cssi/SciSciGPT-SciSciNet huggingface dataset:
A living vector field reveals constraints on galactose network induction in yeast

When a cell encounters a new environment, its transcriptional response can be constrained by its history. For example, yeast cells in galactose induce GAL genes with a speed and unanimity that depends on previous nutrient conditions. Cellular memory of long-term glucose exposure delays GAL induction and makes it highly variable with in a cell population, while other nutrient histories lead to rapid, uniform responses. To investigate how cell-level gene expression dynamics produce population-level phenotypes, we built living vector fields from thousands of single-cell time courses of the proteins Gal3p and Gal1p as cells switched to galactose from various nutrient histories. We show that, after sustained glucose exposure, the lack of these GAL transducers leads to induction delays that are long but also variable; that cellular resources constrain induction; and that bimodally distributed expression levels arise from lineage selection-a subpopulation of cells induces more quickly and outcompetes the rest. Our results illuminate cellular memory in this important model system and illustrate how resources and randomness interact to shape the response of a population to a new environment.
Enantiopure N-Acyldihydropyridones as Synthetic Intermediates: Asymmetric Synthesis of (-)-Septicine and (-)-Tylophorine.

A concise asymmetric synthesis of (−)-septicine (1) and (−)-tylophorine (2) was accomplished with a high degree of stereocontrol in eight and nine steps, respectively. Addition of 4-(1-butenyl)magnesium bromide to 1-acylpyridinium salt 3, prepared in situ from 4-methoxy-3-(triisopropylsilyl)pyridine and the chloroformate of (−)-trans-2-(α-cumyl)cyclohexanol, gave a 91% yield of diastereomerically pure dihydropyridone 7. Oxidative cleavage of 7 and subsequent reduction provided alcohol 6 in 81% yield. Conversion of 6 to the chloride followed by treatment with sodium methoxide gave indolizidinone 9 in high yield. Bromination and conjugate reduction of 9 with L-Selectride, and trapping the intermediate enolate with N-(5-chloro-2-pyridyl)triflimide, provided bromovinyl triflate 11. Palladium-catalyzed cross-coupling of excess (3,4-dimethoxyphenyl)zinc bromide and 11 gave (−)-septicine (1). On the basis of this synthesis, (−)-1 was assigned the R configuration. Reaction of 1 with vanadium(V) trifluoride oxide ...
Reaction of α-(N-Carbamoyl)alkylcuprates with Propargyl Substrates: Synthetic Route to α-Amino Allenes and Δ3-Pyrrolines

Carbamate deprotonation followed by treatment with CuCN·2LiCl affords α-(N-carbamoyl)alkylcuprates which react with propargyl halides, mesylates, tosylates, phosphates, acetates, and epoxides to give α-(N-carbamoyl) allenes via an anti-SN2‘ substitution process. Propargyl halides, sulfonates, and phosphates give good yields of carbamoyl allenes, while the acetates afford low yields. Propargyl substrates undergo regiospecific SN2‘ substitution in the absence of severe steric hindrance. The α-(N-carbamoyl) allenes can be cyclized to 2-oxazolidinones or deprotected to afford the free amines which can be cyclized to Δ3-pyrrolines with either AgNO3 or Ru3(CO)12.
The crystal and molecular structure of the sulfamide (t-BuNH)2SO2

Abstract : The molecular structure of (t-BuNH)2SO2 has been determined by single-crystal X-ray diffraction methods. The compound crystallizes in the monoclinic system, space group P2 sub 1/c, with a = 9.720(4), b = 9.887(4), c = 12.076(6) A, beta = 90.46(4) deg, and Z = 4. The symmetry of (t-BuNH)2SO2 is C sub 2, and the t-Bu groups have a distal relationship with respect to each other. (Author)
Higher Education Professionals Navigating Anti-Immigration Policy for Undocumented Students:

Higher education professionals are critical to undocumented students’ educational success as their access to higher education is heavily dependent on being able to interpret and navigate policies. ...
Strategic Sorting: The Role of Ordeals in Health Care

Ordeals are burdens placed on individuals that yield no benefits to others; hence they represent a dead-weight loss. Ordeals – the most common is waiting time – play a prominent role in rationing health care. The recipients most willing to bear them are those receiving the greatest benefit from scarce health-care resources. Health care is heavily subsidized; hence, moral hazard leads to excess use. Ordeals are intended to discourage expenditures yielding little benefit while simultaneously avoiding the undesired consequences of rationing methods such as quotas or pricing. This analysis diagnoses the economic underpinnings of ordeals. Subsidies for nursing-home care versus home care illustrate.
Increase in weighting of vision vs. proprioception associated with force field adaptation

Hand position can be estimated by vision and proprioception (position sense). The brain is thought to weight and integrate these percepts to form a multisensory estimate of hand position with which to guide movement. Force field adaptation, a type of cerebellum-dependent motor learning, is associated with both motor and proprioceptive changes. The cerebellum has connections with multisensory parietal regions; however, it is unknown if force adaptation is associated with changes in multisensory perception. If force adaptation affects all relevant sensory modalities similarly, the brain’s weighting of vision vs. proprioception should be maintained. Alternatively, if force perturbation is interpreted as somatosensory unreliability, vision may be up-weighted relative to proprioception. We assessed visuo-proprioceptive weighting with a perceptual estimation task before and after subjects performed straight-ahead reaches grasping a robotic manipulandum. Each subject performed one session with a clockwise or counter-clockwise velocity-dependent force field, and one session in a null field. Subjects increased their weight of vision vs. proprioception in the force field session relative to the null session, regardless of force field direction, in the straight-ahead dimension (F1,44 = 5.13, p = 0.029). This suggests that force field adaptation is associated with an increase in the brain’s weighting of vision vs. proprioception.
End-to-End Optimization of High-Throughput DNA Sequencing.

Abstract At the core of Illumina's high-throughput DNA sequencing platforms lies a biophysical surface process that results in a random geometry of clusters of homogeneous short DNA fragments typically hundreds of base pairs long—bridge amplification. The statistical properties of this random process and the lengths of the fragments are critical as they affect the information that can be subsequently extracted, that is, density of successfully inferred DNA fragment reads. The ensembles of overlapping DNA fragment reads are then used to computationally reconstruct the much longer target genome sequence. The success of the reconstruction in turn depends on having a sufficiently large ensemble of DNA fragments that are sufficiently long. In this article using stochastic geometry, we model and optimize the end-to-end flow cell synthesis and target genome sequencing process, linking and partially controlling the statistics of the physical processes to the success of the final computational step. Based on a rou...
Stratification for Multiplicative Character Sums

We prove a stratification result for certain families of $n$-dimensional (complete algebraic) multiplicative character sums. The character sums we consider are sums of products of $r$ multiplicative characters evaluated at rational functions, and the families (with $nr$ parameters) are obtained by allowing each of the $r$ rational functions to be replaced by an "offset", i.e. a translate, of itself. For very general such families, we show that the stratum of the parameter space on which the character sum has maximum weight $n+j$ has codimension at least $j\lfloor(r-1)/2(n-1)\rfloor$ for $1\le j\le n-1$ and $\lceil nr/2\rceil$ for $j=n$.
Cone Fields and Topological Sampling in Manifolds with Bounded Curvature

A standard reconstruction problem is how to discover a compact set from a noisy point cloud that approximates it. A finite point cloud is a compact set. This paper proves a reconstruction theorem which gives a sufficient condition, as a bound on the Hausdorff distance between two compact sets, for when certain offsets of these two sets are homotopic in terms of the absence of μ-critical points in an annular region. We reduce the problem of reconstructing a subset from a point cloud to the existence of a deformation retraction from the offset of the subset to the subset itself. The ambient space can be any Riemannian manifold but we focus on ambient manifolds which have nowhere negative curvature (this includes Euclidean space). We get an improvement on previous bounds for the case where the ambient space is Euclidean whenever μ≤0.945 (μź(0,1) by definition). In the process, we prove stability theorems for μ-critical points when the ambient space is a manifold.
Scalar Green function bounds for instantaneous shock location and one-dimensional stability of viscous shock waves

In this paper, we investigate and prove the nonlinear stability of viscous shock wave solutions of a scalar viscous conservation law, using the methods developed for general systems of conservation laws by Howard, Mascia, Zumbrun and others, based on instantaneous tracking of the location of the perturbed viscous shock wave. In some sense, this paper extends the treatment in a previous expository work of Zumbrun ["Instantaneous shock location ..."] on Burgers equation to the general case, giving an exposition of these methods in the simplest setting of scalar equations. In particular we give by a rescaling argument a simple treatment of nonlinear stability in the small-amplitude case.
Testing conditional independence of discrete distributions

We study the problem of testing *conditional independence* for discrete distributions. Specifically, given samples from a discrete random variable (X, Y, Z) on domain [l1]×[l2] × [n], we want to distinguish, with probability at least 2/3, between the case that X and Y are conditionally independent given Z from the case that (X, Y, Z) is є-far, in l1-distance, from every distribution that has this property. Conditional independence is a concept of central importance in probability and statistics with important applications in various scientific domains. As such, the statistical task of testing conditional independence has been extensively studied in various forms within the statistics and econometrics community for nearly a century. Perhaps surprisingly, this problem has not been previously considered in the framework of distribution property testing and in particular no tester with *sublinear* sample complexity is known, even for the important special case that the domains of X and Y are binary. The main algorithmic result of this work is the first conditional independence tester with sublinear sample complexity. To complement our upper bound, we prove information-theoretic lower bounds establishing that the sample complexity of our algorithm is optimal, up to constant factors, for a number of settings. Specifically, for the prototypical setting when l1, l2 = O(1), we show that the sample complexity of testing conditional independence (upper bound and matching lower bound) is [complex formula not displayed] We also achieve sublinear sample complexity for the general case of arbitrary l1, l2, and n. To obtain our tester, we employ a variety of tools, including (1) a subtle weighted adaptation of the ”flattening” technique, and (2) the design and analysis of an optimal (unbiased) estimator for the following statistical problem of independent interest: Given a degree-d polynomial Q∶ℝn → ℝ and sample access to a distribution p over [n], estimate Q(p1, …, pn) up to small additive error. Obtaining tight variance analyses for specific estimators of this form has been a major technical hurdle in distribution testing. As an important contribution of this work, we develop a general theory providing tight variance bounds for *all* such estimators.
Compact feature-aware Hermite-style high-order surface reconstruction

High-order surface reconstruction is now a commonly used technique for mesh generation and adaptation, geometric and physical modeling, and high-order numerical methods for solving partial differential equations (PDEs). However, surface reconstruction from a relatively coarse mesh remained a challenging problem, especially for surfaces with sharp features. In this paper, we introduce a new method to address this challenge by improving the previous state of the art, including continuous moving frames (CMF) and weighted averaging of local fittings (WALF) (Eng Comput 28 (2012)), in two aspects. First, we significantly improve the robustness of reconstruction from coarse meshes using a Hermite-style least squares approximation to incorporate normals of the surface and tangents of the feature curves. Second, we ensure both $$G^{0}$$ continuity and high-order accuracy of the reconstruction near sharp features by generating parametric surface elements with an iterative feature-aware parameterization. We present the theoretical framework of our method and compare it against point-based methods in terms of accuracy and stability. We demonstrate the use of the proposed technique in generating high-order meshes for finite element methods, and show that it enables nearly identical solutions as using the meshes generated from exact geometry, while allowing additional flexibility.
SIX LITTLE SQUARES AND HOW THEIR NUMBERS GROW

We find the numbers of 3 � 3 magic, semimagic, and magilatin squares, as functions either of the magic sum or of an upper bound on the entries in the square. Our results on magic and semimagic squares differ from previous ones in that we require the entries in the square to be distinct from each other and we derive our results not by ad hoc reasoning but from the general geometric and algebraic method of our paper "An enumer- ative geometry for magic and magilatin labellings". Here we illustrate that method with a detailed analysis of 3 � 3 squares.
Parallel correlation clustering on big graphs

Given a similarity graph between items, correlation clustering (CC) groups similar items together and dissimilar ones apart. One of the most popular CC algorithms is KwikCluster: an algorithm that serially clusters neighborhoods of vertices, and obtains a 3-approximation ratio. Unfortunately, in practice KwikCluster requires a large number of clustering rounds, a potential bottleneck for large graphs. We present C4 and ClusterWild!, two algorithms for parallel correlation clustering that run in a polylogarithmic number of rounds, and provably achieve nearly linear speedups. C4 uses concurrency control to enforce serializability of a parallel clustering process, and guarantees a 3-approximation ratio. ClusterWild! is a coordination free algorithm that abandons consistency for the benefit of better scaling; this leads to a provably small loss in the 3 approximation ratio. We demonstrate experimentally that both algorithms outperform the state of the art, both in terms of clustering accuracy and running time. We show that our algorithms can cluster billion-edge graphs in under 5 seconds on 32 cores, while achieving a 15 x speedup.
Complexity Results and Fast Methods for Optimal Tabletop Rearrangement with Overhand Grasps

This paper studies the underlying combinatorial structure of a class of object rearrangement problems, which appear frequently in applications. The problems involve multiple, similar-geometry objec...
Physiology-Aware Rural Ambulance Routing

The ultimate objective of medical cyber-physical systems is to enhance the effectiveness of patient care. During emergency patient transport from rural medical facility to center tertiary hospital, real-time monitoring of the patient in the ambulance by a physician expert at the tertiary center is crucial. The physician experts can provide vital assistance to the ambulance EMT personnel. While telemetry healthcare services using mobile networks may enable remote real-time monitoring of transported patients, physiologic measures and tracking are at least as important and requires the existence of high-fidelity communication coverage. However, the wireless networks along the roads especially in rural areas can range from 4G to low-speed 2G, some parts with communication breakage. From a patient care perspective, transport during critical illness can make route selection patient state dependent. Prompt decisions with the relative advantage of a longer more secure bandwidth route versus a shorter, more rapid transport route but with less secure bandwidth must be made. The trade-off between route selection and the quality of wireless communication is an important optimization problem which unfortunately has remained unaddressed by prior work.In this paper, we propose a novel physiology-aware route scheduling approach for emergency ambulance transport of rural patients with acute, high risk diseases in need of continuous remote monitoring. We mathematically model the problem into an NP-hard graph theory problem, and approximate a solution based on a trade-off between communication coverage and shortest path. We profile communication along two major routes in a large rural hospital settings in Illinois, and use the traces to manifest the concept. Further, we design our algorithms and run preliminary experiments for scalability analysis. We believe that our scheduling techniques can become a compelling aid that enables an always-connected remote monitoring system in emergency patient transfer scenarios aimed to prevent morbidity and mortality with early diagnosis and effective treatment.
One-shot Coresets: The Case of k-Clustering

Scaling clustering algorithms to massive data sets is a challenging task. Recently, several successful approaches based on data summarization methods, such as coresets and sketches, were proposed. While these techniques provide provably good and small summaries, they are inherently problem dependent - the practitioner has to commit to a fixed clustering objective before even exploring the data. However, can one construct small data summaries for a wide range of clustering problems simultaneously? In this work, we affirmatively answer this question by proposing an efficient algorithm that constructs such one-shot summaries for k-clustering problems while retaining strong theoretical guarantees.
Sarkozy's Theorem for P-Intersective Polynomials

We define a necessary and sufficient condition on a polynomial $h\in \mathbb{Z}[x]$ to guarantee that every set of natural numbers of positive upper density contains a nonzero difference of the form $h(p)$ for some prime $p$. Moreover, we establish a quantitative estimate on the size of the largest subset of ${1,2,\dots,N}$ which lacks the desired arithmetic structure, showing that if deg$(h)=k$, then the density of such a set is at most a constant times $(\log N)^{-c}$ for any $c<1/(2k-2)$. We also discuss how an improved version of this result for $k=2$ and a relative version in the primes can be obtained with some additional known methods.
New instability results for high-dimensional nearest neighbor search

Consider a dataset of n(d) points generated independently from R^d according to a common p.d.f. f"d with support(f"d)=[0,1]^d and sup{f"d(R^d)} growing sub-exponentially in d. We prove that: (i) if n(d) grows sub-exponentially in d, then, for any query point q->@?[0,1]^d and any @e>0, the ratio of the distance between any two dataset points and q-> is less that 1+@e with probability ->1 as d->~; (ii) if n(d)>[4(1+@e)]^d for large d, then for all q->@?[0,1]^d (except a small subset) and any @e>0, the distance ratio is less than 1+@e with limiting probability strictly bounded away from one. Moreover, we provide preliminary results along the lines of (i) when f"d=N(@m->"d,@S"d).
Infinitesimal rigidity of collapsed gradient steady Ricci solitons in dimension three

The only known example of collapsed three-dimensional complete gradient steady Ricci solitons so far is the 3D cigar soliton $N^2\times \mathbb{R}$, the product of Hamilton's cigar soliton $N^2$ and the real line $\mathbb{R}$ with the product metric. R. Hamilton has conjectured that there should exist a family of collapsed positively curved three-dimensional complete gradient steady solitons, with $\mathsf{S}^1$-symmetry, connecting the 3D cigar soliton. In this paper, we make the first initial progress and prove that the infinitesimal deformation at the 3D cigar soliton is non-essential. In Appendix A, we show that the 3D cigar soliton is the unique complete nonflat gradient steady Ricci soliton in dimension three that admits two commuting Killing vector fields.
PRIIME: A generic framework for interactive personalized interesting pattern discovery

The traditional frequent pattern mining algorithms generate an exponentially large number of patterns of which a substantial proportion are not much significant for many data analysis endeavors. Discovery of a small number of personalized interesting patterns from the large output set according to a particular user's interest is an important as well as challenging task. Existing works on pattern summarization do not solve this problem from the personalization viewpoint. In this work, we propose an interactive pattern discovery framework named PRIIME which identifies a set of interesting patterns for a specific user without requiring any prior input on the interestingness measure of patterns from the user. The proposed framework is generic to support discovery of the interesting set, sequence and graph type patterns. We develop a softmax classification based iterative learning algorithm that uses a limited number of interactive feedback from the user to learn her interestingness profile, and use this profile for pattern recommendation. To handle sequence and graph type patterns PRIIME adopts a neural net (NN) based unsupervised feature construction approach. We also develop a strategy that combines exploration and exploitation to select patterns for feedback. We show experimental results on several real-life datasets to validate the performance of the proposed method. We also compare with the existing methods of interactive pattern discovery to show that our method is substantially superior in performance. To portray the applicability of the framework, we present a case study from the real-estate domain.
A NOTE ON CLOSED 3-BRAIDS

This is a review article about knots and links of braid index 3. Its goal is to gather together, in one place, some of the tools that are special to knots and links of braid index 3, in a form that could be useful for those who have a need to calculate, and need to know precisely all the exceptional cases.
Best-Worst Scaling More Reliable than Rating Scales: A Case Study on Sentiment Intensity Annotation

Rating scales are a widely used method for data annotation; however, they present several challenges, such as difficulty in maintaining inter- and intra-annotator consistency. Best–worst scaling (BWS) is an alternative method of annotation that is claimed to produce high-quality annotations while keeping the required number of annotations similar to that of rating scales. However, the veracity of this claim has never been systematically established. Here for the first time, we set up an experiment that directly compares the rating scale method with BWS. We show that with the same total number of annotations, BWS produces significantly more reliable results than the rating scale.
Transcendental equations satisfied by the individual zeros of Riemann $\zeta$, Dirichlet and modular $L$-functions

We consider the non-trivial zeros of the Riemann $\zeta$-function and two classes of $L$-functions; Dirichlet $L$-functions and those based on level one modular forms. We show that there are an infinite number of zeros on the critical line in one-to-one correspondence with the zeros of the cosine function, and thus enumerated by an integer $n$. From this it follows that the ordinate of the $n$-th zero satisfies a transcendental equation that depends only on $n$. Under weak assumptions, we show that the number of solutions of this equation already saturates the counting formula on the entire critical strip. We compute numerical solutions of these transcendental equations and also its asymptotic limit of large ordinate. The starting point is an explicit formula, yielding an approximate solution for the ordinates of the zeros in terms of the Lambert $W$-function. Our approach is a novel and simple method, that takes into account $\arg L$, to numerically compute non-trivial zeros of $L$-functions. The method is surprisingly accurate, fast and easy to implement. Employing these numerical solutions, in particular for the $\zeta$-function, we verify that the leading order asymptotic expansion is accurate enough to numerically support Montgomery's and Odlyzko's pair correlation conjectures, and also to reconstruct the prime number counting function. Furthermore, the numerical solutions of the exact transcendental equation can determine the ordinates of the zeros to any desired accuracy. We also study in detail Dirichlet $L$-functions and the $L$-function for the modular form based on the Ramanujan $\tau$-function, which is closely related to the bosonic string partition function.
Decision Tree Design for Classification in Crowdsourcing Systems

In this paper, we present a novel sequential paradigm for classification in crowdsourcing systems. Considering that workers are unreliable and they perform the tests with errors, we study the construction of decision trees so as to minimize the probability of mis-classification. By exploiting the connection between probability of mis-classification and entropy at each level of the decision tree, we propose two algorithms for decision tree design. Furthermore, the worker assignment problem is studied when workers can be assigned to different tests of the decision tree to provide a trade-off between classification cost and resulting error performance. Numerical results are presented for illustration.
A refinement of multi-dimensional persistence

We study the multi-dimensional persistence of Carlsson and Zomorodian and obtain a finer classification based upon the higher tor-modules of a persistence module. We propose a variety structure on the set of isomorphism classes of these modules, and present several examples. We also provide a geometric interpretation for the higher tor-modules of homology modules of multi-filtered simplicial complexes.
Adversarial Diversity and Hard Positive Generation

State-of-the-art deep neural networks suffer from a fundamental problem – they misclassify adversarial examples formed by applying small perturbations to inputs. In this paper, we present a new psychometric perceptual adversarial similarity score (PASS) measure for quantifying adversarial images, introduce the notion of hard positive generation, and use a diverse set of adversarial perturbations – not just the closest ones – for data augmentation. We introduce a novel hot/cold approach for adversarial example generation, which provides multiple possible adversarial perturbations for every single image. The perturbations generated by our novel approach often correspond to semantically meaningful image structures, and allow greater flexibility to scale perturbation-amplitudes, which yields an increased diversity of adversarial images. We present adversarial images on several network topologies and datasets, including LeNet on the MNIST dataset, and GoogLeNet and ResidualNet on the ImageNet dataset. Finally, we demonstrate on LeNet and GoogLeNet that fine-tuning with a diverse set of hard positives improves the robustness of these networks compared to training with prior methods of generating adversarial images.
Characteristic classes of cameral covers

We study the stack $$ \mathrm{\mathcal{M}} $$ of cameral covers for a complex reductive group G, introduced by Donagi and Gaitsgory. We compute its cohomology ring H* ( $$ \mathrm{\mathcal{M}} $$ , ℚ). In the special case G = GL(n), $$ \mathrm{\mathcal{M}} $$ is the stack of spectral covers. We also compute the cohomology ring of the stack of abstract regular G-Higgs bundles.
Non-embeddable extensions of embedded minors

Abstract A graph G is weakly 4-connected if it is 3-connected, has at least five vertices, and for every pair ( A , B ) such that A ∪ B = V ( G ) , | A ∩ B | = 3 and no edge has one end in A − B and the other in B − A , one of the induced subgraphs G [ A ] , G [ B ] has at most four edges. We describe a set of constructions that starting from a weakly 4-connected planar graph G produce a finite list of non-planar weakly 4-connected graphs, each having a minor isomorphic to G , such that every non-planar weakly 4-connected graph H that has a minor isomorphic to G has a minor isomorphic to one of the graphs in the list. Our main result is more general and applies in particular to polyhedral embeddings in any surface.
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space

Few prior works study deep learning on point sets. PointNet is a pioneer in this direction. However, by design PointNet does not capture local structures induced by the metric space points live in, limiting its ability to recognize fine-grained patterns and generalizability to complex scenes. In this work, we introduce a hierarchical neural network that applies PointNet recursively on a nested partitioning of the input point set. By exploiting metric space distances, our network is able to learn local features with increasing contextual scales. With further observation that point sets are usually sampled with varying densities, which results in greatly decreased performance for networks trained on uniform densities, we propose novel set learning layers to adaptively combine features from multiple scales. Experiments show that our network called PointNet++ is able to learn deep point set features efficiently and robustly. In particular, results significantly better than state-of-the-art have been obtained on challenging benchmarks of 3D point clouds.
Transformationally decoupling clustering and tracer bias

Gaussianizing transformations are used statistically in many non-cosmological fields, but in cosmology, we are only starting to apply them. Here I explain a strategy of analyzing the 1-point function (PDF) of a spatial field, together with the ‘essential’ clustering statistics of the Gaussianized field, which are invariant to a local transformation. In cosmology, if the tracer sampling is sufficient, this achieves two important goals. First, it can greatly multiply the Fisher information, which is negligible on nonlinear scales in the usual δ statistics. Second, it decouples clustering statistics from a local bias description for tracers such as galaxies.
Bridging the Gap between Stochastic Gradient MCMC and Stochastic Optimization

Stochastic gradient Markov chain Monte Carlo (SG-MCMC) methods are Bayesian analogs to popular stochastic optimization methods; however, this connection is not well studied. We explore this relationship by applying simulated annealing to an SGMCMC algorithm. Furthermore, we extend recent SG-MCMC methods with two key components: i) adaptive preconditioners (as in ADAgrad or RMSprop), and ii) adaptive element-wise momentum weights. The zerotemperature limit gives a novel stochastic optimization method with adaptive elementwise momentum weights, while conventional optimization methods only have a shared, static momentum weight. Under certain assumptions, our theoretical analysis suggests the proposed simulated annealing approach converges close to the global optima. Experiments on several deep neural network models show state-of-the-art results compared to related stochastic optimization algorithms.
Deep Reinforcement Learning for Mention-Ranking Coreference Models

Coreference resolution systems are typically trained with heuristic loss functions that require careful tuning. In this paper we instead apply reinforcement learning to directly optimize a neural mention-ranking model for coreference evaluation metrics. We experiment with two approaches: the REINFORCE policy gradient algorithm and a reward-rescaled max-margin objective. We find the latter to be more effective, resulting in significant improvements over the current state-of-the-art on the English and Chinese portions of the CoNLL 2012 Shared Task.
Design and Performance of the X-ray Polarimeter X-Calibur

X-ray polarimetry promises to give qualitatively new information about high-energy astrophysical sources, such as binary black hole systems, micro-quasars, active galactic nuclei, neutron stars, and gamma-ray bursts. We designed, built and tested a X-ray polarimeter, X-Calibur, to be used in the focal plane of the balloon-borne InFOCμS grazing incidence X-ray telescope. X-Calibur combines a low-Z scatterer with a Cadmium Zinc Telluride (CZT) detector assembly to measure the polarization of 20–80 keV X-rays making use of the fact that polarized photons scatter preferentially perpendicular to the electric field orientation. X-Calibur achieves a high detection efficiency of ≃80%. The X-Calibur detector assembly is completed, tested, and fully calibrated. The response to a polarized X-ray beam was measured successfully at the Cornell High Energy Synchrotron Source. This paper describes the design, calibration and performance of the X-Calibur polarimeter. In principle, a similar space-borne scattering polarimeter could operate over the broader 2–100 keV energy band.
The fractional unstable obstacle problem

Abstract We study a model for combustion on a boundary. Specifically, we study certain generalized solutions of the equation ( − Δ ) s u = χ { u > c } for 0 s 1 and an arbitrary constant c . Our main object of study is the free boundary ∂ { u > c } . We study the behavior of the free boundary and prove an upper bound for the Hausdorff dimension of the singular set. We also show that when s ≤ 1 ∕ 2 certain symmetric solutions are stable; however, when s > 1 ∕ 2 these solutions are not stable and therefore not minimizers of the corresponding functional.
Statistical analysis of an AGN sample with simultaneous UV and X-ray observations with Swift

I report on the statistical analysis of a sample of about 100 AGN with simultaneous UV and Xray observations with Swift. I found clear correlations between the X-ray spectral slope αX, the UV slope αUV, and the optical-to-x-ray spectral slope αoxwith the Eddington ratio L/LEdd. I also report on the bolometric corrections for L0.2−2.0keV and L5100. A major aspect of the statistical analysis is multi-variate analysis statistical tools such as the Principal Component Analysis (PCA) and cluster analysis. This analysis shows that the main driver of the AGN properties in this sample is the Eddington ratio L/LEdd. Although separating Seyfert 1s into NLS1s and BLS1s is a good classification, with the 2000 km s−1 cutoff line it is still arbitrary. A better classification scheme may be to separate AGN into low and high L/LEdd AGN as suggested from the cluster analysis.
Person Re-identification in Appearance Impaired Scenarios

Person re-identification is critical in surveillance applications. Current approaches rely on appearance based features extracted from a single or multiple shots of the target and candidate matches. These approaches are at a disadvantage when trying to distinguish between candidates dressed in similar colors or when targets change their clothing. In this paper we propose a dynamics-based feature to overcome this limitation. The main idea is to capture soft biometrics from gait and motion patterns by gathering dense short trajectories (tracklets) which are Fisher vector encoded. To illustrate the merits of the proposed features we introduce three new "appearance-impaired" datasets. Our experiments on the original and the appearance impaired datasets demonstrate the benefits of incorporating dynamics-based information with appearance-based information to re-identification algorithms.
Nodal sets of Laplace eigenfunctions: proof of Nadirashvili’s conjecture and of the lower bound in Yau’s conjecture

Let $u$ be a harmonic function in the unit ball $B(0,1) \subset \mathbb{R}^n$, $n \geq 3$, such that $u(0)=0$. Nadirashvili conjectured that there exists a positive constant $c$, depending on the dimension $n$ only, such that $H^{n-1}(\{u=0 \}\cap B) \geq c$. We prove Nadirashvili's conjecture as well as its counterpart on $C^\infty$-smooth Riemannian manifolds. The latter yields the lower bound in Yau's conjecture. Namely, we show that for any compact $C^\infty$-smooth Riemannian manifold $M$ (without boundary) of dimension $n$ there exists $c>0$ such that for any Laplace eigenfunction $\varphi_\lambda$ on $M$, which corresponds to the eigenvalue $\lambda$, the following inequality holds: $c \sqrt \lambda \leq H^{n-1}(\{\varphi_\lambda =0\})$.
Feature Engineering for Predictive Modeling Using Reinforcement Learning.

Feature engineering is a crucial step in the process of predictive modeling. It involves the transformation of given feature space, typically using mathematical functions, with the objective of reducing the modeling error for a given target. However, there is no well-defined basis for performing effective feature engineering. It involves domain knowledge, intuition, and most of all, a lengthy process of trial and error. The human attention involved in overseeing this process significantly influences the cost of model generation. We present a new framework to automate feature engineering. It is based on performance driven exploration of a transformation graph, which systematically and compactly enumerates the space of given options. A highly efficient exploration strategy is derived through reinforcement learning on past examples.
Noncommutative mixed (Artin) motives and their motivic Hopf dg algebras

This article is the sequel to (Marcolli and Tabuada in Sel Math 20(1):315–358, 2014). We start by developing a theory of noncommutative (=NC) mixed motives with coefficients in any commutative ring. In particular, we construct a symmetric monoidal triangulated category of NC mixed motives, over a base field k, and a full subcategory of NC mixed Artin motives. Making use of Hochschild homology, we then apply Ayoub’s weak Tannakian formalism to these motivic categories. In the case of NC mixed motives, we obtain a motivic Hopf dg algebra, which we describe explicitly in terms of Hochschild homology and complexes of exact cubes. In the case of NC mixed Artin motives, we compute the associated Hopf dg algebra using solely the classical category of mixed Artin–Tate motives. Finally, we establish a short exact sequence relating the Hopf algebra of continuous functions on the absolute Galois group with the motivic Hopf dg algebras of the base field k and of its algebraic closure. Along the way, we describe the behavior of Ayoub’s weak Tannakian formalism with respect to orbit categories and relate the category of NC mixed motives with Voevodsky’s category of mixed motives.
Patterned dynamics of delay-coupled swarms with random communication graphs

Swarm and modular robotics are an emerging area in control of autonomous systems. However, coordinating a large group of interacting autonomous agents requires careful consideration of the logistical issues involved. In particular, inter-agent communication generally involves time delay, and bandwidth restrictions limit the number of neighbors with which each agent in the swarm can communicate. In this paper, we analyze coherent pattern dynamics of groups of delay-coupled agents, where the communication network is an Erdos-Renyi graph. We show that overall motion patterns for a globally-coupled swarm persist under decreasing network connectivity, and derive the bifurcation structure scaling relations for the emergence of different swarming behaviors as a function of the average network degree. We show excellent agreement between the theoretical scaling results and numerical simulations.
Towards a proof of the 24-cell conjecture

This review paper is devoted to the problems of sphere packings in 4 dimensions. The main goal is to find reasonable approaches for solutions to problems related to densest sphere packings in 4-dimensional Euclidean space. We consider two long-standing open problems: the uniqueness of maximum kissing arrangements in 4 dimensions and the 24-cell conjecture. Note that a proof of the 24-cell conjecture also proves that the lattice packing D4 is the densest sphere packing in 4 dimensions.
The \(\tau\) Magnetic Dipole Moment at Future Lepton Colliders

Author(s): Howard, Jessica N; Rajaraman, Arvind; Riley, Rebecca; Tait, Tim MP | Abstract: The magnetic moment of the $\tau$ lepton is an interesting quantity that is potentially sensitive to physics beyond the Standard Model. Electroweak gauge invariance implies that a heavy new physics contribution to it takes the form of an operator which involves the Higgs boson, implying that rare Higgs decays are able to probe the same physics as $a_\tau$. We examine the prospects for rare Higgs decays at future high energy lepton (electron or muon) colliders, and find that such a project collecting a few ab$^{-1}$ would be able to advance our understanding of this physics by roughly a factor of 10 compared to the expected reach of the high luminosity LHC.
Exploring computation-communication tradeoffs in camera systems

Cameras are the defacto sensor. The growing demand for real-time and low-power computer vision, coupled with trends towards high-efficiency heterogeneous systems, has given rise to a wide range of image processing acceleration techniques at the camera node and in the cloud. In this paper, we characterize two novel camera systems that use acceleration techniques to push the extremes of energy and performance scaling, and explore the computation-communication tradeoffs in their design. The first case study targets a camera system designed to detect and authenticate individual faces, running solely on energy harvested from RFID readers. We design a multi-accelerator SoC design operating in the sub-mW range, and evaluate it with real-world workloads to show performance and energy efficiency improvements over a general purpose microprocessor. The second camera system supports a 16-camera rig processing over 32 Gb/s of data to produce real-time 3D-360° virtual reality video. We design a multi-FPGA processing pipeline that outperforms CPU and GPU configurations by up to 10× in computation time, producing panoramic stereo video directly from the camera rig at 30 frames per second. We find that an early data reduction step, either before complex processing or offloading, is the most critical optimization for in-camera systems.
Detecting Statistical Interactions from Neural Network Weights

Interpreting neural networks is a crucial and challenging task in machine learning. In this paper, we develop a novel framework for detecting statistical interactions captured by a feedforward multilayer neural network by directly interpreting its learned weights. Depending on the desired interactions, our method can achieve significantly better or similar interaction detection performance compared to the state-of-the-art without searching an exponential solution space of possible interactions. We obtain this accuracy and efficiency by observing that interactions between input features are created by the non-additive effect of nonlinear activation functions, and that interacting paths are encoded in weight matrices. We demonstrate the performance of our method and the importance of discovered interactions via experimental results on both synthetic datasets and real-world application datasets.
Longitudinal mapping knot invariant for SU(2)

The knot coloring polynomial defined by Eisermann for a finite pointed group is generalized to an infinite pointed group as the longitudinal mapping invariant of a knot. In turn, this can be though...
Do we need higher-order probabilities and, if so, what do they mean?

The apparent failure of individual probabilistic expressions to distinguish uncertainty about truths from uncertainty about probabilistic assessments have prompted researchers to seek formalisms where the two types of uncertainties are given notational distinction. This paper demonstrates that the desired distinction is already a built-in feature of classical probabilistic models, thus, specialized notations are unnecessary.
Some properties of and open problems on Hessian nilpotent polynomials

In the recent progress [BE1], [M], [Z1] and [Z2], the well-known Jacobian conjecture ([BCW], [E]) has been reduced to a problem on HN (Hessian nilpotent) polynomials (the polynomials whose Hessian matrix are nilpotent) and their (deformed) inversion pairs. In this paper, we prove several results on HN polynomials, their (deformed) inversion pairs as well as the associated symmetric polynomial or formal maps. We also propose some open problems for further study of these objects.
Addressing Limited Data for Textual Entailment Across Domains

We seek to address the lack of labeled data (and high cost of annotation) for textual entailment in some domains. To that end, we first create (for experimental purposes) an entailment dataset for the clinical domain, and a highly competitive supervised entailment system, ENT, that is effective (out of the box) on two domains. We then explore self-training and active learning strategies to address the lack of labeled data. With self-training, we successfully exploit unlabeled data to improve over ENT by 15% F-score on the newswire domain, and 13% F-score on clinical data. On the other hand, our active learning experiments demonstrate that we can match (and even beat) ENT using only 6.6% of the training data in the clinical domain, and only 5.8% of the training data in the newswire domain.
Differential Gerstenhaber algebras of generalized complex structures

Associated to every generalized complex structure is a differential Gerstenhaber algebra (DGA). When the generalized complex structure deforms, so does the associated DGA. In this paper, we identify the infinitesimal conditions when the DGA is invariant as the generalized complex structure deforms. We prove that the infinitesimal condition is always integrable. When the underlying manifold is a holomorphic Poisson nilmanifolds, or simply a group in the general, and the geometry is invariant, we find a general construction to solve the infinitesimal conditions under some geometric conditions. Examples and counterexamples of existence of solutions to the infinitesimal conditions are given.
Complex random matrices have no real eigenvalues

Let ζ = ξ + iξ′ where ξ,ξ′ are iid copies of a mean zero, variance one, subgaussian random variable. Let Nn be an n × n random matrix with entries that are iid copies of ζ. We prove that there exis...
The replica-symmetric prediction for compressed sensing with Gaussian matrices is exact

This paper considers the fundamental limit of compressed sensing for i.i.d. signal distributions and i.i.d. Gaussian measurement matrices. Its main contribution is a rigorous characterization of the asymptotic mutual information (MI) and minimum mean-square error (MMSE) in this setting. Under mild technical conditions, our results show that the limiting MI and MMSE are equal to the values predicted by the replica method from statistical physics. This resolves a well-known problem that has remained open for over a decade.
Pinned algebraic distances determined by Cartesian products in $\mathbb{F}_p^2$

Let $p$ be an odd prime and $A \subseteq \mathbb{F}_p$ be a subset of the finite field with $p$ elements. We show that $A \times A \subseteq \mathbb{F}_p^2$ determines at least a constant multiple of $\min\{p, |A|^{3/2}\}$ distinct pinned algebraic distances.
On the convergence of soft potential dynamics to hard sphere dynamics

We address a question raised in the work of Gallagher, Saint-Raymond and Texier (From Newton to Boltzmann: Hard Spheres and Short-range Potentials, Zurich Lectures in Advanced Mathematics, EMS, 2013) that concerns the convergence of soft-potential dynamics to hard sphere dynamics. In the case of two particles, we establish that hard sphere dynamics is the limit of soft sphere dynamics in the weak-star topology of BV. We view our result as establishing a topological method by which to construct weak solutions to the ODE of hard sphere motion.