cssi/SciSciGPT-SciSciNet huggingface dataset:
How to quantize n outputs of a binary symmetric channel to n − 1 bits?

Suppose that Yn is obtained by observing a uniform Bernoulli random vector Xn through a binary symmetric channel with crossover probability α. The “most informative Boolean function” conjecture postulates that the maximal mutual information between Yn and any Boolean function b(Xn) is attained by a dictator function. In this paper, we consider the “complementary” case in which the Boolean function is replaced by f : {0, 1}n → {0, 1}n−1, namely, an n − 1 bit quantizer, and show that I(f(Xn); Yn) ≤ (n − 1)·(1 − h(α)) for any such f. Thus, in this case, the optimal function is of the form f (xn) = (x 1 ,…, x n−1 ).
Compactifications of S-arithmetic Quotients for the Projective General Linear Group

Let F be a global field, let S be a nonempty finite set of places of F which contains the archimedean places of F, let \(d\geqslant 1\), and let \(X =\prod _{v\in S} X_v\) where \(X_v\) is the symmetric space (resp., Bruhat-Tits building) associated to \({{\mathrm{PGL}}}_d(F_v)\) if v is archimedean (resp., non-archimedean). In this paper, we construct compactifications \(\Gamma \backslash \bar{X}\) of the quotient spaces \(\Gamma \backslash X\) for S-arithmetic subgroups \(\Gamma \) of \({{\mathrm{PGL}}}_d(F)\). The constructions make delicate use of the maximal Satake compactification of \(X_v\) (resp., the polyhedral compactification of \(X_v\) of Gerardin and Landvogt) for v archimedean (resp., non-archimedean). We also consider a variant of \(\bar{X}\) in which we use the standard Satake compactification of \(X_v\) (resp., the compactification of \(X_v\) due to Werner).
A high-order HDG method for the Biot’s consolidation model

Abstract We propose a novel high-order HDG method for the Biot’s consolidation model in poroelasticity. We present optimal h -version error analysis for both the semi-discrete and full-discrete (combined with temporal backward differentiation formula) schemes. Numerical tests are provided to demonstrate the performance of the method.
Distributed Lagrangian methods for network resource allocation

Motivated by broad applications in various fields of engineering, we study network resource allocation problems where the goal is to optimally allocate a fixed portion of resources over a network of nodes. In these problems, due to the large scale of the network and complicated interconnections between nodes, any solution must be implemented in parallel and based only on local data resulting in a need for distributed algorithms. In this paper, we propose a distributed Lagrangian method, which requires only local computation and communication. Our focus is to understand the performance of this algorithm on the underlying network topology. Specifically, we obtain an upper bound on the rate of convergence of the algorithm as a function of the size and the topology of the underlying network. The effectiveness and applicability of the proposed method is demonstrated by its use in solving the important economic dispatch problem in power systems, specifically on the benchmark IEEE-14 and IEEE-118 bus test systems.
Spectral Super-Resolution With Prior Knowledge

We address the problem of super-resolution frequency recovery using prior knowledge of the structure of a spectrally sparse, undersampled signal. In many applications of interest, some structure information about the signal spectrum is often known. The prior information might be simply knowing precisely some signal frequencies or the likelihood of a particular frequency component in the signal. We devise a general semidefinite program to recover these frequencies using theories of positive trigonometric polynomials. Our theoretical analysis shows that, given sufficient prior information, perfect signal reconstruction is possible using signal samples no more than thrice the number of signal frequencies. Numerical experiments demonstrate great performance enhancements using our method. We show that the nominal resolution necessary for the grid-free results can be improved if prior information is suitably employed.
A Tempt to Unify Heterogeneous Driving Databases using Traffic Primitives

A multitude of publicly-available driving datasets and data platforms have been raised for autonomous vehicles (AV). However, the heterogeneities of databases in size, structure and driving context make existing datasets practically ineffective due to a lack of uniform frameworks and searchable indexes. In order to overcome these limitations on existing public datasets, this paper proposes a data unification framework based on traffic primitives with ability to automatically unify and label heterogeneous traffic data. This is achieved by two steps: 1) Carefully arrange raw multidimensional time series driving data into a relational database and then 2) automatically extract labeled and indexed traffic primitives from traffic data through a Bayesian nonparametric learning method. Finally, we evaluate the effectiveness of our developed framework using the collected real vehicle data.
Feature Learning in Deep Neural Networks - Studies on Speech Recognition Tasks

Abstract: Recent studies have shown that deep neural networks (DNNs) perform significantly better than shallow networks and Gaussian mixture models (GMMs) on large vocabulary speech recognition tasks. In this paper, we argue that the improved accuracy achieved by the DNNs is the result of their ability to extract discriminative internal representations that are robust to the many sources of variability in speech signals. We show that these representations become increasingly insensitive to small perturbations in the input with increasing network depth, which leads to better speech recognition performance with deeper networks. We also show that DNNs cannot extrapolate to test samples that are substantially different from the training examples. If the training data are sufficiently representative, however, internal features learned by the DNN are relatively stable with respect to speaker differences, bandwidth differences, and environment distortion. This enables DNN-based recognizers to perform as well or better than state-of-the-art systems based on GMMs or shallow networks without the need for explicit model adaptation or feature normalization.
Signatures of infinity: Nonergodicity and resource scaling in prediction, complexity, and learning

We introduce a simple analysis of the structural complexity of infinite-memory processes built from random samples of stationary, ergodic finite-memory component processes. Such processes are familiar from the well known multiarm Bandit problem. We contrast our analysis with computation-theoretic and statistical inference approaches to understanding their complexity. The result is an alternative view of the relationship between predictability, complexity, and learning that highlights the distinct ways in which informational and correlational divergences arise in complex ergodic and nonergodic processes. We draw out consequences for the resource divergences that delineate the structural hierarchy of ergodic processes and for processes that are themselves hierarchical.
Testing unateness nearly optimally

We present an O(n2/3/є2)-query algorithm that tests whether an unknown Boolean function f∶{0,1}n→ {0,1} is unate (i.e., every variable is either non-decreasing or non-increasing) or є-far from unate. The upper bound is nearly optimal given the Ω(n2/3) lower bound of Chen, Waingarten and Xie (2017). The algorithm builds on a novel use of the binary search procedure and its analysis over long random paths.
MGNC-CNN: A Simple Approach to Exploiting Multiple Word Embeddings for Sentence Classification

We introduce a novel, simple convolution neural network (CNN) architecture ‐ multi-group norm constraint CNN (MGNC-CNN) ‐ that capitalizes on multiple sets of word embeddings for sentence classification. MGNCCNN extracts features from input embedding sets independently and then joins these at the penultimate layer in the network to form a final feature vector. We then adopt a group regularization strategy that differentially penalizes weights associated with the subcomponents generated from the respective embedding sets. This model is much simpler than comparable alternative architectures and requires substantially less training time. Furthermore, it is flexible in that it does not require input word embeddings to be of the same dimensionality. We show that MGNC-CNN consistently outperforms baseline models.
Clustering Time Series with Nonlinear Dynamics: A Bayesian Non-Parametric and Particle-Based Approach

We propose a general statistical framework for clustering multiple time series that exhibit nonlinear dynamics into an a-priori-unknown number of sub-groups. Our motivation comes from neuroscience, where an important problem is to identify, within a large assembly of neurons, subsets that respond similarly to a stimulus or contingency. Upon modeling the multiple time series as the output of a Dirichlet process mixture of nonlinear state-space models, we derive a Metropolis-within-Gibbs algorithm for full Bayesian inference that alternates between sampling cluster assignments and sampling parameter values that form the basis of the clustering. The Metropolis step employs recent innovations in particle-based methods. We apply the framework to clustering time series acquired from the prefrontal cortex of mice in an experiment designed to characterize the neural underpinnings of fear.
SEVEN: Deep SEmi-supervised verification networks

Verification determines whether two samples belong to the same class or not, and has important applications such as face and fingerprint verification, where thousands or millions of categories are present but each category has scarce labeled examples, presenting two major challenges for existing deep learning models. We propose a deep semi-supervised model named SEmi-supervised VErification Network (SEVEN) to address these challenges. The model consists of two complementary components. The generative component addresses the lack of supervision within each category by learning general salient structures from a large amount of data across categories. The discriminative component exploits the learned general features to mitigate the lack of supervision within categories, and also directs the generative component to find more informative structures of the whole data manifold. The two components are tied together in SEVEN to allow an end-to-end training of the two components. Extensive experiments on four verification tasks demonstrate that SEVEN significantly outperforms other state-of-the-art deep semi-supervised techniques when labeled data are in short supply. Furthermore, SEVEN is competitive with fully supervised baselines trained with a larger amount of labeled data. It indicates the importance of the generative component in SEVEN.
Listen, attend, and walk: neural mapping of navigational instructions to action sequences

We propose a neural sequence-to-sequence model for direction following, a task that is essential to realizing effective autonomous agents. Our alignment-based encoder-decoder model with long short-term memory recurrent neural networks (LSTM-RNN) translates natural language instructions to action sequences based upon a representation of the observable world state. We introduce a multi-level aligner that empowers our model to focus on sentence "regions" salient to the current world state by using multiple abstractions of the input sentence. In contrast to existing methods, our model uses no specialized linguistic resources (e.g., parsers) or task-specific annotations (e.g., seed lexicons). It is therefore generalizable, yet still achieves the best results reported to-date on a benchmark single-sentence dataset and competitive results for the limited-training multi-sentence setting. We analyze our model through a series of ablations that elucidate the contributions of the primary components of our model.
Ext and local cohomology modules of face rings of simplicial posets

Abstract There are a large number of theorems detailing the homological properties of the Stanley–Reisner ring of a simplicial complex. Here we attempt to generalize some of these results to the case of a simplicial poset. By investigating the combinatorics of certain modules associated with the face ring of a simplicial poset from a topological viewpoint, we extend some results of Miyazaki and Grabe to a wider setting.
ABC implies a Zsigmondy principle for ramification

Abstract Let K be a number field or a function field of characteristic 0. If K is a number field, assume the abc-conjecture for K. We prove a variant of Zsigmondy's theorem for ramified primes in preimage fields of rational functions in K ( x ) that are not postcritically finite. For example, suppose K is a number field and f ∈ K [ x ] is not postcritically finite, and let K n be the field generated by the nth iterated preimages under f of β ∈ K . We show that for all large n, there is a prime of K that ramifies in K n and does not ramify in K m for any m n .
Towards an Understanding of Neural Networks in Natural-Image Spaces

Two major uncertainties, dataset bias and adversarial examples, prevail in state-of-the-art AI algorithms with deep neural networks. In this paper, we present an intuitive explanation for these issues as well as an interpretation of the performance of deep networks in a natural-image space. The explanation consists of two parts: the philosophy of neural networks and a hypothetical model of natural-image spaces. Following the explanation, we 1) demonstrate that the values of training samples differ, 2) provide incremental boost to the accuracy of a CIFAR-10 classifier by introducing an additional "random-noise" category during training, 3) alleviate over-fitting thereby enhancing the robustness against adversarial examples by detecting and excluding illusive training samples that are consistently misclassified. Our overall contribution is therefore twofold. First, while most existing algorithms treat data equally and have a strong appetite for more data, we demonstrate in contrast that an individual datum can sometimes have disproportionate and counterproductive influence and that it is not always better to train neural networks with more data. Next, we consider more thoughtful strategies by taking into account the geometric and topological properties of natural-image spaces to which deep networks are applied.
Voyager 2 constraints on plasmoid-based transport at Uranus

A magnetosphere controls a planet's evolution by suppressing or enhancing atmospheric loss to space. In situ measurements of Uranus' magnetosphere from the Voyager 2 flyby in 1986 provide the only direct evidence of magnetospheric transport processes responsible for this atmospheric escape at Uranus. Analysis of high‐resolution Voyager 2 magnetic field data in Uranus' magnetotail reveals the presence of a loop‐like plasmoid filled with planetary plasma traveling away from the planet. This first plasmoid observation in an Ice Giant magnetosphere elucidates that (1) both internal and external forces play a role in Uranus' magnetospheric dynamics, (2) magnetic reconnection contributes to the circulation of plasma and magnetic flux at Uranus, and (3) plasmoids may be a dominant transport mechanism for mass loss through Uranus' magnetotail.
AlleleAnalyzer: a tool for personalized and allele-specific sgRNA design

The CRISPR/Cas system is a highly specific genome editing tool capable of distinguishing alleles differing by even a single base pair. Target sites might carry genetic variations that are not distinguishable by sgRNA designing tools based on one reference genome. AlleleAnalyzer is an open-source software that incorporates single-nucleotide variants and short insertions and deletions to design sgRNAs for precisely editing 1 or multiple haplotypes of a sequenced genome, currently supporting 11 Cas proteins. It also leverages patterns of shared genetic variation to optimize sgRNA design for different human populations. AlleleAnalyzer is available at https://github.com/keoughkath/AlleleAnalyzer .
Designing for Mobile and Immersive Visual Analytics in the Field

Data collection and analysis in the field is critical for operations in domains such as environmental science and public safety. However, field workers currently face data- and platform-oriented issues in efficient data collection and analysis in the field, such as limited connectivity, screen space, and attentional resources. In this paper, we explore how visual analytics tools might transform field practices by more deeply integrating data into these operations. We use a design probe coupling mobile, cloud, and immersive analytics components to guide interviews with ten experts from five domains to explore how visual analytics could support data collection and analysis needs in the field. The results identify shortcomings of current approaches and target scenarios and design considerations for future field analysis systems. We embody these findings in FieldView, an extensible, open-source prototype designed to support critical use cases for situated field analysis. Our findings suggest the potential for integrating mobile and immersive technologies to enhance data's utility for various field operations and new directions for visual analytics tools to transform fieldwork.
Approaches to concurrency control in distributed data base systems

Whenever multiple users or programs access a data base concurrently, the problem of concurrency control arises. The problem is to synchronize concurrent interactions so that each reads consistent data from the data base, writes consistent data, and is ultimately processed to completion. In a distributed data base this problem is exacerbated because a concurrency control mechanism at one site cannot instantaneously know about interactions at other sites. No fewer than 30 papers on this topic have appeared to date. Our purpose is to survey this literature, concentrating on three approaches—locking, majority consensus, and SDD-1 protocols—which together subsume the bulk of the literature. **
Poissonian pair correlation in higher dimensions

Abstract Let ( x n ) n = 1 ∞ be a sequence on the torus T (normalized to length 1). A sequence ( x n ) is said to have Poissonian pair correlation if, for all s > 0 , lim N → ∞ ⁡ 1 N # { 1 ≤ m ≠ n ≤ N : | x m − x n | ≤ s N } = 2 s . It is known that this implies uniform distribution of the sequence ( x n ) . Hinrichs, Kaltenbock, Larcher, Stockinger & Ullrich extended this result to higher dimensions and showed that sequences ( x n ) in [ 0 , 1 ] d that satisfy, for all s > 0 , lim N → ∞ ⁡ 1 N # { 1 ≤ m ≠ n ≤ N : ‖ x m − x n ‖ ∞ ≤ s N 1 / d } = ( 2 s ) d are also uniformly distributed. We prove the same result for the extension by the Euclidean norm: if a sequence ( x n ) in T d satisfies, for all s > 0 , lim N → ∞ ⁡ 1 N # { 1 ≤ m ≠ n ≤ N : ‖ x m − x n ‖ 2 ≤ s N 1 / d } = ω d s d , where ω d is the volume of the unit ball, then ( x n ) is uniformly distributed. Our approach shows that Poissonian Pair Correlation implies an exponential sum estimate that resembles and implies the Weyl criterion.
Robust Bi-Tempered Logistic Loss Based on Bregman Divergences

We introduce a temperature into the exponential function and replace the softmax output layer of the neural networks by a high-temperature generalization. Similarly, the logarithm in the loss we use for training is replaced by a low-temperature logarithm. By tuning the two temperatures, we create loss functions that are non-convex already in the single layer case. When replacing the last layer of the neural networks by our bi-temperature generalization of the logistic loss, the training becomes more robust to noise. We visualize the effect of tuning the two temperatures in a simple setting and show the efficacy of our method on large datasets. Our methodology is based on Bregman divergences and is superior to a related two-temperature method that uses the Tsallis divergence.
Detecting substance-related problems in narrative investigation summaries of child abuse and neglect using text mining and machine learning.

Abstract Background State child welfare agencies collect, store, and manage vast amounts of data. However, they often do not have the right data, or the data is problematic or difficult to inform strategies to improve services and system processes. Considerable resources are required to read and code these text data. Data science and text mining offer potentially efficient and cost-effective strategies for maximizing the value of these data. Objective The current study tests the feasibility of using text mining for extracting information from unstructured text to better understand substance-related problems among families investigated for abuse or neglect. Method A state child welfare agency provided written summaries from investigations of child abuse and neglect. Expert human reviewers coded 2956 investigation summaries based on whether the caseworker observed a substance-related problem. These coded documents were used to develop, train, and validate computer models that could perform the coding on an automated basis. Results A set of computer models achieved greater than 90% accuracy when judged against expert human reviewers. Fleiss kappa estimates among computer models and expert human reviewers exceeded .80, indicating that expert human reviewer ratings are exchangeable with the computer models. Conclusion These results provide compelling evidence that text mining procedures can be a cost-effective and efficient solution for extracting meaningful insights from unstructured text data. Additional research is necessary to understand how to extract the actionable insights from these under-utilized stores of data in child welfare.
An Unsupervised Autoregressive Model for Speech Representation Learning.

This paper proposes a novel unsupervised autoregressive neural model for learning generic speech representations. In contrast to other speech representation learning methods that aim to remove noise or speaker variabilities, ours is designed to preserve information for a wide range of downstream tasks. In addition, the proposed model does not require any phonetic or word boundary labels, allowing the model to benefit from large quantities of unlabeled data. Speech representations learned by our model significantly improve performance on both phone classification and speaker verification over the surface features and other supervised and unsupervised approaches. Further analysis shows that different levels of speech information are captured by our model at different layers. In particular, the lower layers tend to be more discriminative for speakers, while the upper layers provide more phonetic content.
Distributed Voltage Control with Communication Delays

The increased penetration of volatile renewable energy into distribution networks necessities more efficient distributed (VAR) voltage control. In these control algorithms each bus regulates its voltage fluctuations based on local voltage measurements and communication to other buses. Recent studies have shown that the communication between buses is necessary to solve the general voltage control problem. However, existing literature provides only synchronous voltage control algorithms, which are clearly infeasible in large networks. In this paper, we design a distributed asynchronous feedback voltage control algorithm. The main contributions of our algorithm are: 1) it only requires local communication between neighbors in the network, 2) it enforces hard reactive power limits at every iteration, 3) it converges to hard voltage limits, 4) it converges to optimal reactive power control, 5) it is provably robust to communication delays. We prove the algorithms convergence assuming linear relationship between voltage and reactive power. We simulate the algorithm using the full nonlinear AC power flow model. Our simulations show the effectiveness of our algorithm on realistic networks with both static and fluctuating loads, even in the presence of communication delays.
Psychological stress in the workplace

A sound theory of psychological stress must be capable of helping us understand the variations in the ways individuals appraise adaptational transactions with their environments. On the basis of knowledge gained from traditional research methods, there are three main strategies for reducing stress in the workplace. One is to alter the conditions of work so that they are either less stressful or less counterproductive for effective coping. The second strategy is to try to help those who are having difficulty adapting to conditions that are impossible or difficult to change to cope more effectively. The third strategy is transactional and requires that we identify the individual or group relationships with the work setting that are stressful, and try to change them for the individual or group on the basis of relational findings. Emotion-focused coping consists of efforts to regulate the emotional distress caused by harm or threat.
Reactive Control Meets Runtime Verification: A Case Study of Navigation

This paper presents an application of specification based runtime verification techniques to control mobile robots in a reactive manner. In our case study, we develop a layered control architecture where runtime monitors constructed from formal specifications are embedded into the navigation stack. We use temporal logic and regular expressions to describe safety requirements and mission specifications, respectively. An immediate benefit of our approach is that it leverages simple requirements and objectives of traditional control applications to more complex specifications in a non-intrusive and compositional way. Finally, we demonstrate a simulation of robots controlled by the proposed architecture and we discuss further extensions of our approach.
Risk, External Adjustment and Capital Flows

Abstract This paper studies the process of external adjustment. I develop an open economy model with endowment and preference shocks that can account for the empirical behavior of real exchange rates, interest rates and consumption in the U.S. and Europe. The model includes cross border holdings of bonds and equity, and financial frictions that impede international risk-sharing. I find that external adjustment following endowment shocks predominantly takes place via trade flows, consistent with the intertemporal approach to the current account. In contrast, preference shocks that change investors' risk aversion induce adjustment via the trade and valuation channels; where the latter includes the effects of unexpected capital gains and loss on existing cross border holdings and changes in the expected future return differentials between foreign assets and liabilities. The model estimates imply that the valuation channel of external adjustment is more important for the U.S. than the trade channel. Consistent with this implication, I show that forecasts of future return differentials contributed most to the volatility of the U.S. net foreign asset position in the post Bretton-Woods era.
Maximum likelihood estimation of Jakes' Doppler power spectrum parameters for SIMO channels using the Whittle approximation

We derive methods for asymptotic maximum likelihood (ML) estimation of Jakes' Doppler power spectrum parameters from noisy estimates of the complex fading envelope under the correlated single-input multi-output (SIMO) fading scenario. The Whittle approximation to the likelihood function is utilized to estimate the Doppler spread, noise variance, and spatial channel correlation parameters. We also compute the asymptotic Cramer-Rao bounds (CRBs) for the unknown parameters. Numerical simulations demonstrate the performance of the proposed methods.
PacBio Amplicon Sequencing Method To Measure Pilin Antigenic Variation Frequencies of Neisseria gonorrhoeae.

ABSTRACT Gene diversification is a common mechanism pathogens use to alter surface structures to aid in immune avoidance. Neisseria gonorrhoeae uses a gene conversion-based diversification system to alter the primary sequence of the gene encoding the major subunit of the pilus, pilE. Antigenic variation occurs when one of the nonexpressed 19 silent copies donates part of its DNA sequence to pilE. We have developed a method using Pacific Biosciences (PacBio) amplicon sequencing and custom software to determine pilin antigenic variation frequencies. The program analyzes 37 variable regions across the strain FA1090 1-81-S2 pilE gene and can be modified to determine sequence variation from other starting pilE sequences or other diversity generation systems. Using this method, we measured pilin antigenic variation frequencies for various derivatives of strain FA1090 and showed we can also analyze pilin antigenic variation frequencies during macrophage infection. IMPORTANCE Diversity generation systems are used by many unicellular organism to provide subpopulations of cell with different properties that are available when needed. We have developed a method using the PacBio DNA sequencing technology and a custom computer program to analyze the pilin antigenic variation system of the organism that is the sole cause of the sexually transmitted infection, gonorrhea.
The Role of the Business Press in Creating and Disseminating Information around Earnings Announcements

This paper investigates the role of the business press in creating and disseminating information around earnings announcements by examining different motivations of trading volume. We find that pre...
Decelerated carbon cycling by ectomycorrhizal fungi is controlled by substrate quality and community composition

Interactions between symbiotic ectomycorrhizal (EM) and free-living saprotrophs can result in significant deceleration of leaf litter decomposition. While this phenomenon is widely cited, its generality remains unclear, as both the direction and magnitude of EM fungal effects on leaf litter decomposition have been shown to vary among studies. Here we explicitly examine how contrasting leaf litter types and EM fungal communities may lead to differential effects on carbon (C) and nitrogen (N) cycling. Specifically, we measured the response of soil nutrient cycling, litter decay rates, litter chemistry and fungal community structure to the reduction of EM fungi (via trenching) with a reciprocal litter transplant experiment in adjacent Pinus- or Quercus-dominated sites. We found clear evidence of EM fungal suppression of C and N cycling in the Pinus-dominated site, but no suppression in the Quercus-dominated site. Additionally, in the Pinus-dominated site, only the Pinus litter decay rates were decelerated by EM fungi and were associated with decoupling of litter C and N cycling. Our results support the hypothesis that EM fungi can decelerate C cycling via N competition, but strongly suggest that the 'Gadgil effect' is dependent on both substrate quality and EM fungal community composition. We argue that understanding tree host traits as well as EM fungal functional diversity is critical to a more mechanistic understanding of how EM fungi mediate forest soil biogeochemical cycling.
The early radiologic findings in the adult chest after cardiopulmonary bypass surgery

Postoperative chest radiographs of 70 adults who had undergone cardiopulmonary bypass surgery were evaluated. The distribution of pulmonary collapse/consolidation and pleural effusions, and the pattern of radiopacities related to drainage tubes were considered. Radiologic manifestations related to sternotomy were noted.
Automatic Prostate Zonal Segmentation Using Fully Convolutional Network With Feature Pyramid Attention

Our main objective in the paper is to develop a novel deep learning-based algorithm for automatic segmentation of prostate zones and to evaluate the performance of the algorithm on an additional independent testing dataset in comparison with inter-reader agreement between two experts. With IRB approval and HIPAA compliance, we designed a novel convolutional neural network (CNN) for automatic segmentation of the prostatic transition zone (TZ) and peripheral zone (PZ) on T2-weighted (T2w) 3 Tesla (3T) MRI. The total study cohort included 359 MRI scans of patients in subcohorts; 313 scans from a deidentified publicly available dataset (SPIE-AAPM-NCI PROSTATEX challenge) and 46 scans from a large U.S. tertiary referral center (external testing dataset (ETD)). The TZ and PZ contours were manually annotated by research fellows, supervised by expert genitourinary (GU) radiologists. The model was developed using 250 patients and tested internally using the remaining 63 patients from the PROSTATEX (internal testing dataset (ITD)) and tested again (n=46) externally using the ETD. The Dice Similarity Coefficient (DSC) was used to evaluate the segmentation performance. DSCs for PZ and TZ were 0.74±0.08 and 0.86±0.07 in the ITD respectively. In the ETD, DSCs for PZ and TZ were 0.74±0.07 and 0.79±0.12, respectively. The inter-reader consistency (Expert 2 vs. Expert 1) were 0.71±0.13 (PZ) and 0.75±0.14 (TZ). This novel DL algorithm enabled automatic segmentation of PZ and TZ with high accuracy on both ITD and ETD without a performance difference for PZ and less than 10% TZ difference. In the ETD, the proposed method can be comparable to experts in the segmentation of prostate zones. Part of our source code and datasets with annotations is available at https://github.com/ykl-ucla/prostate_zonal_seg.
Active and Repressed Chromatin Domains Exhibit Distinct Nucleosome Segregation during DNA Replication

Chromatin domains and their associated structures must be faithfully inherited through cellular division to maintain cellular identity. However, accessing the localized strategies preserving chromatin domain inheritance, specifically the transfer of parental, pre-existing nucleosomes with their associated post-translational modifications (PTMs) during DNA replication, is challenging in living cells. We devised an inducible, proximity-dependent labeling system to irreversibly mark replication-dependent H3.1 and H3.2 histone-containing nucleosomes at desired loci in mouse embryonic stem cells so that their fate after DNA replication could be followed. Strikingly, repressed chromatin domains are preserved through local re-deposition of parental nucleosomes. In contrast, nucleosomes decorating active chromatin domains do not exhibit such preservation. Notably, altering cell fate leads to an adjustment of the positional inheritance of parental nucleosomes that reflects the corresponding changes in chromatin structure. These findings point to important mechanisms that contribute to parental nucleosome segregation to preserve cellular identity.
Equivariant isomorphisms of Ext and Tor modules

Abstract In this article we establish equivariant isomorphisms of Ext and Tor modules over different relative complete intersections. More precisely, for a commutative ring Q, this paper investigates how Ext Q / ( f ) ⁎ ( M , N ) and Tor ⁎ Q / ( f ) ( M , N ) change when one varies f among all Koszul-regular sequences of a fixed length such that f M = 0 and f N = 0 . Of notable interest is how the theory of perturbations is used to establish isomorphisms of certain DG modules.
Dispersion characterization and pulse prediction with machine learning

In this work, we demonstrate the efficacy of neural networks in the characterization of dispersive media. We also develop a neural network to make predictions for input probe pulses that propagate through a nonlinear dispersive medium, which may be applied to predicting optimal pulse shapes for a desired output. The setup requires only a single pulse for the probe, providing considerable simplification of the current method of dispersion characterization that requires frequency scanning across the entirety of the gain and absorption features. We show that the trained networks are able to predict pulse profiles as well as dispersive features that are nearly identical to their experimental counterparts. We anticipate that the use of machine learning in conjunction with optical communication and sensing methods, both classical and quantum, can provide signal enhancement and experimental simplifications even in the face of highly complex, layered non-linear light-matter interactions.
The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning Rate Procedure For Least Squares

Minimax optimal convergence rates for numerous classes of stochastic convex optimization problems are well characterized, where the majority of results utilize iterate averaged stochastic gradient descent (SGD) with polynomially decaying step sizes. In contrast, the behavior of SGD’s final iterate has received much less attention despite the widespread use in practice. Motivated by this observation, this work provides a detailed study of the following question: what rate is achievable using the final iterate of SGD for the streaming least squares regression problem with and without strong convexity? First, this work shows that even if the time horizon T (i.e. the number of iterations that SGD is run for) is known in advance, the behavior of SGD’s final iterate with any polynomially decaying learning rate scheme is highly sub-optimal compared to the statistical minimax rate (by a condition number factor in the strongly convex case and a factor of $\sqrt{T}$ in the non-strongly convex case). In contrast, this paper shows that Step Decay schedules, which cut the learning rate by a constant factor every constant number of epochs (i.e., the learning rate decays geometrically) offer significant improvements over any polynomially decaying step size schedule. In particular, the behavior of the final iterate with step decay schedules is off from the statistical minimax rate by only log factors (in the condition number for the strongly convex case, and in T in the non-strongly convex case). Finally, in stark contrast to the known horizon case, this paper shows that the anytime (i.e. the limiting) behavior of SGD’s final iterate is poor (in that it queries iterates with highly sub-optimal function value infinitely often, i.e. in a limsup sense) irrespective of the step size scheme employed. These results demonstrate the subtlety in establishing optimal learning rate schedules (for the final iterate) for stochastic gradient procedures in fixed time horizon settings.
Experimental evolution for niche breadth in bacteriophage T4 highlights the importance of structural genes.

Ecologists have long studied the evolution of niche breadth, including how variability in environments can drive the evolution of specialism and generalism. This concept is of particular interest in viruses, where niche breadth evolution may explain viral disease emergence, or underlie the potential for therapeutic measures like phage therapy. Despite the significance and potential applications of virus-host interactions, the genetic determinants of niche breadth evolution remain underexplored in many bacteriophages. In this study, we present the results of an evolution experiment with a model bacteriophage system, Escherichia virus T4, in several host environments: exposure to Escherichia coli C, exposure to E. coli K-12, and exposure to both E. coli C and E. coli K-12. This experimental framework allowed us to investigate the phenotypic and molecular manifestations of niche breadth evolution. First, we show that selection on different hosts led to measurable changes in phage productivity in all experimental populations. Second, whole-genome sequencing of experimental populations revealed signatures of selection. Finally, clear and consistent patterns emerged across the host environments, especially the presence of new mutations in phage structural genes-genes encoding proteins that provide morphological and biophysical integrity to a virus. A comparison of mutations found across functional gene categories revealed that structural genes acquired significantly more mutations than other categories. Our findings suggest that structural genes are central determinants in bacteriophage niche breadth.
Bounding singular surfaces via Chern numbers

We prove the existence of a bound on the number of steps of the minimal model program for singular surfaces in terms of discrepancies and Chern numbers. As an application, we prove that given $$R\in {\mathbb {R}}_{>0}$$ and $$\epsilon \in (0,1)$$ , the class $${\mathcal {F}}(R,\epsilon )$$ of 2-dimensional pairs (X, D) of general type with $$\epsilon $$ -klt singularities, D with standard coefficients, and $$4c_2(X,D)-c_1^2(X,D)\le R$$ , forms a bounded family.
Tempered fractional Brownian motion: wavelet estimation, modeling and testing

Abstract The Davenport spectrum is a modification of the classical Kolmogorov spectrum for the inertial range of turbulence that accounts for non-scaling low frequency behavior. Like the classical fractional Brownian motion vis-a-vis the Kolmogorov spectrum, tempered fractional Brownian motion (tfBm) is a new model that displays the Davenport spectrum. The autocorrelation of the increments of tfBm displays semi-long range dependence (hyperbolic and quasi-exponential decays over moderate and large scales, respectively), a phenomenon that has been observed in a wide range of applications from wind speeds to geophysics to finance. In this paper, we use wavelets to construct the first estimation method for tfBm and a simple and computationally efficient test for fBm vs tfBm alternatives. The properties of the wavelet estimator and test are mathematically and computationally established. An application of the methodology shows that tfBm is a better model than fBm for a geophysical flow data set.
Verification and Control for Finite-Time Safety of Stochastic Systems via Barrier Functions

This paper studies the problem of enforcing safety of a stochastic dynamical system over a finite time horizon. We use stochastic barrier functions as a means to quantity the probability that a system exits a given safe region of the state space in finite time. A barrier certificate condition that bounds the infinitesimal generator of the system, and hence bounds the expected value of the barrier function over the time horizon, is recast as a sum-of-squares optimization problem for efficient numerical computation. Unlike prior works, the proposed certificate condition includes a state-dependent bound on the infinitesimal generator, allowing for tighter probability bounds. Moreover, for stochastic systems for which the drift dynamics are affine-in-control, we propose a method for synthesizing polynomial state feedback controllers that achieve a specified probability of safety. Two case studies are presented that benchmark and illustrate the performance of our method.
A Matrix-free Likelihood Method for Exploratory Factor Analysis of High-dimensional Gaussian Data.

This technical note proposes a novel profile likelihood method for estimating the covariance parameters in exploratory factor analysis of high-dimensional Gaussian datasets with fewer observations ...
Decentralized Heading Control With Rate Constraints Using Pulse-Coupled Oscillators

Decentralized heading control is crucial for robotic network operations, such as surveillance, exploration, and cooperative construction. However, few results consider decentralized heading control when the speed of heading adjustment is restricted. In this article, we propose a simple hybrid-dynamical model based on pulse-coupled oscillators for decentralized heading control in mobile robots while accounting for the constraint on the rate of heading change. The pulse-coupled oscillator model is effective in coordinating the phase of oscillator networks and, hence, is promising for robotic heading coordination given that both phase and heading evolve on the same 1-D torus. However, existing pulse-coupled oscillator results require the phase adjustment to be instantaneous, which cannot hold for robot heading adjustment due to physical limitations. We propose a generalization to the standard pulse-coupled oscillator model to allow for the phase to adjust at a finite rate, yet still have the oscillator network converge to the desired state, making our approach applicable to robotic heading coordination under rate constraints. We provide rigorous mathematical proofs for the achievement of both synchronized and desynchronized heading relationships and experimentally verify the results using extensive tests on a multi-robot platform.
Bifunctional nickel and copper electrocatalysts for CO2 reduction and the oxygen evolution reaction

In this study, a bifunctional electrocatalyst for CO2 reduction and the O2 evolution reaction (OER) was constructed from the electrodeposition of cuprous oxide (Cu2O) and Ni on a carbon substrate. Different Ni thicknesses on Cu2O were achieved by varying the time of chronopotentiometric deposition of Ni. Electrochemical CO2 reduction was carried out at −0.89 V and −1.89 V vs. RHE, and it was found that formate and CO were the two major products. Cu2O modified with a Ni overlayer with a thickness of ∼700 nm resulted in the highest formate faradaic efficiency of 18%, and Cu2O resulted in highest CO faradaic efficiency of 7.9%. The enhanced faradaic efficiency for formate is attributed to the synergistic effect between Ni and Cu2O due to maximized amounts of exposed bimetallic sites that facilitate CO2 reduction. The electrocatalyst also produces ∼9 times more current density than previous studies using Ni–Cu2O electrocatalysts for the OER. The ability of the Ni–Cu2O thin films to catalyze both the OER and CO2 reduction allows them to be incorporated in the first demonstration of a two-electrode CO2 conversion device with a bifunctional catalyst. In this architecture, the device produces formate and CO with faradaic efficiencies of 16.0% and 19.7%, respectively.
DDM: Fast Near-Optimal Multi-Robot Path Planning Using Diversified-Path and Optimal Sub-Problem Solution Database Heuristics

We propose a novel centralized and decoupled algorithm, DDM , for solving multi-robot path planning problems in grid graphs, targeting on-demand and automated warehouse-like settings. Two settings are studied: a traditional one whose objective is to move a set of robots from their respective initial vertices to the goal vertices as quickly as possible, and a dynamic one which requires frequent re-planning to accommodate for goal configuration adjustments. Among other techniques, DDM is mainly enabled through exploiting two innovative heuristics: path diversification and optimal sub-problem solution databases. The two heuristics attack two distinct phases of a decoupling-based planner: while path diversification allows the more effective use of the entire workspace for robot travel, optimal sub-problem solution databases facilitate the fast resolution of local path conflicts. Extensive evaluation demonstrates that DDM achieves high levels of scalability and solution quality close to the optimum.
A Longitudinal Study of Retirement Transition and Advance Directive Completion.

BACKGROUND AND OBJECTIVES Advance directives (AD) help to ensure patients' wishes are honored and contribute to improved end-of-life care. According to normative life course theory, retirement is a significant role change that signals a transition into the third age and its socially prescribed activities. This study tested whether transitioning from work to retirement prompted AD completion. RESEARCH DESIGN AND METHODS The sample included Health and Retirement Study participants who, in 2012, had no ADs and were not completely retired (N = 919). Three waves of data were analyzed using multinomial logistic regression to test associations between retirement transition and advance directive completion. RESULTS By 2014, 21% had completed ADs and another 17% completed them by 2016. Those who completely retired between 2012 and 2014 were almost twice as likely to complete ADs between 2014 and 2016. Graduated increase in level of retirement between 2012 and 2014 was associated with higher odds of new AD possession in 2016, but did not reach statistical significance at p < .05. DISCUSSION AND IMPLICATIONS The period following retirement may be an optimal time to encourage patients and clients who have not already done so to complete advance directives.