cssi/SciSciGPT-SciSciNet huggingface dataset:
Some supercongruences occurring in truncated hypergeometric series

Abstract For the purposes of this paper supercongruences are congruences between terminating hypergeometric series and quotients of p -adic Gamma functions that are stronger than those one can expect to prove using commutative formal group laws. We prove a number of such supercongruences by using classical hypergeometric transformation formulae. These formulae, most of which are decades or centuries old, allow us to write the terminating series as the ratio of products of Γ-values. At this point sums have become quotients . Writing these Γ-quotients as Γ p -quotients, we are in a situation that is well-suited for proving p -adic congruences. These Γ p -functions can be p -adically approximated by their Taylor series expansions. Sometimes there is cancellation of the lower order terms, leading to stronger congruences. Using this technique we prove, among other things, a conjecture of Kibelbek and a strengthened version of a conjecture of van Hamme.
Streaming for Aibohphobes: Longest Palindrome with Mismatches

A palindrome is a string that reads the same as its reverse, such as "aibohphobia" (fear of palindromes). Given a metric and an integer d>0, a d-near-palindrome} is a string of Hamming distance at most d from its reverse. We study the natural problem of identifying the longest d-near-palindrome in data streams. The problem is relevant to the analysis of DNA databases, and to the task of repairing recursive structures in documents such as XML and JSON. We present the first streaming algorithm for the longest d-near-palindrome problem that returns a d-near-palindrome whose length is within a multiplicative (1+\eps)-factor of the longest d-near-palindrome. Our algorithm also returns the set of mismatched indices in the d-near-palindrome, and uses O{\frac{d\log^7 n}{\eps\log(1+\eps)}} bits of space, and O{\frac{d\log^6 n}{\eps\log(1+\eps)}} update time per arrival symbol. We show that for d=o(\sqrt{n}), any randomized algorithm with multiplicative approximation (1+\eps) that succeeds with probability at least 1-1/n requires \Omega(d\log n) space. We further obtain a streaming algorithm that returns a d-near-palindrome whose length is within an additive E-error of the longest d-near-palindrome. The algorithm uses O{\frac{dn\log^6 n}{E}} bits of space and O{\frac{dn\log^5 n}{E}} update time. As before, we show that any randomized streaming algorithm that solves the longest d-near-palindrome problem for additive error E with probability at least 1-\frac{1}{n}, uses \Omega\left(\frac{dn}{E}\right) space. Finally, we give an exact two-pass algorithm that solves the longest d-near-palindrome problem using O{d^2\sqrt{n}\log^6 n} bits of space.
Model Predictive Climate Control of Connected and Automated Vehicles for Improved Energy Efficiency

This paper considers an application of model predictive control to automotive air conditioning (A/C) system in future connected and automated vehicles (CAVs) with battery electric or hybrid electric powertrains. A control-oriented prediction model for A/C system is proposed, identified, and validated against a higher fidelity simulation model (CoolSim). Based on the developed prediction model, a nonlinear model predictive control (NMPC) problem is formulated and solved online to minimize the energy consumption of the A/C system. Simulation results illustrate the desirable characteristics of the proposed NMPC solution such as being able to enforce physical constraints of the A/C system and maintain cabin temperature within a specified range. Moreover, it is shown that by utilizing the vehicle speed preview and through coordinated adjustment of the cabin temperature constraints, energy efficiency improvements of up to 9% can be achieved.
Adversarial Deep Averaging Networks for Cross-Lingual Sentiment Classification

In recent years great success has been achieved in sentiment classification for English, thanks in part to the availability of copious annotated resources. Unfortunately, most languages do not enjo...
An inverse free electron laser acceleration-driven Compton scattering X-ray source.

The generation of X-rays and γ-rays based on synchrotron radiation from free electrons, emitted in magnet arrays such as undulators, forms the basis of much of modern X-ray science. This approach has the drawback of requiring very high energy, up to the multi-GeV-scale, electron beams, to obtain the required photon energy. Due to the limit in accelerating gradients in conventional particle accelerators, reaching high energy typically demands use of instruments exceeding 100's of meters in length. Compact, less costly, monochromatic X-ray sources based on very high field acceleration and very short period undulators, however, may enable diverse, paradigm-changing X-ray applications ranging from novel X-ray therapy techniques to active interrogation of sensitive materials, by making them accessible in energy reach, cost and size. Such compactness and enhanced energy reach may be obtained by an all-optical approach, which employs a laser-driven high gradient accelerator based on inverse free electron laser (IFEL), followed by a collision point for inverse Compton scattering (ICS), a scheme where a laser is used to provide undulator fields. We present an experimental proof-of-principle of this approach, where a TW-class CO2 laser pulse is split in two, with half used to accelerate a high quality electron beam up to 84 MeV through the IFEL interaction, and the other half acts as an electromagnetic undulator to generate up to 13 keV X-rays via ICS. These results demonstrate the feasibility of this scheme, which can be joined with other techniques such as laser recirculation to yield very compact photon sources, with both high peak and average brilliance, and with energies extending from the keV to MeV scale. Further, use of the IFEL acceleration with the ICS interaction produces a train of high intensity X-ray pulses, thus enabling a unique tool synchronized with a laser pulse for ultra-fast strobe, pump-probe experimental scenarios.
Windows into Geometric Events: Data Structures for Time-Windowed Querying of Temporal Point Sets.

We study geometric data structures for sets of point-based temporal events, answering time-windowed queries, i.e., given a contiguous time interval we answer common geometric queries about the point events with time stamps in this interval. The geometric queries we consider include queries based on the skyline, convex hull, and proximity relations of the point set. We provide space efficient data structures which answer queries in polylogarithmic time.
HIERARCHICAL CONFIGURATIONS FOR CROSS-CORRELATION INTERFEROMETERS WITH MANY ELEMENTS

Array configurations built on a hierarchy of simple elements have excellent properties for cross-correlation imaging interferometers including a smooth distribution of measured Fourier components, high angular resolution, low side lobes, and compact array size. Compared to arrays with a Gaussian distribution of antenna separations, hierarchical arrays (H-arrays) produce beams with higher angular resolution and a tighter concentration of the total power (encircled energy) within a smaller area around the main beam. An attractive feature of H-arrays is their simplicity. The relationships between the Fourier coverage and the array configuration are easy enough to understand that they can be adjusted to achieve different design goals without the need for numerical optimization. H-arrays will be useful for future multi-element interferometers.
Beyond graph energy: Norms of graphs and matrices

Abstract In 1978 Gutman introduced the energy of a graph as the sum of the absolute values of graph eigenvalues, and ever since then graph energy has been intensively studied. Since graph energy is the trace norm of the adjacency matrix, matrix norms provide a natural background for its study. Thus, this paper surveys research on matrix norms that aims to expand and advance the study of graph energy. The focus is exclusively on the Ky Fan and the Schatten norms, both generalizing and enriching the trace norm. As it turns out, the study of extremal properties of these norms leads to numerous analytic problems with deep roots in combinatorics. The survey brings to the fore the exceptional role of Hadamard matrices, conference matrices, and conference graphs in matrix norms. In addition, a vast new matrix class is studied, a relaxation of symmetric Hadamard matrices. The survey presents solutions to just a fraction of a larger body of similar problems bonding analysis to combinatorics. Thus, open problems and questions are raised to outline topics for further investigation.
Mean-variance type controls involving a hidden Markov chain: models and numerical approximation

Motivated by applications arising in networked systems, this work examines controlled regime-switching systems that stem from a mean-variance formulation. A main point is that the switching process is a hidden Markov chain. An additional piece of information, namely, a noisy observation of switching process corrupted by white noise is available. We focus on minimizing the variance subject to a fixed terminal expectation. Using the Wonham filter, we convert the partially observed system to a completely observable one first. Since closed-form solutions are virtually impossible be obtained, a Markov chain approximation method is used to devise a computational scheme. Convergence of the algorithm is obtained. A numerical example is provided to demonstrate the results.
Evolution of a semidiscrete system modeling the scattering of acoustic waves by a piezoelectric solid

We consider a model problem of the scattering of linear acoustic waves in free homogeneous space by an elastic solid. The stress tensor in the solid combines the effect of a linear dependence of strains with the influence of an existing electric field. The system is closed using Gauss’s law for the associated electric displacement. Well-posedness of the system is studied by its reformulation as a first order in space and time differential system with help of an elliptic lifting operator. We then proceed to studying a semidiscrete formulation, corresponding to an abstract Finite Element discretization in the electric and elastic fields, combined with an abstract Boundary Element approximation of a retarded potential representation of the acoustic field. The results obtained with this approach improve estimates obtained with Laplace domain techniques. While numerical experiments illustrating convergence of a fully discrete version of this problem had already been published, we demonstrate some properties of the full model with some simulations for the two dimensional case.
Minimal Resolutions of Dominant and Semidominant Ideals

Abstract We introduce new classes of monomial ideals: dominant, p-semidominant, and GNP ideals. The families of dominant and 1-semidominant ideals extend those of complete and almost complete intersections, while the family of GNP ideals extends that of generic ideals. We show that dominant ideals give a complete characterization of when the Taylor resolution is minimal, 1-semidominant ideals are Scarf, and the minimal resolutions of 2-semidominant ideals can be obtained from their Taylor resolutions by eliminating faces and facets of equal multidegree, in arbitrary order. We also generalize a theorem of Bayer, Peeva and Sturmfels by proving that GNP ideals are Scarf.
CASO: Cost-Aware Secure Outsourcing of General Computational Problems

Computation outsourcing is an integral part of cloud computing. It enables end-users to outsource their computational tasks to the cloud and utilize the shared cloud resources in a pay-per-use manner. However, once the tasks are outsourced, the end-users will lose control of their data, which may result in severe security issues especially when the data is sensitive. To address this problem, secure outsourcing mechanisms have been proposed to ensure security of the end-users’ outsourced data. In this paper, we investigate outsourcing of general computational problems which constitute the mathematical basics for problems emerged from various fields such as engineering and finance. To be specific, we propose affine mapping based schemes for the problem transformation and outsourcing so that the cloud is unable to learn any key information from the transformed problem. Meanwhile, the overhead for the transformation is limited to an acceptable level compared to the computational savings introduced by the outsourcing itself. Furthermore, we develop cost-aware schemes to balance the trade-offs between end-users’ various security demands and computational overhead. We also propose a verification scheme to ensure that the end-users will always receive a valid solution from the cloud. Our extensive complexity and security analysis show that our proposed Cost-Aware Secure Outsourcing (CASO) scheme is both practical and effective.
Motivic connective K -theories and the cohomology of A(1)

We make some computations in stable motivic homotopy theory over Spec ℂ, completed at 2. Using homotopy fixed points and the algebraic K-theory spectrum, we construct over ℂ a motivic analogue of the real K-theory spectrum KO. We also establish a theory of motivic connective covers over ℂ to obtain a motivic version of ko. We establish an Adams spectral sequence for computing motivic ko-homology. The E2-term of this spectral sequence involves Ext groups over the subalgebra A(1) of the motivic Steenrod algebra. We make several explicit computations of these E2-terms in interesting special cases.
Piecewise Flat Curvature and Ricci Flow in Three Dimensions

Discrete forms of the scalar, sectional and Ricci curvatures are constructed on simplicial piecewise flat triangulations of smooth manifolds, depending directly on the simplicial structure and a choice of dual tessellation. This is done by integrating over volumes which include appropriate samplings of hinges for each type of curvature, with the integrals based on the parallel transport of vectors around hinges. Computations for triangulations of a diverse set of manifolds show these piecewise flat curvatures to converge to their smooth values. The Ricci curvature also gives a piecewise flat Ricci flow as a fractional rate of change of edge-lengths, again converging to the smooth Ricci flow for the manifolds tested.
Time window temporal logic

Abstract This paper introduces time window temporal logic (TWTL), a rich expressive language for describing various time bounded specifications. In particular, the syntax and semantics of TWTL enable the compact representation of serial tasks, which are prevalent in various applications including robotics, sensor systems, and manufacturing systems. This paper also discusses the relaxation of TWTL formulae with respect to the deadlines of the tasks. Efficient automata-based frameworks are presented to solve synthesis, verification and learning problems. The key ingredient to the presented solution is an algorithm to translate a TWTL formula to an annotated finite state automaton that encodes all possible temporal relaxations of the given formula. Some case studies are presented to illustrate the expressivity of the logic and the proposed algorithms.
Instance Embedding Transfer to Unsupervised Video Object Segmentation

We propose a method for unsupervised video object segmentation by transferring the knowledge encapsulated in image-based instance embedding networks. The instance embedding network produces an embedding vector for each pixel that enables identifying all pixels belonging to the same object. Though trained on static images, the instance embeddings are stable over consecutive video frames, which allows us to link objects together over time. Thus, we adapt the instance networks trained on static images to video object segmentation and incorporate the embeddings with objectness and optical flow features, without model retraining or online fine-tuning. The proposed method outperforms state-of-the-art unsupervised segmentation methods in the DAVIS dataset and the FBMS dataset.
PDE-constrained optimization in medical image analysis

PDE-constrained optimization problems find many applications in medical image analysis, for example, neuroimaging, cardiovascular imaging, and oncologic imaging. We review the related literature and give examples of the formulation, discretization, and numerical solution of PDE-constrained optimization problems for medical imaging. We discuss three examples. The first is image registration, the second is data assimilation for brain tumor patients, and the third is data assimilation in cardiovascular imaging. The image registration problem is a classical task in medical image analysis and seeks to find pointwise correspondences between two or more images. Data assimilation problems use a PDE-constrained formulation to link a biophysical model to patient-specific data obtained from medical images. The associated optimality systems turn out to be sets of nonlinear, multicomponent PDEs that are challenging to solve in an efficient way. The ultimate goal of our work is the design of inversion methods that integrate complementary data, and rigorously follow mathematical and physical principles, in an attempt to support clinical decision making. This requires reliable, high-fidelity algorithms with a short time-to-solution. This task is complicated by model and data uncertainties, and by the fact that PDE-constrained optimization problems are ill-posed in nature, and in general yield high-dimensional, severely ill-conditioned systems after discretization. These features make regularization, effective preconditioners, and iterative solvers that, in many cases, have to be implemented on distributed-memory architectures to be practical, a prerequisite. We showcase state-of-the-art techniques in scientific computing to tackle these challenges.
Online Convex Optimization with Stochastic Constraints

This paper considers online convex optimization (OCO) with stochastic constraints, which generalizes Zinkevich's OCO over a known simple fixed set by introducing multiple stochastic functional constraints that are i.i.d. generated at each round and are disclosed to the decision maker only after the decision is made. This formulation arises naturally when decisions are restricted by stochastic environments or deterministic environments with noisy observations. It also includes many important problems as special case, such as OCO with long term constraints, stochastic constrained convex optimization, and deterministic constrained convex optimization. To solve this problem, this paper proposes a new algorithm that achieves $O(\sqrt{T})$ expected regret and constraint violations and $O(\sqrt{T}\log(T))$ high probability regret and constraint violations. Experiments on a real-world data center scheduling problem further verify the performance of the new algorithm.
A simplified approach to recovery conditions for low rank matrices

Recovering sparse vectors and low-rank matrices from noisy linear measurements has been the focus of much recent research. Various reconstruction algorithms have been studied, including l 1 and nuclear norm minimization as well as l p minimization with p &#60; 1. These algorithms are known to succeed if certain conditions on the measurement map are satisfied. Proofs for the recovery of matrices have so far been much more involved than in the vector case. In this paper, we show how several classes of recovery conditions can be extended from vectors to matrices in a simple and transparent way, leading to the best known restricted isometry and nullspace conditions for matrix recovery. Our results rely on the ability to “vectorize” matrices through the use of a key singular value inequality.
Expansion properties for finite subdivision rules I

Among Thurston maps (orientation-preserving, postcritically finite branched coverings of the 2-sphere to itself), those that arise as subdivision maps of a finite subdivision rule form a special family. For such maps, we investigate relationships between various notions of expansion—combinatorial, dynamical, algebraic, and coarse-geometric.
Noncrossing ordinal classification

Ordinal data are often seen in real applications. Regular multicategory classification methods are not designed for this data type and a more proper treatment is needed. We consider a framework of ordinal classification which pools the results from binary classifiers together. An inherent difficulty of this framework is that the class prediction can be ambiguous due to boundary crossing. To fix this issue, we propose a noncrossing ordinal classification method which materializes the framework by imposing noncrossing constraints. An asymptotic study of the proposed method is conducted. We show by simulated and data examples that the proposed method can improve the classification performance for ordinal data without the ambiguity caused by boundary crossings.
Entity Identification as Multitasking

Standard approaches in entity identification hard-code boundary detection and type prediction into labels and perform Viterbi. This has two disadvantages: 1. the runtime complexity grows quadratically in the number of types, and 2. there is no natural segment-level representation. In this paper, we propose a neural architecture that addresses these disadvantages. We frame the problem as multitasking, separating boundary detection and type prediction but optimizing them jointly. Despite its simplicity, this architecture performs competitively with fully structured models such as BiLSTM-CRFs while scaling linearly in the number of types. Furthermore, by construction, the model induces type-disambiguating embeddings of predicted mentions.
Group actions and Helly's theorem

Abstract We describe a connection between the combinatorics of generators for certain groups and the combinatorics of Helly's 1913 theorem on convex sets. We use this connection to prove fixed point theorems for actions of these groups on nonpositively curved metric spaces. These results are encoded in a property that we introduce called “property FA r ”, which reduces to Serre's property FA when r = 1 . The method applies to S-arithmetic groups in higher Q-rank, to simplex reflection groups (including some nonarithmetic ones), and to higher rank Chevalley groups over polynomial and other rings (for example SL n ( Z [ x 1 , … , x d ] ) , n > 2 ).
Calculated Avoidance: Math Anxiety Predicts Math Avoidance in Effort-Based Decision-Making

Math anxiety-negative feelings toward math-is hypothesized to be associated with the avoidance of math-related activities such as taking math courses and pursuing STEM careers. However, there is little experimental evidence for the math anxiety-avoidance link. Such evidence is important for formulating how to break this relationship. We hypothesize that math avoidance emerges when one perceives the costs of effortful math engagement to outweigh its benefits and that this perception depends on individual differences in math anxiety. To test this hypothesis, we developed an effort-based decision-making task in which participants chose between solving easy, low-reward problems and hard, high-reward problems in both math and nonmath contexts. Higher levels of math anxiety were associated with a tendency to select easier, low-reward problems over harder, high-reward math (but not word) problems. Addressing this robust math anxiety-avoidance link has the potential to increase interest and success in STEM fields.
Cubic Regularization with Momentum for Nonconvex Optimization

Momentum is a popular technique to accelerate the convergence in practical training, and its impact on convergence guarantee has been well-studied for first-order algorithms. However, such a successful acceleration technique has not yet been proposed for second-order algorithms in nonconvex this http URL this paper, we apply the momentum scheme to cubic regularized (CR) Newton's method and explore the potential for acceleration. Our numerical experiments on various nonconvex optimization problems demonstrate that the momentum scheme can substantially facilitate the convergence of cubic regularization, and perform even better than the Nesterov's acceleration scheme for CR. Theoretically, we prove that CR under momentum achieves the best possible convergence rate to a second-order stationary point for nonconvex optimization. Moreover, we study the proposed algorithm for solving problems satisfying an error bound condition and establish a local quadratic convergence rate. Then, particularly for finite-sum problems, we show that the proposed algorithm can allow computational inexactness that reduces the overall sample complexity without degrading the convergence rate.
Towards Robust Product Packing with a Minimalistic End-Effector

Advances in sensor technologies, object detection algorithms, planning frameworks and hardware designs have motivated the deployment of robots in warehouse automation. A variety of such applications, like order fulfillment or packing tasks, require picking objects from unstructured piles and carefully arranging them in bins or containers. Desirable solutions need to be low-cost, easily deployable and controllable, making minimalistic hardware choices desirable. The challenge in designing an effective solution to this problem relates to appropriately integrating multiple components, so as to achieve a robust pipeline that minimizes failure conditions. The current work proposes a complete pipeline for solving such packing tasks, given access only to RGB-D data and a single robot arm with a vacuum-based end-effector, which is also used as a pushing finger. To achieve the desired level of robustness, three key manipulation primitives are identified, which take advantage of the environment and simple operations to successfully pack multiple cubic objects. The overall approach is demonstrated to be robust to execution and perception errors. The impact of each manipulation primitive is evaluated by considering different versions of the proposed pipeline, which incrementally introduce reasoning about object poses and corrective manipulation actions.
Mutant huntingtin fails to directly impair brain mitochondria

Although the mechanisms by which mutant huntingtin (mHtt) results in Huntington's disease (HD) remain unclear, mHtt-induced mitochondrial defects were implicated in HD pathogenesis. The effect of mHtt could be mediated by transcriptional alterations, by direct interaction with mitochondria, or by both. In the present study, we tested a hypothesis that mHtt directly damages mitochondria. To test this hypothesis, we applied brain cytosolic fraction from YAC128 mice, containing mHtt, to brain non-synaptic and synaptic mitochondria from wild-type mice and assessed mitochondrial respiration with a Clark-type oxygen electrode, membrane potential and Ca2+ uptake capacity with tetraphenylphosphonium (TPP+ )- and Ca2+ -sensitive electrodes, respectively, and, reactive oxygen species production with Amplex Red assay. The amount of mHtt bound to mitochondria following incubation with mHtt-containing cytosolic fraction was greater than the amount of mHtt bound to brain mitochondria isolated from YAC128 mice. Despite mHtt binding to wild-type mitochondria, no abnormalities in mitochondrial functions were detected. This is consistent with our previous results demonstrating the lack of defects in brain mitochondria isolated from R6/2 and YAC128 mice. This, however, could be because of partial loss of mitochondrially bound mHtt during the isolation procedure. Consequently, we increased the amount of mitochondrially bound mHtt by incubating brain non-synaptic and synaptic mitochondria isolated from YAC128 mice with mHtt-containing cytosolic fraction. Despite the enrichment of YAC128 brain mitochondria with mHtt, mitochondrial functions (respiration, membrane potential, reactive oxygen species production, Ca2+ uptake capacity) remained unchanged. Overall, our results suggest that mHtt does not directly impair mitochondrial functions, arguing against the involvement of this mechanism in HD pathogenesis. Open Science: This manuscript was awarded with the Open Materials Badge For more information see: https://cos.io/our-services/open-science-badges/.
UltraShiver: Lateral Force Feedback on a Bare Fingertip via Ultrasonic Oscillation and Electroadhesion

We propose a new lateral force feedback device, the UltraShiver, which employs a combination of in-plane ultrasonic oscillation (around 30 kHz) and out-of-plane electroadhesion. It can achieve a strong active lateral force (400 mN) on the bare fingertip while operating silently. The lateral force is a function of pressing force, lateral vibration velocity, and electroadhesive voltage, as well as the relative phase between the velocity and voltage. In this paper, we perform experiments to investigate characteristics of the UltraShiver and their influence on lateral force. A lumped-parameter model is developed to understand the physical underpinnings of these influences. The model with frequency-weighted electroadhesion forces shows good agreement with experimental results. In addition, a Gaussian-like potential well is rendered as an application of the UltraShiver.
Old and New Nearly Optimal Polynomial Root-Finders

Univariate polynomial root-finding has been studied for four millennia and still remains the subject of intensive research. Hundreds if not thousands of efficient algorithms for this task have been proposed and analyzed. Two nearly optimal solution algorithms have been devised in 1995 and 2016, based on recursive factorization of a polynomial and subdivision iterations, respectively, but both of them are superseded in practice by Ehrlich’s functional iterations. By combining factorization techniques with Ehrlich’s and subdivision iterations we devise a variety of new root-finders. They match or supersede the known algorithms in terms of their estimated complexity for root-finding on the complex plane, in a disc, and in a line segment and promise to be practically competitive.
Consensual sex work: An overview of sex-workers' human dignity in law, philosophy, and Abrahamic religions

Abstract This article explores the dignity of consensual sex-workers through multiple prisms, namely: comparative law, philosophy, and Abrahamic religions. A deontological multidisciplinary approach becomes indispensable for discussing a multi-faceted phenomenon such as sex-work. In discussing the legal aspect of the sex workers' dignity, this article analyzes two notable Supreme Court decisions that focus on the human dignity issues associated with sex work: Bedford decision of the Canadian Supreme Court and Jordan decision of the Supreme Court of South Africa. Further, this paper examines the sex workers' case, philosophically, in light of Malby's working model of human dignity. It adopts Malby's model to assess the dignity of sex workers through examining sex-workers' autonomy in their relationships with clients, pimps, brothels, and authorities. This research also provides an overview of the human dignity of sex-workers in Abrahamic religions: Judaism, Christianity, and Islam. Finally, it compares sex work to same sex marriage and dwarf tossing as types of victimless crimes to emphasize the leading role of human dignity in carrying out ethical transformation. This article is restricted to consensual sex work and it draws many information from interviews with sex workers available on YouTube and Social Media. It aims at emphasizing the distinction between the dignity of the profession (sex work) and the dignity of the professionals (sex workers) throughout the multiple prisms. This distinction shows that consensual sex work does not necessarily deprive sex workers of their inherent human dignity and autonomy, and thus does not deprive them of their basic human rights.
Narratives as Networks: Predicting Memory from the Structure of Naturalistic Events

ABSTRACT Human life consists of a multitude of diverse and interconnected events. However, extant research has focused on how humans segment and remember discrete events from continuous input, with far less attention given to how the structure of connections between events impacts memory. We conducted an fMRI study in which subjects watched and recalled a series of realistic audiovisual narratives. By transforming narratives into networks of events, we found that more central events—those with stronger semantic or causal connections to other events—were better remembered. During encoding, central events evoked larger hippocampal event boundary responses associated with memory consolidation. During recall, high centrality predicted stronger activation in cortical areas involved in episodic recollection, and more similar neural representations across individuals. Together, these results suggest that when humans encode and retrieve complex real-world experiences, the reliability and accessibility of memory representations is shaped by their location within a network of events.
Retinal Stabilization Reveals Limited Influence of Extraretinal Signals on Heading Tuning in the Medial Superior Temporal Area

Heading perception in primates depends heavily on visual optic-flow cues. Yet during self-motion, heading percepts remain stable, even though smooth-pursuit eye movements often distort optic flow. According to theoretical work, self-motion can be represented accurately by compensating for these distortions in two ways: via retinal mechanisms or via extraretinal efference-copy signals, which predict the sensory consequences of movement. Psychophysical evidence strongly supports the efference-copy hypothesis, but physiological evidence remains inconclusive. Neurons that signal the true heading direction during pursuit are found in visual areas of monkey cortex, including the dorsal medial superior temporal area (MSTd). Here we measured heading tuning in MSTd using a novel stimulus paradigm, in which we stabilize the optic-flow stimulus on the retina during pursuit. This approach isolates the effects on neuronal heading preferences of extraretinal signals, which remain active while the retinal stimulus is prevented from changing. Our results from 3 female monkeys demonstrate a significant but small influence of extraretinal signals on the preferred heading directions of MSTd neurons. Under our stimulus conditions, which are rich in retinal cues, we find that retinal mechanisms dominate physiological corrections for pursuit eye movements, suggesting that extraretinal cues, such as predictive efference-copy mechanisms, have a limited role under naturalistic conditions.SIGNIFICANCE STATEMENT Sensory systems discount stimulation caused by an animal's own behavior. For example, eye movements cause irrelevant retinal signals that could interfere with motion perception. The visual system compensates for such self-generated motion, but how this happens is unclear. Two theoretical possibilities are a purely visual calculation or one using an internal signal of eye movements to compensate for their effects. The latter can be isolated by experimentally stabilizing the image on a moving retina, but this approach has never been adopted to study motion physiology. Using this method, we find that extraretinal signals have little influence on activity in visual cortex, whereas visually based corrections for ongoing eye movements have stronger effects and are likely most important under real-world conditions.
%svy_logistic_regression: A generic SAS macro for simple and multiple logistic regression and creating quality publication-ready tables using survey or non-survey data

Introduction Reproducible research is increasingly gaining interest in the research community. Automating the production of research manuscript tables from statistical software can help increase the reproducibility of findings. Logistic regression is used in studying disease prevalence and associated factors in epidemiological studies and can be easily performed using widely available software including SAS, SUDAAN, Stata or R. However, output from these software must be processed further to make it readily presentable. There exists a number of procedures developed to organize regression output, though many of them suffer limitations of flexibility, complexity, lack of validation checks for input parameters, as well as inability to incorporate survey design. Methods We developed a SAS macro, %svy_logistic_regression, for fitting simple and multiple logistic regression models. The macro also creates quality publication-ready tables using survey or non-survey data which aims to increase transparency of data analyses. It further significantly reduces turn-around time for conducting analysis and preparing output tables while also addressing the limitations of existing procedures. In addition, the macro allows for user-specific actions to handle missing data as well as use of replication-based variance estimation methods. Results We demonstrate the use of the macro in the analysis of the 2013-2014 National Health and Nutrition Examination Survey (NHANES), a complex survey designed to assess the health and nutritional status of adults and children in the United States. The output presented here is directly from the macro and is consistent with how regression results are often presented in the epidemiological and biomedical literature, with unadjusted and adjusted model results presented side by side. Conclusions The SAS code presented in this macro is comprehensive, easy to follow, manipulate and to extend to other areas of interest. It can also be incorporated quickly by the statistician for immediate use. It is an especially valuable tool for generating quality, easy to review tables which can be incorporated directly in a publication.
Machine Learning Based IoT Edge Node Security Attack and Countermeasures

Advances in technology have enabled tremendous progress in the development of a highly connected ecosystem of ubiquitous computing devices collectively called the Internet of Things (IoT). Ensuring the security of IoT devices is a high priority due to the sensitive nature of the collected data. Physically Unclonable Functions (PUFs) have emerged as critical hardware primitive for ensuring the security of IoT nodes. Malicious modeling of PUF architectures has proven to be difficult due to the inherently stochastic nature of PUF architectures. Extant approaches to malicious PUF modeling assume that a priori knowledge and physical access to the PUF architecture is available for malicious attack on the IoT node. However, many IoT networks make the underlying assumption that the PUF architecture is sufficiently tamper-proof, both physically and mathematically. In this work, we show that knowledge of the underlying PUF structure is not necessary to clone a PUF. We present a novel non-invasive, architecture independent, machine learning attack for strong PUF designs with a cloning accuracy of 93.5% and improvements of up to 48.31% over an alternative, two-stage brute force attack model. We also propose a machine-learning based countermeasure, discriminator, which can distinguish cloned PUF devices and authentic PUFs with an average accuracy of 96.01%. The proposed discriminator can be used for rapidly authenticating millions of IoT nodes remotely from the cloud server.
Generalized Lyndon Factorizations of Infinite Words

A generalized lexicographic order on words is a lexicographic order where the total order of the alphabet depends on the position of the comparison. A generalized Lyndon word is a finite word which is strictly smallest among its class of rotations with respect to a generalized lexicographic order. This notion can be extended to infinite words: an infinite generalized Lyndon word is an infinite word which is strictly smallest among its class of suffixes. We prove a conjecture of Dolce, Restivo, and Reutenauer: every infinite word has a unique nonincreasing factorization into finite and infinite generalized Lyndon words. When this factorization has finitely many terms, we characterize the last term of the factorization. Our methods also show that the infinite generalized Lyndon words are precisely the words with infinitely many generalized Lyndon prefixes.
Presentation Matters: The Effect of Wrapping Neatness on Gift Attitudes

While gift-givers typically wrap gifts prior to presenting them, little is known about the effect of how the gift is wrapped on recipients’ expectations and attitudes toward the gift inside. We propose that when recipients open a gift from a friend, they like it less when the giver has wrapped it neatly as opposed to sloppily and we draw on expectation disconfirmation theory to explain the effect. Specifically, recipients set higher (lower) expectations for neatly (sloppily)-wrapped gifts, making it harder (easier) for the gifts to meet these expectations, resulting in contrast effects that lead to less (more) positive attitudes toward the gifts once unwrapped. However, when the gift-giver is an acquaintance, there is ambiguity in the relationship status and wrapping neatness serves as a cue about the relationship rather than the gift itself. This leads to assimilation effects where the recipient likes the gift more when neatly wrapped. We assess these effects across three studies and find that they hold for desirable, neutral, and undesirable gifts, as well as with both hypothetical and real gifts.
Effect of random time changes on Loewner hulls

Loewner hulls are determined by their real-valued driving functions. We study the geometric effect on the Loewner hulls when the driving function is composed with a random time change, such as the inverse of an α-stable subordinator. In contrast to SLE, we show that for a large class of random time changes, the time-changed Brownian motion process does not generate a simple curve. Further we develop criteria which can be applied in many situations to determine whether the Loewner hull generated by a time-changed driving function is simple or non-simple. To aid our analysis of an example with a time-changed deterministic driving function, we prove a deterministic result that a driving function that moves faster than atr for r∈(0,1/2) generates a hull that leaves the real line tangentially.
The Beauville-Bogomolov class as a characteristic class

Let X be any compact Kahler manifold deformation equivalent to the Hilbert scheme of length n subschemes on a K3 surface, n>1. We construct over XxX a rank 2n-2 reflexive twisted sheaf E, which is locally free away from the diagonal. The characteristic classes of E are invariant under the diagonal action of an index two subgroup of the monodromy group. Given a point x in X, the restriction E_x of E to {x}xX has the following properties. (1) The characteristic class k_i(E_x) in H^{i,i}(X,Q) can not be expressed as a polynomial in classes of lower degree, if 1<i<(n+1)/2. (2) The Beauville-Bogomolov class is equal to c_2(TX)+2k_2(E_x).
FINANCIAL INCLUSION: A VERITABLE TOOL FOR INCLUSIVE GROWTH AND POVERTY ALLEVIATION IN NIGERIA

In recent times, inclusive growth has become a national policy objective worldwide. In the context of Nigeria’s growth planning, it is a relatively new terminology which got the attention of policy makers in October 2012.Inclusive growth has the literal meaning of the two words that refers to both the pace and the pattern of the economic growth. It basically means, broad based, shared, and pro-poor growth. In Nigeria, the formal financial system provides services to about 35% of the economically active population while the remaining 65% are excluded from access to financial services. These 65% are often served by the informal financial sector some of which are not regulated. The non-regulation of the activities of the informal sector has serious implications for the Central Bank of Nigeria’s ability to exercise its mandate of promoting monetary stability and sound financial system .Economic growth and development in any economy is propelled by the quality of goods and services provided within that economy as well as their competiveness with other economies. One of the major factors that have constrained the pace of economic growth and development is lack of access to sustainable financial services by a greater proportion of the populace. Inclusive growth will follow at a faster pace when every segment of the populace engages in the productive process through access to sustainable financial services. Access to financial services could assist to alleviate poverty and promote both growth and standard of living when the economic active-poor and the disadvantaged are enabled to save, borrow and reinvest for the growth of the real sector of the economy. In this, paper an effort has been made to understand the inclusive growth phenomenon, its need and sustainable financial inclusion as a veritable tool to attain it, relative to Nigerian experience.
Dynamic changes in prescription opioids from 2006 to 2017 in Texas.

Background The US is experiencing an epidemic of opioid overdoses which may be at least partially due to an over-reliance on opioid analgesics in the treatment of chronic non-cancer pain and subsequent escalation to heroin or illicit fentanyl. As Texas was reported to be among the lowest in the US for opioid use and misuse, further examination of this state is warranted. Materials and Methods This study was conducted to quantify prescription opioid use in Texas. Data was obtained from the publicly available US Drug Enforcement Administration's Automation of Reports and Consolidated Orders System (ARCOS) which monitors controlled substances transactions from manufacture to commercial distribution. Data for 2006-2017 from Texas for ten prescription opioids including eight primarily used to relieve pain (codeine, fentanyl, hydrocodone, hydromorphone, meperidine, morphine, oxycodone, oxymorphone) and two (buprenorphine and methadone) for the treatment of an Opioid Use Disorder (OUD) were examined. Results The change in morphine mg equivalent (MME) of all opioids (+23.3%) was only slightly greater than the state's population gains (21.1%). Opioids used to treat an OUD showed pronounced gains (+90.8%) which were four-fold faster than population growth. Analysis of individual agents revealed pronounced elevations in codeine (+387.5%), hydromorphone (+106.7%), and oxycodone (+43.6%) and a reduction in meperidine (-80.3%) in 2017 relative to 2006. Methadone in 2017 accounted for a greater portion (39.5%) of the total MME than hydrocodone, oxycodone, morphine, hydromorphone, oxymorphone, and meperidine, combined. There were differences between urban and rural areas in the changes in hydrocodone and buprenorphine. Conclusions Collectively, these findings indicate that continued vigilance is needed in Texas to appropriately treat pain and an OUD while minimizing the potential for prescription opioid diversion and misuse. Texas may lead the US in a return to pre-opioid epidemic prescription levels.