cssi/SciSciGPT-SciSciNet huggingface dataset:
Approximately Optimal Wireless Broadcasting

We study a wireless broadcast network, where a single source reliably communicates independent messages to multiple destinations, with the potential aid of relays and cooperation between destinations. The wireless nature of the medium is captured by the broadcast nature of transmissions as well as the superposition of transmitted signals plus independent Gaussian noise at the received signal at any radio. We propose a scheme that can achieve rate tuples within a constant gap away from the cut-set bound, where the constant is independent of channel coefficients and power constraints. First, for a deterministic broadcast network, we propose a new coding scheme, constructed by adopting a “receiver-centric” viewpoint, that uses quantize-and-forward relaying as an inner code concatenated with an outer Marton code for the induced deterministic broadcast channel. This scheme is shown to achieve the cut-set bound evaluated with product form distributions. This result is then lifted to the Gaussian network by using a deterministic network called the discrete superposition network as a formal quantization interface. This two-stage construction circumvents the difficulty involved in working with a vector nonlinear non-Gaussian broadcast channel that arises if we construct a similar scheme directly for the Gaussian network.
What Makes a Neural Code Convex

Neural codes allow the brain to represent, process, and store information about the world. Combinatorial codes, comprised of binary patterns of neural activity, encode information via the collective behavior of populations of neurons. A code is called convex if its codewords correspond to regions defined by an arrangement of convex open sets in Euclidean space. Convex codes have been observed experimentally in many brain areas, including sensory cortices and the hippocampus, where neurons exhibit convex receptive fields. What makes a neural code convex? That is, how can we tell from the intrinsic structure of a code if there exists a corresponding arrangement of convex open sets? In this work, we provide a complete characterization of local obstructions to convexity. This motivates us to define max intersection-complete codes, a family guaranteed to have no local obstructions. We then show how our characterization enables one to use free resolutions of Stanley--Reisner ideals in order to detect violations...
Microscopic details of a fluid/thin film triple line.

In recent years, there has been a considerable interest in the mechanics of soft objects meeting fluid interfaces (elasto-capillary interactions). In this work we experimentally examine the case of a fluid resting on a thin film of rigid material which, in turn, is resting on a fluid substrate. To simplify complexity, we adapt the experiment to a one-dimensional contact geometry and examine the behaviour of polystyrene and polycarbonate films directly with confocal microscopy. We find that the fluid meets the film in a manner consistent with the Young–Dupre equation when the film is thick, but transitions to what appears similar to a Neumann-like balance when the thickness is decreased. However, on closer investigation we find that the true contact angle is always given by the Young construction. The apparent paradox is a result of macroscopically measured angles not being directly related to true microscopic contact angles when curvature is present. We model the effect with an Euler–Bernoulli beam on a Winkler foundation as well as with an equivalent energy-based capillary model. Notably, the models highlight several important lengthscales and the complex interplay of tension, gravity, and bending in the problem.
Differentially Private Significance Tests for Regression Coefficients

Many data producers seek to provide users access to confidential data without unduly compromising data subjects’ privacy and confidentiality. One general strategy is to require users to do analyses...
Polynomial Time and Sample Complexity for Non-Gaussian Component Analysis: Spectral Methods

Author(s): Tan, Yan Shuo; Vershynin, Roman | Abstract: The problem of Non-Gaussian Component Analysis (NGCA) is about finding a maximal low-dimensional subspace $E$ in $\mathbb{R}^n$ so that data points projected onto $E$ follow a non-gaussian distribution. Although this is an appropriate model for some real world data analysis problems, there has been little progress on this problem over the last decade. In this paper, we attempt to address this state of affairs in two ways. First, we give a new characterization of standard gaussian distributions in high-dimensions, which lead to effective tests for non-gaussianness. Second, we propose a simple algorithm, \emph{Reweighted PCA}, as a method for solving the NGCA problem. We prove that for a general unknown non-gaussian distribution, this algorithm recovers at least one direction in $E$, with sample and time complexity depending polynomially on the dimension of the ambient space. We conjecture that the algorithm actually recovers the entire $E$.
Foams stabilized by tricationic amphiphilic surfactants

Abstract The unique surface properties of amphiphilic molecules have made them widely used in applications where foaming, emulsifying or coating processes are needed. Novel surfactant architectures with multi-cephalic and multi-tailed molecules have reportedly enhanced their anti-bacterial activity in connection with tail length and the nature of the head group, but their ability to produce and stabilize foam is mostly unknown. Here we report on experiments with tris-cationic, triple-headed, double- and single-tailed amphiphiles and their foamability and foam stability with respect to head group, tail number and tail length. The amphiphiles are composed of an aromatic mesitylene core and three benzylic ammonium bromide groups, with alkyl chains attached to one or two of the head groups. Whereas shorter (14 carbons in length) double-tailed molecules are found to produce very stable foams, foams made with single tail molecules of the same length show poor foamability and stability, and foams with longer (16 carbons in length) double-tail molecules do not foam with the methods used. By contrast, the structure of the non-tail-bearing head group (trimethylammonium vs. pyridinium) has no impact on foamability. Furthermore, observations of the coarsening rate at nearly constant liquid content indicate that the enhanced foam stability is a result of lower gas permeability through the surfactant monolayer. Finally, the critical aggregation concentration (CAC) of the surfactants demonstrates to be a good predictor of foamability and foam stability for these small molecule surfactants. These results inform how surfactant architecture can be tailored to produce stable foams.
High-dimensional multivariate mediation with application to neuroimaging data

Mediation analysis is an important tool in the behavioral sciences for investigating the role of intermediate variables that lie in the path between a treatment and an outcome variable. The influence of the intermediate variable on the outcome is often explored using a linear structural equation model (LSEM), with model coefficients interpreted as possible effects. While there has been significant research on the topic, little work has been done when the intermediate variable (mediator) is a high-dimensional vector. In this work, we introduce a novel method for identifying potential mediators in this setting called the directions of mediation (DMs). DMs linearly combine potential mediators into a smaller number of orthogonal components, with components ranked based on the proportion of the LSEM likelihood each accounts for. This method is well suited for cases when many potential mediators are measured. Examples of high-dimensional potential mediators are brain images composed of hundreds of thousands of voxels, genetic variation measured at millions of single nucleotide polymorphisms (SNPs), or vectors of thousands of variables in large-scale epidemiological studies. We demonstrate the method using a functional magnetic resonance imaging study of thermal pain where we are interested in determining which brain locations mediate the relationship between the application of a thermal stimulus and self-reported pain.
Berezin transforms on noncommutative polydomains

This paper is an attempt to unify the multivariable operator model theory for ball-like domains and commutative polydiscs, and extend it to a more general class of noncommutative polydomains. An important role in our study is played by noncommutative Berezin transforms associated with the elements of the polydomain.
Energy-Efficient Building HVAC Control Using Hybrid System LBMPC

Abstract Improving the energy-efficiency of heating, ventilation, and air-conditioning (HVAC) systems has the potential to realize large economic and societal benefits. This paper concerns the system identification of a hybrid system model of a building-wide HVAC system and its subsequent control using a hybrid system formulation of learning-based model predictive control (LBMPC). Here, the learning refers to model updates to the hybrid system model that incorporate the heating effects due to occupancy, solar effects, outside air temperature (OAT), and equipment, in addition to integrator dynamics inherently present in low-level control. Though we make significant modeling simplifications, our corresponding controller that uses this model is able to experimentally achieve a large reduction in energy usage without any degradations in occupant comfort. It is in this way that we justify the modeling simplifications that we have made. We conclude by presenting results from experiments on our building HVAC testbed, which show an average of 1.5MWh of energy savings per day (p = 0.002) with a 95% confidence interval of 1.0MWh to 2.1MWh of energy savings.
AttriGuard: A Practical Defense Against Attribute Inference Attacks via Adversarial Machine Learning

Users in various web and mobile applications are vulnerable to attribute inference attacks, in which an attacker leverages a machine learning classifier to infer a target user's private attributes (e.g., location, sexual orientation, political view) from its public data (e.g., rating scores, page likes). Existing defenses leverage game theory or heuristics based on correlations between the public data and attributes. These defenses are not practical. Specifically, game-theoretic defenses require solving intractable optimization problems, while correlation-based defenses incur large utility loss of users' public data. In this paper, we present AttriGuard, a practical defense against attribute inference attacks. AttriGuard is computationally tractable and has small utility loss. Our AttriGuard works in two phases. Suppose we aim to protect a user's private attribute. In Phase I, for each value of the attribute, we find a minimum noise such that if we add the noise to the user's public data, then the attacker's classifier is very likely to infer the attribute value for the user. We find the minimum noise via adapting existing evasion attacks in adversarial machine learning. In Phase II, we sample one attribute value according to a certain probability distribution and add the corresponding noise found in Phase I to the user's public data. We formulate finding the probability distribution as solving a constrained convex optimization problem. We extensively evaluate AttriGuard and compare it with existing methods using a real-world dataset. Our results show that AttriGuard substantially outperforms existing methods. Our work is the first one that shows evasion attacks can be used as defensive techniques for privacy protection.
MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels

Recent deep networks are capable of memorizing the entire data even when the labels are completely random. To overcome the overfitting on corrupted labels, we propose a novel technique of learning another neural network, called MentorNet, to supervise the training of the base deep networks, namely, StudentNet. During training, MentorNet provides a curriculum (sample weighting scheme) for StudentNet to focus on the sample the label of which is probably correct. Unlike the existing curriculum that is usually predefined by human experts, MentorNet learns a data-driven curriculum dynamically with StudentNet. Experimental results demonstrate that our approach can significantly improve the generalization performance of deep networks trained on corrupted training data. Notably, to the best of our knowledge, we achieve the best-published result on WebVision, a large benchmark containing 2.2 million images of real-world noisy labels. The code are at this https URL
Searching for Hyperbolicity

This paper is an expanded version of a talk given at the AWM Research Symposium 2017. It is intended as a gentle introduction to geometric group theory with a focus on the notion of hyperbolicity, a theme that has inspired the field from its inception to current-day research. The last section includes a discussion of some current approaches to extending techniques from hyperbolic groups to more general classes of groups.
A New Approach to Numerical Computation of Hausdorff Dimension of Iterated Function Systems: Applications to Complex Continued Fractions

In a previous paper (Falk and Nussbaum, in $$C^m$$ Eigenfunctions of Perron–Frobenius operators and a new approach to numerical computation of hausdorff dimension: applications in $$\mathbb {R}^1$$ , 2016. ArXiv e-prints arXiv:1612.00870 ), the authors developed a new approach to the computation of the Hausdorff dimension of the invariant set of an iterated function system or IFS and studied some applications in one dimension. The key idea, which has been known in varying degrees of generality for many years, is to associate to the IFS a parametrized family of positive, linear, Perron-Frobenius operators $$L_s$$ . In our context, $$L_s$$ is studied in a space of $$C^m$$ functions and is not compact. Nevertheless, it has a strictly positive $$C^m$$ eigenfunction $$v_s$$ with positive eigenvalue $$\lambda _s$$ equal to the spectral radius of $$L_s$$ . Under appropriate assumptions on the IFS, the Hausdorff dimension of the invariant set of the IFS is the value $$s=s_*$$ for which $$\lambda _s =1$$ . To compute the Hausdorff dimension of an invariant set for an IFS associated to complex continued fractions, (which may arise from an infinite iterated function system), we approximate the eigenvalue problem by a collocation method using continuous piecewise bilinear functions. Using the theory of positive linear operators and explicit a priori bounds on the partial derivatives of the strictly positive eigenfunction $$v_s$$ , we are able to give rigorous upper and lower bounds for the Hausdorff dimension $$s_*$$ , and these bounds converge to $$s_*$$ as the mesh size approaches zero. We also demonstrate by numerical computations that improved estimates can be obtained by the use of higher order piecewise tensor product polynomial approximations, although the present theory does not guarantee that these are strict upper and lower bounds. An important feature of our approach is that it also applies to the much more general problem of computing approximations to the spectral radius of positive transfer operators, which arise in many other applications.
Hierarchical density order embeddings

By representing words with probability densities rather than point vectors, probabilistic word embeddings can capture rich and interpretable semantic information and uncertainty. The uncertainty information can be particularly meaningful in capturing entailment relationships -- whereby general words such as "entity" correspond to broad distributions that encompass more specific words such as "animal" or "instrument". We introduce density order embeddings, which learn hierarchical representations through encapsulation of probability densities. In particular, we propose simple yet effective loss functions and distance metrics, as well as graph-based schemes to select negative samples to better learn hierarchical density representations. Our approach provides state-of-the-art performance on the WordNet hypernym relationship prediction task and the challenging HyperLex lexical entailment dataset -- while retaining a rich and interpretable density representation.
Adjoint twisted Alexander polynomials of genus one two-bridge knots

We give explicit formulas for the adjoint twisted Alexander polynomial and nonabelian Reidemeister torsion of genus one two-bridge knots.
AMR Parsing using Stack-LSTMs

We present a transition-based AMR parser that directly generates AMR parses from plain text. We use Stack-LSTMs to represent our parser state and make decisions greedily. In our experiments, we show that our parser achieves very competitive scores on English using only AMR training data. Adding additional information, such as POS tags and dependency trees, improves the results further.
Video Compression Through Image Interpolation

An ever increasing amount of our digital communication, media consumption, and content creation revolves around videos. We share, watch, and archive many aspects of our lives through them, all of which are powered by strong video compression. Traditional video compression is laboriously hand designed and hand optimized. This paper presents an alternative in an end-to-end deep learning codec. Our codec builds on one simple idea: Video compression is repeated image interpolation. It thus benefits from recent advances in deep image interpolation and generation. Our deep video codec outperforms today’s prevailing codecs, such as H.261, MPEG-4 Part 2, and performs on par with H.264.
On finite generation of the section ring of the determinant of cohomology line bundle

For $C$ a stable curve of arithmetic genus $g\ge 2$, and $\mathcal{D}$ the determinant of cohomology line bundle on $\operatorname{Bun}_{\operatorname{SL}(r)}(C)$, we show the section ring for the pair $(\operatorname{Bun}_{\operatorname{SL}(r)}(C), \mathcal{D})$ is finitely generated. Three applications are given.
The Threshold for Integer Homology in Random d-Complexes

Let $$Y \sim Y_d(n,p)$$Y~Yd(n,p) denote the Bernoulli random d-dimensional simplicial complex. We answer a question of Linial and Meshulam from 2003, showing that the threshold for vanishing of homology $$H_{d-1}(Y; \mathbb {Z})$$Hd-1(YźZ) is less than $$40d (d+1) \log n / n$$40d(d+1)logn/n. This bound is tight, up to a constant factor which depends on d.
Batched Stochastic Gradient Descent with Weighted Sampling

We analyze a batched variant of Stochastic gradient descent (SGD) with weighted sampling distribution for smooth and non-smooth objective functions. We show that by distributing the batches computationally, a significant speedup in the convergence rate is provably possible compared to either batched sampling or weighted sampling alone. We propose several computationally efficient schemes to approximate the optimal weights and compute proposed sampling distributions explicitly for the least squares and hinge loss problems. We show both analytically and experimentally that substantial gains can be obtained.
A submodular-supermodular procedure with applications to discriminative structure learning

In this paper, we present an algorithm for minimizing the difference between two sub-modular functions using a variational framework which is based on (an extension of) the concave-convex procedure [17]. Because several commonly used metrics in machine learning, like mutual information and conditional mutual information, are submodular, the problem of minimizing the difference of two submodular problems arises naturally in many machine learning applications. Two such applications are learning discriminatively structured graphical models and feature selection under computational complexity constraints. A commonly used metric for measuring discriminative capacity is the EAR measure which is the difference between two conditional mutual information terms. Feature selection taking complexity considerations into account also fall into this framework because both the information that a set of features provide and the cost of computing and using the features can be modeled as submodular functions. This problem is NP-hard, and we give a polynomial time heuristic for it. We also present results on synthetic data to show that classifiers based on discriminative graphical models using this algorithm can significantly outperform classifiers based on generative graphical models.
Cut, Paste and Learn: Surprisingly Easy Synthesis for Instance Detection

A major impediment in rapidly deploying object detection models for instance detection is the lack of large annotated datasets. For example, finding a large labeled dataset containing instances in a particular kitchen is unlikely. Each new environment with new instances requires expensive data collection and annotation. In this paper, we propose a simple approach to generate large annotated instance datasets with minimal effort. Our key insight is that ensuring only patch-level realism provides enough training signal for current object detector models. We automatically ‘cut’ object instances and ‘paste’ them on random backgrounds. A naive way to do this results in pixel artifacts which result in poor performance for trained models. We show how to make detectors ignore these artifacts during training and generate data that gives competitive performance on real data. Our method outperforms existing synthesis approaches and when combined with real images improves relative performance by more than 21% on benchmark datasets. In a cross-domain setting, our synthetic data combined with just 10% real data outperforms models trained on all real data.
Polynomial bounds for invariant functions separating orbits

Abstract In a representation of a linear algebraic group G , polynomial invariant functions almost always fail to separate orbits. Unless G is reductive, the ring of invariant polynomials may not be finitely generated. Also the number and complexity of the generators may grow rapidly with the size of the representation. We instead consider an extension of the polynomial ring by introducing a “quasi-inverse” that computes the inverse of a function where defined. With the addition of the quasi-inverse, we write straight line programs defining functions that separate the orbits of any linear algebraic group G . The number of these programs and their length have polynomial bounds in the parameters of the representation.
Reconstruction from periodic nonlinearities, with applications to HDR imaging

We consider the problem of reconstructing signals and images from periodic nonlinearities. For such problems, we design a measurement scheme that supports efficient reconstruction; moreover, our method can be adapted to extend to compressive sensing-based signal and image acquisition systems. Our techniques can be potentially useful for reducing the measurement complexity of high dynamic range (HDR) imaging systems, with little loss in reconstruction quality. Several numerical experiments on real data demonstrate the effectiveness of our approach.
Prioritized Threshold Allocation for Distributed Frequency Response

Higher penetration of renewable generation will increase the demand for adequate (and cost-effective) controllable resources on the grid that can mitigate and contain the contingencies locally before it can cause a network-wide collapse. However, end-use constraints can potentially lead to load unavailability when an event occurs, leading to unreliable demand response services. Sensors measurements and knowledge of the local load dynamics could be leveraged to improve the performance of load control algorithms. In the context of hierarchical frequency response using ensemble of switching loads, we present a metric to evaluate the fitness of each device in successfully providing the ancillary service. Furthermore a fitness-based assignment of control set-points is formulated which achieves reliable performance under different operating conditions. Monte Carlo simulations of ensembles of electric water heaters and residential air-conditioners are performed to evaluate the proposed control algorithm.
“Degenerate” 3-dimensional Sklyanin algebras are monomial algebras

Abstract The 3-dimensional Sklyanin algebras, S a , b , c , form a flat family parametrized by points ( a , b , c ) ∈ P 2 − D where D is a set of 12 points. When ( a , b , c ) ∈ D , the algebras having the same defining relations as the 3-dimensional Sklyanin algebras are called “degenerate Sklyanin algebras”. C. Walton showed they do not have the same properties as the non-degenerate ones. Here we prove that a degenerate Sklyanin algebra is isomorphic to the free algebra on u , v , and w , modulo either the relations u 2 = v 2 = w 2 = 0 or the relations u v = v w = w u = 0 . These monomial algebras are Zhang twists of each other. Therefore all degenerate Sklyanin algebras have the same category of graded modules. A number of properties of the degenerate Sklyanin algebras follow from this observation. We exhibit a quiver Q and an ultramatricial algebra R such that if S is a degenerate Sklyanin algebra then the categories QGr S , QGr k Q , and Mod R , are equivalent. Here QGr ( − ) denotes the category of graded right modules modulo the full subcategory of graded modules that are the sum of their finite-dimensional submodules. The group of cube roots of unity, μ 3 , acts as automorphisms of the free algebra on two variables, F , in such a way that QGr S is equivalent to QGr ( F ⋊ μ 3 ) .
A note on Banach principle for JW-algebras

In the sequel we establish the Banach Principle for semifinite JW-algebras without direct summand of type I2, which extends the recent results of Chilin and Litvinov on the Banach Principle for semifinite von Neumann algebras to the case of JW-algebras.
Towards Eco-friendly Database Management Systems.

Database management systems (DBMSs) have largely ignored the task of managing the energy consumed during query processing. Both economical and environmental factors now require that DBMSs pay close attention to energy consumption. In this paper we approach this issue by considering energy consumption as a first-class performance goal for query processing in a DBMS. We present two concrete techniques that can be used by a DBMS to directly manage the energy consumption. Both techniques trade energy consumption for performance. The first technique, called PVC, leverages the ability of modern processors to execute at lower processor voltage and frequency. The second technique, called QED, uses query aggregation to leverage common components of queries in a workload. Using experiments run on a commercial DBMS and MySQL, we show that PVC can reduce the processor energy consumption by 49% of the original consumption while increasing the response time by only 3%. On MySQL, PVC can reduce energy consumption by 20% with a response time penalty of only 6%. For simple selection queries with no predicate overlap, we show that QED can be used to gracefully trade response time for energy, reducing energy consumption by 54% for a 43% increase in average response time. In this paper we also highlight some research issues in the emerging area of energy-efficient data processing.
Variational Inference via $\chi$ Upper Bound Minimization

Variational inference (VI) is widely used as an efficient alternative to Markov chain Monte Carlo. It posits a family of approximating distributions $q$ and finds the closest member to the exact posterior $p$. Closeness is usually measured via a divergence $D(q || p)$ from $q$ to $p$. While successful, this approach also has problems. Notably, it typically leads to underestimation of the posterior variance. In this paper we propose CHIVI, a black-box variational inference algorithm that minimizes $D_{\chi}(p || q)$, the $\chi$-divergence from $p$ to $q$. CHIVI minimizes an upper bound of the model evidence, which we term the $\chi$ upper bound (CUBO). Minimizing the CUBO leads to improved posterior uncertainty, and it can also be used with the classical VI lower bound (ELBO) to provide a sandwich estimate of the model evidence. We study CHIVI on three models: probit regression, Gaussian process classification, and a Cox process model of basketball plays. When compared to expectation propagation and classical VI, CHIVI produces better error rates and more accurate estimates of posterior variance.
Optimal decomposition and recombination of isostatic geometric constraint systems for designing layered materials

Abstract Optimal recursive decomposition (or DR-planning) is crucial for analyzing, designing, solving or finding realizations of geometric constraint systems. While the optimal DR-planning problem is NP-hard even for (general) 2D bar–joint constraint systems, we describe an O ( n 3 ) algorithm for a broad class of constraint systems that are isostatic or underconstrained. The algorithm achieves optimality by using the new notion of a canonical DR-plan that also meets various desirable, previously studied criteria. In addition, we leverage recent results on Cayley configuration spaces to show that the indecomposable systems – that are solved at the nodes of the optimal DR-plan by recombining solutions to child systems – can be minimally modified to become decomposable and have a small DR-plan, leading to efficient realization algorithms. We show formal connections to well-known problems such as completion of underconstrained systems. Well suited to these methods are classes of constraint systems that can be used to efficiently model, design and analyze quasi-uniform (aperiodic) and self-similar, layered material structures. We formally illustrate by modeling silica bilayers as body–hyperpin systems and cross-linking microfibrils as pinned line-incidence systems. A software implementation of our algorithms and videos demonstrating the software are publicly available online (visit http://cise.ufl.edu/~tbaker/drp/index.html ).
Evolving the management of acute perioperative pain towards opioid free protocols: a narrative review.

Objective: Identification of pain as the fifth vital sign has resulted in over-prescription and overuse of opioids in the US, with addiction reaching epidemic proportions. In Europe, and more recen...
Automated selection of uniform regions for CT image quality detection

CT images are widely used in pathology detection and follow-up treatment procedures. Accurate identification of pathological features requires diagnostic quality CT images with minimal noise and artifact variation. In this work, a novel Fourier-transform based metric for image quality (IQ) estimation is presented that correlates to additive CT image noise. There is presently a need to optimize contrast enhancement to ensure As Low As Reasonably Achievable (ALARA) dosage with diagnostic IQ. In the proposed method, two windowed CT image subset regions are analyzed together to identify the extent of variation in the corresponding Fourier-domain spectrum. The two square windows are chosen such that their center pixels coincide and one window is a subset of the other. The Fourier-domain spectral difference between these two sub-sampled windows is then used to isolate spatial regions-of-interest (ROI) with low variation (ROI-LV) and high variation (ROI-HV), respectively. Finally, the number of pixels within the spatial ROI-LV is correlated with image acquisition parameters that serve as IQ metrics. We observe that the number of pixels in thresholded ROI-LV regions strongly correlate with the imaging tube current (in mA) in phantom CT images (r = 0.9648, p = 0.0018, R2 = 0.9308) and patient abdominal CT images (r = 0.9283, R2 = 0.8617). Thus, the proposed method can be useful to quantitatively estimate the diagnostic CT image quality.
Active acoustic metamaterials reconfigurable in real time

A major limitation of current acoustic metamaterials is that their acoustic properties are either locked into place once fabricated or are only modestly tunable, tying them to the particular application for which they are designed. We present a design approach that yields active metamaterials whose physical structure is fixed, yet their local acoustic response can be changed almost arbitrarily and in real time by configuring the digital electronics that control the metamaterial acoustic properties. We demonstrate this approach experimentally by designing a metamaterial slab configured to act as a very thin acoustic lens that manipulates differently three identical, consecutive pulses incident on the lens. Moreover, we show that the slab can be configured to simultaneously implement various roles, such as that of a lens and a beam steering device. Finally, we show that the metamaterial slab is suitable for efficient second harmonic acoustic imaging devices capable of overcoming the diffraction limit of linear lenses. These advantages demonstrate the versatility of this active metamaterial and highlight its broad applicability, in particular, to acoustic imaging.
Universal Three-Dimensional Optical Logic

We experimentally demonstrate a universal logic gate and extend to fully 3D NAND and NOR. A weak pulse triggers the collapse and spatiotemporal instability of a stronger pulse. Extremely powerful new computational strategies appear possible.
The Gaussian Radon transform and machine learning

There has been growing recent interest in probabilistic interpretations of kernel-based methods as well as learning in Banach spaces. The absence of a useful Lebesgue measure on an infinite-dimensional reproducing kernel Hilbert space is a serious obstacle for such stochastic models. We propose an estimation model for the ridge regression problem within the framework of abstract Wiener spaces and show how the support vector machine solution to such problems can be interpreted in terms of the Gaussian Radon transform.
Quantum Phases in Entropic Dynamics

In the Entropic Dynamics framework, the dynamics is driven by maximizing entropy subject to appropriate constraints. In this work, we bring Entropic Dynamics one-step closer to full equivalence with quantum theory by identifying constraints that lead to wave functions that remain single-valued even for multi-valued phases by recognizing the intimate relation between quantum phases, gauge symmetry, and charge quantization.
Graphlet decomposition: framework, algorithms, and applications

From social science to biology, numerous applications often rely on graphlets for intuitive and meaningful characterization of networks. While graphlets have witnessed a tremendous success and impact in a variety of domains, there has yet to be a fast and efficient framework for computing the frequencies of these subgraph patterns. However, existing methods are not scalable to large networks with billions of nodes and edges. In this paper, we propose a fast, efficient, and parallel framework as well as a family of algorithms for counting k-node graphlets. The proposed framework leverages a number of theoretical combinatorial arguments that allow us to obtain significant improvement on the scalability of graphlet counting. For each edge, we count a few graphlets and obtain the exact counts of others in constant time using the combinatorial arguments. On a large collection of $$300+$$300+ networks from a variety of domains, our graphlet counting strategies are on average $$460{\times }$$460× faster than existing methods. This brings new opportunities to investigate the use of graphlets on much larger networks and newer applications as we show in the experiments. To the best of our knowledge, this paper provides the largest graphlet computations to date.
Learning to Recognize Objects by Retaining Other Factors of Variation

Most ConvNets formulate object recognition from natural images as a single task classification problem, and attempt to learn features useful for object categories, but invariant to other factors of variation such as pose and illumination. They do not explicitly learn these other factors, instead, they usually discard them by pooling and normalization. Here, we take the opposite approach: we train ConvNets for object recognition by retaining other factors (pose in our case) and learning them jointly with object category. We design a new multi-task leaning (MTL) ConvNet, named disentangling CNN (disCNN), which explicitly enforces the disentangled representations of object identity and pose, and is trained to predict object categories and pose transformations. disCNN achieves significantly better object recognition accuracies than the baseline CNN trained solely to predict object categories on the iLab-20M dataset, a large-scale turntable dataset with detailed pose and lighting information. We further show that the pretrained features on iLab-20M generalize to both Washington RGB-D and ImageNet datasets, and the pretrained dis-CNN features are significantly better than the pretrained baseline CNN features for fine-tuning on ImageNet.
Quantitative Stratification and the Regularity of Harmonic Maps and Minimal Currents

We introduce techniques for turning estimates on the infinitesimal behavior of solutions to nonlinear equations (statements concerning tangent cones and blow ups) into more effective control. In the present paper, we focus on proving regularity theorems for stationary and minimizing harmonic maps and minimal currents. There are several aspects to our improvements of known estimates. First, we replace known estimates on the Hausdorff dimension of singular sets by estimates on their Minkowski r-content, or equivalently, on the volumes of their r-tubular neighborhoods. Second, we give improved regularity control with respect to the number of derivatives bounded and/or on the norm in which the derivatives are bounded. As an example of the former, our results for minimizing harmonic maps f : M->N between riemannian manifolds include a priori bounds in W^{1,p}\cap W^{2,p} for all p < 3. These are the first such bounds involving second derivatives in general dimensions. Finally, the quantity we control is actually provides much stronger information than follows from a bound on the L^p norm of derivatives. Namely, we obtain L^p bounds for the inverse of the regularity scale r_f(x)=max{r:sup_{B_r(x)}(r|\nabla f|+r^2|\nabla^2 f|)\leq 1}. Applications to minimal hypersufaces include a priori L^p bounds for the second fundamental form A for all p < 7. Previously known bounds were for p < 2+\epsilon(n). Again, the full theorem is much stronger and yields Lp bounds for the corresponding regularity scale r_A(x)=max{r:sup_{B_r(x)}rA\leq 1}. In outline, our discussion follows that of an earlier paper in which we proved analogous estimates in the context of noncollapsed riemannian manifolds with a lower bound on Ricci curvature. These were applied to Einstein manifolds. A key point in all of these arguments is to establish the relevant quantitative differentiation theorem.
On graph stream clustering with side information

Graph clustering becomes an important problem due to emerging applications involving the web, social networks and bio-informatics. Recently, many such applications generate data in the form of streams. Clustering massive, dynamic graph streams is significantly challenging because of the complex structures of graphs and computational difficulties of continuous data. Meanwhile, a large volume of side information is associated with graphs, which can be of various types. The examples include the properties of users in social network activities, the meta attributes associated with web click graph streams and the location information in mobile communication networks. Such attributes contain extremely useful information and has the potential to improve the clustering process, but are neglected by most recent graph stream mining techniques. In this paper, we define a unified distance measure on both link structures and side attributes for clustering. In addition, we propose a novel optimization framework DMO, which can dynamically optimize the distance metric and make it adapt to the newly received stream data. We further introduce a carefully designed statistics SGS(C) which consume constant storage spaces with the progression of streams. We demonstrate that the statistics maintained are sufficient for the clustering process as well as the distance optimization and can be scalable to massive graphs with side attributes. We will present experiment results to show the advantages of the approach in graph stream clustering with both links and side information over the baselines.
Conservative integrators for a toy model of weak turbulence

Abstract Weak turbulence is a phenomenon by which a system generically transfers energy from low to high wave numbers, while persisting for all finite time. It has been conjectured by Bourgain that the 2D defocusing nonlinear Schrodinger equation (NLS) on the torus has this dynamic, and several analytical and numerical studies have worked towards addressing this point. In the process of studying the conjecture, Colliander, Keel, Staffilani, Takaoka, and Tao introduced a “toy model” dynamical system as an approximation of NLS, which has been subsequently studied numerically. In this work, we formulate and examine several numerical schemes for integrating this model equation. The model has two invariants, and our schemes aim to conserve at least one of them. We prove convergence in some cases, and our numerical studies show that the schemes compare favorably to others, such as Trapezoidal Rule and fixed step fourth order Runge–Kutta. The preservation of the invariants is particularly important in the study of weak turbulence as the energy transfer tends to occur on long time scales.
Modular verification of interrupt-driven software

Interrupts have been widely used in safety-critical computer systems to handle outside stimuli and interact with the hardware, but reasoning about interrupt-driven software remains a difficult task. Although a number of static verification techniques have been proposed for interrupt-driven software, they often rely on constructing a monolithic verification model. Furthermore, they do not precisely capture the complete execution semantics of interrupts such as nested invocations of interrupt handlers. To overcome these limitations, we propose an abstract interpretation framework for static verification of interrupt-driven software that first analyzes each interrupt handler in isolation as if it were a sequential program, and then propagates the result to other interrupt handlers. This iterative process continues until results from all interrupt handlers reach a fixed point. Since our method never constructs the global model, it avoids the up-front blowup in model construction that hampers existing, non-modular, verification techniques. We have evaluated our method on 35 interrupt-driven applications with a total of 22,541 lines of code. Our results show the method is able to quickly and more accurately analyze the behavior of interrupts.
Mechanistic constitutive model for wormlike micelle solutions with flow-induced structure formation

Abstract We present a tensor constitutive model for predicting stress and flow-induced structure formation in dilute wormlike micellar solutions. The micellar solution is treated as a dilute suspension of rigid Brownian rods whose length varies dynamically. Consistent with the mechanism presented by Turner and Cates [J. Phys.: Condens. Matter 4, 3719 (1992)], flow-induced alignment of the rods is assumed to promote increase of rod length that corresponds to the formation of flow-induced structures observed in experiments. At very high deformation rate, hydrodynamic stresses causes the rod length to decrease. These mechanisms are implemented in a phenomenological equation governing the evolution of rod length, with the number density of rods appropriately modified to ensure conservation of surfactant mass. The model leads first to an increase in both shear and extensional viscosity as deformation rate increases and then to a decrease at higher rates. If the rate constant for flow-induced rod growth is sufficiently large, the model predicts a multivalued relation between stress and deformation rate in both shear and uniaxial extension. Predictions for shear and extensional flow at steady state are in reasonable agreement with experimental results. By design, the model is simple enough to serve as a tractable constitutive relation for computational fluid dynamics studies.
Scalable Sparse Subspace Clustering by Orthogonal Matching Pursuit

Subspace clustering methods based on l1, l2 or nuclear norm regularization have become very popular due to their simplicity, theoretical guarantees and empirical success. However, the choice of the regularizer can greatly impact both theory and practice. For instance, l1 regularization is guaranteed to give a subspace-preserving affinity (i.e., there are no connections between points from different subspaces) under broad conditions (e.g., arbitrary subspaces and corrupted data). However, it requires solving a large scale convex optimization problem. On the other hand, l2 and nuclear norm regularization provide efficient closed form solutions, but require very strong assumptions to guarantee a subspace-preserving affinity, e.g., independent subspaces and uncorrupted data. In this paper we study a subspace clustering method based on orthogonal matching pursuit. We show that the method is both computationally efficient and guaranteed to give a subspace-preserving affinity under broad conditions. Experiments on synthetic data verify our theoretical analysis, and applications in handwritten digit and face clustering show that our approach achieves the best trade off between accuracy and efficiency. Moreover, our approach is the first one to handle 100,000 data points.
Stimulation-Based Control of Dynamic Brain Networks

The ability to modulate brain states using targeted stimulation is increasingly being employed to treat neurological disorders and to enhance human performance. Despite the growing interest in brain stimulation as a form of neuromodulation, much remains unknown about the network-level impact of these focal perturbations. To study the system wide impact of regional stimulation, we employ a data-driven computational model of nonlinear brain dynamics to systematically explore the effects of targeted stimulation. Validating predictions from network control theory, we uncover the relationship between regional controllability and the focal versus global impact of stimulation, and we relate these findings to differences in the underlying network architecture. Finally, by mapping brain regions to cognitive systems, we observe that the default mode system imparts large global change despite being highly constrained by structural connectivity. This work forms an important step towards the development of personalized stimulation protocols for medical treatment or performance enhancement.
Top-Down Learning for Structured Labeling with Convolutional Pseudoprior

Current practice in convolutional neural networks (CNN) remains largely bottom-up and the role of top-down process in CNN for pattern analysis and visual inference is not very clear. In this paper, we propose a new method for structured labeling by developing convolutional pseudoprior (ConvPP) on the ground-truth labels. Our method has several interesting properties: (1) compared with classic machine learning algorithms like CRFs and Structural SVM, ConvPP automatically learns rich convolutional kernels to capture both short- and long- range contexts; (2) compared with cascade classifiers like Auto-Context, ConvPP avoids the iterative steps of learning a series of discriminative classifiers and automatically learns contextual configurations; (3) compared with recent efforts combining CNN models with CRFs and RNNs, ConvPP learns convolution in the labeling space with improved modeling capability and less manual specification; (4) compared with Bayesian models like MRFs, ConvPP capitalizes on the rich representation power of convolution by automatically learning priors built on convolutional filters. We accomplish our task using pseudo-likelihood approximation to the prior under a novel fixed-point network structure that facilitates an end-to-end learning process. We show state-of-the-art results on sequential labeling and image labeling benchmarks.