cssi/SciSciGPT-SciSciNet huggingface dataset:
Adic Finiteness: Bounding Homology and Applications

ABSTRACTWe prove the versions of amplitude inequalities of Iversen, Foxby and Iyengar, and Frankild and Sather-Wagstaff that replace finite generation conditions with adic finiteness conditions. As an application, we prove that a local ring R of prime characteristic is regular if and only if for some proper ideal &#x1d51f; the derived local cohomology complex RΓ&#x1d51f;(R) has finite flat dimension when viewed through some positive power of the Frobenius endomorphism.
Lose the Views: Limited Angle CT Reconstruction via Implicit Sinogram Completion

Computed Tomography (CT) reconstruction is a fundamental component to a wide variety of applications ranging from security, to healthcare. The classical techniques require measuring projections, called sinograms, from a full 180Â° view of the object. However, obtaining a full-view is not always feasible, such as when scanning irregular objects that limit flexibility of scanner rotation. The resulting limited angle sinograms are known to produce highly artifact-laden reconstructions with existing techniques. In this paper, we propose to address this problem using CTNet - a system of 1D and 2D convolutional neural networks, that operates directly on a limited angle sinogram to predict the reconstruction. We use the x-ray transform on this prediction to obtain a "completed" sinogram, as if it came from a full 180Â°view. We feed this to standard analytical and iterative reconstruction techniques to obtain the final reconstruction. We show with extensive experimentation on a challenging real world dataset that this combined strategy outperforms many competitive baselines. We also propose a measure of confidence for the reconstruction that enables a practitioner to gauge the reliability of a prediction made by CTNet. We show that this measure is a strong indicator of quality as measured by the PSNR, while not requiring ground truth at test time. Finally, using a segmentation experiment, we show that our reconstruction also preserves the 3D structure of objects better than existing solutions.
Is Machine Learning in Power Systems Vulnerable

Recent advances in Machine Learning (ML) have led to its broad adoption in a series of power system applications, ranging from meter data analytics, renewable/load/price forecasting to grid security assessment. Although these data-driven methods yield state-of-the-art performances in many tasks, the robustness and security of applying such algorithms in modern power grids have not been discussed. In this paper, we attempt to address the issues regarding the security of ML applications in power systems. We first show that most of the current ML algorithms proposed in power systems are vulnerable to adversarial examples, which are maliciously crafted input data. We then adopt and extend a simple yet efficient algorithm for finding subtle perturbations, which could be used for generating adversaries for both categorical (e.g., user load profile classification) and sequential applications (e.g., renewables generation forecasting). Case studies on classification of power quality disturbances and forecast of building loads demonstrate the vulnerabilities of current ML algorithms in power networks under our adversarial designs. These vulnerabilities call for design of robust and secure ML algorithms for real world applications.
The Corporate Keepers of International Law

In transborder environmental protection, territorial disputes, internet governance, anticorruption, international human rights, and humanitarian law, private businesses are increasingly supporting the implementation and enforcement of international law. This Article analyzes the various ways that corporate decision making contributes to this phenomenon, and assesses its prospects for enhancing international law's existing enforcement paradigms. In doing so, the Article opens new ground for scholarly and policy consideration of the proper role of corporations in the global legal order.
Part Segmentation for Highly Accurate Deformable Tracking in Occlusions via Fully Convolutional Neural Networks

Successfully tracking the human body is an important perceptual challenge for robots that must work around people. Existing methods fall into two broad categories: geometric tracking and direct pose estimation using machine learning. While recent work has shown direct estimation techniques can be quite powerful, geometric tracking methods using point clouds can provide a very high level of 3D accuracy which is necessary for many robotic applications. However these approaches can have difficulty in clutter when large portions of the subject are occluded. To overcome this limitation, we propose a solution based on fully convolutional neural networks (FCN). We develop an optimized Fast-FCN network architecture for our application which allows us to filter observed point clouds and improve tracking accuracy while maintaining interactive frame rates. We also show that this model can be trained with a limited number of examples and almost no manual labelling by using an existing geometric tracker and data augmentation to automatically generate segmentation maps. We demonstrate the accuracy of our full system by comparing it against an existing geometric tracker, and show significant improvement in these challenging scenarios.
Uniform confidence bands for nonparametric errors-in-variables regression

Abstract This paper develops a method to construct uniform confidence bands for a nonparametric regression function where a predictor variable is subject to a measurement error. We allow for the distribution of the measurement error to be unknown, but assume the availability of validation data or repeated measurements on the latent predictor variable. The proposed confidence band builds on the deconvolution kernel estimation and a novel application of the multiplier bootstrap method. We establish asymptotic validity of the proposed confidence band. To our knowledge, this is the first paper to derive asymptotically valid uniform confidence bands for nonparametric errors-in-variables regression.
Locating Objects Without Bounding Boxes

Recent advances in convolutional neural networks (CNN) have achieved remarkable results in locating objects in images. In these networks, the training procedure usually requires providing bounding boxes or the maximum number of expected objects. In this paper, we address the task of estimating object locations without annotated bounding boxes which are typically hand-drawn and time consuming to label. We propose a loss function that can be used in any fully convolutional network (FCN) to estimate object locations. This loss function is a modification of the average Hausdorff distance between two unordered sets of points. The proposed method has no notion of bounding boxes, region proposals, or sliding windows. We evaluate our method with three datasets designed to locate people’s heads, pupil centers and plant centers. We outperform state-of-the-art generic object detectors and methods fine-tuned for pupil tracking.
Temporal FiLM: Capturing Long-Range Sequence Dependencies with Feature-Wise Modulations.

Learning representations that accurately capture long-range dependencies in sequential inputs --- including text, audio, and genomic data --- is a key problem in deep learning. Feed-forward convolutional models capture only feature interactions within finite receptive fields while recurrent architectures can be slow and difficult to train due to vanishing gradients. Here, we propose Temporal Feature-Wise Linear Modulation (TFiLM) --- a novel architectural component inspired by adaptive batch normalization and its extensions --- that uses a recurrent neural network to alter the activations of a convolutional model. This approach expands the receptive field of convolutional sequence models with minimal computational overhead. Empirically, we find that TFiLM significantly improves the learning speed and accuracy of feed-forward neural networks on a range of generative and discriminative learning tasks, including text classification and audio super-resolution.
MaxGap Bandit: Adaptive Algorithms for Approximate Ranking

This paper studies the problem of adaptively sampling from K distributions (arms) in order to identify the largest gap between any two adjacent means. We call this the MaxGap-bandit problem. This problem arises naturally in approximate ranking, noisy sorting, outlier detection, and top-arm identification in bandits. The key novelty of the MaxGap bandit problem is that it aims to adaptively determine the natural partitioning of the distributions into a subset with larger means and a subset with smaller means, where the split is determined by the largest gap rather than a pre-specified rank or threshold. Estimating an arm’s gap requires sampling its neighboring arms in addition to itself, and this dependence results in a novel hardness parameter that characterizes the sample complexity of the problem. We propose elimination and UCB-style algorithms and show that they are minimax optimal. Our experiments show that the UCB-style algorithms require 6-8x fewer samples than non-adaptive sampling to achieve the same error.
An informatics software stack for point defect-derived opto-electronic properties: the Asphalt Project

Computational acceleration of performance metric-based materials discovery via high-throughput screening and machine learning methods is becoming widespread. Nevertheless, development and optimization of the opto-electronic properties that depend on dilute concentrations of point defects in new materials have not significantly benefited from these advances. Here, the authors present an informatics and simulation suite to computationally accelerate these processes. This will enable faster and more fundamental materials research, and reduce the cost and time associated with the materials development cycle. Analogous to the new avenues enabled by current first-principles-based property databases, this type of framework will open entire new research frontiers as it proliferates.
Practical task allocation for software fault-tolerance and its implementation in embedded automotive systems

Due to the advent of active safety features and automated driving capabilities, the complexity of embedded computing systems within automobiles continues to increase. Such advanced driver assistance systems (ADAS) are inherently safety-critical and must tolerate failures in any subsystem. However, fault-tolerance in safety-critical systems has been traditionally supported by hardware replication, which is prohibitively expensive in terms of cost, weight, and size for the automotive market. Recent work has studied the use of software-based fault-tolerance techniques that utilize task-level hot and cold standbys to tolerate fail-stop processor and task failures. The benefit of using standbys is maximal when a task and any of its standbys obey the placement constraint of not being co-located on the same processor. We propose a new heuristic based on a “tiered” placement constraint, and show that our heuristic produces a better task assignment that saves at least one processor up to 40% of the time relative to the best known heuristic to date. We then introduce a task allocation algorithm that, for the first time to our knowledge, leverages the run-time attributes of cold standbys. Our empirical study finds that our heuristic uses no more than one additional processor in most cases relative to an optimal allocation that we construct for evaluation purposes using a creative technique. We also extend our heuristic to support mixed-criticality systems which allow for overload operation. We have designed and implemented our software fault-tolerance framework in AUTOSAR, an automotive industry standard. We use this implementation to provide an experimental evaluation of our task-level fault-tolerance features. Finally, we present an analysis of the worst-case behavior of our task recovery features.
Classical conditioning drives learned reward prediction signals in climbing fibers across the lateral cerebellum

Classical models of cerebellar learning posit that climbing fibers operate according to a supervised learning rule to instruct changes in motor output by signaling the occurrence of movement errors. However, cerebellar output is also associated with non-motor behaviors, and recently with modulating reward association pathways in the VTA. To test how the cerebellum processes reward related signals in the same type of classical conditioning behavior typically studied to evaluate reward processing in the VTA and striatum, we have used calcium imaging to visualize instructional signals carried by climbing fibers across the lateral cerebellum in mice before and after learning. We find distinct climbing fiber responses in three lateral cerebellar regions that can each signal reward prediction. These instructional signals are well suited to guide cerebellar learning based on reward expectation and enable a cerebellar contribution to reward driven behaviors, suggesting a broad role for the lateral cerebellum in reward-based learning.
Spatial refinements and Khovanov homology

We review the construction and context of a stable homotopy refinement of Khovanov homology.
Compressive Sensing with a Multiple Convex Sets Domain

In this paper, we study a general framework for compressive sensing assuming the existence of the prior knowledge that x* belongs to the union of multiple convex sets, ${x^{\ast}} \in { \cup _i}{\mathcal{C}_i}$. In fact, by proper choices of these convex sets in the above framework, the problem can be transformed to well known CS problems such as the phase retrieval, quantized compressive sensing, and model-based CS. First we analyze the impact of this prior knowledge on the minimum number of measurements M to guarantee the uniqueness of the solution. Then we formulate a universal objective function for signal recovery, which is both computationally inexpensive and flexible. Then, an algorithm based on multiplicative weight update and proximal gradient descent is proposed and analyzed for signal reconstruction. Finally, we investigate as to how we can improve the signal recovery by introducing regularizers into the objective function.
A wearable therapy and technology garment for kids: the underlying super hero.

Children today live in a fast-paced, ever-changing and sensory overloaded society. With anxiety and depression in children becoming more present within today's youth, the need to address these concerns including increased health and behavioral issues are imperative. Giving children the right tools to combat these conditions and potentially dangerous disorders will help them traverse their daily lives while assisting them with their physical, mental, and emotional well-being. Depression and anxiety can have negative effects on children that directly affect their learning capabilities and functionalities to do so. Wearable therapy garments were created for these situations and are an easy way to support wellness within these types of conditions. Fabric and clothing can make a big difference in a child's life and apparel can be used to help reduce stress and anxiety disorders through functional garments in a way that can contribute to a happier, healthier, and reduced stress childhood. Hugs have been proven to reduce anxiety, therefore, compression that simulates that feeling has a positive effect on children. Compression garments exist for children, however, how can these garments be elevated with functional wearable technology, and design aesthetic to incorporate fun, kid-friendly design elements that create body positivity and well-being.
A frame semantic overview of NLP-based information extraction for cancer-related EHR notes.

Abstract Objective There is a lot of information about cancer in Electronic Health Record (EHR) notes that can be useful for biomedical research provided natural language processing (NLP) methods are available to extract and structure this information. In this paper, we present a scoping review of existing clinical NLP literature for cancer. Methods We identified studies describing an NLP method to extract specific cancer-related information from EHR sources from PubMed, Google Scholar, ACL Anthology, and existing reviews. Two exclusion criteria were used in this study. We excluded articles where the extraction techniques used were too broad to be represented as frames (e.g., document classification) and also where very low-level extraction methods were used (e.g. simply identifying clinical concepts). 78 articles were included in the final review. We organized this information according to frame semantic principles to help identify common areas of overlap and potential gaps. Results Frames were created from the reviewed articles pertaining to cancer information such as cancer diagnosis, tumor description, cancer procedure, breast cancer diagnosis, prostate cancer diagnosis and pain in prostate cancer patients. These frames included both a definition as well as specific frame elements (i.e. extractable attributes). We found that cancer diagnosis was the most common frame among the reviewed papers (36 out of 78), with recent work focusing on extracting information related to treatment and breast cancer diagnosis. Conclusion The list of common frames described in this paper identifies important cancer-related information extracted by existing NLP techniques and serves as a useful resource for future researchers requiring cancer information extracted from EHR notes. We also argue, due to the heavy duplication of cancer NLP systems, that a general purpose resource of annotated cancer frames and corresponding NLP tools would be valuable.
Control of cytokinesis by β-adrenergic receptors indicates an approach for regulating cardiomyocyte endowment

One million patients with congenital heart disease (CHD) live in the United States. They have a lifelong risk of developing heart failure. Current concepts do not sufficiently address mechanisms of heart failure development specifically for these patients. Here, analysis of heart tissue from an infant with tetralogy of Fallot with pulmonary stenosis (ToF/PS) labeled with isotope-tagged thymidine demonstrated that cardiomyocyte cytokinesis failure is increased in this common form of CHD. We used single-cell transcriptional profiling to discover that the underlying mechanism of cytokinesis failure is repression of the cytokinesis gene ECT2, downstream of β-adrenergic receptors (β-ARs). Inactivation of the β-AR genes and administration of the β-blocker propranolol increased cardiomyocyte division in neonatal mice, which increased the number of cardiomyocytes (endowment) and conferred benefit after myocardial infarction in adults. Propranolol enabled the division of ToF/PS cardiomyocytes in vitro. These results suggest that β-blockers could be evaluated for increasing cardiomyocyte division in patients with ToF/PS and other types of CHD.
Self-Assembled Chromophore–Catalyst Bilayer for Water Oxidation in a Dye-Sensitized Photoelectrosynthesis Cell

A new approach is described here for the preparation of water-oxidation photoanodes in a dye-sensitized photoelectrosynthesis cell for water splitting. It is based on a chromophore (C)–catalyst (Ca...
Precise optical probing of perceptual detection

We developed and used holographic two-photon optogenetic stimulation to probe the detection of evoked neuronal activity at cellular and single action potential resolution, with millisecond precision, while ruling out detection of indirect effects.
A note on the optimal degree of the weak gradient of the stabilizer free weak Galerkin finite element method

Abstract Recently, a new stabilizer free weak Galerkin method (SFWG) is proposed, which is easier to implement. The idea is to raise the degree of polynomials j for computing weak gradient. It is shown that if j ≥ j 0 for some j 0 , then SFWG achieves the optimal rate of convergence. However, large j will cause some numerical difficulties. To improve the efficiency of SFWG and avoid numerical locking, in this note, we provide the optimal j 0 with rigorous mathematical proof.
Norm inequalities for the Fourier coefficients of some almost periodic functions

Abstract Using C. Fefferman’s embedding of a charge space in a measure space allows us to apply standard interpolation theorems to prove norm inequalities for Besicovitch almost periodic functions. This yields an analogue of Paley’s Inequality for the Fourier coefficients of periodic functions.
Statistical analysis of variability in TnSeq data across conditions using zero-inflated negative binomial regression

Deep sequencing of transposon mutant libraries (or TnSeq) is a powerful method for probing essentiality of genomic loci under different environmental conditions. Various analytical methods have been described for identifying conditionally essential genes whose tolerance for insertions varies between two conditions. However, for large-scale experiments involving many conditions, a method is needed for identifying genes that exhibit significant variability in insertions across multiple conditions. In this paper, we introduce a novel statistical method for identifying genes with significant variability of insertion counts across multiple conditions based on Zero-Inflated Negative Binomial (ZINB) regression. Using likelihood ratio tests, we show that the ZINB distribution fits TnSeq data better than either ANOVA or a Negative Binomial (in a generalized linear model). We use ZINB regression to identify genes required for infection of M. tuberculosis H37Rv in C57BL/6 mice. We also use ZINB to perform a analysis of genes conditionally essential in H37Rv cultures exposed to multiple antibiotics. Our results show that, not only does ZINB generally identify most of the genes found by pairwise resampling (and vastly out-performs ANOVA), but it also identifies additional genes where variability is detectable only when the magnitudes of insertion counts are treated separately from local differences in saturation, as in the ZINB model.
Replica Wormholes and the Entropy of Hawking Radiation

The information paradox can be realized in anti-de Sitter spacetime joined to a Minkowski region. In this setting, we show that the large discrepancy between the von Neumann entropy as calculated by Hawking and the requirements of unitarity is fixed by including new saddles in the gravitational path integral. These saddles arise in the replica method as complexified wormholes connecting different copies of the black hole. As the replica number n → 1, the presence of these wormholes leads to the island rule for the computation of the fine-grained gravitational entropy. We discuss these replica wormholes explicitly in two-dimensional Jackiw-Teitelboim gravity coupled to matter.
On the complex Cayley Grassmannian

Abstract We define a torus action on the (complex) Cayley Grassmannian X. Using this action, we prove that X is a singular variety. We also show that the singular locus is smooth and has the same cohomology ring as that of C P 5 . Furthermore, we identify the singular locus with a quotient of G 2 C by a parabolic subgroup.
Environmental Conscious Design: An Introduction To EDST

Industrial companies are facing more pressure to improve the recyclability performance of their new designed products. The concept of Environmental Conscious Design (ECD) has been adopted to assist more concerning about the environmental impact of the products at the early stage of designing. This new trend requires that the design strategy should be modified to integrate environmental constraints. This paper develops an Environmental Design Support Tool (EDST) to help designers improve their product in aspect of the environment. The proposed EDST is capable of evaluating the new designed product in terms of environmental sustainability, i.e., material selection, recyclability evaluation, and disassemblability analyses.
Taking down the wall: Transition and inequality

In this article, we investigate the main determinants of income inequality in the transition countries of Central and Eastern Europe and the Commonwealth of Independent States during the period 1990–2018. To this end, we address a major methodological challenge that lies at the core of the cross‐country literature on income inequality: the potential endogeneity of income per capita, which is largely ignored by most empirical studies. We adopt a two‐pronged empirical strategy by (1) using trading partners’ weighted average real GDP as an instrumental variable and (2) estimating the model via the two‐stage least squares approach for static models and the generalized method of moments estimator for dynamic models. Our empirical findings are consistent with the Kuznets curve that illustrates a nonlinear relationship between income inequality and the level of economic development. We also find that the redistributive impact of fiscal policy is statistically insignificant and taxation and government spending appear to have the opposing effects on income inequality in transition economies over the sample period.
Regulatory Avoidance and Spillover: The Effects of Environmental Regulation on Emissions at Coal-Fired Power Plants

The Clean Air Act’s National Ambient Air Quality Standards (NAAQS) and New Source Review (NSR) permitting work in conjunction to improve ambient air quality in the United States. However, all previous studies of the NAAQS ignore this joint nature and focus solely on the effects of NAAQS non-attainment designation on various economic outcomes. Additionally, previous studies ignore the potentiality of regulatory spillover, i.e., abatement for one pollutant decreasing emissions of other, non-regulated pollutants, from the NAAQS. In this paper, we first examine the actions of plant managers who face varying degrees of regulatory oversight. We then estimate the differential effects of the NAAQS and NSR on emissions at coal-fired power plants, while also examining spillover effects. Collectively, this study adds to the literature on environmental regulation in two key ways. First, we use comprehensive data on facility-level NSR permit receipt to examine the differential effects of the NAAQS on plants regulated jointly by NSR and NAAQS non-attainment and those plants regulated only by NAAQS non-attainment. We investigate how the monitoring of areas with non-attainment designation leads to the avoidance of regulatory scrutiny by local plants and find that regulated facilities not subjected to the technological requirements of NSR decreased $$\hbox {NO}_{\mathrm{{x}}}$$ emissions by 20%. We also examine the mechanisms through which this abatement occurs, e.g., technology, use of cleaner inputs. Second, we identify the spillover effects of the NAAQS for certain pollutants by examining the effects of disparate non-attainment designations from the emissions in question. We find a significant decrease in $$\hbox {NO}_{\mathrm{{x}}}$$ and $$\hbox {CO}_{{2}}$$ emissions as a result of carbon monoxide and $$\hbox {SO}_{{2}}$$ -affected non-attainment designation, respectively. We provide evidence that regulatory spillover in this case is the result of different emission control strategies.
Stagnant zone formation in a 2D bed of circular and elongated grains under penetration

When a large blunt object penetrates into a granular material, the force response exhibits an initial nonlinear relationship with depth that is widely attributed to the formation of a shear-jammed stagnant zone. We present an experimental study of a model 2D granular system that preserves common aspects of slow granular penetration, such as a linear force law for large depths, while also allowing for direct and precise measurement of a stagnant zone. We also probe structural anisotropies throughout the bed and examine their characteristics inside and outside the stagnant zone. Finally, we simultaneously consider effects of concave elongated particle shapes (dimers) and find that while most results are not affected, the relationship between local structure and strain suggests a distinct fragility in the stagnant zone.
Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML

An important research direction in machine learning has centered around developing meta-learning algorithms to tackle few-shot learning. An especially successful algorithm has been Model Agnostic Meta-Learning (MAML), a method that consists of two optimization loops, with the outer loop finding a meta-initialization, from which the inner loop can efficiently learn new tasks. Despite MAML's popularity, a fundamental open question remains -- is the effectiveness of MAML due to the meta-initialization being primed for rapid learning (large, efficient changes in the representations) or due to feature reuse, with the meta initialization already containing high quality features? We investigate this question, via ablation studies and analysis of the latent representations, finding that feature reuse is the dominant factor. This leads to the ANIL (Almost No Inner Loop) algorithm, a simplification of MAML where we remove the inner loop for all but the (task-specific) head of the underlying neural network. ANIL matches MAML's performance on benchmark few-shot image classification and RL and offers computational improvements over MAML. We further study the precise contributions of the head and body of the network, showing that performance on the test tasks is entirely determined by the quality of the learned features, and we can remove even the head of the network (the NIL algorithm). We conclude with a discussion of the rapid learning vs feature reuse question for meta-learning algorithms more broadly.
Prefrontal somatostatin interneurons encode fear memory.

Theories stipulate that memories are encoded within networks of cortical projection neurons. Conversely, GABAergic interneurons are thought to function primarily to inhibit projection neurons and thereby impose network gain control, an important but purely modulatory role. Here we show in male mice that associative fear learning potentiates synaptic transmission and cue-specific activity of medial prefrontal cortex somatostatin (SST) interneurons and that activation of these cells controls both memory encoding and expression. Furthermore, the synaptic organization of SST and parvalbumin interneurons provides a potential circuit basis for SST interneuron-evoked disinhibition of medial prefrontal cortex output neurons and recruitment of remote brain regions associated with defensive behavior. These data suggest that, rather than constrain mnemonic processing, potentiation of SST interneuron activity represents an important causal mechanism for conditioned fear. Cummings and Clem demonstrate that cue-related activation and plasticity of prefrontal interneurons cause disinhibition of a distributed brain network that underlies fear memory.
Model misspecification, Bayesian versus credibility estimation, and Gibbs posteriors

In the context of predicting future claims, a fully Bayesian analysis – one that specifies a statistical model, prior distribution, and updates using Bayes's formula – is often viewed as the gold-s...
Ketamine disinhibits dendrites and enhances calcium signals in prefrontal dendritic spines

A subanesthetic dose of ketamine causes acute psychotomimetic symptoms and sustained antidepressant effects. In prefrontal cortex, the prevailing disinhibition hypothesis posits that N-methyl-d-aspartate receptor (NMDAR) antagonists such as ketamine act preferentially on GABAergic neurons. However, cortical interneurons are heterogeneous. In particular, somatostatin-expressing (SST) interneurons selectively inhibit dendrites and regulate synaptic inputs, yet their response to systemic NMDAR antagonism is unknown. Here, we report that ketamine acutely suppresses the activity of SST interneurons in the medial prefrontal cortex of the awake mouse. The deficient dendritic inhibition leads to greater synaptically evoked calcium transients in the apical dendritic spines of pyramidal neurons. By manipulating NMDAR signaling via GluN2B knockdown, we show that ketamine's actions on the dendritic inhibitory mechanism has ramifications for frontal cortex-dependent behaviors and cortico-cortical connectivity. Collectively, these results demonstrate dendritic disinhibition and elevated calcium levels in dendritic spines as important local-circuit alterations driven by the administration of subanesthetic ketamine.
The Dynamic Shift Detector: An algorithm to identify changes in parameter values governing populations.

Environmental factors interact with internal rules of population regulation, sometimes perturbing systems to alternate dynamics though changes in parameter values. Yet, pinpointing when such changes occur in naturally fluctuating populations is difficult. An algorithmic approach that can identify the timing and magnitude of parameter shifts would facilitate understanding of abrupt ecological transitions with potential to inform conservation and management of species. The “Dynamic Shift Detector” is an algorithm to identify changes in parameter values governing temporal fluctuations in populations with nonlinear dynamics. The algorithm examines population time series data for the presence, location, and magnitude of parameter shifts. It uses an iterative approach to fitting subsets of time series data, then ranks the fit of break point combinations using model selection, assigning a relative weight to each break. We examined the performance of the Dynamic Shift Detector with simulations and two case studies. Under low environmental/sampling noise, the break point sets selected by the Dynamic Shift Detector contained the true simulated breaks with 70–100% accuracy. The weighting tool generally assigned breaks intentionally placed in simulated data (i.e., true breaks) with weights averaging >0.8 and those due to sampling error (i.e., erroneous breaks) with weights averaging <0.2. In our case study examining an invasion process, the algorithm identified shifts in population cycling associated with variations in resource availability. The shifts identified for the conservation case study highlight a decline process that generally coincided with changing management practices affecting the availability of hostplant resources. When interpreted in the context of species biology, the Dynamic Shift Detector algorithm can aid management decisions and identify critical time periods related to species’ dynamics. In an era of rapid global change, such tools can provide key insights into the conditions under which population parameters, and their corresponding dynamics, can shift.
Genus Six Curves, K3 Surfaces, and Stable Pairs

A general smooth curve of genus six lies on a quintic del Pezzo surface. In \cite{AK11}, Artebani and Kond\=o construct a birational period map for genus six curves by taking ramified double covers of del Pezzo surfaces. The map is not defined for special genus six curves. In this paper, we construct a smooth Deligne-Mumford stack $\mathfrak{P}_0$ parametrizing certain stable surface-curve pairs which essentially resolves this map. Moreover, we give an explicit description of pairs in $\mathfrak{P}_0$ containing special curves.
Scattered sentences have few separable randomizations

In the paper Randomizations of Scattered Sentences, Keisler showed that if Martin’s axiom for aleph one holds, then every scattered sentence has few separable randomizations, and asked whether the conclusion could be proved in ZFC alone. We show here that the answer is “yes”. It follows that the absolute Vaught conjecture holds if and only if every $$L_{\omega _1\omega }$$ -sentence with few separable randomizations has countably many countable models.
A strategy map’s effect on the feedback that middle managers pass along to upper management

Feedback from lower-level employees helps upper management know when a strategy revision is necessary. Middle managers have significant control over upper management’s access to lower-level employee feedback because they decide whether to pass along that feedback when they receive it. A strategy map may help mitigate bias in these middle managers’ feedback reporting decisions. In my experiment, participants act as middle managers who receive one of two types of lower-level employee feedback: positive feedback or negative feedback. Without a strategy map, participants over-report positive feedback, under-report negative feedback, and exhibit motivated reasoning toward negative feedback. With a strategy map, participants over-report positive feedback less, under-report negative feedback at the same rate, and their motivated reasoning against negative feedback no longer affects their reporting willingness. Thus, a strategy map could help de-bias some of the feedback middle managers pass along to upper management.
K -theoretic Tate–Poitou duality and the fiber of the cyclotomic trace

Let $$p\in {\mathbb {Z}}$$ be an odd prime. We prove a spectral version of Tate–Poitou duality for the algebraic K-theory spectra of number rings with p inverted. This identifies the homotopy type of the fiber of the cyclotomic trace $$K({\mathcal {O}}_{F})^{\scriptscriptstyle \wedge }_{p}\mathchoice{\longrightarrow }{\rightarrow }{\rightarrow }{\rightarrow }TC({\mathcal {O}}_{F})^{\scriptscriptstyle \wedge }_{p}$$ after taking a suitably connective cover. As an application, we identify the homotopy type at odd primes of the homotopy fiber of the cyclotomic trace for the sphere spectrum in terms of the algebraic K-theory of $${\mathbb {Z}}$$ .
International Investment Law and Domestic Investment Rules: Tracing the Upstream and Downstream Flows

International investment law and domestic law governing foreign investment strongly influence one another and indeed operate in a relationship of co-dependency or interoperability. Yet the flows between the two bodies of law, and their respective modalities of influence, remain generally unexplored in international legal theory. To shed light on this important phenomenon, this article traces the ways in which international investment law can affect the content of domestic investment law, using theories of international law compliance as a lens for such an understanding. It then proposes a set of pathways by which domestic law can influence the content of international investment rules. International law thus depends upon national law not only for its implementation but for its very content. Indeed, the regime of investment law will not tolerate significant discrepancies between the two. An appreciation of this dynamic is critical to evaluating the prospects of improvements to international investment law and can inform the ongoing discussions among stakeholders to this end.
Generation of a Retina Reporter hiPSC Line to Label Progenitor, Ganglion, and Photoreceptor Cell Types.

Purpose Early in mammalian eye development, VSX2, BRN3b, and RCVRN expression marks neural retinal progenitors (NRPs), retinal ganglion cells (RGCs), and photoreceptors (PRs), respectively. The ability to create retinal organoids from human induced pluripotent stem cells (hiPSC) holds great potential for modeling both human retinal development and retinal disease. However, no methods allowing the simultaneous, real-time monitoring of multiple specific retinal cell types during development currently exist. Methods CRISPR/Cas9-mediated homology-directed repair (HDR) in hiPSCs facilitated the replacement of the VSX2 (Progenitor), BRN3b (Ganglion), and RCVRN (Photoreceptor) stop codons with sequences encoding a viral P2A peptide fused to Cerulean, green fluorescent protein, and mCherry reporter genes, respectively, to generate a triple transgenic reporter hiPSC line called PGP1. This was accomplished by co-electroporating HDR templates and sgRNA/Cas9 vectors into hiPSCs followed by antibiotic selection. Functional validation of the PGP1 hiPSC line included the ability to generate retinal organoids, with all major retinal cell types, displaying the expression of the three fluorescent reporters consistent with the onset of target gene expression. Disaggregated organoids were also analyzed by fluorescence-activated cell sorting and fluorescent populations were tested for the expression of the targeted gene. Results Retinal organoids formed from the PGP1 line expressed appropriate fluorescent proteins consistent with the differentiation of NRPs, RGCs, and PRs. Organoids produced from the PGP1 line expressed transcripts consistent with the development of all major retinal cell types. Conclusions and translational relevance The PGP1 line offers a powerful new tool to study retinal development, retinal reprogramming, and therapeutic drug screening.
Averages Along the Primes: Improving and Sparse Bounds

Consider averages along the prime integers $ \mathbb P $ given by \begin{equation*} \mathcal{A}_N f (x) = N ^{-1} \sum_{ p \in \mathbb P \;:\; p\leq N} (\log p) f (x-p). \end{equation*} These averages satisfy a uniform scale-free $ \ell ^{p}$-improving estimate. For all $ 1< p < 2$, there is a constant $ C_p$ so that for all integer $ N$ and functions $ f$ supported on $ [0,N]$, there holds \begin{equation*} N ^{-1/p' }\lVert \mathcal{A}_N f\rVert_{\ell^{p'}} \leq C_p N ^{- 1/p} \lVert f\rVert_{\ell^p}. \end{equation*} The maximal function $ \mathcal{A}^{\ast} f =\sup_{N} \lvert \mathcal{A}_N f \rvert$ satisfies $ (p,p)$ sparse bounds for all $ 1< p < 2$. The latter are the natural variants of the scale-free bounds. As a corollary, $ \mathcal{A}^{\ast} $ is bounded on $ \ell ^{p} (w)$, for all weights $ w$ in the Muckenhoupt $A_p$ class. No prior weighted inequalities for $ \mathcal{A}^{\ast} $ were known.
Deep Learning Based Precoding for the MIMO Gaussian Wiretap Channel

A novel precoding method based on supervised deep neural networks is introduced for the multiple-input multiple-output Gaussian wiretap channel. The proposed deep learning (DL)-based precoding learns the input covariance matrix through offline training over a large set of input channels and their corresponding covariance matrices for efficient, reliable, and secure transmission of information. Furthermore, by spending time in offline training, this method remarkably reduces the computation complexity in real-time applications. Compared to traditional precoding methods, the proposed DL-based precoding is significantly faster and reaches near-capacity secrecy rates. DL based precoding is also more robust than transitional precoding approaches to the number of antennas at the eavesdropper. This new approach to precoding is promising in applications in which delay and complexity are critical.
Exploring Reading Strategy Use in Native and L2 Readers

Research comparing the reading strategy use of native and second language (L2) readers has often relied on self-report measures and has, at times, been conducted without measures of reading profici...
Application of a ‘Jacobi Identity’ for Vertex Operator Algebras to Zeta Values and Differential Operators

We explain how to use a certain new ‘Jacobi identity’ for vertex operator algebras, announced in a previous paper (math.QA/9909178), to interpret and generalize S. Bloch's recent work relating values of the Riemann zeta function at negative integers to a certain Lie algebra of operators.
Non-malleability Against Polynomial Tampering.

We present the first explicit construction of a non-malleable code that can handle tampering functions that are bounded-degree polynomials. Prior to our work, this was only known for degree-1 polynomials (affine tampering functions), due to Chattopadhyay and Li (STOC 2017). As a direct corollary, we obtain an explicit non-malleable code that is secure against tampering by bounded-size arithmetic circuits.
Humanitarian Negotiation with Parties to Armed Conflict: The Role of Laws and Principles in the Discourse

This article examines the role of international humanitarian law (IHL) and humanitarian principles in the discourse of humanitarian negotiation. The article is based on extensive, semi-structured interviews conducted with 53 humanitarian practitioners about their experiences engaging in negotiations in the field. The article proceeds in four parts. Part 1 discusses two key factors at play during humanitarian negotiation processes. The first factor is the counterpart’s familiarity with relevant legal and normative frameworks. The second factor is the interests that can drive counterparts’ behavior. Part 2 presents a framework for understanding how the interaction of these two factors — familiarity and interest-alignment — can shape the discourse of humanitarian negotiation. Part 3 addresses the impact of these same issues on the humanitarian side of the negotiation. In particular, there is the possibility that humanitarian actors themselves might also lack familiarity with IHL and/or humanitarian principles and might find that their interests exist in tension with humanitarian laws and principles. The final section offers concluding remarks.
Assembly of Gold Nanowires on Gold Nanostripe Arrays: Simulation and Experiment

Nanowires (NWs) have a large aspect ratio and large available surface area, which have made them a potential platform for applications in biosensors as well as electronic/optic and power storage de...
Sources of GeV Photons and the Fermi Results

This chapter presents the elaborated lecture notes on Sources of GeV Photons and the Fermi Results given by Charles D. Dermer at the 40th Saas-Fee Advanced Course on "Astrophysics at Very High Energies". The Fermi Gamma-ray Space Telescope made important discoveries and established new results in various areas of astrophysics: from our solar system to remote gamma-ray bursts, from pulsar physics to limits on dark matter and Lorentz invariance violations. The author gives a broad overview of these results by discussing GeV instrumentation and the GeV sky as seen by Fermi, the Fermi catalogs on gamma-ray sources, pulsars and active galactic nuclei, relativistic jet physics and blazars, gamma-rays from cosmic rays in the Galaxy, from star-forming galaxies and from clusters of galaxies, the diffuse extra-galactic gamma-ray background, micro-quasars, radio galaxies, the extragalactic background light, gamma-ray bursts, Fermi acceleration, ultra-high energy cosmic rays, and black holes.
Induction of recurrent break cluster genes in neural progenitor cells differentiated from embryonic stem cells in culture

Mild replication stress enhances appearance of dozens of robust recurrent genomic break clusters, termed RDCs, in cultured primary mouse neural stem and progenitor cells (NSPCs). Robust RDCs occur within genes ("RDC-genes") that are long and have roles in neural cell communications and/or have been implicated in neuropsychiatric diseases or cancer. We sought to develop an in vitro approach to determine whether specific RDC formation is associated with neural development. For this purpose, we adapted a system to induce neural progenitor cell (NPC) development from mouse embryonic stem cell (ESC) lines deficient for XRCC4 plus p53, a genotype that enhances DNA double-strand break (DSB) persistence to enhance detection. We tested for RDCs by our genome-wide DSB identification approach that captures DSBs via their ability to join to specific genomic Cas9/single-guide RNA-generated bait DSBs. In XRCC4/p53-deficient ESCs, we detected seven RDCs, all of which were in genes and two of which were robust. In contrast, in NPCs derived from these ESC lines we detected 29 RDCs, a large fraction of which were robust and associated with long, transcribed neural genes that were also robust RDC-genes in primary NSPCs. These studies suggest that many RDCs present in NSPCs are developmentally influenced to occur in this cell type and indicate that induced development of NPCs from ESCs provides an approach to rapidly elucidate mechanistic aspects of NPC RDC formation.