cssi/SciSciGPT-SciSciNet huggingface dataset:
SARS-CoV-2 Total and Subgenomic RNA Viral Load in Hospitalized Patients.

BACKGROUND: Previous studies demonstrated that SARS-CoV-2 RNA can be detected for weeks after infection. The significance of this finding is unclear and, in most patients, does not represent active infection. Detection of subgenomic RNA has been proposed to represent productive infection and may be a useful marker for monitoring infectivity. METHODS: We used RT-qPCR to quantify total and subgenomic nucleocapsid (sgN) and envelope (sgE) transcripts in 185 SARS-CoV-2 positive nasopharyngeal swab samples collected on hospital admission and to relate to symptom duration. RESULTS: We find that all transcripts decline at the same rate; however, sgE becomes undetectable before other transcripts. The median duration of symptoms to a negative test is 14 days for sgE and 25 days for sgN. There is a linear decline in subgenomic compared to total RNA suggesting subgenomic transcript copy number is dependent on copy number of total transcripts. The mean difference between total and sgN is 16-fold and the mean difference between total and sgE is 137-fold. This relationship is constant over duration of symptoms allowing prediction of subgenomic copy number from total copy number. CONCLUSIONS: Subgenomic RNA may be no more useful in determining infectivity than a copy number threshold determined for total RNA.
Fast classification rates without standard margin assumptions

We consider the classical problem of learning rates for classes with finite VC dimension. It is well known that fast learning rates up to $O\left(\frac{d}{n}\right)$ are achievable by the empirical risk minimization algorithm (ERM) if low noise or margin assumptions are satisfied. These usually require the optimal Bayes classifier to be in the class, and it has been shown that when this is not the case, the fast rates cannot be achieved even in the noise free case. In this paper, we further investigate the question of the fast rates under the misspecification, when the Bayes classifier is not in the class (also called the agnostic setting). First, we consider classification with a reject option, namely Chow's reject option model, and show that by slightly lowering the impact of hard instances, a learning rate of order $O\left(\frac{d}{n}\log \frac{n}{d}\right)$ is always achievable in the agnostic setting by a specific learning algorithm. Similar results were only known under special versions of margin assumptions. We also show that the performance of the proposed algorithm is never worse than the performance of ERM. Based on those results, we derive the necessary and sufficient conditions for classification (without a reject option) with fast rates in the agnostic setting achievable by improper learners. This simultaneously extends the work of Massart and N\'{e}d\'{e}lec (Ann. of Statistics, 2006), which studied this question in the case where the Bayesian optimal rule belongs to the class, and the work of Ben-David and Urner (COLT, 2014), which allows the misspecification but is limited to the no noise setting. Our result also provides the first general setup in statistical learning theory in which an improper learning algorithm may significantly improve the learning rate for non-convex losses.
Rational functions of Degree Four that Permute the Projective Line over a Finite Field

Recently, rational functions of degree three that permute the projective line P1(Fq) over a finite field Fq were determined by Ferraguti and Micheli. In the present paper, using a different method,...
Personal Wealth, Self-Employment, and Business Ownership

We study the effect of personal wealth on entrepreneurial decisions using data on mineral payments from Texas shale drilling to individuals throughout the United States. Large cash windfalls increase business formation by 0.8 to 2.1 percentage points, but do not affect transitions to self-employment. By contrast, cash windfalls significantly extend self-employment spells, but do not affect the duration of business ownership. Our findings help reconcile contrasting findings in prior work: liquidity constraints have different effects on entrepreneurial activity that may depend on the entrepreneur’s motivations.
Trade Law in a Data-Driven Economy: The Need for Modesty and Resilience

This article examines the social challenges posed by the data-driven economy, their implications for international trade law, the current trade negotiating context involving distinct models advanced by the United States, European Union, and China, and a way forward that can both enhance trade and regulatory efficacy. It examines seven interrelated risks. They are the rise of ‘winner-take-all’ companies; social control through public and private surveillance; social polarization; premature deindustrialization; national security threats; cybersecurity risks; and threats to personal privacy. In response to these risks, the article contends that trade agreements should be deferential to national regulation, while supporting mechanisms for regulatory learning and adaptation. In this spirit, the article advances a governance framework that goes beyond ‘liberalization' and that foregrounds the importance of building resilience and engaging in regulatory problem solving.
Sample Complexity Bounds for Two Timescale Value-based Reinforcement Learning Algorithms

Two timescale stochastic approximation (SA) has been widely used in value-based reinforcement learning algorithms. In the policy evaluation setting, it can model the linear and nonlinear temporal difference learning with gradient correction (TDC) algorithms as linear SA and nonlinear SA, respectively. In the policy optimization setting, two timescale nonlinear SA can also model the greedy gradient-Q (Greedy-GQ) algorithm. In previous studies, the non-asymptotic analysis of linear TDC and Greedy-GQ has been studied in the Markovian setting, with diminishing or accuracy-dependent stepsize. For the nonlinear TDC algorithm, only the asymptotic convergence has been established. In this paper, we study the non-asymptotic convergence rate of two timescale linear and nonlinear TDC and Greedy-GQ under Markovian sampling and with accuracy-independent constant stepsize. For linear TDC, we provide a novel non-asymptotic analysis and show that it attains an $\epsilon$-accurate solution with the optimal sample complexity of $\mathcal{O}(\epsilon^{-1}\log(1/\epsilon))$ under a constant stepsize. For nonlinear TDC and Greedy-GQ, we show that both algorithms attain $\epsilon$-accurate stationary solution with sample complexity $\mathcal{O}(\epsilon^{-2})$. It is the first non-asymptotic convergence result established for nonlinear TDC under Markovian sampling and our result for Greedy-GQ outperforms the previous result orderwisely by a factor of $\mathcal{O}(\epsilon^{-1}\log(1/\epsilon))$.
Does Length Impact Engagement? Length Limits of Posts and Microblogging Behavior.

Managers of social media platforms aggressively compete for users to stimulate user generated content on their sites. In this study, we delve into one tactic of this competition which has become increasingly popular, but has received limited attention, the extension of character limits. Despite its popularity, the impact of changing these limits on user engagement and behavior is unclear. On the one hand, increasing limits might yield more low quality and rambling posts, thereby alienating the users who favor microblogs for their streamlined and efficient content. On the other hand, increasing limits might give users the freedom they need to more effectively communicate ideas. We delve into this tension by exploiting a natural experiment, the phased rollout of length limit extensions on the Chinese microblog, Weibo. As expected, findings from more than 7,000 users suggest that extending length limits increases the average length of posts. However, results also indicate that the magnitude of the effect is infinitesimal (only 2 characters). Further, results indicate that extending length limits significantly increases user engagement by enticing users into posting more actively. Finally, it appears that accessing the platform through tethered or mobile connection strongly moderates these effects; inasmuch as the observed changes to post length and volumes are only observed for PC users (versus mobile-only users). This suggests a potential risk in extending length limits by shifting the platform away from mobile-only users.
Staged Repositioning in Endoscopic Endonasal Odontoidectomy Maximizes Decompression while Allowing Preservation of the C1 Anterior Arch: A Technical Note.

Background Preservation of the anterior arch of C1 in endoscopic endonasal odontoidectomy has been proposed as an alternative to complete C1 arch resections, potentially affording less destabilization of the craniocervical junction. Nonetheless, this approach may limit the decompression achieved. In this case, intraoperative repositioning allowed maximal decompression while preserving the anterior arch of C1. Methods A 79-year-old woman presented with suboccipital pain caused by an expansile and compressive mass centered on the dens. Notably, the mass occluded both vertebral arteries resulting in small cerebellar strokes. An endoscopic endonasal approach for diagnosis and decompression was performed followed by posterior fixation. Results Given the significant compression, the patient was initially positioned in slight cervical extension. After rhinopharyngeal flap harvest, the top half of the anterior arch of C1 was resected, maintaining its structural integrity. The odontoidectomy was completed flush to the superior border of the reduced C1 arch. After an intraoperative computed tomography (CT) scan, performed in a neutral position, the patient was then repositioned with cervical flexion. This maneuver presented the residual odontoid above the C1 arch, but, given the partial removal of the dens, it did not result in any change in neuromonitoring. Further odontoid resection was then completed and follow-up CT scan revealed maximal dens removal, extending below the C1 anterior arch in neutral position. Conclusions In cases of odontoid/atlantoaxial pathology causing significant neural compression, staged intraoperative repositioning can safely maximize the odontoidectomy, while affording preservation of the structural integrity of the anterior arch of C1.
Natural Synthesis of Provably-Correct Data-Structure Manipulations

This paper presents natural synthesis, which generalizes the proof-theoretic synthesis technique to support very expressive logic theories. This approach leverages the natural proof methodology and reduces an intractable, unbounded-size synthesis problem to a tractable, bounded-size synthesis problem, which is amenable to be handled by modern inductive synthesis engines. The synthesized program admits a natural proof and is a provably-correct solution to the original synthesis problem. We explore the natural synthesis approach in the domain of imperative data-structure manipulations and present a novel syntax-guided synthesizer based on natural synthesis. The input to our system is a program template together with a rich functional specification that the synthesized program must meet. Our system automatically produces a program implementation along with necessary proof artifacts, namely loop invariants and ranking functions, and guarantees the total correctness with a natural proof. Experiments show that our natural synthesizer can efficiently produce provably-correct implementations for sorted lists and binary search trees. To our knowledge, this is the first system that can automatically synthesize these programs, their functional correctness and their termination in tandem from bare-bones control flow skeletons.
Decentralized Deep Learning Using Momentum-Accelerated Consensus

We consider the problem of decentralized deep learning where multiple agents collaborate to learn from a distributed dataset. While several decentralized deep learning approaches exist, the majority consider a central parameter-server topology for aggregating the model parameters from the agents. However, such a topology may be inapplicable in networked systems such as ad-hoc mobile networks, field robotics, and power network systems where direct communication with the central parameter server may be inefficient. In this context, we propose and analyze a novel decentralized deep learning algorithm where the agents interact over a fixed communication topology (without a central server). Our algorithm is based on the heavy-ball acceleration method used in gradient-based optimization. We propose a novel consensus protocol where each agent shares with its neighbors its model parameters and gradient-momentum values during the optimization process. We consider nonconvex objective functions and theoretically analyze our algorithm’s performance. We present several empirical comparisons with competing decentralized learning methods to demonstrate the efficacy of our approach under different communication topologies.
Floorplanning by graph dualization: L-shaped modules

We propose a novel technique for constructing a floorplan from an adjacency requirement -- represented by a graphG. The algorithm finds a geometric dual ofG involving both rectangular and L-shaped modules. This is the first dualization technique which permits L-shaped modules. We can test inO(n3/2) time ifG admits an L-shaped dual and construct one, if it exists, inO(n2) time, wheren is the number of modules.
Toxoplasma gondii secreted effectors co-opt host repressor complexes to inhibit necroptosis

Summary Toxoplasma gondii translocates effector proteins into its host cell to subvert various host pathways. T. gondii effector TgIST blocks the transcription of interferon-stimulated genes to reduce immune defense. Interferons upregulate numerous genes, including protein kinase R (PKR), which induce necrosome formation to activate mixed-lineage-kinase-domain-like (MLKL) pseudokinase and induce necroptosis. Whether these interferon functions are targeted by Toxoplasma is unknown. Here, we examine secreted effectors that localize to the host cell nucleus and find that the chronic bradyzoite stage secretes effector TgNSM that targets the NCoR/SMRT complex, a repressor for various transcription factors, to inhibit interferon-regulated genes involved in cell death. TgNSM acts with TgIST to block IFN-driven expression of PKR and MLKL, thus preventing host cell necroptotic death and protecting the parasite’s intracellular niche. The mechanism of action of TgNSM uncovers a role of NCoR/SMRT in necroptosis, assuring survival of intracellular cysts and chronic infection.
Financial Reporting and Moral Sentiments

Abstract Scholars have long suspected that people behave differently when their actions will be observed by or revealed to others. We hypothesize that financial reporting that reveals managers' actions will lead managers to take actions that better align with investor interests. We test this hypothesis with an experiment in which we manipulate the availability of a financial report that reveals managerial actions. Our evidence shows that financial reporting leads a manager to choose reinvestment and resource-sharing actions that better align with investor interests, even when the investor can impose no cost or confer no reward on the manager. This effect holds when the investor can shut down the firm and take a sizable portion of the assets. Our evidence suggests that financial reporting's economic value comes not only from its traditional contracting function, but also because managers care about investors' moral evaluations of them that are enabled by reporting.
Properties of HYMNs in examples of four-color, five-color, and six-color adinkras

The mathematical concept of a "Banchoff index" associated with discrete Morse functions for oriented triangular meshes has been shown to correspond to the height assignments of nodes in adinkras. In recent work there has been introduced the concept of "Banchoff matrices" leading to HYMNs - height yielding matrix numbers. HYMNs map the shape of an adinkra to a set of eigenvalues derived from Banchoff matrices. In the context of some examples of four-color, minimal five-color, and minimal six-color adinkras, properties of the HYMNs are explored.
On the number of edges in maximally linkless graphs

A maximally linkless graph is a graph that can be embedded in $\mathbb{R}^3$ without any links, but cannot be embedded in such a way if any other edge is added to the graph. Recently, a family of maximally linkless graphs was found with $m=3n-3$ edges. We improve upon this by demonstrating a new family of maximally linkless graphs with $m\le \frac{14}{5}n$ edges.
Variance Reduction via Primal-Dual Accelerated Dual Averaging for Nonsmooth Convex Finite-Sums

We study structured nonsmooth convex finite-sum optimization that appears widely in machine learning applications, including support vector machines and least absolute deviation. For the primal-dual formulation of this problem, we propose a novel algorithm called \emph{Variance Reduction via Primal-Dual Accelerated Dual Averaging (\vrpda)}. In the nonsmooth and general convex setting, \vrpda~has the overall complexity $O(nd\log\min \{1/\epsilon, n\} + d/\epsilon )$ in terms of the primal-dual gap, where $n$ denotes the number of samples, $d$ the dimension of the primal variables, and $\epsilon$ the desired accuracy. In the nonsmooth and strongly convex setting, the overall complexity of \vrpda~becomes $O(nd\log\min\{1/\epsilon, n\} + d/\sqrt{\epsilon})$ in terms of both the primal-dual gap and the distance between iterate and optimal solution. Both these results for \vrpda~improve significantly on state-of-the-art complexity estimates, which are $O(nd\log \min\{1/\epsilon, n\} + \sqrt{n}d/\epsilon)$ for the nonsmooth and general convex setting and $O(nd\log \min\{1/\epsilon, n\} + \sqrt{n}d/\sqrt{\epsilon})$ for the nonsmooth and strongly convex setting, in a much more simple and straightforward way. Moreover, both complexities are better than \emph{lower} bounds for general convex finite sums that lack the particular (common) structure that we consider. Our theoretical results are supported by numerical experiments, which confirm the competitive performance of \vrpda~compared to state-of-the-art.
Fairness of Exposure in Stochastic Bandits

Contextual bandit algorithms have become widely used for recommendation in online systems (e.g. marketplaces, music streaming, news), where they now wield substantial influence on which items get exposed to the users. This raises questions of fairness to the items -- and to the sellers, artists, and writers that benefit from this exposure. We argue that the conventional bandit formulation can lead to an undesirable and unfair winner-takes-all allocation of exposure. To remedy this problem, we propose a new bandit objective that guarantees merit-based fairness of exposure to the items while optimizing utility to the users. We formulate fairness regret and reward regret in this setting, and present algorithms for both stochastic multi-armed bandits and stochastic linear bandits. We prove that the algorithms achieve sub-linear fairness regret and reward regret. Beyond the theoretical analysis, we also provide empirical evidence that these algorithms can fairly allocate exposure to different arms effectively.
“As Long As I’m Me”: From Personhood to Personal Identity in Dementia and Decision-making

As older people begin to develop dementia, we confront ethical questions about when and how to intervene in their increasingly compromised decision-making. The prevailing approach in bioethics to tackling this challenge has been to develop theories of “decision-making capacity” based on the same characteristics that entitle the decisions of moral persons to respect in general. This article argues that this way of thinking about the problem has missed the point. Because the disposition of property is an identity-dependent right, what matters in dementia and decision-making is an individual’s personal identity with their prior self, not their moral personhood. Therefore, in considering when and how we ought to intervene in the decision-making of those with dementia, we must look to the philosophy of personal identity rather than personhood.
Expanding Cost-Aware Function Execution with Multidimensional Notions of Cost.

Recent advances in networking technology and serverless architectures have enabled automated distribution of compute workloads at the function level. As heterogeneity and physical distribution of computing resources increase, so too does the need to effectively use those resources. This is especially true when leveraging multiple compute resources in the form of local, distributed, and cloud resources. Adding to the complexity of the problem is different notions of "cost" when it comes to using these resources. Tradeoffs exist due to the inherent difference between costs of computation for the end user. For example, deploying a workload on the cloud could be much faster than using local resources but using the cloud incurs a financial cost. Here, the end user is presented with the tradeoff between time and money. We describe preliminary work towards Delts+, a framework that integrates multidimensional cost objectives, cost tradeoffs, and optimization under constraints.
CLTR: An End-to-End, Transformer-Based System for Cell-Level Table Retrieval and Table Question Answering

We present the first end-to-end, transformer-based table question answering (QA) system that takes natural language questions and massive table corpora as inputs to retrieve the most relevant tables and locate the correct table cells to answer the question. Our system, CLTR, extends the current state-of-the-art QA over tables model to build an end-to-end table QA architecture. This system has successfully tackled many real-world table QA problems with a simple, unified pipeline. Our proposed system can also generate a heatmap of candidate columns and rows over complex tables and allow users to quickly identify the correct cells to answer questions. In addition, we introduce two new open domain benchmarks, E2E_WTQ and E2E_GNQ, consisting of 2,005 natural language questions over 76,242 tables. The benchmarks are designed to validate CLTR as well as accommodate future table retrieval and end-to-end table QA research and experiments. Our experiments demonstrate that our system is the current state-of-the-art model on the table retrieval task and produces promising results for end-to-end table QA.
The Benson - Symonds invariant for ordinary and signed permutation modules

Abstract The signed permutation modules are a simultaneous generalization of the ordinary permutation modules and the twisted permutation modules of the symmetric group. In a recent paper Dave Benson and Peter Symonds defined a new invariant γ G ( M ) for a finite dimensional module M of a finite group G which attempts to quantify how close a module is to being projective. In this paper, we determine this invariant for all the signed permutation modules of the symmetric group using tools from representation theory and combinatorics.
On a divisor of the central binomial coefficient

It is well known that for all $$n\ge 1$$ the number $$n+1$$ is a divisor of the central binomial coefficient $${2n\atopwithdelims ()n}$$ . Since the nth central binomial coefficient equals the number of lattice paths from (0, 0) to (n, n) by unit steps north or east, a natural question is whether there is a way to partition these paths into sets of $$n+1$$ paths or $$n+1$$ equinumerous sets of paths. The Chung–Feller theorem gives an elegant answer to this question. We pose and deliver an answer to the analogous question for $$2n-1$$ , another divisor of $${2n\atopwithdelims ()n}$$ . We then show our main result follows from a more general observation regarding binomial coefficients $${n\atopwithdelims ()k}$$ with n and k relatively prime. A discussion of the case where n and k are not relatively prime is also given, highlighting the limitations of our methods. Finally, we come full circle and give a novel interpretation of the Catalan numbers.
PARP7 mono-ADP-ribosylates the agonist conformation of the androgen receptor in the nucleus.

We recently described a signal transduction pathway that contributes to androgen receptor (AR) regulation based on site-specific ADP-ribosylation by PARP7, a mono-ADP-ribosyltransferase implicated in several human cancers. ADP-ribosylated AR is recognized by PARP9/DTX3L, a heterodimeric complex that contains an ADP-ribose reader (PARP9) and a ubiquitin E3 ligase (DTX3L). Here, we have characterized the cellular and biochemical requirements for AR ADP-ribosylation by PARP7. We found that the reaction requires nuclear localization of PARP7 and an agonist-induced conformation of AR. PARP7 contains a Cys3His1-type zinc finger (ZF), which also is critical for AR ADP-ribosylation. The Parp7 ZF is required for efficient nuclear import by a nuclear localization signal (NLS) encoded in PARP7, but rescue experiments indicate the ZF makes a contribution to AR ADP-ribosylation that is separable from the effect on nuclear transport. ZF mutations do not detectably reduce PARP7 catalytic activity and binding to AR, but they do result in the loss of PARP7 enhancement of AR-dependent transcription of the MYBPC1 gene. Our data reveals critical roles for AR conformation and the PARP7 ZF in AR ADP-ribosylation and AR-dependent transcription.
DECODE: a Deep-learning framework for Condensing enhancers and refining boundaries with large-scale functional assays.

Motivation Mapping distal regulatory elements, such as enhancers, is a cornerstone for elucidating how genetic variations may influence diseases. Previous enhancer-prediction methods have used either unsupervised approaches or supervised methods with limited training data. Moreover, past approaches have implemented enhancer discovery as a binary classification problem without accurate boundary detection, producing low-resolution annotations with superfluous regions and reducing the statistical power for downstream analyses (e.g. causal variant mapping and functional validations). Here, we addressed these challenges via a two-step model called Deep-learning framework for Condensing enhancers and refining boundaries with large-scale functional assays (DECODE). First, we employed direct enhancer-activity readouts from novel functional characterization assays, such as STARR-seq, to train a deep neural network for accurate cell-type-specific enhancer prediction. Second, to improve the annotation resolution, we implemented a weakly supervised object detection framework for enhancer localization with precise boundary detection (to a 10 bp resolution) using Gradient-weighted Class Activation Mapping. Results Our DECODE binary classifier outperformed a state-of-the-art enhancer prediction method by 24% in transgenic mouse validation. Furthermore, the object detection framework can condense enhancer annotations to only 13% of their original size, and these compact annotations have significantly higher conservation scores and genome-wide association study variant enrichments than the original predictions. Overall, DECODE is an effective tool for enhancer classification and precise localization. Availability and implementation DECODE source code and pre-processing scripts are available at decode.gersteinlab.org. Supplementary information Supplementary data are available at Bioinformatics online.
Is beauty skin deep

Abstract We investigate if the perceived attractiveness of mutual fund managers influences mutual fund flows. We hand-collect professional photographs of managers and use machine learning algorithms to develop two objective proxies of attractiveness. We find that, after controlling for fund characteristics, performance measures and manager characteristics, mutual funds managed by ‘attractive’ managers receive higher fund flows. Our results are robust to matched sample analysis, Heckman two-stage selection, alternate model specifications. Attractiveness bias is predominantly witnessed within retail investors and does not entail superior fund performance. Our results suggest that mutual fund investors exhibit a bias for seemingly attractive mutual fund managers.
Voluntary Disclosure in Light of Control Concerns

The centrality of private information in the design of accounting institutions has been explored via agency models that address control concerns as well as disclosure models that amplify valuation issues. Somewhat surprisingly, the joint analysis of control and valuation considerations, and their implication for firms’ voluntary disclosure practices have not received much attention. Our paper addresses this shortcoming. With embedded control and valuation concerns, the nature of the firm’s disclosure in the stock market is altered profoundly – disclosure is two-tailed or intermediate, but not single-tailed as is the norm in the disclosure literature bereft of control problems. The severity of the control problem is also altered in that disclosure changes managerial incentives to acquire and exploit private information.
Scalable resetting algorithms for synchronization of pulse-coupled oscillators over rooted directed graphs

Abstract We study the problem of robust global synchronization of pulse-coupled oscillators (PCOs) over directed graphs. It is known that when the digraphs are strongly connected, global synchronization can be achieved by using a class of deterministic binary set-valued resetting controllers (Poveda and Teel, 2019). However, for large-scale networks, these algorithms are not scalable because some of their tuning parameters have upper bounds of the order O 1 N , where N is the number of agents. This paper resolves this scalability issue by presenting several new results about global synchronization of PCOs with more general network topologies using the frameworks of deterministic and stochastic hybrid dynamical systems. First, we establish that similar deterministic binary resetting algorithms can achieve robust global and fixed-time synchronization in any rooted acyclic digraph. Moreover, in this case, we show that the synchronization dynamics are now scalable as the tuning parameters of the algorithm are network independent, i.e., of order O ( 1 ) . However, the algorithms cannot be further extended to all rooted digraphs. We establish this new impossibility result by introducing a counter-example with a particular rooted digraph for which global synchronization cannot be achieved, irrespective of the tuning parameters. Nevertheless, we show that if the binary resetting algorithms are modified by accommodating an Erdos–Renýi type random graph model, then the resulting stochastic resetting dynamics will guarantee global synchronization almost surely for all rooted digraphs and, moreover, the tuning parameters of the dynamics are network independent. Stability and robustness properties of the resetting algorithms are studied using the tools from set-valued hybrid dynamical systems. Numerical simulations are provided at the end of the paper for demonstration of the main results.
Dynamical height growth: left, right, and total orbits

Let $S$ be a set of dominant rational self-maps on $\mathbb{P}^N$. We study the arithmetic and dynamical degrees of infinite sequences of $S$ obtained by sequentially composing elements of $S$ on the right and left. We then apply this insight to dynamical Galois theory.
Unpacking Human Teachers’ Intentions for Natural Interactive Task Learning

Interactive Task Learning (ITL) is an emerging research agenda that studies the design of complex intelligent robots that can acquire new knowledge through natural human teacher-robot learner interactions. ITL methods are particularly useful for designing intelligent robots whose behavior can be adapted by humans collaborating with them. Various research communities are contributing methods for ITL and a large subset of this research is robot-centered with a focus on developing algorithms that can learn online, quickly. This paper studies the ITL problem from a human-centered perspective to provide guidance for robot design so that human teachers can naturally teach ITL robots. In this paper, we present 1) a qualitative bidirectional analysis of an interactive teaching study (N=10) through which we characterize various aspects of actions intended and executed by human teachers when teaching a robot; 2) an in-depth discussion of the teaching approach employed by two participants to understand the need for personal adaptation to individual teaching styles; and 3) requirements for ITL robot design based on our analyses and informed by a computational theory of collaborative interactions, SharedPlans.
P-sort: an open-source software for cerebellar neurophysiology

Algorithms that perform spike sorting depend on waveforms to cluster spikes. However, a cerebellar Purkinje-cell produces two types of spikes; simple and complex spikes. A complex spike coincides w...
Pretty Good Phone Privacy

To receive service in today's cellular architecture, phones uniquely identify themselves to towers and thus to operators. This is now a cause of major privacy violations, as operators now sell and leak identity and location data of hundreds of millions of mobile users. In this paper, we take an end-to-end perspective on the cellular architecture and find key points of decoupling that enable us to protect user identity and location privacy with no changes to physical infrastructure, no added latency, and no requirement of direct cooperation from existing operators. We describe Pretty Good Phone Privacy (PGPP) and demonstrate how our modified backend stack (NGC) works with real phones to provide ordinary yet privacy-preserving connectivity. We explore inherent privacy and efficiency tradeoffs in a simulation of a large metropolitan region. We show how PGPP maintains today's control overheads while significantly improving user identity and location privacy.
Bounds for Learning Lossless Source Coding

This paper asks a basic question: how much training is required to beat a universal source coder? Traditionally, there have been two types of source coders: fixed, optimum coders such as Huffman coders; and universal source coders, such as Lempel-Ziv. The paper considers a third type of source coders: learned coders. These are coders that are trained on data of a particular type, and then used to encode new data of that type. This is a type of coder that has recently become popular for (lossy) image and video coding. The paper evaluates two criteria for performance of learned coders: the average performance over training data, and a guaranteed performance for all training except for some error probability Pe, which is PAC learning. In both cases the coders are evaluated with respect to redundancy. The paper considers the independent identically distributed (IID) binary case and binary Markov chains. In both cases it is shown that the amount of training data required is very moderate: to code sequences of length $l$ the amount of training data required to beat a universal source coder is $m=K\frac{l}{\log l}$ , where the constant $K$ depends on the case considered.
An entrainment-based model for annular wakes, with applications to airborne wind energy

Several novel wind energy systems produce wakes with annular cross-sections, which are qualitatively different from the wakes with circular cross-sections commonly generated by conventional horizontal-axis wind turbines and by compact obstacles. Since wind farms use arrays of hundreds of turbines, good analytical wake models are essential for efficient wind farm planning. Several models already exist for circular wakes; however, none have yet been proposed for annular wakes, making it impossible to estimate their array performance. We use the entrainment hypothesis to develop a reduced-order model for the shape and flow velocity of an annular wake from a generic toroidal obstacle. Our model consists of a set of three ordinary differential equations, which we solve numerically. In addition, by assuming that the annular wake does not drift radially, we further reduce the problem to a model comprising only two differential equations, which we solve analytically. Both of our models are in good agreement with previously published large eddy simulation results.
TMS Bursts Can Modulate Local and Networks Oscillations During Lower-Limb Movement.

Purpose Lower-limb motor functions involve processing information via both motor and cognitive control networks. Measuring oscillations is a key element in communication within and between cortical networks during high-order motor functions. Increased midfrontal theta oscillations are related to improved lower-limb motor performances in patients with movement disorders. Noninvasive neuromodulation approaches have not been explored extensively to understand the oscillatory mechanism of lower-limb motor functions. This study aims to examine the effects of repetitive transcranial magnetic stimulation on local and network EEG oscillations in healthy elderly subjects. Methods Eleven healthy elderly subjects (67-73 years) were recruited via advertisements, and they underwent both active and sham stimulation procedures in a random, counterbalanced design. Transcranial magnetic stimulation bursts (θ-transcranial magnetic stimulation; 4 pulses/second) were applied over the midfrontal lead (vertex) before a GO-Cue pedaling task, and signals were analyzed using time-frequency methods. Results Transcranial magnetic stimulation bursts increase the theta activity in the local (p = 0.02) and the associated network during the lower-limb pedaling task (p = 0.02). Furthermore, after task-related transcranial magnetic stimulation burst sessions, increased resting-state alpha activity was observed in the midfrontal region (p = 0.01). Conclusions This study suggests the ability of midfrontal transcranial magnetic stimulation bursts to directly modulate local and network oscillations in a frequency manner during lower-limb motor task. Transcranial magnetic stimulation burst-induced modulation may provide insights into the functional roles of oscillatory activity during lower-limb movement in normal and disease conditions.
4D-Net for Learned Multi-Modal Alignment

We present 4D-Net, a 3D object detection approach, which utilizes 3D Point Cloud and RGB sensing information, both in time. We are able to incorporate the 4D information by performing a novel dynamic connection learning across various feature representations and levels of abstraction, as well as by observing geometric constraints. Our approach outperforms the state-of-the-art and strong baselines on the Waymo Open Dataset. 4D-Net is better able to use motion cues and dense image information to detect distant objects more successfully.
Design Strategy Network: A Deep Hierarchical Framework to Represent Generative Design Strategies in Complex Action Spaces

Generative design problems often encompass complex action spaces that may be divergent over time, contain state-dependent constraints, or involve hybrid (discrete and continuous) domains. To address those challenges, this work introduces Design Strategy Network (DSN), a data-driven deep hierarchical framework that can learn strategies over these arbitrary complex action spaces. The hierarchical architecture decomposes every action decision into first predicting a preferred spatial region in the design space and then outputting a probability distribution over a set of possible actions from that region. This framework comprises a convolutional encoder to work with image-based design state representations, a multi-layer perceptron to predict a spatial region, and a weight-sharing network to generate a probability distribution over unordered set-based inputs of feasible actions. Applied to a truss design study, the framework learns to predict the actions of human designers in the study, capturing their truss generation strategies in the process. Results show that DSNs significantly outperform non-hierarchical methods of policy representation, demonstrating their superiority in complex action space problems.
A Novel Variable Stiffness Soft Robotic Gripper

Compliant grasping is crucial for secure handling objects not only vary in shapes but also in mechanical properties. We propose a novel soft robotic gripper with decoupled stiffness and shape control capability for performing adaptive grasping with minimum system complexity. The proposed soft fingers conform to object shapes facilitating the handling of objects of different types, shapes, and sizes. Each soft gripper finger has a length constraining mechanism (an articulable rigid backbone) and is powered by pneumatic muscle actuators. We derive the kinematic model of the gripper and use an empirical approach to simultaneously map input pressures to stiffness control and bending deformation of fingers. We use these mappings to demonstrate decoupled stiffness and shape (bending) control of various grasping configurations. We conduct tests to quantify the grip quality when holding objects as the gripper changes orientation, the ability to maintain the grip as the gripper is subjected to translational and rotational movements, and the external force perturbations required to release the object from the gripper under various stiffness and shape (bending) settings. The results validate the proposed gripper's performance and show how the decoupled stiffness and shape control can improve the grasping quality in soft robotic grippers.
Associations between acoustic features of maternal speech and infants' emotion regulation following a social stressor.

Caregiver voices may provide cues to mobilize or calm infants. This study examined whether maternal prosody predicted changes in infants' biobehavioral state after the still face, a stressor in which the mother withdraws and reinstates social engagement. Ninety-four dyads participated in the study (infant age 4-8 months). Infants' heart rate and respiratory sinus arrhythmia (measuring cardiac vagal tone) were derived from an electrocardiogram (ECG). Infants' behavioral distress was measured by negative vocalizations, facial expressions, and gaze aversion. Mothers' vocalizations were measured via a composite of spectral analysis and spectro-temporal modulation using a two-dimensional fast Fourier transformation of the audio spectrogram. High values on the maternal prosody composite were associated with decreases in infants' heart rate (β = -.26, 95% CI: [-0.46, -0.05]) and behavioral distress (β = -.23, 95% CI: [-0.42, -0.03]), and increases in cardiac vagal tone in infants whose vagal tone was low during the stressor (1 SD below mean β = .39, 95% CI: [0.06, 0.73]). High infant heart rate predicted increases in the maternal prosody composite (β = .18, 95% CI: [0.03, 0.33]). These results suggest specific vocal acoustic features of speech that are relevant for regulating infants' biobehavioral state and demonstrate mother-infant bi-directional dynamics.
Using Machine Learning to Predict Game Outcomes Based on Player-Champion Experience in League of Legends

League of Legends (LoL) is the most widely played multiplayer online battle arena (MOBA) game in the world. An important aspect of LoL is competitive ranked play, which utilizes a skill-based matchmaking system to form fair teams. However, players’ skill levels vary widely depending on which champion, or hero, that they choose to play as. In this paper, we propose a method for predicting game outcomes in ranked LoL games based on players’ experience with their selected champion. Using a deep neural network, we found that game outcomes can be predicted with 75.1% accuracy after all players have selected champions, which occurs before gameplay begins. Our results have important implications for playing LoL and matchmaking. Firstly, individual champion skill plays a significant role in the outcome of a match, regardless of team composition. Secondly, even after the skill-based matchmaking, there is still a wide variance in team skill before gameplay begins. Finally, players should only play champions that they have mastered, if they want to win games.
Random polynomials: Central limit theorems for the real roots

The number of real roots has been a central subject in the theory of random polynomials and random functions since the fundamental papers of Littlewood, Offord, and Kac in the 1940s. The main task here is to determine the limiting distribution of this random variable. In 1974, Maslova famously proved a central limit theorem (CLT) for the number of real roots of Kac polynomials. It has remained the only limiting theorem available for the number of real roots for more than four decades. In this paper, using a new approach, we derive a general CLT for the number of real roots of a large class of random polynomials with coefficients growing polynomially. Our result both generalizes and strengthens Maslova’s theorem.
Developers perception of peer code review in research software development

Research software is software developed by and/or used by researchers, across a wide variety of domains, to perform their research. Because of the complexity of research software, developers cannot conduct exhaustive testing. As a result, researchers have lower confidence in the correctness of the output of the software. Peer code review, a standard software engineering practice, has helped address this problem in other types of software. Peer code review is less prevalent in research software than it is in other types of software. In addition, the literature does not contain any studies about the use of peer code review in research software. Therefore, through analyzing developers perceptions, the goal of this work is to understand the current practice of peer code review in the development of research software, identify challenges and barriers associated with peer code review in research software, and present approaches to improve the peer code review in research software. We conducted interviews and a community survey of research software developers to collect information about their current peer code review practices, difficulties they face, and how they address those difficulties. We received 84 unique responses from the interviews and surveys. The results show that while research software teams review a large amount of their code, they lack formal process, proper organization, and adequate people to perform the reviews. Use of peer code review is promising for improving the quality of research software and thereby improving the trustworthiness of the underlying research results. In addition, by using peer code review, research software developers produce more readable and understandable code, which will be easier to maintain.
The Brachistochrone: An Excellent Problem for All Levels of Physics Students

The classic brachistrochrone problem is standard material in intermediate mechanics. Many variations exist including some accessible to introductory students. While a quantitative solution isn’t feasible in introductory classes, qualitative discussions can be very beneficial since kinematics, Newton’s laws, energy conservation, and motion along curved trajectories all play a role. In this work, we describe an activity focusing on a qualitative understanding of the brachistochrone and examine the performance of freshmen, juniors, and graduate students. The activity can be downloaded at https://w3.physics.arizona.edu/undergrad/teaching-resources.
Recession-specific recoveries: L’s, U’s and everything in between

Abstract We relax the assumption that recessions are all alike and propose a new model of output growth that allows for recession-specific recoveries. Output growth is modeled as the weighted average of Markov-switching processes that temporarily alter the level of real GDP ( U -shaped) and those with permanent effects ( L -shaped), where the recession-specific weight is endogenously estimated. Only the 1969–70 and 2007–09 recessions are characterized exclusively as U and L , respectively. The other 85% of U.S. recessions reflect a weighted combination of the two shapes, suggesting multiple sources of recessions. With respect to fitting output growth, our model outperforms those that generate either U - or L -shaped recoveries and the model-implied paths closely track the level of actual U.S. real GDP during recessions and recoveries.