cssi/SciSciGPT-SciSciNet huggingface dataset:
ZnO nanowire and nanobelt platform for nanotechnology

Abstract Semiconducting zinc oxide nanowires (NWs) and nanobelts (NBs) are a unique group of quasi-one-dimensional nanomaterial. This review mainly focuses on the rational synthesis, structure analysis, novel properties and unique applications of zinc oxide NWs and NBs in nanotechnology. First, we will discuss rational design of synthetic strategies and the synthesis of NWs via vapor phase and chemical growth approaches. Secondly, the vapor–solid process for synthesis of oxide based nanostructures will be described in details. We will illustrate the polar surface dominated growth phenomena, such as the formation of nanosprings, nanorings and nanohelices of single-crystal zinc oxide. Third, we will describe the unique and novel electrical, optoelectronic, field emission, and mechanical properties of individual NWs and NBs. Finally, we will illustrate some novel devices and applications made using NWs as ultra-sensitive chemical and biological nanosensors, solar cell, light emitting diodes, nanogenerators, and nano-piezotronic devices. ZnO is ideal for nanogenerators for converting nano-scale mechanical energy into electricity owing to its coupled piezoelectric and semiconductive properties. The devices designed based on this coupled characteristic are the family of piezotronics, which is a new and unique group of electronic components that are controlled by external forces/pressure.
Synthesis of 5'-amino-5'-phosphonate analogues of pyrimidine nucleoside monophosphates.

The 5‘-amino-5‘-phosphono derivatives of cytidine, cytosine arabinoside (ara-C), and uridine have been prepared via the corresponding nucleoside aldehydes. Phosphite addition to imines derived from the nucleoside aldehydes and p-methoxybenzylamine was efficient, and use of this amine allowed cleavage of the products to the parent amino phosphonic acids. The phosphite additions proved to be diastereoselective, with the cytidine and uridine derivatives favoring the 5‘S stereochemistry and the ara-C derivative favoring the 5‘R isomer. The stereochemistry of one cytidine derivative was established by single-crystal diffraction analysis, and detailed comparisons of the 13C NMR data allowed assignments of the other amino phosphonates.
Synthesis of programmable tetra-ortho-substituted biaryl compounds using Diels-Alder cycloadditions/cycloreversions of disubstituted alkynyl stannanes.

Orthogonally functionalized, programmable biaryl templates have been synthesized utilizing aryl acetylenic stannanes and oxygenated dienes in a cycloaddition/cycloreversion strategy. Sequential functionalization of each of the four ortho positions has been demonstrated. Subsequent resolution of a representative anilino phenol has been accomplished. Additionally, a highly active anhydrous, boroxine-based Suzuki coupling protocol has been developed for conversion of unreactive aryl chlorides.
Quantum Ensembles of Silicon Nanoparticles: Discrimination of Static and Dynamic Photoluminescence Quenching Processes

Porous silicon photoluminescence is characterized by a broad emission band that displays unusually long (tens to hundreds of microseconds), wavelength-dependent emissive lifetimes. The photolumines...
Thermal buoyancy and Marangoni convection in a two fluid layered system

Thermal buoyancy and surface tension driven convection is numerically investigated in a system with two immiscible fluids. The geometry investigated has an open cavity configuration with the lighter fluid situated on top of the heavier fluid, forming a stable layered system. The upper fluid meniscus and the interface are assumed to be flat and undeformable in the calculations. The governing equations and boundary/interface conditions are solved by a control volume-based finite difference scheme for two pairs of immiscible fluids; the water-hexadecane system and a so-called generic system. The steady-state calculations predict dramatically different flows when interfacial tension effects are included or excluded from the system model. These differences are particularly appreciable in surface tension-dominated flows, that are typical of microgravity situations. Complex flow patterns, with induced secondary flows, are noticed in both the fluids. In general, the overall system heat transfer is found to increase with increases in the thermal buoyancy and surface tension effects, but the behavior of the system flow and thermal fields is not easy to characterize when different combinations of these forces are considered.
Programmable bacteria induce durable tumor regression and systemic antitumor immunity

Synthetic biology is driving a new era of medicine through the genetic programming of living cells1,2. This transformative approach allows for the creation of engineered systems that intelligently sense and respond to diverse environments, ultimately adding specificity and efficacy that extends beyond the capabilities of molecular-based therapeutics3-6. One particular area of focus has been the engineering of bacteria as therapeutic delivery systems to selectively release therapeutic payloads in vivo7-11. Here we engineered a non-pathogenic Escherichia coli strain to specifically lyse within the tumor microenvironment and release an encoded nanobody antagonist of CD47 (CD47nb)12, an anti-phagocytic receptor that is commonly overexpressed in several human cancer types13,14. We show that delivery of CD47nb by tumor-colonizing bacteria increases activation of tumor-infiltrating T cells, stimulates rapid tumor regression, prevents metastasis and leads to long-term survival in a syngeneic tumor model in mice. Moreover, we report that local injection of CD47nb-expressing bacteria stimulates systemic tumor-antigen-specific immune responses that reduce the growth of untreated tumors, providing proof-of-concept for an abscopal effect induced by an engineered bacterial immunotherapy. Thus, engineered bacteria may be used for safe and local delivery of immunotherapeutic payloads leading to systemic antitumor immunity.
DNA methyltransferase inhibition reduces inflammation-induced colon tumorigenesis

Chronic inflammation is strongly associated with an increased risk of developing colorectal cancer. DNA hypermethylation of CpG islands alters the expression of genes in cancer cells and plays an important role in carcinogenesis. Chronic inflammation is also associated with DNA methylation alterations and in a mouse model of inflammation-induced colon tumorigenesis, we previously demonstrated that inflammation-induced tumours have 203 unique regions with DNA hypermethylation compared to uninflamed epithelium. To determine if altering inflammation-induced DNA hypermethylation reduces tumorigenesis, we used the same mouse model and treated mice with the DNA methyltransferase (DNMT) inhibitor decitabine (DAC) throughout the tumorigenesis time frame. DAC treatment caused a significant reduction in colon tumorigenesis. The tumours that did form after DAC treatment had reduced inflammation-specific DNA hypermethylation and alteration of expression of associated candidate genes. When compared, inflammation-induced tumours from control (PBS-treated) mice were enriched for cell proliferation associated gene expression pathways whereas inflammation-induced tumours from DAC-treated mice were enriched for interferon gene signatures. To further understand the altered tumorigenesis, we derived tumoroids from the different tumour types. Interestingly, tumoroids derived from inflammation-induced tumours from control mice maintained many of the inflammation-induced DNA hypermethylation alterations and had higher levels of DNA hypermethylation at these regions than tumoroids from DAC-treated mice. Importantly, tumoroids derived from inflammation-induced tumours from the DAC-treated mice proliferated more slowly than those derived from the inflammation-induced tumours from control mice. These studies suggest that inhibition of inflammation-induced DNA hypermethylation may be an effective strategy to reduce inflammation-induced tumorigenesis.
Differential Effector Response of Amnion- and Adipose-Derived Mesenchymal Stem Cells to Inflammation; Implications for Intradiscal Therapy.

Intervertebral disc degeneration (IVDD) is a progressive condition marked by tissue destruction and inflammation. The therapeutic effector functions of mesenchymal stem cells (MSCs) makes them an attractive therapy for patients with IVDD. While several sources of MSCs exist, the optimal choice for use in the inflamed IVD remains a significant question. Adipose (AD)- and amnion (AM)-derived MSCs have several advantages compared with other sources, however, no study has directly compared the impact of IVDD inflammation on their effector functions. Human MSCs were cultured in media with or without supplementation of interleukin-1β (IL-1β) and tumor necrosis factor-α at concentrations reportedly produced by IVDD cells. MSC proliferation and production of pro- and anti-inflammatory cytokines were quantified following 24 and 48 h of culture. Additionally, the osteogenic and chondrogenic potential of AD- and AM-MSCs was characterized via histology and biochemical analysis following 28 days of culture. In inflammatory culture, AM-MSCs produced significantly more anti-inflammatory IL-10 (14.47 ± 2.39 pg/ml; p = 0.004) and larger chondrogenic pellets (5.67 ± 0.26 mm2 ; p = 0.04) with greater percent area staining positively for glycosaminoglycan (82.03 ± 3.26%; p < 0.001) compared with AD-MSCs (0.00 ± 0.00 pg/ml; 2.76 ± 0.18 mm2 ; 34.75 ± 2.49%; respectively). Conversely, AD-MSCs proliferated more resulting in higher cell numbers (221,000 ± 8,021 cells; p = 0.048) and produced higher concentrations of pro-inflammatory cytokines prostaglandin E2 (1,118.30 ± 115.56 pg/ml; p = 0.030) and IL-1β (185.40 ± 7.63 pg/ml; p = 0.010) compared with AM-MSCs (109,667 ± 5,696 cells; 1,291.40 ± 78.47 pg/ml; 144.10 ± 4.57 pg/ml; respectively). AD-MSCs produced more mineralized extracellular matrix (3.34 ± 0.05 relative absorbance units [RAU]; p < 0.001) compared with AM-MSCs (1.08 ± 0.06 RAU). Under identical inflammatory conditions, a different effector response was observed with AM-MSCs producing more anti-inflammatories and demonstrating enhanced chondrogenesis compared with AD-MSCs, which produced more pro-inflammatory cytokines and demonstrated enhanced osteogenesis. These findings may begin to help inform researchers which MSC source may be optimal for IVD regeneration. © 2019 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 37:2445-2456, 2019.
Sparse grid discontinuous Galerkin methods for the Vlasov-Maxwell system

Abstract In this paper, we develop sparse grid discontinuous Galerkin (DG) schemes for the Vlasov-Maxwell (VM) equations. The VM system is a fundamental kinetic model in plasma physics, and its numerical computations are quite demanding, due to its intrinsic high-dimensionality and the need to retain many properties of the physical solutions. To break the curse of dimensionality, we consider the sparse grid DG methods that were recently developed in [20] , [21] for transport equations. Such methods are based on multiwavelets on tensorized nested grids and can significantly reduce the numbers of degrees of freedom. We formulate two versions of the schemes: sparse grid DG and adaptive sparse grid DG methods for the VM system. Their key properties and implementation details are discussed. Accuracy and robustness are demonstrated by numerical tests, with emphasis on comparison of the performance of the two methods, as well as with their full grid counterparts.
Some topics in the kinetics of protein aggregation

Abstract Preliminary results are presented for the kinetics of phase separation in three distinct models of protein aggregation. The first is a model of the formation of spherical microcrystals of insulin via an initial formation of fractal clusters of insulin. The results of our Brownian dynamics study of this model are in qualitative agreement with a recent experimental study [1] of microcrystal formation from aqueous mixtures of insulin. A second work involves a theory for the formation of metastable bundles of sickle hemoglobin from fibers, based on a recent generic theory of bundle formation [2]. We also discuss a model for the microscopic formation of these fibers. Finally, we discuss preliminary results for the kinetics of cluster formation for a six patch model of protein crystallization.
Bayesian games with intentions

Abstract We define Bayesian games with intentions by introducing a distinction between “intended” and “actual” actions, generalizing both Bayesian games and (static) psychological games Geanakoplos et al. (1989) . We propose a new solution concept for this framework and prove that Nash equilibria in static psychological games correspond to a special class of equilibria as defined in our setting. We also show how the actual/intended divide can be used to implement the distinction between “real” outcomes and “reference” outcomes so crucial to prospect theory, and how some of the core insights of prospect theory can thereby be captured using Bayesian games with intentions.
Time-Resolved X-Ray Microscopy for Materials Science

X-ray microscopy has been an indispensable tool to image nanoscale properties for materials research. One of its recent advances is extending microscopic studies to the time domain to visualize the...
Measurement partitioning and observational equivalence in state estimation

This paper studies observability of linear systems from both algebraic and graph-theoretic arguments, and further draws a parallel between the two. We show that a set of critical measurements (for state-space observability) can be partitioned into two types: α and β. This partitioning is driven by different graphical (or algebraic) methods used to define the corresponding measurements. Subsequently, we describe observational equivalence, i.e., given an α (or β) measurement, say yi, what is the set of measurements equivalent to yi, such that only one measurement in this set is required? Since α and β measurements are cast using different algebraic and graphical characteristics, their equivalence sets are also derived using different algebraic and graph-theoretic principles. The need to make such equivalence arises in areas, e.g., meter placement for power systems, where relevant lines of study include: (a) to guarantee state-space observability with as few sensors as possible; and, (b) to find candidate replacement measurements when a sensor incurs a fault. We illustrate the related concepts on a simple, yet insightful, system digraph.
Trigonometric solutions of the associative Yang-Baxter equation

We classify trigonometric solutions to the associative Yang-Baxter equation (AYBE) for A = Mat_n, the associative algebra of n-by-n matrices. The AYBE was first presented in a 2000 article by Marcelo Aguiar and also independently by Alexandre Polishchuk. Trigonometric AYBE solutions limit to solutions of the classical Yang-Baxter equation. We find that such solutions of the AYBE are equal to special solutions of the quantum Yang-Baxter equation (QYBE) classified by Gerstenhaber, Giaquinto, and Schack (GGS), divided by a factor of q - q^{-1}, where q is the deformation parameter q = exp(h). In other words, when it exists, the associative lift of the classical r-matrix coincides with the quantum lift up to a factor. We give explicit conditions under which the associative lift exists, in terms of the combinatorial classification of classical r-matrices through Belavin-Drinfeld triples. The results of this paper illustrate nontrivial connections between the AYBE and both classical (Lie) and quantum bialgebras.
Stochastic optimization for Markov modulated networks with application to delay constrained wireless scheduling

We consider a wireless system with a small number of delay constrained users and a larger number of users without delay constraints. We develop a scheduling algorithm that reacts to time varying channels and maximizes throughput (to within a desired proximity), stabilizes all queues, and satisfies the delay constraints. The problem is solved by reducing the constrained optimization to a set of weighted stochastic shortest path problems, which act as natural generalizations of max-weight policies to Markov modulated networks. We also present performance bounds when the shortest path problems are solved inexactly, and discuss the additional complexity as compared to systems without delay constraints. The solution technique is general and applies to other constrained stochastic network optimization problems.
Approximation and limit theorems for quantum stochastic models with unbounded coefficients

Abstract We prove a limit theorem for quantum stochastic differential equations with unbounded coefficients which extends the Trotter–Kato theorem for contraction semigroups. From this theorem, general results on the convergence of approximations and singular perturbations are obtained. The results are illustrated in several examples of physical interest.
An information-theoretic analysis of Thompson sampling

We provide an information-theoretic analysis of Thompson sampling that applies across a broad range of online optimization problems in which a decision-maker must learn from partial feedback. This analysis inherits the simplicity and elegance of information theory and leads to regret bounds that scale with the entropy of the optimal-action distribution. This strengthens preexisting results and yields new insight into how information improves performance.
Hidden symmetries via hidden extensions

This paper introduces a new approach to finding knots and links with hidden symmetries using "hidden extensions", a class of hidden symmetries defined here. We exhibit a family of tangle complements in the ball whose boundaries have symmetries with hidden extensions, then we further extend these to hidden symmetries of some hyperbolic link complements.
F-threshold functions: syzygy gap fractals and the two-variable homogeneous case

Abstract In this article we study F-pure thresholds (and, more generally, F-thresholds) of homogeneous polynomials in two variables over a field of characteristic p > 0 . Passing to a field extension, we factor such a polynomial into a product of powers of pairwise prime linear forms, and to this collection of linear forms we associate a special type of function called a syzygy gap fractal. We use this syzygy gap fractal to study, at once, the collection of all F-pure thresholds of all polynomials constructed with the same fixed linear forms. This allows us to describe the structure of the denominator of such an F-pure threshold, showing in particular that whenever the F-pure threshold differs from its expected value its denominator is a multiple of p. This answers a question of Schwede in the two-variable homogeneous case. In addition, our methods give an algorithm to compute F-pure thresholds of homogenous polynomials in two variables.
Are generalized cut-set bounds tight for the deterministic interference channel?

We propose the idea of extended networks, which is constructed by replicating the users in the two-user deterministic interference channel (DIC) and designing the interference structure among them, such that any rate that can be achieved by each user in the original network can also be achieved simultaneously by all replicas of that user in the extended network. We demonstrate that by carefully designing extended networks and applying the generalized cut-set (GCS) bound to them, we can derive a tight converse for the two-user DIC. Furthermore, we generalize our techniques to the three-user DIC, and demonstrate that the proposed approach also results in deriving a tight converse for the three-user DIC in the symmetric case.
Truncated Horizon Policy Search: Combining Reinforcement Learning & Imitation Learning

In this paper, we propose to combine imitation and reinforcement learning via the idea of reward shaping using an oracle. We study the effectiveness of the near-optimal cost-to-go oracle on the planning horizon and demonstrate that the cost-to-go oracle shortens the learner's planning horizon as function of its accuracy: a globally optimal oracle can shorten the planning horizon to one, leading to a one-step greedy Markov Decision Process which is much easier to optimize, while an oracle that is far away from the optimality requires planning over a longer horizon to achieve near-optimal performance. Hence our new insight bridges the gap and interpolates between imitation learning and reinforcement learning. Motivated by the above mentioned insights, we propose Truncated HORizon Policy Search (THOR), a method that focuses on searching for policies that maximize the total reshaped reward over a finite planning horizon when the oracle is sub-optimal. We experimentally demonstrate that a gradient-based implementation of THOR can achieve superior performance compared to RL baselines and IL baselines even when the oracle is sub-optimal.
Streamed learning: one-pass SVMs

We present a streaming model for large-scale classification (in the context of l2-SVM) by leveraging connections between learning and computational geometry. The streaming model imposes the constraint that only a single pass over the data is allowed. The l2-SVM is known to have an equivalent formulation in terms of the minimum enclosing ball (MEB) problem, and an efficient algorithm based on the idea of core sets exists (CVM) [Tsang et al., 2005]. CVM learns a (1+Ɛ)-approximate MEB for a set of points and yields an approximate solution to corresponding SVM instance. However CVM works in batch mode requiring multiple passes over the data. This paper presents a single-pass SVM which is based on the minimum enclosing ball of streaming data. We show that the MEB updates for the streaming case can be easily adapted to learn the SVM weight vector in a way similar to using online stochastic gradient updates. Our algorithm performs polylogarithmic computation at each example, and requires very small and constant storage. Experimental results show that, even in such restrictive settings, we can learn efficiently in just one pass and get accuracies comparable to other state-of-the-art SVM solvers (batch and online). We also give an analysis of the algorithm, and discuss some open issues and possible extensions.
PT-spike: a precise-time-dependent single spike neuromorphic architecture with efficient supervised learning

One of the most exciting advancements in Artificial Intelligence (AI) over the last decade is the wide adoption of Artificial Neural Networks (ANNs), such as Deep Neural Network (DNN) and Convolutional Neural Network (CNN), in real world applications. However, the underlying massive amounts of computation and storage requirement greatly challenge their applicability in resource-limited platforms like drone, mobile phone and IoT devices etc. The third generation of neural network model-Spiking Neural Network (SNN), inspired by the working mechanism and efficiency of human brain, has emerged as a promising solution for achieving more impressive computing and power efficiency within light-weighted devices (e.g. single chip). However, the relevant research activities have been narrowly carried out on conventional rate-based spiking system designs for fulfilling the practical cognitive tasks, underestimating SNN's energy efficiency, throughput and system flexibility. Although the time-based SNN can be more attractive conceptually, its potentials are not unleashed in realistic applications due to lack of efficient coding and practical learning schemes. In this work, a Precise-Time-Dependent Single Spike Neuromorphic Architecture, namely "PT-Spike", is developed to bridge this gap. Three constituent hardware-favorable techniques: precise single-spike temporal encoding, efficient supervised temporal learning and fast asymmetric decoding are proposed accordingly to boost the energy efficiency and data processing capability of the time-based SNN at a more compact neural network model size when executing real cognitive tasks. Simulation results show that "PT-Spike" demonstrates significant improvements in network size, processing efficiency and power consumption with marginal classification accuracy degradation, when compared with the rate-based SNN and ANN under the similar network configuration.
Circumnavigation of an unknown target using UAVs with range and range rate measurements

This paper presents two control algorithms enabling a UAV to circumnavigate an unknown target using range and range rate (i.e., the derivative of range) measurements. Given a prescribed orbit radius, both control algorithms (i) tend to drive the UAV toward the tangent of prescribed orbit when the UAV is outside or on the orbit, and (ii) apply zero control input if the UAV is inside the desired orbit. The algorithms differ in that, the first algorithm is smooth and unsaturated while the second algorithm is non-smooth and saturated. By analyzing properties associated with the bearing angle of the UAV relative to the target and through proper design of Lyapunov functions, it is shown that both algorithms produce the desired orbit for an arbitrary initial state. Two examples are provided as a proof of concept.
City-Identification of Flickr Videos Using Semantic Acoustic Features

City-identification of videos aims to determine the likelihood of a video belonging to a set of cities. In this paper, we present an approach using only audio, thus we do not use any additional modality such as images, user-tags or geo-tags. In this manner, we show to what extent the city-location of videos correlates to their acoustic information. Success in this task suggests improvements can be made to complement the other modalities. In particular, we present a method to compute and use semantic acoustic features to perform city-identification and the features show semantic evidence of the identification. The semantic evidence is given by a taxonomy of urban sounds and expresses the potential presence of these sounds in the city-soundtracks. We used the MediaEval Placing Task set, which contains Flickr videos labeled by city. In addition, we used the UrbanSound8K set containing audio clips labeled by sound-type. Our method improved the state-of-the-art performance and provides a novel semantic approach to this task.
Quantifying the cognitive extent of science

Abstract While the modern science is characterized by an exponential growth in scientific literature, the increase in publication volume clearly does not reflect the expansion of the cognitive boundaries of science. Nevertheless, most of the metrics for assessing the vitality of science or for making funding and policy decisions are based on productivity. Similarly, the increasing level of knowledge production by large science teams, whose results often enjoy greater visibility, does not necessarily mean that “big science” leads to cognitive expansion. Here we present a novel, big-data method to quantify the extents of cognitive domains of different bodies of scientific literature independently from publication volume, and apply it to 20 million articles published over 60–130 years in physics, astronomy, and biomedicine. The method is based on the lexical diversity of titles of fixed quotas of research articles. Owing to large size of quotas, the method overcomes the inherent stochasticity of article titles to achieve
Kleitman's conjecture about families of given size minimizing the number of k-chains

Abstract A central theorem in combinatorics is Sperner's Theorem, which determines the maximum size of a family F ⊆ P ( n ) that does not contain a 2-chain F 1 ⊊ F 2 . Erdős later extended this result and determined the largest family not containing a k-chain F 1 ⊊ … ⊊ F k . Erdős and Katona and later Kleitman asked how many such chains must appear in families whose size is larger than the corresponding extremal result. This question was resolved for 2-chains by Kleitman in 1966, who showed that amongst families of size M in P ( n ) , the number of 2-chains is minimized by a family whose sets are taken as close to the middle layer as possible. He also conjectured that the same conclusion should hold for all k, not just 2. The best result on this question is due to Das, Gan and Sudakov who showed that Kleitman's conjecture holds for families whose size is at most the size of the k + 1 middle layers of P ( n ) , provided k ≤ n − 6 . Our main result is that for every fixed k and e > 0 , if n is sufficiently large then Kleitman's conjecture holds for families of size at most ( 1 − e ) 2 n , thereby establishing Kleitman's conjecture asymptotically. Our proof is based on ideas of Kleitman and Das, Gan and Sudakov. Several open problems are also given.
Error bounds for compressed sensing algorithms with group sparsity: A unified approach

Abstract In compressed sensing, in order to recover a sparse or nearly sparse vector from possibly noisy measurements, the most popular approach is l 1 -norm minimization. Upper bounds for the l 2 -norm of the error between the true and estimated vectors are given in [1] and reviewed in [2] , while bounds for the l 1 -norm are given in [3] . When the unknown vector is not conventionally sparse but is “group sparse” instead, a variety of alternatives to the l 1 -norm have been proposed in the literature, including the group LASSO, sparse group LASSO, and group LASSO with tree structured overlapping groups. However, no error bounds are available for any of these modified objective functions. In the present paper, a unified approach is presented for deriving upper bounds on the error between the true vector and its approximation, based on the notion of decomposable and γ -decomposable norms. The bounds presented cover all of the norms mentioned above, and also provide a guideline for choosing norms in future to accommodate alternate forms of sparsity.
Encog: library of interchangeable machine learning models for Java and C#

This paper introduces the Encog library for Java and C#, a scalable, adaptable, multiplatform machine learning framework that was first released in 2008. Encog allows a variety of machine learning models to be applied to data sets using regression, classification, and clustering. Various supported machine learning models can be used interchangeably with minimal recoding. Encog uses efficient multithreaded code to reduce training time by exploiting modern multicore processors. The current version of Encog can be downloaded from http://www.encog.org.
Super-resolution imaging using the spatial-frequency filtered intensity fluctuation correlation

We report an experimental demonstration of a nonclassical imaging mechanism with super-resolving power beyond the Rayleigh limit. When the classical image is completely blurred out due to the use of a small imaging lens, by taking advantage of the intensity fluctuation correlation of thermal light, the demonstrated camera recovered the image of the resolution testing gauge. This method could be adapted to long distance imaging, such as satellite imaging, which requires large diameter camera lenses to achieve high image resolution.
The LOOP Estimator: Adjusting for Covariates in Randomized Experiments

Background:When conducting a randomized controlled trial, it is common to specify in advance the statistical analyses that will be used to analyze the data. Typically, these analyses will involve a...
An Alternative View: When Does SGD Escape Local Minima?

Stochastic gradient descent (SGD) is widely used in machine learning. Although being commonly viewed as a fast but not accurate version of gradient descent (GD), it always finds better solutions than GD for modern neural networks. In order to understand this phenomenon, we take an alternative view that SGD is working on the convolved (thus smoothed) version of the loss function. We show that, even if the function $f$ has many bad local minima or saddle points, as long as for every point $x$, the weighted average of the gradients of its neighborhoods is one point convex with respect to the desired solution $x^*$, SGD will get close to, and then stay around $x^*$ with constant probability. More specifically, SGD will not get stuck at "sharp" local minima with small diameters, as long as the neighborhoods of these regions contain enough gradient information. The neighborhood size is controlled by step size and gradient noise. Our result identifies a set of functions that SGD provably works, which is much larger than the set of convex functions. Empirically, we observe that the loss surface of neural networks enjoys nice one point convexity properties locally, therefore our theorem helps explain why SGD works so well for neural networks.
Strong solutions to the three-dimensional compressible viscoelastic fluids

Abstract The existence and uniqueness of the local strong solution to the three-dimensional compressible viscoelastic fluids near the equilibrium are established. In addition to the uniform estimates on the velocity, some essential uniform estimates on the density and the deformation gradient are also obtained.
Mesenchymal stem cells promote mesenteric vasodilation through hydrogen sulfide and endothelial nitric oxide

Stem cells have been shown to improve survival, mesenteric perfusion, and histological injury scores following intestinal ischemia. These benefits may be due to the paracrine release of hydrogen su...
Compatibility for Probabilistic Theories

We define an index of compatibility for a probabilistic theory (PT). Quantum mechanics with index 0 and classical probability theory with index 1 are at the two extremes. In this way, quantum mechanics is at least as incompatible as any PT. We consider a PT called a concrete quantum logic that may have compatibility index strictly between 0 and 1, but we have not been able to show this yet. Finally, we show that observables in a PT can be represented by positive, vector-valued measures. 1 Observables in Probabilistic Theories This paper is based on the stimulating article [1] by Busch, Heinosaari and Schultz. The authors should be congratulated for introducing a useful new tool for measuring the compatibility of a probabilistic theory (PT). In this paper, we present a simpler, but coarser, measure of compatibility that we believe will also be useful. A probabilistic theory is a σ-convex subset K of a real Banach space V . That is, if 0 ≤ λi ≤ 1 with ∑ λi = 1 and vi ∈ K, i = 1, 2, . . ., then ∑ λivi converges in norm to an element of K. We call the elements of K states. There is no loss of generality in assuming that K generates V in the sense that the closed linear hull of K equals V . Denote the collection of Borel
Reduced complexity detection for network-coded slotted ALOHA using sphere decoding

Network-coded slotted ALOHA (NCSA) is a refinement to the classic slotted ALOHA protocol which improves throughput by enabling multiple source transmissions per ALOHA slot using physical-layer network coding (PNC). The receiver detects the network-coded combination of bits during every slot and recovers information bits by solving a system of linear equations. This work develops a receiver capable of detecting the network-coded combination of bits during a slot considering an arbitrary number of sources, orthogonal modulation, and a block fading channel. Maximum-likelihood detection of the network-coded symbol at the receiver becomes complex as the number of sources is increased. To reduce this complexity, sphere decoding is applied at the receiver to limit the number of constellation symbols the receiver must consider for detection. The system is simulated for two modulation orders and two through five sources, and error-rate performance results are provided.
Automatic Summarization of Student Course Feedback

Student course feedback is generated daily in both classrooms and online course discussion forums. Traditionally, instructors manually analyze these responses in a costly manner. In this work, we propose a new approach to summarizing student course feedback based on the integer linear programming (ILP) framework. Our approach allows different student responses to share co-occurrence statistics and alleviates sparsity issues. Experimental results on a student feedback corpus show that our approach outperforms a range of baselines in terms of both ROUGE scores and human evaluation.
Multimodal factor analysis

A multimodal system with Poisson, Gaussian, and multinomial observations is considered. A generative graphical model that combines multiple modalities through common factor loadings is proposed. In this model, latent factors are like summary objects that has latent factor scores in each modality, and the observed objects are represented in terms of such summary objects. This potentially brings about a significant dimensionality reduction. It also naturally enables a powerful means of clustering based on a diverse set of observations. An expectation-maximization (EM) algorithm to find the model parameters is provided. The algorithm is tested on a Twitter dataset which consists of the counts and geographical coordinates of hashtag occurrences, together with the bag of words for each hashtag. The resultant factors successfully localizes the hashtags in all dimensions: counts, coordinates, topics. The algorithm is also extended to accommodate von Mises-Fisher distribution, which is used to model the spherical coordinates.
Rigid inner forms of real and $p$-adic groups

We dene a new cohomology set H 1 (u ! W;Z ! G) for an ane algebraic group G and a nite central subgroup, both dened over a local eld of characteristic zero, which is an enlargement of the usual rst Galois cohomology set of G. We show how this set can be used to give a precise conjectural description of the internal structure and endoscopic transfer of tempered L-packets for arbitrary connected reductive groups that extends the well-known conjectural description for quasi-split groups. In the case of real groups, we show that this description is correct using Shelstad’s work.
Sum-of-Squares Certificates for Maxima of Random Tensors on the Sphere.

For an $n$-variate order-$d$ tensor $A$, define $ A_{\max} := \sup_{\| x \|_2 = 1} \langle A , x^{\otimes d} \rangle$ to be the maximum value taken by the tensor on the unit sphere. It is known that for a random tensor with i.i.d $\pm 1$ entries, $A_{\max} \lesssim \sqrt{n\cdot d\cdot\log d}$ w.h.p. We study the problem of efficiently certifying upper bounds on $A_{\max}$ via the natural relaxation from the Sum of Squares (SoS) hierarchy. Our results include: - When $A$ is a random order-$q$ tensor, we prove that $q$ levels of SoS certifies an upper bound $B$ on $A_{\max}$ that satisfies \[ B ~~~~\leq~~ A_{\max} \cdot \biggl(\frac{n}{q^{\,1-o(1)}}\biggr)^{q/4-1/2} \quad \text{w.h.p.} \] Our upper bound improves a result of Montanari and Richard (NIPS 2014) when $q$ is large. - We show the above bound is the best possible up to lower order terms, namely the optimum of the level-$q$ SoS relaxation is at least \[ A_{\max} \cdot \biggl(\frac{n}{q^{\,1+o(1)}}\biggr)^{q/4-1/2} \ . \] - When $A$ is a random order-$d$ tensor, we prove that $q$ levels of SoS certifies an upper bound $B$ on $A_{\max}$ that satisfies \[ B ~~\leq ~~ A_{\max} \cdot \biggl(\frac{\widetilde{O}(n)}{q}\biggr)^{d/4 - 1/2} \quad \text{w.h.p.} \] For growing $q$, this improves upon the bound certified by constant levels of SoS. This answers in part, a question posed by Hopkins, Shi, and Steurer (COLT 2015), who established the tight characterization for constant levels of SoS.
Transient behavior near liquid-gas interface at supercritical pressure

Abstract Numerical heat and mass transfer analysis of a configuration where a cool liquid hydrocarbon is suddenly introduced to a hotter gas at supercritical pressure shows that a well-defined phase equilibrium can be established before substantial growth of typical hydrodynamic instabilities. The equilibrium values at the interface quickly reach near-steady values. Sufficiently thick diffusion layers form quickly around the liquid-gas interface (e.g., 3–10 μ m for the liquid phase and 10–30 μ m for the gas phase in 10–100 μ s), where density variations become increasingly important with pressure as mixing of species is enhanced. While the hydrocarbon vaporizes and the gas condenses for all analyzed pressures, the net mass flux across the interface reverses as pressure is increased, showing that a clear vaporization-driven problem at low pressures may present condensation at higher pressures. This is achieved while heat still conducts from gas to liquid. Analysis of fundamental thermodynamic laws on a fixed-mass element containing the diffusion layers proves the thermodynamic viability of the obtained results.
A Poisson-Gaussian Denoising Dataset With Real Fluorescence Microscopy Images

Fluorescence microscopy has enabled a dramatic development in modern biology. Due to its inherently weak signal, fluorescence microscopy is not only much noisier than photography, but also presented with Poisson-Gaussian noise where Poisson noise, or shot noise, is the dominating noise source. To get clean fluorescence microscopy images, it is highly desirable to have effective denoising algorithms and datasets that are specifically designed to denoise fluorescence microscopy images. While such algorithms exist, no such datasets are available. In this paper, we fill this gap by constructing a dataset - the Fluorescence Microscopy Denoising (FMD) dataset - that is dedicated to Poisson-Gaussian denoising. The dataset consists of 12,000 real fluorescence microscopy images obtained with commercial confocal, two-photon, and wide-field microscopes and representative biological samples such as cells, zebrafish, and mouse brain tissues. We use image averaging to effectively obtain ground truth images and 60,000 noisy images with different noise levels. We use this dataset to benchmark 10 representative denoising algorithms and find that deep learning methods have the best performance. To our knowledge, this is the first real microscopy image dataset for Poisson-Gaussian denoising purposes and it could be an important tool for high-quality, real-time denoising applications in biomedical research.
Toward Crowd-Sensitive Path Planning

If a robot can predict crowds in parts of its environment that are inaccessible to its sensors, then it can plan to avoid them. This paper proposes a fast, online algorithm that learns average crowd densities in different areas. It also describes how these densities can be incorporated into existing navigation architectures. In simulation across multiple challenging crowd scenarios, the robot reaches its target faster, travels less, and risks fewer collisions than if it were to plan with the traditional A* algorithm.
On residue maps for affine curves

Abstract We establish several compatibility results between residue maps in etale and Galois cohomology that arise naturally in the analysis of smooth affine algebraic curves having good reduction over discretely valued fields. These results are needed, and in fact have already been used, for the study of finiteness properties of the unramified cohomology of function fields of affine curves over number fields.
Geometry-based next frame prediction from monocular video

We consider the problem of next frame prediction from video input. A recurrent convolutional neural network is trained to predict depth from monocular video input, which, along with the current video image and the camera trajectory, can then be used to compute the next frame. Unlike prior next-frame prediction approaches, we take advantage of the scene geometry and use the predicted depth for generating the next frame prediction. Our approach can produce rich next frame predictions which include depth information attached to each pixel. Another novel aspect of our approach is that it predicts depth from a sequence of images (e.g. in a video), rather than from a single still image. We evaluate the proposed approach on the KITTI dataset, a standard dataset for benchmarking tasks relevant to autonomous driving. The proposed method produces results which are visually and numerically superior to existing methods that directly predict the next frame. We show that the accuracy of depth prediction improves as more prior frames are considered.
Weyl character formula in KK-theory

The purpose of this paper is to begin an exploration of connections between the Baum-Connes conjecture in operator $\K$-theory and the geometric representation theory of reductive Lie groups. Our initial goal is very modest, and we shall not stray far from the realm of compact groups, where geometric representation theory amounts to elaborations of the Weyl character formula such as the Borel-Weil-Bott theorem. We shall recast the topological $\K$-theory approach to the Weyl character formula, due basically to Atiyah and Bott, in the language of Kasparov's $\K\K$-theory. Then we shall show how, contingent on the Baum-Connes conjecture, our $\K\K$-theoretic Weyl character formula can be carried over to noncompact groups.
Semigroups of $L$-space knots and nonalgebraic iterated torus knots

Algebraic knots are known to be iterated torus knots and to admit L-space surgeries. However, Hedden proved that there are iterated torus knots that admit L-space surgeries but are not algebraic. We present an infinite family of such examples, with the additional property that no nontrivial linear combination of knots in this family is concordant to a linear combination of algebraic knots. The proof uses the Ozsvath-Stipsicz-Szabo Upsilon function, and also introduces a new invariant of L-space knots, the formal semigroup.
Imaging through glass diffusers using densely connected convolutional networks

Computational imaging through scatter generally is accomplished by first characterizing the scattering medium so that its forward operator is obtained and then imposing additional priors in the form of regularizers on the reconstruction functional to improve the condition of the originally ill-posed inverse problem. In the functional, the forward operator and regularizer must be entered explicitly or parametrically (e.g., scattering matrices and dictionaries, respectively). However, the process of determining these representations is often incomplete, prone to errors, or infeasible. Recently, deep learning architectures have been proposed to instead learn both the forward operator and regularizer through examples. Here, we propose for the first time, to our knowledge, a convolutional neural network architecture called “IDiffNet” for the problem of imaging through diffuse media and demonstrate that IDiffNet has superior generalization capability through extensive tests with well-calibrated diffusers. We also introduce the negative Pearson correlation coefficient (NPCC) loss function for neural net training and show that the NPCC is more appropriate for spatially sparse objects and strong scattering conditions. Our results show that the convolutional architecture is robust to the choice of prior, as demonstrated by the use of multiple training and testing object databases, and capable of achieving higher space–bandwidth product reconstructions than previously reported.
Double Total Ramifications for Curves of Genus 2

Inside the moduli space of curves of genus 2 with 2 marked points we consider the loci of curves admitting a map to P^1 of degree d totally ramified over the two marked points, for d>= 2. Such loci have codimension two. We compute the class of their compactifications in the moduli space of stable curves. Several results will be deduced from this computation.
On the Frobenius complexity of determinantal rings

Abstract We compute the Frobenius complexity for the determinantal ring of prime characteristic p obtained by modding out the 2 × 2 minors of an m × n matrix of indeterminates, where m > n ⩾ 2 . We also show that, as p → ∞ , the Frobenius complexity approaches m − 1 .