finding 855 strong Lehmer pairs. These are compared to the corresponding zeros of ζ′(s) in the same range.
Leaky roots and stable Gauss-Lucas theorems

ABSTRACTLet p:C→C be a polynomial. The Gauss-Lucas theorem states that its critical points, p′(z)=0, are contained in the convex hull of its roots. In a recent quantitative version, Totik shows tha...
Representing Images in 200 Bytes: Compression via Triangulation

A rapidly increasing portion of internet traffic is dominated by requests from mobile devices with limited and metered bandwidth constraints. To satisfy these requests, it has become standard practice for websites to transmit small and extremely compressed image previews as part of the initial page load process to improve responsiveness. Increasing thumbnail compression beyond the capabilities of existing codecs is therefore an active research direction. In this work, we concentrate on extreme compression rates, where the size of the image is typically 200 bytes or less. First, we propose a novel approach for image compression that, unlike commonly used methods, does not rely on block-based statistics. We use an approach based on an adaptive triangulation of the target image, devoting more triangles to high entropy regions of the image. Second, we present a novel algorithm for encoding the triangles. The results show favorable statistics, in terms of PSNR and SSIM, over both the JPEG and the WebP standards.
Minimal subfamilies and the probabilistic interpretation for modulus on graphs

The notion of p-modulus of a family of objects on a graph is a measure of the richness of such families. We develop the notion of minimal subfamilies using the method of Lagrangian duality for p-modulus. We show that minimal subfamilies have at most |E| elements and that these elements carry a weight related to their “importance” in relation to the corresponding p-modulus problem. When $$p=2$$ , this measure of importance is in fact a probability measure and modulus can be thought as trying to minimize the expected overlap in the family.
Smooth Embeddings of Rational Homology Balls

Abstract The rational homology balls B n appeared in Fintushel and Sternʼs rational blow-down construction [3] and were subsequently used in [10] , [4] to construct exotic smooth manifolds with small Euler numbers. We show that a large class of smooth 4-manifolds have all the B n s for odd n ⩾ 3 embedded in them. In particular, we give explicit examples, using Kirby calculus, of several families of smooth embeddings of the rational homology balls B n .
Computable randomness and betting for computable probability spaces

Unlike Martin-L\"of randomness and Schnorr randomness, computable randomness has not been defined, except for a few ad hoc cases, outside of Cantor space. This paper offers such a definition (actually, several equivalent definitions), and further, provides a general method for abstracting "bit-wise" definitions of randomness from Cantor space to arbitrary computable probability spaces. This same method is also applied to give machine characterizations of computable and Schnorr randomness for computable probability spaces, extending the previously known results. The paper contains a new type of randomness---endomorphism randomness---which the author hopes will shed light on the open question of whether Kolmogorov-Loveland randomness is equivalent to Martin-L\"of randomness. The last section contains ideas for future research.
Photocatalytic activity of pulsed laser deposited TiO2 thin films

Abstract Nanostructured TiO 2 thin films were prepared by pulsed laser deposition (PLD) on indium-doped tin oxide (ITO) substrates. Results from X-ray photoelectron spectroscopy (XPS) show that Ti 2p core level peaks shift toward the lower binding energy with decrease in the buffer gas pressure (O 2 :Ar = 1:1). This suggests that oxygen vacancies are created under insufficient oxygen conditions. Anatase-to-rutile ratio is also found to be system pressure dependent. Under deposition pressure of 100 Pa, only anatase phase was observed even at 1073 K substrate temperature which is much higher that the bulk anatase-to-rutile phase transformation temperature. The deposited TiO 2 thin films were fabricated as photoanodes for photoelectrochemical (PEC) studies. PEC measurements on TiO 2 photoanodes show that the flatband potential ( V fb ) increases by 0.088 eV on absolute vacuum energy scale (AVS) with decrease in the deposition pressure from 100 to 33 Pa at 873 K. The highest incident photon to current conversion efficiency [IPCE( λ )] of 2.5 to 6% at λ = 320 nm was obtained from the thin films prepared at substrate temperature of 873 K. Combining the results from XPS and PEC studies, we conclude that the deposition pressure affects the concentration of the oxygen vacancies which changes the electronic structure of the TiO 2 . With reference to photoelectrochemical catalytic performance, our results suggest that it is possible to adjust the Fermi energy level and structure of TiO 2 thin films by controlling the buffer gas pressure and temperature to align the energy of the flatband potential ( V fb ) with respect to specific redox species in the electrolyte.
Generalized Uniformly Optimal Methods for Nonlinear Programming

Uniformly optimal convex programming algorithms have been designed to achieve the optimal complexity bounds for convex optimization problems regardless of the level of smoothness of the objective function. In this paper, we present a generic framework to extend such existing algorithms to solve more general nonlinear, possibly nonconvex, optimization problems. The basic idea is to incorporate a local search step (gradient descent or Quasi-Newton iteration) into the uniformly optimal convex programming methods, and then enforce a monotone decreasing property of the function values computed along the trajectory. While optimal methods for nonconvex programming are not generally known, algorithms of these types will achieve the best known complexity for nonconvex problems, and the optimal complexity for convex ones without requiring any problem parameters. As a consequence, we can have a unified treatment for a general class of nonlinear programming problems regardless of their convexity and smoothness level. In particular, we show that the accelerated gradient and level methods, both originally designed for solving convex optimization problems only, can be used for solving both convex and nonconvex problems uniformly. In a similar vein, we show that some well-studied techniques for nonlinear programming, e.g., Quasi-Newton iteration, can be embedded into optimal convex optimization algorithms to possibly further enhance their numerical performance. Our theoretical and algorithmic developments are complemented by some promising numerical results obtained for solving a few important nonconvex and nonlinear data analysis problems in the literature.
Nauty in Macaulay2

We introduce a new Macaulay2 package, Nauty, which gives access to powerful methods on graphs provided by the software nauty by Brendan McKay. The primary motivation for accessing nauty is to determine if two graphs are isomorphic. We also implement methods to generate families of graphs restricted in various ways using tools provided with the software nauty. INTRODUCTION. Let G and H be two finite, simple, undirected graphs on the common vertex set V with edge sets E(G) and E(H), respectively. We say that G and H are isomorphic if there is a bijection j from V to itself which preserves edges, that is,fu; vg2 E(G) if and only iffj(u);j(v)g2 E(H). Determining whether two given graphs are isomorphic is known as the Graph Isomorphism problem. When Garey and Johnson wrote their classic book (GJ) on the complexity of algorithms, they specified twelve problems of ambiguous complexity, one of which was the Graph Isomorphism problem. Unfortunately, it is still unknown if the Graph Isomorphism problem is P or NP-complete. Moreover, the problem is of such notoriety that some have even begun referring to a new complexity class, GI, of problems which reduce in polynomial time to the Graph Isomorphism problem (J). Despite this, there exists computer software which is capable of determining whether two graphs are isomorphic in reasonable time. One such piece of software is nauty (N) by McKay. The nauty software is written in highly portable C and is designed to, above all else, compute whether two graphs are isomorphic. It also includes an extensive family of tools, collectively called gtools, to generate systematic modifications of graphs, to generate specific families of graphs, to generate random graphs, to filter a set of graphs for given properties, and to canonically relabel graphs. Most of these features would be beneficial to any computer software that handles graphs. The package EdgeIdeals (FHT) by Francisco, Hoefel, and Van Tuyl implements structures and methods for manipulating graphs (and hypergraphs) within Macaulay2 (M2), a software system by Grayson and Stillman designed to aid in research of commutative algebra and algebraic geometry. We introduce a new package, Nauty, for Macaulay2, which provides an interface with nauty. 1 Most of the aforementioned tools in gtools are accessible through Nauty. In particular, the methods of perhaps the greatest interest are areIsomorphic, filterGraphs, generateGraphs, and generateRandomGraphs. The remainder of this note is broken in to two sections: the first describes briefly the theoretical underpinnings of nauty and the second gives an example session of using Nauty along with a few useful caveats. CANONICAL LABELLINGS. In (M), McKay describes the improved algorithms which he developed to canonically label a graph; these algorithms are the heart of nauty and are summarised in (N). We recall briefly the theoretical ideas which make such algorithms useful.
Statistical Optimal Transport via Factored Couplings

We propose a new method to estimate Wasserstein distances and optimal transport plans between two probability distributions from samples in high dimension. Unlike plug-in rules that simply replace the true distributions by their empirical counterparts, our method promotes couplings with low transport rank, a new structural assumption that is similar to the nonnegative rank of a matrix. Regularizing based on this assumption leads to drastic improvements on high-dimensional data for various tasks, including domain adaptation in single-cell RNA sequencing data. These findings are supported by a theoretical analysis that indicates that the transport rank is key in overcoming the curse of dimensionality inherent to data-driven optimal transport.
Third cohomology and fusion categories

It was observed recently that for a fixed finite group $G$, the set of all Drinfeld centres of $G$ twisted by 3-cocycles form a group, the so-called group of modular extensions (of the representation category of $G$), which is isomorphic to the third cohomology group of $G$. We show that for an abelian $G$, pointed twisted Drinfeld centres of $G$ form a subgroup of the group of modular extensions. We identify this subgroup with a group of quadratic extensions containing $G$ as a Lagrangian subgroup, the so-called group of Lagrangian extensions of $G$. We compute the group of Lagrangian extensions, thereby providing an interpretation of the internal structure of the third cohomology group of an abelian $G$ in terms of fusion categories.
Design, Analysis and Application of A Volumetric Convolutional Neural Network

Abstract The design, analysis and application of a volumetric convolutional neural network (VCNN) are studied in this work. Although many CNNs have been proposed in the literature, their design is empirical. In the design of the VCNN, we propose a feed-forward K-means clustering algorithm to determine the filter number and size at each convolutional layer systematically. For the analysis of the VCNN, the cause of confusing classes in the output of the VCNN is explained by analyzing the relationship between the filter weights (also known as anchor vectors) from the last fully-connected layer to the output. Furthermore, a hierarchical clustering method followed by a random forest classification method is proposed to boost the classification performance among confusing classes. For the application of the VCNN, we examine the 3D shape classification problem and conduct experiments on a popular ModelNet40 dataset. The proposed VCNN offers the state-of-the-art performance among all volume-based CNN methods.
The refined Coates–Sinnott conjecture for characteristic p global fields

Abstract This article is concerned with proving a refined function field analogue of the Coates–Sinnott conjecture, formulated in the number field context in 1974. Our main theorem calculates the Fitting ideal of a certain even Quillen K -group in terms of special values of L -functions. The techniques employed are directly inspired by recent work of Greither and Popescu in the equivariant Iwasawa theory of arbitrary global fields. They rest on the results of Greither and Popescu on the Galois module structure of certain naturally defined Picard 1-motives associated to an arbitrary Galois extension of function fields.
Tensor powers of rank 1 Drinfeld modules and periods

Abstract We study tensor powers of rank 1 sign-normalized Drinfeld A-modules, where A is the coordinate ring of an elliptic curve over a finite field. Using the theory of A-motives, we find explicit formulas for the A-action of these modules. Then, by developing the theory of vector-valued Anderson generating functions, we give formulas for the period lattice of the associated exponential function.
Integrally Closed Ideals on Log Terminal Surfaces are Multiplier Ideals

We show that all integrally closed ideals on log terminal surfaces are multiplier ideals by extending an existing proof for smooth surfaces.
Optimal Asymptotic Entrainment of Phase-Reduced Oscillators

We derive optimal periodic controls for entrainment of a self-driven oscillator to a desired frequency. The alternative objectives of minimizing power and maximizing frequency range of entrainment are considered. A state space representation of the oscillator is reduced to a linearized phase model, and the optimal periodic control is computed from the phase response curve using formal averaging and the calculus of variations. Computational methods are used to calculate the periodic orbit and the phase response curve, and a numerical method for approximating the optimal controls is introduced. Our method is applied to asymptotically control the period of spiking neural oscillators modeled using the Hodgkin-Huxley equations. This example illustrates the optimality of entrainment controls derived using phase models when applied to the original state space system.Copyright © 2011 by ASME
Computing the Feasible Spaces of Optimal Power Flow Problems

The solution to an optimal power flow (OPF) problem provides a minimum cost operating point for an electric power system. The performance of OPF solution techniques strongly depends on the problem's feasible space. This paper presents an algorithm that is guaranteed to compute the entire feasible spaces of small OPF problems to within a specified discretization tolerance. Specifically, the feasible space is computed by discretizing certain of the OPF problem's inequality constraints to obtain a set of power flow equations. All solutions to the power flow equations at each discretization point are obtained using the Numerical Polynomial Homotopy Continuation algorithm. To improve computational tractability, “bound tightening” and “grid pruning” algorithms use convex relaxations to preclude consideration of many discretization points that are infeasible for the OPF problem. The proposed algorithm is used to generate the feasible spaces of two small test cases.
Dynamic teaching in sequential decision making environments

We describe theoretical bounds and a practical algorithm for teaching a model by demonstration in a sequential decision making environment. Unlike previous efforts that have optimized learners that watch a teacher demonstrate a static policy, we focus on the teacher as a decision maker who can dynamically choose different policies to teach different parts of the environment. We develop several teaching frameworks based on previously defined supervised protocols, such as Teaching Dimension, extending them to handle noise and sequences of inputs encountered in an MDP. We provide theoretical bounds on the learnability of several important model classes in this setting and suggest a practical algorithm for dynamic teaching.
Decentralized formation control with a quadratic lyapunov function

In this paper, we investigate a decentralized formation control algorithm for an undirected formation control model. Unlike other formation control problems where only the shape of a configuration counts, we emphasize here also its Euclidean embedding. By following this decentralized formation control law, the agents will converge to certain equilibrium of the control system. In particular, we show that there is a quadratic Lyapunov function associated with the formation control system whose unique local (global) minimum point is the target configuration. In view of the fact that there exist multiple equilibria (in fact, a continuum of equilibria) of the formation control system, and hence there are solutions of the system which converge to some equilibria other than the target configuration, we apply simulated annealing, as a heuristic method, to the formation control law to fix this problem. Simulation results show that sample paths of the modified stochastic system approach the target configuration.
On semiparametric exponential family graphical models

We propose a new class of semiparametric exponential family graphical models for the analysis of high dimensional mixed data. Different from the existing mixed graphical models, we allow the nodewise conditional distributions to be semiparametric generalized linear models with unspecified base measure functions. Thus, one advantage of our method is that it is unnecessary to specify the type of each node and the method is more convenient to apply in practice. Under the proposed model, we consider both problems of parameter estimation and hypothesis testing in high dimensions. In particular, we propose a symmetric pairwise score test for the presence of a single edge in the graph. Compared to the existing methods for hypothesis tests, our approach takes into account of the symmetry of the parameters, such that the inferential results are invariant with respect to the dierent parametrizations of the same edge. Thorough numerical simulations and a real data example are provided to back up our theoretical results.
The Galois group of the category of mixed Hodge–Tate structures

The category $$\mathrm{MHT}_{\mathbb {Q}}$$ of mixed Hodge–Tate structures over $${\mathbb {Q}}$$ is a mixed Tate category. Thanks to the Tannakian formalism it is equivalent to the category of graded comodules over a commutative graded Hopf algebra $${{{\mathcal {H}}}}_\bullet = \oplus _{n=0}^\infty {{{\mathcal {H}}}}_n$$ over $${\mathbb {Q}}$$ . Since the category $$\mathrm{MHT}_{\mathbb {Q}}$$ has homological dimension one, $${{{\mathcal {H}}}}_\bullet $$ is isomorphic to the commutative graded Hopf algebra provided by the tensor algebra of the graded vector space given by the sum of $$\mathrm{Ext}_{\mathrm{MHT}_{\mathbb {Q}}}^1({\mathbb {Q}}(0), {\mathbb {Q}}(n)) = {\mathbb {C}}/(2\pi i)^n{\mathbb {Q}}$$ over $$n>0$$ . However this isomorphism is not natural in any sense, e.g. does not exist in families. We give a natural construction of the Hopf algebra $${{{\mathcal {H}}}}_\bullet $$ . Namely, let $${\mathbb {C}}^*_{\mathbb {Q}}:={\mathbb {C}}^* \otimes {\mathbb {Q}}$$ . Set $$\begin{aligned} {{{\mathcal {A}}}}_\bullet ({\mathbb {C}}):= {\mathbb {Q}}\oplus \bigoplus _{n=1}^\infty {\mathbb {C}}_{\mathbb {Q}}^* \otimes _{\mathbb {Q}}{\mathbb {C}}^{\otimes n-1}. \end{aligned}$$ We provide it with a commutative graded Hopf algebra structure, such that $${{{\mathcal {H}}}}_\bullet = {{{\mathcal {A}}}}_\bullet ({\mathbb {C}})$$ . This implies another construction of the big period map $${{{\mathcal {H}}}}_n \longrightarrow {\mathbb {C}}_{\mathbb {Q}}^* \otimes {\mathbb {C}}$$ from Goncharov (JAMS 12(2):569–618, 1999. arXiv:alg-geom/9601021 , Annales de la Faculte des Sciences de Toulouse XXV(2–3):397–459, 2016. arXiv:1510.07270 ). Generalizing this, we introduce a notion of a Tate dg-algebra (R, k(1)), and assign to it a Hopf dg-algebra $${{{\mathcal {A}}}}_\bullet (R)$$ . For example, the Tate algebra $$({\mathbb {C}}, 2\pi i {\mathbb {Q}})$$ gives rise to the Hopf algebra $$\mathcal{A}_\bullet ({\mathbb {C}})$$ . Another example of a Tate dg-algebra $$(\Omega _X^\bullet , 2\pi i{\mathbb {Q}})$$ is provided by the holomorphic de Rham complex $$\Omega _X^\bullet $$ of a complex manifold X. The sheaf of Hopf dg-algebras $${{{\mathcal {A}}}}_\bullet (\Omega _X^\bullet )$$ describes a dg-model of the derived category of variations of Hodge–Tate structures on X. The cobar complex of $$\mathcal{A}_\bullet (\Omega _X^\bullet )$$ is a dg-model for the rational Deligne cohomology of X. We consider a variant of our construction which starting from Fontaine’s period rings $$\mathrm{B}_{\mathrm{crys}}$$ / $$\mathrm{B}_{\mathrm{st}}$$ produces graded/dg Hopf algebras which we relate to the p-adic Hodge theory.
Indecomposability for differential algebraic groups

Abstract We study a notion of indecomposability in differential algebraic groups which is inspired by both model theory and differential algebra. After establishing some basic definitions and results, we prove an indecomposability theorem for differential algebraic groups. The theorem establishes a sufficient criterion for the subgroup of a differential algebraic group generated by an infinite family of subvarieties to be a differential algebraic subgroup. This theorem is used for various definability results. For instance, we show that every noncommutative almost simple differential algebraic group is perfect, solving a problem of Cassidy and Singer. We also establish numerous bounds on Kolchin polynomials, some of which seem to be of a nature not previously considered in differential algebraic geometry; in particular, we establish bounds on the Kolchin polynomial of the generators of the differential field of definition of a differential algebraic variety.
An example of Crepant Resolution Conjecture in two steps

We study the relation among the genus 0 Gromov-Witten theories of the three spaces $\mathcal{X}\leftarrow\mathcal{Z}\leftarrow Y$, where $\mathcal{X}=[\c^2/\z_3]$, $\mathcal{Z}$ is obtained by a weighted blowup at the stacky point of $\mathcal{X}$, and $Y$ is the crepant resolution of the $A_2$ singularity. We formulate and verify a statement similar to the Crepant Resolution Conjecture of Bryan and Graber.b
Performance limits on the classification of Kronecker-structured models

Kronecker-structured (K-S) models have been proposed for the efficient representation, processing, and classification of multidimensional signals such as images and video. In this paper, we study the classification performance of Kronecker-structured models in two asymptotic regimes. First, we study the diversity order, the slope of the error probability as the signal noise power goes to zero. We derive an exact expression for the diversity order as a function of the signal and subspace dimensions of a K-S model. Next, we study the classification capacity, the maximum rate at which the number of classes can grow as the signal dimension goes to infinity. We derive upper and lower bounds on the prelog factor of the classification capacity. Finally, we evaluate the empirical classification performance of K-S models, showing that they agree with the diversity order analysis.
Real-Time Deep Learning at the Edge for Scalable Reliability Modeling of Si-MOSFET Power Electronics Converters

With the significant growth of advanced high-frequency power converters, online monitoring and active reliability assessment of power electronic devices are extremely crucial. This paper presents a transformative approach, named deep learning reliability awareness of converters at the edge (Deep RACE), for real-time reliability modeling and prediction of high-frequency MOSFET power electronic converters. Deep RACE offers a holistic solution which comprises algorithm advances, and full system integration (from the cloud down to the edge node) to create a near real-time reliability awareness. On the algorithm side, this paper proposes a deep learning algorithmic solution based on stacked long short-term memory for collective reliability training and inference across collective MOSFET converters based on device resistance changes. Deep RACE also proposes an integrative edge-to-cloud solution to offer a scalable decentralized devices-specific reliability monitoring, awareness, and modeling. The MOSFET convertors are Internet-of-Things (IoT) devices which have been empowered with edge real-time deep learning processing capabilities. The proposed Deep RACE solution has been prototyped and implemented through learning from MOSFET data set provided by NASA. Our experimental results show an average miss prediction of 8.9% over five different devices which is a much higher accuracy compared to well-known classical approaches (Kalman filter and particle filter). Deep RACE only requires 26-ms processing time and 1.87-W computing power on edge IoT device.
Central stability homology

We give a new categorical way to construct the central stability homology of Putman and Sam and explain how it can be used in the context of representation stability and homological stability. In contrast to them, we cover categories with infinite automorphism groups. We also connect central stability homology to Randal-Williams and Wahl’s work on homological stability. We also develop a criterion that implies that functors that are polynomial in the sense of Randal-Williams and Wahl are centrally stable in the sense of Putman.
Frobenius and Homological Dimensions of Complexes

It is proved that a module M over a Noetherian local ring R of prime characteristic and positive dimension has finite flat dimension if $${\text {Tor}}_i^R({}^{e}\!R, M)=0$$ for $${\text {dim}}\,R$$ consecutive positive values of i and infinitely many e. Here $${}^{e}\!R$$ denotes the ring R viewed as an R-module via the eth iteration of the Frobenius endomorphism. In the case R is Cohen–Macualay, it suffices that the Tor vanishing above holds for a single $$e\geqslant \log _p e(R)$$, where e(R) is the multiplicity of the ring. This improves a result of Dailey et al. (J Commut Algebra), as well as generalizing a theorem due to Miller (Contemp Math 331:207–234, 2003) from finitely generated modules to arbitrary modules. We also show that if R is a complete intersection ring then the vanishing of $${\text {Tor}}_i^R({}^{e}\!R, M)$$ for single positive values of i and e is sufficient to imply M has finite flat dimension. This extends a result of Avramov and Miller (Math Res Lett 8(1–2):225–232, 2001).
RoarNet: A Robust 3D Object Detection based on RegiOn Approximation Refinement

We present RoarNet, a new approach for 3D object detection from 2D image and 3D Lidar point clouds. Based on two stage object detection framework ([1], [2]) with PointNet [3] as our backbone network, we suggest several novel ideas to improve 3D object detection performance. The first part of our method, RoarNet_2D, estimates the 3D poses of objects from a monocular image, which approximates where to examine further, and derives multiple candidates that are geometrically feasible. This step significantly narrows down feasible 3D regions, which otherwise requires demanding processing of 3D point clouds in a huge search space. Then the second part, RoarNet_3D, takes the candidate regions and conducts in-depth inferences to conclude final poses in a recursive manner. Inspired by PointNet, RoarNet_3D processes 3D point clouds directly without any loss of data, leading to precise detection. We evaluate our method in KITTI, a 3D object detection benchmark. Our result shows that RoarNet has superior performance to state-of-the-art methods that are publicly available. Remarkably, RoarNet also outperforms state-of-the-art methods even in settings where Lidar and camera are not time synchronized, which is practically important for actual driving environment.
Knock-In Rat Lines with Cre Recombinase at the Dopamine D1 and Adenosine 2a Receptor Loci

Genetically modified mice have become standard tools in neuroscience research. Our understanding of the basal ganglia in particular has been greatly assisted by BAC mutants with selective transgene expression in striatal neurons forming the direct or indirect pathways. However, for more sophisticated behavioral tasks and larger intracranial implants, rat models are preferred. Furthermore, BAC lines can show variable expression patterns depending upon genomic insertion site. We therefore used CRISPR/Cas9 to generate two novel knock-in rat lines specifically encoding Cre recombinase immediately after the dopamine D1 receptor (Drd1a) or adenosine 2a receptor (Adora2a) loci. Here, we validate these lines using in situ hybridization and viral vector mediated transfection to demonstrate selective, functional Cre expression in the striatal direct and indirect pathways, respectively. We used whole-genome sequencing to confirm the lack of off-target effects and established that both rat lines have normal locomotor activity and learning in simple instrumental and Pavlovian tasks. We expect these new D1-Cre and A2a-Cre rat lines will be widely used to study both normal brain functions and neurological and psychiatric pathophysiology.
Designing Secure Ethereum Smart Contracts: A Finite State Machine Based Approach

The adoption of blockchain-based distributed computation platforms is growing fast. Some of these platforms, such as Ethereum, provide support for implementing smart contracts, which are envisioned to have novel applications in a broad range of areas, including finance and the Internet-of-Things. However, a significant number of smart contracts deployed in practice suffer from security vulnerabilities, which enable malicious users to steal assets from a contract or to cause damage. Vulnerabilities present a serious issue since contracts may handle financial assets of considerable value, and contract bugs are non-fixable by design. To help developers create more secure smart contracts, we introduce FSolidM, a framework rooted in rigorous semantics for designing contracts as Finite State Machines (FSM). We present a tool for creating FSM on an easy-to-use graphical interface and for automatically generating Ethereum contracts. Further, we introduce a set of design patterns, which we implement as plugins that developers can easily add to their contracts to enhance security and functionality.
Capacitated Covering Problems in Geometric Spaces

We consider the following capacitated covering problem. We are given a set P of n points and a set $${\mathcal {B}}$$ of balls from some metric space, and a positive integer U that represents the capacity of each of the balls in $${\mathcal {B}}$$ . We would like to compute a subset $${\mathcal {B}}' \subseteq {\mathcal {B}}$$ of balls and assign each point in P to some ball in $${\mathcal {B}}'$$ that contains it, so that the number of points assigned to any ball is at most U. The objective function that we would like to minimize is the cardinality of $${\mathcal {B}}'$$ . We consider this problem in arbitrary metric spaces as well as Euclidean spaces of constant dimension. In the metric setting, even the uncapacitated version of the problem is hard to approximate to within a logarithmic factor. In the Euclidean setting, the best known approximation guarantee in dimensions 3 and higher is logarithmic in the number of points. Thus we focus on obtaining “bi-criteria” approximations. In particular, we are allowed to expand the balls in our solution by some factor, but optimal solutions do not have that flexibility. Our main result is that allowing constant factor expansion of the input balls suffices to obtain constant approximations for this problem. In fact, in the Euclidean setting, only $$1+\epsilon $$ factor expansion is sufficient for any $$\epsilon > 0$$ , with the approximation factor being a polynomial in $$1/\epsilon $$ . We obtain these results using a unified scheme for rounding the natural LP relaxation; this scheme may be useful for other capacitated covering problems. We also complement these bi-criteria approximations by obtaining hardness of approximation results that shed light on our understanding of these problems.
Equilibrium-State Density Profiles of Centrifuged Cakes of Flocculated Suspensions

Many advanced ceramics are formed by colloidal consolidation using techniques such as sedimentation, centrifugation, or pressure filtration. In all these, the minimization of density variations of various colloidally-consolidated cakes has not been examined systematically until recently. Schilling et al. used γ-ray densitometry to study the density variations in a sedimented cake and showed that the sediment of a flocculated alumina suspension exhibited significant density variations within the cake whereas the sediment of a dispersed alumina suspension showed a constant density profile. Auzerais et al. used a medical X-ray computer tomography (CT) to study the settling of dispersed and flocculated silica suspensions and obtained similar results: The sedimented cake of a dispersed silica suspension was fairly uniform whereas those of flocculated silica suspensions exhibited significant density variations. However, Shih et al. examined pressure-filtered cakes of flocculated alumina suspensions with γ-ray densitometry and showed that the density profiles of pressure-filtered cakes of flocculated alumina suspensions were uniform, in contrast to their sedimentation counterparts which showed significant density variations. These studies illustrate that how the local density varies within a consolidated cake depends not only on the suspension conditions but also on how the suspensions are consolidated, e.g., the consolidation pressure.
On Wind Speed Sensor Configurations and Altitude Control in Airborne Wind Energy Systems

Real-time altitude control of airborne wind energy (AWE) systems can improve performance by allowing turbines to track favorable wind speeds across a range of operating altitudes. The current work explores the performance implications of deploying an AWE system with sensor configurations that provide different amounts of data to characterize wind speed profiles. We examine various control objectives that balance trade-offs between exploration and exploitation, and use a persistence model to generate a probabilistic wind speed forecast to inform control decisions. We assess system performance by comparing power production against baselines such as omniscient control and stationary flight. We show that with few sensors, control strategies that reward exploration are favored. We also show that with comprehensive sensing, the implications of choosing a sub-optimal control strategy decrease. This work informs and motivates the need for future research exploring online learning algorithms to characterize vertical wind speed profiles.
Convergent Evidence for Predispositional Effects of Brain Gray Matter Volume on Alcohol Consumption

Abstract Background Alcohol use has been reliably associated with smaller subcortical and cortical regional gray matter volumes (GMVs). Whether these associations reflect shared predisposing risk factors or causal consequences of alcohol use remains poorly understood. Methods Data came from 3 neuroimaging samples (N = 2423), spanning childhood or adolescence to middle age, with prospective or family-based data. First, we identified replicable GMV correlates of alcohol use. Next, we used family-based and longitudinal data to test whether these associations may plausibly reflect a predispositional liability for alcohol use or a causal consequence of alcohol use. Finally, we used heritability, gene-set enrichment, and transcriptome-wide association study approaches to evaluate whether genome-wide association study–defined genomic risk for alcohol consumption is enriched for genes that are preferentially expressed in regions that were identified in our neuroimaging analyses. Results Smaller right dorsolateral prefrontal cortex (DLPFC) (i.e., middle and superior frontal gyri) and insula GMVs were associated with increased alcohol use across samples. Family-based and prospective longitudinal data suggest that these associations are genetically conferred and that DLPFC GMV prospectively predicts future use and initiation. Genomic risk for alcohol use was enriched in gene sets that were preferentially expressed in the DLPFC and was associated with replicable differential gene expression in the DLPFC. Conclusions These data suggest that smaller DLPFC and insula GMV plausibly represent genetically conferred predispositional risk factors for, as opposed to consequences of, alcohol use. DLPFC and insula GMV represent promising biomarkers for alcohol-consumption liability and related psychiatric and behavioral phenotypes.
Design of a High Step-up DC-DC Power Converter with Voltage Multiplier Cells and Reduced Losses on Semiconductors for Photovoltaic Systems

A high step-up dc-dc converter based on an isolated dc-dc converter with voltage multiplier cells for photovoltaic systems is essentially introduced in this paper. The proposed converter can provide a high step-up voltage gain. The switch voltage stress and losses on semiconductors are significantly reduced through this work Furthermore, the proposed converter can reliably offer and provide continuous input current which can be basically used for integrating photovoltaic systems to convert 30 V to 480 V dc bus. The ripple on the input current is minimized due to the isolated converter, and the proposed converter is fed by a single input voltage. The operation modes and the characteristics of the aforementioned converter are thoroughly analyzed. The components selection, simulation results and experiment results are mainly verified by using MATALB Simulink. Consequently, a 360W hardware prototype is implemented to validate the design and the theory.
Chromatin-informed inference of transcriptional programs in gynecologic and basal breast cancers

Chromatin accessibility data can elucidate the developmental origin of cancer cells and reveal the enhancer landscape of key oncogenic transcriptional regulators. We develop a computational strategy called PSIONIC (patient-specific inference of networks informed by chromatin) to combine chromatin accessibility data with large tumor expression data and model the effect of enhancers on transcriptional programs in multiple cancers. We generate a new ATAC-seq data profiling chromatin accessibility in gynecologic and basal breast cancer cell lines and apply PSIONIC to 723 patient and 96 cell line RNA-seq profiles from ovarian, uterine, and basal breast cancers. Our computational framework enables us to share information across tumors to learn patient-specific TF activities, revealing regulatory differences between and within tumor types. PSIONIC-predicted activity for MTF1 in cell line models correlates with sensitivity to MTF1 inhibition, showing the potential of our approach for personalized therapy. Many identified TFs are significantly associated with survival outcome. To validate PSIONIC-derived prognostic TFs, we perform immunohistochemical analyses in 31 uterine serous tumors for ETV6 and 45 basal breast tumors for MITF and confirm that the corresponding protein expression patterns are also significantly associated with prognosis. Epigenomic data on chromatin accessibility and transcription factor occupancy can reveal enhancer landscapes in cancer. Here, the authors develop a computational strategy called PSIONIC (patient-specific inference of networks informed by chromatin) to model the impact of enhancers on transcriptional programs in gynecologic and basal breast cancers.
Direct laser writing of birefringent photonic crystals for the infrared spectral range

Infrared optical photonic crystals fabricated using direct laser writing, which is based on the two-photon polymerization of suitable monomers, have received substantial interest since the emergence of this process. Two-photon polymerization could be a disruptive technology for the fabrication of all-dielectric photonic crystals in the infrared spectral range, as it allows the synthesis of large scale arrays of uniform structures with arbitrary geometries and arrangements. However, all-dielectric photonic crystals that provide birefringent optical responses in the infrared spectral range have not yet been demonstrated using direct laser writing techniques. Here, the authors explore the form birefringence observed in photonic crystals composed of arrays of subwavelength-sized slanted polymer microwires. The photonic crystals investigated here were fabricated in a single fabrication step using direct laser writing of an infrared transparent photoresist (IP-Dip). A strong contrast of the cross-polarized reflectance of photonic crystals as a function of the in-plane orientation is observed in the mid-infrared spectral range at λ ≈ 6.5 μ m. This observation is indicative of an anisotropic optical behavior. Finite element based techniques corroborate the experimentally observed responses qualitatively.Infrared optical photonic crystals fabricated using direct laser writing, which is based on the two-photon polymerization of suitable monomers, have received substantial interest since the emergence of this process. Two-photon polymerization could be a disruptive technology for the fabrication of all-dielectric photonic crystals in the infrared spectral range, as it allows the synthesis of large scale arrays of uniform structures with arbitrary geometries and arrangements. However, all-dielectric photonic crystals that provide birefringent optical responses in the infrared spectral range have not yet been demonstrated using direct laser writing techniques. Here, the authors explore the form birefringence observed in photonic crystals composed of arrays of subwavelength-sized slanted polymer microwires. The photonic crystals investigated here were fabricated in a single fabrication step using direct laser writing of an infrared transparent photoresist (IP-Dip). A strong contrast of the cross-polarized refl...
Energy-Efficient Design in FDSOI

This talk presents options for energy-efficient mixed-signal and digital design in FDSOI technologies. Effective generation of body bias and its use to improve efficiency will be presented on the examples of RF and baseband building blocks, temperature sensors, data converters and voltage regulators. The techniques will be presented within a concept of a RISC-V-based SoC, designed to operate in a very wide voltage range utilizing 28nm FDSOI.
Proteins That Interact with the Mucin-Type Glycoprotein Msb2p Include a Regulator of the Actin Cytoskeleton.

Transmembrane mucin-type glycoproteins can regulate signal transduction pathways. In yeast, signaling mucins regulate mitogen-activated protein kinase (MAPK) pathways that induce cell differentiati...