cssi/SciSciGPT-SciSciNet huggingface dataset:
Scene-centric Joint Parsing of Cross-view Videos

Cross-view video understanding is an important yet under-explored area in computer vision. In this paper, we introduce a joint parsing framework that integrates view-centric proposals into scene-centric parse graphs that represent a coherent scene-centric understanding of cross-view scenes. Our key observations are that overlapping fields of views embed rich appearance and geometry correlations and that knowledge fragments corresponding to individual vision tasks are governed by consistency constraints available in commonsense knowledge. The proposed joint parsing framework represents such correlations and constraints explicitly and generates semantic scene-centric parse graphs. Quantitative experiments show that scene-centric predictions in the parse graph outperform view-centric predictions.
Asymptotic Symmetries of Massless QED in Even Dimensions

We consider the scattering of massless particles coupled to an abelian gauge field in 2n-dimensional Minkowski spacetime. Weinberg’s soft photon theorem is recast as Ward identities for infinitely many new nontrivial symmetries of the massless QED S-matrix, with one such identity arising for each propagation direction of the soft photon. These symmetries are identified as large gauge transformations with angle-dependent gauge parameters that are constant along the null generators of null infinity. Almost all of the symmetries are spontaneously broken in the standard vacuum and the soft photons are the corresponding Goldstone bosons. Our result establishes a relationship between soft theorems and asymptotic symmetry groups in any even dimension.
Massively-Parallel Lossless Data Decompression

Today's exponentially increasing data volumes and the high cost of storage make compression essential for the Big Data industry. Although research has concentrated on efficient compression, fast decompression is critical for analytics queries that repeatedly read compressed data. While decompression can be parallelized somewhat by assigning each data block to a different process, break-through speed-ups require exploiting the massive parallelism of modern multi-core processors and GPUs for data decompression within a block. We propose two new techniques to increase the degree of parallelism during decompression. The first technique exploits the massive parallelism of GPU and SIMD architectures. The second sacrifices some compression efficiency to eliminate data dependencies that limit parallelism during decompression. We evaluate these techniques on the decompressor of the DEFLATE scheme, called Inflate, which is based on LZ77 compression and Huffman encoding. We achieve a 2&#x00D7; speed-up in a head-to-head comparison with several multi core CPU-based libraries, while achieving a 17% energy saving with comparable compression ratios.
Merging the A-and Q-spectral theories

Let G be a graph with adjacency matrix A(G), and let D(G) be the diagonal matrix of the degrees of G: The signless Laplacian Q(G) of G is defined as Q(G):= A(G) +D(G). Cvetkovic called the study of the adjacency matrix the A-spectral theory, and the study of the signless Laplacian{the Q-spectral theory. To track the gradual change of A(G) into Q(G), in this paper it is suggested to study the convex linear combinations A_ (G) of A(G) and D(G) defined by Aα (G) := αD(G) + (1 - α)A(G), 0 ≤ α ≤ 1. This study sheds new light on A(G) and Q(G), and yields, in particular, a novel spectral Turan theorem. A number of open problems are discussed.
Exact Recovery in the Stochastic Block Model

The stochastic block model with two communities, or equivalently the planted bisection model, is a popular model of random graph exhibiting a cluster behavior. In the symmetric case, the graph has two equally sized clusters and vertices connect with probability $p$ within clusters and $q$ across clusters. In the past two decades, a large body of literature in statistics and computer science has focused on providing lower bounds on the scaling of $|p-q|$ to ensure exact recovery. In this paper, we identify a sharp threshold phenomenon for exact recovery: if $\alpha =pn/\log (n)$ and $\beta =qn/\log (n)$ are constant (with $\alpha >\beta $ ), recovering the communities with high probability is possible if $({\alpha +\beta }/{2}) - \sqrt {\alpha \beta }>1$ and is impossible if $({\alpha +\beta }/{2}) - \sqrt {\alpha \beta } . In particular, this improves the existing bounds. This also sets a new line of sight for efficient clustering algorithms. While maximum likelihood (ML) achieves the optimal threshold (by definition), it is in the worst case NP-hard. This paper proposes an efficient algorithm based on a semidefinite programming relaxation of ML, which is proved to succeed in recovering the communities close to the threshold, while numerical experiments suggest that it may achieve the threshold. An efficient algorithm that succeeds all the way down to the threshold is also obtained using a partial recovery algorithm combined with a local improvement procedure.
Loci of curves with subcanonical points in low genus

Inside the moduli space of curves of genus three with one marked point, we consider the locus of hyperelliptic curves with a marked Weierstrass point, and the locus of non-hyperelliptic curves with a marked hyperflex. These loci have codimension two. We compute the classes of their closures in the moduli space of stable curves of genus three with one marked point. Similarly, we compute the class of the closure of the locus of curves of genus four with an even theta characteristic vanishing with order three at a certain point. These loci naturally arise in the study of minimal dimensional strata of Abelian differentials.
Semi-supervised Clustering for Short Text via Deep Representation Learning

In this work, we propose a semisupervised method for short text clustering, where we represent texts as distributed vectors with neural networks, and use a small amount of labeled data to specify our intention for clustering. We design a novel objective to combine the representation learning process and the kmeans clustering process together, and optimize the objective with both labeled data and unlabeled data iteratively until convergence through three steps: (1) assign each short text to its nearest centroid based on its representation from the current neural networks; (2) re-estimate the cluster centroids based on cluster assignments from step (1); (3) update neural networks according to the objective by keeping centroids and cluster assignments fixed. Experimental results on four datasets show that our method works significantly better than several other text clustering methods.
Homotopy colimits in stable representation theory

We study the problem of existence and uniqueness of homotopy colimits in stable representation theory, where one typically does not have model category structures to guarantee that these homotopy colimits exist or have good properties. We get both negative results (homotopy cofibers fail to exist if there exist any objects of positive finite projective dimension!) and positive results (reasonable conditions under which homotopy colimits exist and are unique, even when model category structures fail to exist). Along the way, we obtain relative-homological-algebraic generalizations of classical theorems of Hilton-Rees and Oort. We describe some applications to Waldhausen $K$-theory and to deformation-theoretic methods in stable representation theory.
Additive systems and a theorem of de Bruijn

This paper proves a theorem of de Bruijn that classifies additive systems for the nonnegative integers, that is, families A = (Ai)ieI of sets of nonnegative integers, each set containing 0, such th...
Global Model Interpretation Via Recursive Partitioning

In this work, we propose a simple but effective method to interpret black-box machine learning models globally. That is, we use a compact binary tree, the interpretation tree, to explicitly represent the most important decision rules that are implicitly contained in the black-box machine learning models. This tree is learned from the contribution matrix which consists of the contributions of input variables to predicted scores for each single prediction. To generate the interpretation tree, a unified process recursively partitions the input variable space by maximizing the difference in the average contribution of the split variable between the divided spaces. We demonstrate the effectiveness of our method in diagnosing machine learning models on multiple tasks. Also, it is useful for new knowledge discovery as such insights are not easily identifiable when only looking at single predictions. In general, our work makes it easier and more efficient for human beings to understand machine learning models.
Time dependent scattering from a grating

Computing the electromagnetic field due to a periodic grating is critical for assessing the performance of thin film solar voltaic devices. In this paper we investigate the computation of these fields in the time domain (similar problems also arise in simulating antennas). Assuming a translation invariant periodic grating this reduces to solving the wave equation in a periodic domain. Materials used in practical devices have frequency dependent coefficients, and we provide a first proof of existence and uniqueness for a general class of such materials. Using Convolution Quadrature we can then prove time stepping error estimates. We end with some preliminary numerical results that demonstrate the convergence and stability of the scheme.
The Weyl Problem With Nonnegative Gauss Curvature In Hyperbolic Space

In this paper, we discuss the isometric embedding problem in hyperbolic space with nonnegative extrinsic curvature. We prove a priori bounds for the trace of the second fundamental form and extend the result to -dimensions. We also obtain an estimate for the gradient of the smaller principal curvature in 2 dimensions.
Global regularity for a logarithmically supercritical hyperdissipative Navier–Stokes equation

Let d≥3. We consider the global Cauchy problem for the generalized Navier–Stokes system ∂ t u + ( u ⋅ ∇ ) u = − D 2 u − ∇ p , ∇ ⋅ u = 0 , u ( 0 , x ) = u 0 ( x ) for u:ℝ+×ℝd→ℝd and p:ℝ+×ℝd→ℝ, where u0:ℝd→ℝd is smooth and divergence free, and D is a Fourier multiplier whose symbol m:ℝd→ℝ+ is nonnegative; the case m(ξ)=|ξ| is essentially Navier–Stokes. It is folklore that one has global regularity in the critical and subcritical hyperdissipation regimes m(ξ)=|ξ|α for α≥(d+2)∕4. We improve this slightly by establishing global regularity under the slightly weaker condition that m(ξ)≥|ξ|(d+2)∕4∕g(|ξ|) for all sufficiently large ξ and some nondecreasing function g:ℝ+→ℝ+ such that ∫ 1∞ds∕(sg(s)4)=+∞. In particular, the results apply for the logarithmically supercritical dissipation m(ξ):=|ξ|(d+2)∕4∕log(2+|ξ|2)1∕4.
Order Matters: Sequence to sequence for sets

Abstract: Sequences have become first class citizens in supervised learning thanks to the resurgence of recurrent neural networks. Many complex tasks that require mapping from or to a sequence of observations can now be formulated with the sequence-to-sequence (seq2seq) framework which employs the chain rule to efficiently represent the joint probability of sequences. In many cases, however, variable sized inputs and/or outputs might not be naturally expressed as sequences. For instance, it is not clear how to input a set of numbers into a model where the task is to sort them; similarly, we do not know how to organize outputs when they correspond to random variables and the task is to model their unknown joint probability. In this paper, we first show using various examples that the order in which we organize input and/or output data matters significantly when learning an underlying model. We then discuss an extension of the seq2seq framework that goes beyond sequences and handles input sets in a principled way. In addition, we propose a loss which, by searching over possible orders during training, deals with the lack of structure of output sets. We show empirical evidence of our claims regarding ordering, and on the modifications to the seq2seq framework on benchmark language modeling and parsing tasks, as well as two artificial tasks -- sorting numbers and estimating the joint probability of unknown graphical models.
Fine-grained car detection for visual census estimation

Targeted socio-economic policies require an accurate understanding of a country’s demographic makeup. To that end, the United States spends more than 1 billion dollars a year gathering census data such as race, gender, education, occupation and unemployment rates. Compared to the traditional method of collecting surveys across many years which is costly and labor intensive, data-driven, machine learning-driven approaches are cheaper and faster—with the potential ability to detect trends in close to real time. In this work, we leverage the ubiquity of Google Street View images and develop a computer vision pipeline to predict income, per capita carbon emission, crime rates and other city attributes from a single source of publicly available visual data. We first detect cars in 50 million images across 200 of the largest US cities and train a model to predict demographic attributes using the detected cars. To facilitate our work, we have collected the largest and most challenging fine-grained dataset reported to date consisting of over 2600 classes of cars comprised of images from Google Street View and other web sources, classified by car experts to account for even the most subtle of visual differences. We use this data to construct the largest scale fine-grained detection system reported to date. Our prediction results correlate well with ground truth income data (r=0.82), Massachusetts department of vehicle registration, and sources investigating crime rates, income segregation, per capita carbon emission, and other market research. Finally, we learn interesting relationships between cars and neighborhoods allowing us to perform the first large scale sociological analysis of cities using computer vision techniques.
On designing and testing distributed virtual environments

Distributed real-time DRT systems are among the most complex software systems to design, test, maintain, and evolve. The existence of components distributed over a network often conflicts with real-time requirements, leading to design strategies that depend on domain-specific and even application-specific knowledge. Distributed virtual environment DVE systems are DRT systems that connect multiple users instantly with each other and with a shared virtual space over a network. DVE systems deviate from traditional DRT systemsx in the importance of the quality of the end user experience. We present an analysis of important, but challenging, issues in the design, testing, and evaluation of DVE systems through the lens of experiments with a concrete DVE, OpenSimulator. We frame our observations within six dimensions of well-known design concerns: correctness, fault tolerance/prevention, scalability, time sensitivity, consistency, and overhead of distribution. Furthermore, we place our experimental work in a broader historical context, showing that these challenges are intrinsic to DVEs and suggesting lines of future research. Copyright © 2016 John Wiley & Sons, Ltd.
Incentivized Blockchain-based Social Media Platforms: A Case Study of Steemit

Advances in Blockchain and distributed ledger technologies are driving the rise of incentivized social media platforms over Blockchains, where no single entity can take control of the information and users can receive cryptocurrency as rewards for creating or curating high-quality contents. This paper presents an empirical analysis of Steemit, a key representative of the emerging incentivized social media platforms over Blockchains, to understand and evaluate the actual level of decentralization and the practical effects of cryptocurrency-driven reward system in these modern social media platforms. Similar to Bitcoin, Steemit is operated by a decentralized community, where 21 members are periodically elected to cooperatively operate the platform through the Delegated Proof-of-Stake (DPoS) consensus protocol. Our study performed on 539 million operations performed by 1.12 million Steemit users during the period 2016/03 to 2018/08 reveals that the actual level of decentralization in Steemit is far lower than the ideal level, indicating that the DPoS consensus protocol may not be a desirable approach for establishing a highly decentralized social media platform. In Steemit, users create contents as posts which get curated based on votes from other users. The platform periodically issues cryptocurrency as rewards to creators and curators of popular posts. Although such a reward system is originally driven by the desire to incentivize users to contribute to high-quality contents, our analysis of the underlying cryptocurrency transfer network on the blockchain reveals that more than 16% transfers of cryptocurrency in Steemit are sent to curators suspected to be bots and also finds the existence of an underlying supply network for the bots, both suggesting a significant misuse of the current reward system in Steemit. Our study is designed to provide insights on the current state of this emerging blockchain-based social media platform including the effectiveness of its design and the operation of the consensus protocols and the reward system.
Casting Light on Invisible Cities: Computationally Engaging with Literary Criticism.

Literary critics often attempt to uncover meaning in a single work of literature through careful reading and analysis. Applying natural language processing methods to aid in such literary analyses remains a challenge in digital humanities. While most previous work focuses on “distant reading” by algorithmically discovering high-level patterns from large collections of literary works, here we sharpen the focus of our methods to a single literary theory about Italo Calvino’s postmodern novel Invisible Cities, which consists of 55 short descriptions of imaginary cities. Calvino has provided a classification of these cities into eleven thematic groups, but literary scholars disagree as to how trustworthy his categorization is. Due to the unique structure of this novel, we can computationally weigh in on this debate: we leverage pretrained contextualized representations to embed each city’s description and use unsupervised methods to cluster these embeddings. Additionally, we compare results of our computational approach to similarity judgments generated by human readers. Our work is a first step towards incorporating natural language processing into literary criticism.
ON TENSORING WITH THE STEINBERG REPRESENTATION

Let G be a simple, simply connected algebraic group over an algebraically closed field of prime characteristic p > 0. Recent work of Kildetoft and Nakano and of Sobaje has shown close connections between two long-standing conjectures of Donkin: one on tilting modules and the lifting of projective modules for Frobenius kernels of G and another on the existence of certain filtrations of G-modules. A key question related to these conjectures is whether the tensor product of the rth Steinberg module with a simple module with prth restricted highest weight admits a good filtration. In this paper we verify this statement (i) when p ≥ 2h − 4 (h is the Coxeter number), (ii) for all rank two groups, (iii) for p ≥ 3 when the simple module corresponds to a fundamental weight and (iv) for a number of cases when the rank is less than or equal to five.
Contour location via entropy reduction leveraging multiple information sources

We introduce an algorithm to locate contours of functions that are expensive to evaluate. The problem of locating contours arises in many applications, including classification, constrained optimization, and performance analysis of mechanical and dynamical systems (reliability, probability of failure, stability, etc.). Our algorithm locates contours using information from multiple sources, which are available in the form of relatively inexpensive, biased, and possibly noisy approximations to the original function. Considering multiple information sources can lead to significant cost savings. We also introduce the concept of contour entropy, a formal measure of uncertainty about the location of the zero contour of a function approximated by a statistical surrogate model. Our algorithm locates contours efficiently by maximizing the reduction of contour entropy per unit cost.
Evolution of active categorical image classification via saccadic eye movement

Pattern recognition and classification is a central concern for modern information processing systems. In particular, one key challenge to image and video classification has been that the computational cost of image processing scales linearly with the number of pixels in the image or video. Here we present an intelligent machine (the “active categorical classifier,” or ACC) that is inspired by the saccadic movements of the eye, and is capable of classifying images by selectively scanning only a portion of the image. We harness evolutionary computation to optimize the ACC on the MNIST hand-written digit classification task, and provide a proof-of-concept that the ACC works on noisy multi-class data. We further analyze the ACC and demonstrate its ability to classify images after viewing only a fraction of the pixels, and provide insight on future research paths to further improve upon the ACC presented here.
Mass distribution and skewness for passive scalar transport in pipes with polygonal and smooth cross sections

We extend our previous results characterizing the loading properties of a diffusing passive scalar advected by a laminar shear flow in ducts and channels to more general cross-sectional shapes, including regular polygons and smoothed corner ducts originating from deformations of ellipses. For the case of the triangle, short time skewness is calculated exactly to be positive, while long-time asymptotics shows it to be negative. Monte-Carlo simulations confirm these predictions, and document the time scale for sign change. Interestingly, the equilateral triangle is the only regular polygon with this property, all other polygons possess positive skewness at all times, although this cannot cannot be proved on finite times due to the lack of closed form flow solutions for such geometries. Alternatively, closed form flow solutions can be constructed for smooth deformations of ellipses, and illustrate how the possibility of multiple sign switching in time is unrelated to domain corners. Exact conditions relating the median and the skewness to the mean are developed which guarantee when the sign for the skewness implies front (back) loading properties of the evolving tracer distribution along the pipe. Short and long time asymptotics confirm this condition, and Monte-Carlo simulations verify this at all times.
An algebraic study of extension algebras

We present simple conditions which guarantee a geometric extension algebra to behave like a variant of quasi-hereditary algebras. In particular, standard modules of affine Hecke algebras of type $\sf{BC}$, and the quiver Schur algebras are shown to satisfy the Brauer-Humphreys type reciprocity and the semi-orthogonality property. In addition, we present a new criterion of purity of weights in the geometric side. This yields a proof of Shoji's conjecture on limit symbols of type $\sf{B}$ [T. Shoji, {\it Adv. Stud. Pure Math.} 40 (2004)], and the purity of the exotic Springer fibers [S. Kato, {\it Duke Math. J.} 148 (2009)]. Using this, we describe the leading terms of the $C^{\infty}$-realization of a solution of the Lieb-McGuire system in the appendix. In [S. Kato, {\it Duke Math. J.} 163 (2014)], we apply the results of this paper to the KLR algebras of type $\sf{ADE}$ to establish Kashwara's problem and Lusztig's conjecture.
Higher rank stable pairs and virtual localization

We introduce a higher rank analog of the Pandharipande-Thomas theory of stable pairs on a Calabi-Yau threefold $X$. More precisely, we develop a moduli theory for frozen triples given by the data $O^r(-n)\rightarrow F$ where $F$ is a sheaf of pure dimension 1. The moduli space of such objects does not naturally determine an enumerative theory: that is, it does not naturally possess a perfect symmetric obstruction theory. Instead, we build a zero-dimensional virtual fundamental class by hand, by truncating a deformation-obstruction theory coming from the moduli of objects in the derived category of $X$. This yields the first deformation-theoretic construction of a higher-rank enumerative theory for Calabi-Yau threefolds. We calculate this enumerative theory for local $\mathbb{P}^1$ using the Graber-Pandharipande virtual localization technique.
Non-stable K-theory and extremally rich C⁎-algebras

Abstract We consider the properties of weak cancellation, K 1 -surjectivity, good index theory, and K 1 -injectivity, for the class of extremally rich C ⁎ -algebras, and for the smaller class of isometrically rich C ⁎ -algebras. We establish all four properties for isometrically rich C ⁎ -algebras and for extremally rich C ⁎ -algebras that are either purely infinite or of real rank zero, K 1 -injectivity in the real rank zero case following from a prior result of H. Lin. We also show that weak cancellation implies the other properties for extremally rich C ⁎ -algebras and that the class of extremally rich C ⁎ -algebras with weak cancellation is closed under extensions. Moreover, we consider analogous properties which replace the group K 1 ( A ) with the extremal K-set K e ( A ) as well as two versions of K 0 -surjectivity.
Getzler symbol calculus and deformation quantization

Abstract In this paper we give a construction of Fedosov quantization incorporating the odd variables and an analogous formula to Getzler’s pseudodifferential calculus composition formula is obtained. A Fedosov type connection is constructed on the bundle of Weyl tensor Clifford algebras over the cotangent bundle of a Riemannian manifold. The quantum algebra associated with this connection is used to define a deformation of the exterior algebra of Riemannian manifolds.
Autoencoder-augmented neuroevolution for visual doom playing

Neuroevolution has proven effective at many re-inforcement learning tasks, including tasks with incomplete information and delayed rewards, but does not seem to scale well to high-dimensional controller representations, which are needed for tasks where the input is raw pixel data. We propose a novel method where we train an autoencoder to create a comparatively low-dimensional representation of the environment observation, and then use CMA-ES to train neural network controllers acting on this input data. As the behavior of the agent changes the nature of the input data, the autoencoder training progresses throughout evolution. We test this method in the VizDoom environment built on the classic FPS Doom, where it performs well on a health-pack gathering task.
ON A TWISTED VERSION OF LINNIK AND SELBERG'S CONJECTURE ON SUMS OF KLOOSTERMAN SUMS

We generalise the work of Sarnak-Tsimerman to twisted sums of Kloosterman sums and thus give evidence towards the twisted Linnik-Selberg Conjecture.
A Trajectory from a Vertex to Itself on the Dodecahedron

We prove that there exists a geodesic trajectory on the dodecahedron from a vertex to itself that does not pass through any other vertex.
IQ and Economic Volatility

Do countries with higher levels of national IQ have less volatile growth of real economic output compared with lower-IQ countries? This issue has not been addressed. Using a large sample of countries, a simple bivariate correlation indicates that IQ and economic volatility are negatively related. Estimating a regression model of volatility that includes IQ with a number of control variables, the estimated coefficient on IQ is consistently negative and significant. On average, a one-standard deviation increase in IQ is associated with about a 30 percent reduction in economic volatility. The evidence indicates that higher IQ countries have more stable economies.
UAV Pose Estimation using Cross-view Geolocalization with Satellite Imagery

We propose an image-based cross-view geolocalization method that estimates the global pose of a UAV with the aid of georeferenced satellite imagery. Our method consists of two Siamese neural networks that extract relevant features despite large differences in viewpoints. The input to our method is an aerial UAV image and nearby satellite images, and the output is the weighted global pose estimate of the UAV camera. We also present a framework to integrate our crossview geolocalization output with visual odometry through a Kalman filter. We build a dataset of simulated UAV images and satellite imagery to train and test our networks. We show that our method performs better than previous camera pose estimation methods, and we demonstrate our networks ability to generalize well to test datasets with unseen images. Finally, we show that integrating our method with visual odometry significantly reduces trajectory estimation errors.
Well-posedness for the Navier–Stokes equations in critical mixed-norm Lebesgue spaces

We study the Cauchy problem in n-dimensional space for the system of Navier–Stokes equations in critical mixed-norm Lebesgue spaces. Local well-posedness and global well-posedness of solutions are established in the class of critical mixed-norm Lebesgue spaces. Being in the mixed-norm Lebesgue spaces, both of the initial data and the solutions could be singular at certain points or decaying to zero at infinity with different rates in different spatial variable directions. Some of these singular rates could be very strong, and some of the decaying rates could be significantly slow. Besides other interests, the results of the paper demonstrate the persistence of the anisotropic behavior of the initial data under the evolution. To achieve the goals, fundamental analysis theory such as Young’s inequality, time decaying of solutions for heat equations, the boundedness of the Helmholtz–Leray projection, and the boundedness of the Riesz transform are developed in mixed-norm Lebesgue spaces. These analysis results are topics of independent interests, and they are potentially useful in other problems.
Spine-Inspired Continuum Soft Exoskeleton for Stoop Lifting Assistance

Back injuries are the most prevalent work-related musculoskeletal disorders and represent a major cause of disability. Although innovations in wearable robots aim to alleviate this hazard, the majority of existing exoskeletons are obtrusive because the rigid linkage design limits natural movement, thus causing ergonomic risk. Moreover, these existing systems are typically only suitable for one type of movement assistance, not ubiquitous for a wide variety of activities. To fill in this gap, this letter presents a new wearable robot design approach continuum soft exoskeleton. This spine-inspired wearable robot is unobtrusive and assists both squat and stoops while not impeding walking motion. To tackle the challenge of the unique anatomy of spine that is inappropriate to be simplified as a single degree of freedom joint, our robot is conformal to human anatomy and it can reduce multiple types of forces along the human spine such as the spinae muscle force, shear, and compression force of the lumbar vertebrae. We derived kinematics and kinetics models of this mechanism and established an analytical biomechanics model of human-robot interaction. Quantitative analysis of disc compression force, disc shear force and muscle force was performed in simulation. We further developed a virtual impedance control strategy to deliver force control and compensate hysteresis of Bowden cable transmission. The feasibility of the prototype was experimentally tested on three healthy subjects. The root mean square error of force tracking is 6.63 N (3.3% of the 200 N peak force) and it demonstrated that it can actively control the stiffness to the desired value. This continuum soft exoskeleton represents a feasible solution with the potential to reduce back pain for multiple activities and multiple forces along the human spine.
Exploring Faculty and Student Reflections on Collaborative Teaching in the Honors Seminar Classroom.

University Honors programming in the United States is interdisciplinary and collaborative; from First Year Honors Seminars to capstone research projects for upperclassmen, Honors students embrace multidisciplinary learning and research. This approach, however, does not always translate into the Honors classroom in regards to an incorporation of diverse perspectives of multiple faculty members in a given course. This article utilizes a mixed-methods approach to explore the impact and results of a collaboratively taught Honors Seminar. “Exploring Faculty and Student Reflections on Collaborative Teaching in the Honors Seminar Classroom” departs from the authors’ model of a co-taught Honors Seminar and then moves to an exploration of the student responses, comparing both a pre- and post-course survey, that considers student perceptions of multi-instructor formats. The essay ends with a brief conclusion that addresses some possible challenges to team-taught courses, from scheduling to institutional issues, in the context of Honors programming in an effort to encourage continued discussion about collaborative teaching of Honors Seminars.
Time-richness and phosphatic microsteinkern accumulation in the Cincinnatian (Katian) Ordovician, USA: An example of polycyclic phosphogenic condensation

Abstract Millimeter-scale phosphatic steinkern preservation is a feature of the taxonomically enigmatic Early Cambrian “small shelly faunas”, but this style of preservation is not unique to the Cambrian; it is ubiquitous, if infrequently reported, from the Phanerozoic record. The polycyclic phosphogenic condensation (PPC) model envisions both the genesis and concentration of phosphatic microsteinkerns as natural outcomes of shell bed genesis through episodic sediment starvation. This model predicts that more reworked and condensed shell bed limestones will contain more phosphatic microsteinkerns, but that even the least reworked limestones may contain some phosphatic particles. We test this model through examination of vertical thin sections densely collected through a 10-meter interval from the classic Cincinnatian (upper Katian, middle Maysvillian North American Stage) upper Fairview Formation, Miamitown Shale, and lower Grant Lake formations at four localities near Cincinnati, Ohio. For each of approximately 50 distinguishable limestone depositional units in each locality, a 2 × 2 cm square was selected for study. Each square was assigned a textural classification (mud content of intergranular space) and a breakage rank (pristine to comminuted). Phosphatic particle distribution was quantified both by visual estimation and by particle counting, with counts ranging from none detected to over 1000 per 4 cm2. Our analyses show a strong positive relationship between phosphate content and both textural maturity and fragmentation. This positive relationship is consistent with the PPC model and confirms that textural maturity can reflect the degree of condensation as well as depth-related environmental energy. This finding suggests that shell bed processes of repeated deposition and reworking make a significant contribution to the generation and accumulation of phosphatic particles. If local-scale sedimentary processes and conditions can control this accumulation, temporal changes in phosphatic sediment deposition rates may be linked to earth changes more complexly than through changing ocean chemistry on a global scale.
Harmonia: Wideband Spreading for Accurate Indoor RF Localization

We introduce Harmonia, a new RF-based localization scheme that provides the simplicity, cost, and power advantages of traditional narrowband radios with the decimeter-scale accuracy of ultra wideband localization techniques. Harmonia is an asymmetric tag and anchor system, requiring minimal modifications to existing low-power wireless devices to support highfidelity localization with comparatively modest infrastructure costs. A prototype Harmonia design offers location estimates with an average-case error of 53.4 cm in complex, heavymultipath, indoor environments and captures location estimates at 56 Hz while requiring only 1.7 mA additional power draw for each tag and complying with all US UWB regulations. We believe this architecture's combination of accuracy, update rate, power draw, and system complexity will lead to a new point in the design space.
Adoption of Big Data in Higher Education for Better Institutional Effectiveness

Big data provides many opportunities to broaden existing insights in different fields and higher education also can use the big data for the better institutional performance. This study examines the conceptual models to adopt big data for institutional effectiveness on higher education system. Based on the three relevant entities including institution, faculty, and student, this research provides the relationship between them and construct the negotiable environment as well as the characteristics of relevant indicators. This research suggests the conceptual model that provide how relevant campus members pursuit the better institutional performance and what the individual targeting is. Conceptual model indicates the three main stakeholders exchange specific values under certain type of negotiable interactions and the trade makes better outcomes for institutional effectiveness in the long run. This relationships between the stakeholders generate potential connections with other players outside and the relation creates another chance to broaden range of institutional growth in the higher education system.
Density Deconvolution with Small Berkson Errors

The present paper studies density deconvolution in the presence of small Berkson errors, in particular, when the variances of the errors tend to zero as the sample size grows. It is known that when the Berkson errors are present, in some cases, the unknown density estimator can be obtained by simple averaging without using kernels. However, this may not be the case when Berkson errors are asymptotically small. By treating the former case as a kernel estimator with the zero bandwidth, we obtain the optimal expressions for the bandwidth. We show that the density of Berkson errors acts as a regularizer, so that the kernel estimator is unnecessary when the variance of Berkson errors lies above some threshold that depends on the shapes of the densities in the model and the number of observations.
Is 3D Printing a Threat to Global Trade? The Trade Effects You Didn't Hear About

In the mid-2000s, the production of hearing aids shifted almost entirely to 3D printing. Using difference-in-differences and synthetic control methods, this paper examines the effects of this shift on trade flows. The analysis finds that trade increased roughly 60 percent following the introduction of 3D printing. Revealed comparative advantage was reinforced, with exports growing most rapidly for middle- and high-income countries. The analysis also finds that developing countries increased their imports of hearing aids as a result of the innovation, benefitting consumers. As a robustness check, the paper examines 35 products that are partially 3D printed and finds positive and significant effects on trade. The results counter widespread views that 3D printing will shorten supply chains and reduce trade.
Interleukin-36γ–producing macrophages drive IL-17–mediated fibrosis

Biomaterials induce an immune response and mobilization of macrophages, yet identification and phenotypic characterization of functional macrophage subsets in vivo remain limited. We performed single-cell RNA sequencing analysis on macrophages sorted from either a biologic matrix [urinary bladder matrix (UBM)] or synthetic biomaterial [polycaprolactone (PCL)]. Implantation of UBM promotes tissue repair through generation of a tissue environment characterized by a T helper 2 (TH2)/interleukin (IL)-4 immune profile, whereas PCL induces a standard foreign body response characterized by TH17/IL-17 and fibrosis. Unbiased clustering and pseudotime analysis revealed distinct macrophage subsets responsible for antigen presentation, chemoattraction, and phagocytosis, as well as a small population with expression profiles of both dendritic cells and skeletal muscle after UBM implantation. In the PCL tissue environment, we identified a CD9hi+IL-36γ+ macrophage subset that expressed TH17-associated molecules. These macrophages were virtually absent in mice lacking the IL-17 receptor, suggesting that they might be involved in IL-17-dependent immune and autoimmune responses. Identification and comparison of the unique phenotypical and functional macrophage subsets in mouse and human tissue samples suggest broad relevance of the new classification. These distinct macrophage subsets demonstrate previously unrecognized myeloid phenotypes involved in different tissue responses and provide targets for potential therapeutic modulation in tissue repair and pathology.
Spectral decimation for families of self-similar symmetric Laplacians on the Sierpiński gasket

We construct a one-parameter family of Laplacians on the Sierpinski Gasket that are symmetric and self-similar for the 9-map iterated function system obtained by iterating the standard 3-map iterated function system. Our main result is the fact that all these Laplacians satisfy a version of spectral decimation that builds a precise catalog of eigenvalues and eigenfunctions for any choice of the parameter. We give a number of applications of this spectral decimation. We also prove analogous results for fractal Laplacians on the unit Interval, and this yields an analogue of the classical Sturm-Liouville theory for the eigenfunctions of these one-dimensional Laplacians.
Fivebranes and 4-Manifolds

We describe rules for building 2d theories labeled by 4-manifolds. Using the proposed dictionary between building blocks of 4-manifolds and 2d \(\mathcal{N} = (0,2)\) theories, we obtain a number of results, which include new 3d \(\mathcal{N} = 2\) theories T[M3] associated with rational homology spheres and new results for Vafa–Witten partition functions on 4-manifolds. In particular, we point out that the gluing measure for the latter is precisely the superconformal index of 2d (0, 2) vector multiplet and relate the basic building blocks with coset branching functions. We also offer a new look at the fusion of defect lines/walls, and a physical interpretation of the 4d and 3d Kirby calculus as dualities of 2d \(\mathcal{N} = (0,2)\) theories and 3d \(\mathcal{N} = 2\) theories, respectively.
The importance of better models in stochastic optimization.

Standard stochastic optimization methods are brittle, sensitive to stepsize choice and other algorithmic parameters, and they exhibit instability outside of well-behaved families of objectives. To address these challenges, we investigate models for stochastic optimization and learning problems that exhibit better robustness to problem families and algorithmic parameters. With appropriately accurate models—which we call the aprox family—stochastic methods can be made stable, provably convergent, and asymptotically optimal; even modeling that the objective is nonnegative is sufficient for this stability. We extend these results beyond convexity to weakly convex objectives, which include compositions of convex losses with smooth functions common in modern machine learning. We highlight the importance of robustness and accurate modeling with experimental evaluation of convergence time and algorithm sensitivity.
Advertising and demand for addictive goods: The effects of e-cigarette advertising

This paper studies the effect of e-cigarette advertising on demand for tobacco cigarettes and finds that a proposed ban on e-cigarette advertising could have unintended consequences.
Balanced Datasets Are Not Enough: Estimating and Mitigating Gender Bias in Deep Image Representations

In this work, we present a framework to measure and mitigate intrinsic biases with respect to protected variables –such as gender– in visual recognition tasks. We show that trained models significantly amplify the association of target labels with gender beyond what one would expect from biased datasets. Surprisingly, we show that even when datasets are balanced such that each label co-occurs equally with each gender, learned models amplify the association between labels and gender, as much as if data had not been balanced! To mitigate this, we adopt an adversarial approach to remove unwanted features corresponding to protected variables from intermediate representations in a deep neural network – and provide a detailed analysis of its effectiveness. Experiments on two datasets: the COCO dataset (objects), and the imSitu dataset (actions), show reductions in gender bias amplification while maintaining most of the accuracy of the original models.
A randomized controlled trial of a trauma-informed school prevention program for urban youth: Rationale, design, and methods

Abstract Introduction Youth in disadvantaged urban areas are frequently exposed to chronic stress and trauma, including housing instability, neighborhood violence, and other poverty-related adversities. These exposures increase risk for emotional, behavioral, and academic problems and ultimately, school dropout. Schools are a promising setting in which to address these issues; however, there are few universal, trauma-informed school-based interventions for urban youth. Methods/Design Project POWER (Promoting Options for Wellness and Emotion Regulation) is a randomized controlled trial testing the impact of RAP Club, a trauma-informed intervention for eighth graders that includes mindfulness as a core component. Students in 32 urban public schools (n = 800) are randomly assigned to either RAP Club or a health education active control group. We assess student emotional, behavioral, and academic outcomes using self-report surveys and teacher ratings at baseline, post-intervention, and 4-month follow up. Focus groups and interviews with students, teachers, and principals address program feasibility, acceptability, and fidelity, as well as perceived program impacts. Students complete an additional self-report survey in ninth grade. Schools provide students' academic and disciplinary data for their seventh, eighth, and ninth grade years. In addition, data on program costs are collected to conduct an economic analysis of the intervention and active control programs. Discussion Notable study features include program co-leadership by young adults from the community and building capacity of school personnel for continued program delivery. In addition to testing program impact, we will identify factors related to successful program implementation to inform future program use and dissemination.