cssi/SciSciGPT-SciSciNet huggingface dataset:
More Than DNA Methylation: Does Pleiotropy Drive the Complex Pattern of Evolution of Dnmt1?

DNA methylation is an important chromatin modification that can stably alter gene expression in cells and maintain genome integrity in plants and vertebrates. The function of DNA methylation outside of these well-studied systems, however, is unclear. Insects, in particular, represent an understudied group. Variation in the level of DNA methylation and gains and losses in the maintenance methyltransferase, DNMT1, across the insect tree of life suggests that there is much we don’t understand about DMNT1 function and evolution. One constant across the studies examining patterns of Dnmt1 expression in insects is that expression is consistently high in reproductive tissues compared to somatic tissue. The explanation for this has been that DNMT1 is required in tissues that have high levels of cell division. Our previous study found that downregulation of Dnmt1 expression in the milkweed bug Oncopeltus fasciatus results in the expected reduction of DNA methylation, no global changes in gene expression reflecting changes in DNA methylation, and the loss of the ability to produce viable oocytes. Here, we show that females treated with ds-Dnmt1 RNA during larval development have a more extreme phenotype; they lack oocytes entirely but develop a normal somatic ovary. Our results indicate a specific role for DNMT1 in the formation of gametes and are consistent with data from other systems, including Tribolium castaneum, a species does not have DNA methylation. We propose that DNMT1 has multiple functional roles in addition to methylating DNA, which explains its complex patterns of evolution.
Evaluation of T2Candida Panel for detection of Candida in peritoneal dialysates

Fungal peritonitis in the peritoneal dialysis population is difficult to diagnose promptly due to the inherently slow cultivation-based methods currently required for identification of peritonitis pathogens. Because of the moderate risk for severe complications, the need for rapid diagnostics is considerable. One possible solution to this unmet need is the T2Candida Panel, a new technology designed to detect the most common pathogenic Candida spp. directly from whole blood specimens in as little as a few hours. We hypothesized that this technology could be applied to the detection of Candida in peritoneal dialysate, a matrix not currently approved by the Food and Drug Administration for testing by this system. Remnant dialysate samples from three healthy (noninfected) pediatric peritoneal dialysis patients were spiked with Candida glabrata, serially diluted, and tested in triplicate with unaltered dialysate specimens. The assay detected C. glabrata in 100% of spiked dialysate samples across the full spectrum of dilutions tested, and no assay inhibition or cross-reactivity was noted. These findings suggest one of possibly more applications of this technology. The positive clinical implications of this test will continue to be realized as its use is validated in peritoneal dialysate and other patient specimen types.
Offline state estimation for hybrid systems via nonsmooth variable projection

Abstract We propose an offline algorithm that simultaneously estimates discrete and continuous components of a hybrid system’s state. We formulate state estimation as a continuous optimization problem by relaxing the discrete component and using a robust loss function to accommodate large changes in the continuous component during switching events. Subsequently, we develop a novel nonsmooth variable projection algorithm with Gauss–Newton updates to solve the state estimation problem and prove the algorithm’s global convergence to stationary points. We demonstrate the effectiveness of our approach by comparing it to a state-of-the-art filter bank method, and by applying it to simple piecewise-linear and -nonlinear mechanical systems undergoing intermittent impact.
Modelling the on-going natural selection of educational attainment in contemporary societies.

Abstract There has been substantial increase in education attainment (EA) in both developing and developed countries over the past century. I present a simulation model to examine the potential evolutionary trajectories of EA under current selective pressure in western populations. With the assumption that EA is negatively correlated with fitness and has both a genetic component and a cultural component, I show that when prestige-biased transmission of the EA (i.e. people with more education are more likely to be copied) is present, the phenotype of EA is likely to keep increasing in the short term, yet the genetic component of EA may undergo a constant decline and become the limiting factor in further phenotypic increase.
Streaming and Batch Algorithms for Truss Decomposition

Truss decomposition is a method used to analyze large sparse graphs in order to identify successively better connected subgraphs. Since in many domains the underlying graph changes over time, its associated truss decomposition needs to be updated as well. This work focuses on the problem of incrementally updating an existing truss decomposition and makes the following three significant contributions. First, it presents a theory that identifies how the truss decomposition can change as new edges get added. Second, it develops an efficient incremental algorithm that incorporates various optimizations to update the truss decomposition after every edge addition. These optimizations are designed to reduce the number of edges that are explored by the algorithm. Third, it extends this algorithm to batch updates (i.e., where the truss decomposition needs to be updated after a set of edges are added), which reduces the overall computations that need to be performed. We evaluated the performance of our algorithms on real-world datasets. Our incremental algorithm achieves over 250000x average speedup for inserting an edge in a graph with 10 million edges relative to the non-incremental algorithm. Further, our experiments on batch updates show that our batch algorithm consistently performs better than the incremental algorithm.
A Case Study on using Deep Learning for Network Intrusion Detection

Deep Learning has been very successful in many application domains. However, its usefulness in the context of network intrusion detection has not been systematically investigated. In this paper, we report a case study on using deep learning for both supervised network intrusion detection and unsupervised network anomaly detection. We show that Deep Neural Networks (DNNs) can outperform other machine learning based intrusion detection systems, while being robust in the presence of dynamic IP addresses. We also show that Autoencoders can be effective for network anomaly detection.
An Epidemiological Model of Malaria Accounting for Asymptomatic Carriers

Asymptomatic individuals in the context of malarial disease are subjects who carry a parasite load, but do not show clinical symptoms. A correct understanding of the influence of asymptomatic individuals on transmission dynamics will provide a comprehensive description of the complex interplay between the definitive host (female Anopheles mosquito), intermediate host (human), and agent (Plasmodium parasite). The goal of this article is to conduct a rigorous mathematical analysis of a new compartmentalized malaria model accounting for asymptomatic human hosts for the purpose of calculating the basic reproductive number ([Formula: see text]) and determining the bifurcations that might occur at the onset of disease-free equilibrium. A point of departure of this model from others appearing in the literature is that the asymptomatic compartment is decomposed into two mutually disjoint sub-compartments by making use of the naturally acquired immunity of the population under consideration. After deriving the model, a qualitative analysis is carried out to classify the stability of the equilibria of the system. Our results show that the dynamical system is locally asymptotically stable provided that [Formula: see text]. However, this stability is not global, owning to the occurrence of a sub-critical bifurcation in which additional non-trivial sub-threshold equilibrium solutions appear in response to a specified parameter being perturbed. To ensure that the model does not undergo a backward bifurcation, we demand an auxiliary parameter denoted [Formula: see text] in addition to the threshold constraint [Formula: see text]. The authors hope that this qualitative analysis will fill in the gaps of what is currently known about asymptomatic malaria and aid in designing strategies that assist the further development of malaria control and eradication efforts.
Dynamic and Distributed Online Convex Optimization for Demand Response of Commercial Buildings

We extend the regret analysis of the online distributed weighted dual averaging ( DWDA ) algorithm from Hosseini et al. to the dynamic setting and provide the tightest dynamic regret bound known to date with respect to the time horizon for a distributed online convex optimization (OCO) algorithm. Our bound is linear in the cumulative difference between consecutive optima and does not depend explicitly on the time horizon. We use dynamic-online DWDA ( D-ODWDA ) and formulate a performance-guaranteed distributed online demand response approach for heating, ventilation, and air-conditioning (HVAC) systems of commercial buildings. We show the performance of our approach for fast timescale demand response in numerical simulations and obtain demand response decisions that closely reproduce the centralized optimal ones.
Eligibility for subcutaneous implantable cardioverter-defibrillator in congenital heart disease.

Background Adult congenital heart disease (ACHD) patients can benefit from a subcutaneous implantable cardioverter-defibrillator (S-ICD). Objective The purpose of this study was to assess left- and right-sided S-ICD eligibility in ACHD patients, use machine learning to predict S-ICD eligibility in ACHD patients, and transform 12-lead electrocardiogram (ECG) to S-ICD 3-lead ECG, and vice versa. Methods ACHD outpatients (n = 101; age 42 ± 14 years; 52% female; 85% white; left ventricular ejection fraction [LVEF] 56% ± 9%) were enrolled in a prospective study. Supine and standing 12-lead ECG were recorded simultaneously with a right- and left-sided S-ICD 3-lead ECG. Peak-to-peak QRS and T amplitudes; RR, PR, QT, QTc, and QRS intervals; Tmax, and R/Tmax (31 predictor variables) were tested. Model selection, training, and testing were performed using supine ECG datasets. Validation was performed using standing ECG datasets and an out-of-sample non-ACHD population (n = 68; age 54 ± 16 years; 54% female; 94% white; LVEF 61% ± 8%). Results Forty percent of participants were ineligible for S-ICD. Tetralogy of Fallot patients passed right-sided screening (57%) more often than left-sided screening (21%; McNemar χ2 P = .025). Female participants had greater odds of eligibility (adjusted odds ratio [OR] 5.9; 95% confidence interval [CI] 1.6–21.7; P = .008). Validation of the ridge models was satisfactory for standing left-sided (receiver operating characteristic area under the curve [ROC AUC] 0.687; 95% CI 0.582–0.791) and right-sided (ROC AUC 0.655; 95% CI 0.549–0.762) S-ICD eligibility prediction. Validation of transformation matrices showed satisfactory agreement ( Conclusion Nearly half of the contemporary ACHD population is ineligible for S-ICD. The odds of S-ICD eligibility are greater for female than for male ACHD patients. Machine learning prediction of S-ICD eligibility can be used for screening of S-ICD candidates.
Peak Strouhal frequency of subsonic jet noise as a function of Reynolds number

The narrowband spectral characteristics of the acoustic field generated by subsonic jets at low Reynolds numbers were studied in terms of a dimensionless power spectral density. It was found that the variation of the sound magnitude of the spectrum is an increasing function of the Reynolds number. The maximum level of the spectrum is not a linear function of the Reynolds number. The spectra are insensitive to Reynolds number above a critical value of about 100,000. Up to Reynolds number of this magnitude, the peak Strouhal frequency observed at 30 deg emission angle is identical with that at 90 deg for any exit Mach number. At higher Reynolds number the peak Strouhal number increases with increasing emission angle. The peak Strouhal number was found to be about 0.2, which is in agreement with previous findings that a few discrete modes at a Strouhal number of around 0.2 in low Reynolds number supersonic jets are powerful noise generators.
On the simulation of transonic shock-turbulent boundary layer interactions in cryogenic or heavy gas wind tunnels

Abstract : The role of the basic similitude parameters governing transonic normal shock-turbulent boundary layer interaction effects in cryogenic wind tunnel tests is studied theoretically for the non-separating case. Besides Mach and Reynolds number, these parameters are the wall to total temperatures ratio, specific heat ratio gamma, viscosity-temperature exponent and Prandtl number. The results show that lack of temperature ratio simulation has a significantly adverse effect on interactive skin friction and hence separation onset compared to the adiabatic free flight case; higher gamma's than air also may have some effect. (Author)
Computerized Therapeutic Decision Support

The increasing breadth and depth of clinical information stored electronically has enabled computerized therapeutic decision support systems to provide appropriate and timely advice to providers, which that is tailored specifically to a patient's clinical status. This chapter provides a taxonomy for therapeutic decision support systems in terms of the goals of the advice, the mechanism in which the advice is given, the information needed to generate the advice, and the underlying reasoning used. Examples of applications of therapeutic decision support systems are provided with an assessment of their utility within the complex processes of clinical care. Keywords: therapeutic decision support; computerized patient record; electronic medical record; patient safety; medical errors
WHO IS AFRAID OF THE FRIEDMAN RULE

In this paper, we explore the connection between optimal monetary policy and het- erogeneity among agents. We study a standard monetary economy with two types of agents in which the stationary distribution of money holdings is non-degenerate. Sans type-specific fiscal policy, we show that the zero-nominal-interest rate policy (the Friedman rule) does not maximize type-specific welfare; it may not maximize aggregate social welfare either. Indeed, one or, more surprisingly, both types may benefit if the central bank deviates from the Friedman rule. Our results suggest a positive explanation for why central banks around the world do not implement the Friedman rule.
Remote sensing of soil moisture by Skylab radiometer and scatterometer sensors

A Skylab experiment was designed to evaluate the feasibility of monitoring the moisture content of the soil from space. Data from various Skylab sensors were collected across two test sites while direct measurements of soil moisture were being made at the surface depths of soil. Correlations were obtained between the moisture content of the soil and radiometer sensors (S193 and S194) and the scatterometer instrument (S193). The high correlations obtained indicate that microwave sensors may be quite useful for such measurements in the future.-
Impact of Chromophoric Dissolved Organic Matter on UV Inhibition of Primary Productivity in the Sea

A model was developed to assess the impact of chromophoric dissolved organic matter (CDOM) on phytoplankton production within the euphotic zone. The rate of depth-integrated daily gross primary productivity within the euphotic zone was evaluated as a function of date, latitude, CDONI absorption characteristics, chlorophyll a (chl a) concentration, vertical stratification, and phytoplankton sensitivity to UV radiation (UVR). Results demonstrated that primary production was enhanced in the upper 30 m of the water column by the presence of CDOM, where predicted increases in production due to the removal of damaging UVR more than offset its reduction resulting from the absorption of photosynthetically usable radiation. At greater depths, where little UVR remained, primary production was always reduced due to removal by CDOM of photosynthetically usable radiation. When CDOM was distributed homogeneously within the euphotic zone, the integral over z [(GPP)(sub ez)], was reduced under most bio-optical (i.e. solar zenith angle, and CDOM absorption, and ozone concentration) and photophysiological production at depth was greater than the enhancement of production at the surface.
The Economics of Density: Evidence From the Berlin Wall

This paper develops a quantitative model of internal city structure that features agglomeration and dispersion forces and an arbitrary number of heterogeneous city blocks. The model remains tractable and amenable to empirical analysis because of stochastic shocks to commuting decisions, which yield a gravity equation for commuting flows. To structurally estimate agglomeration and dispersion forces, we use data on thousands of city blocks in Berlin for 1936, 1986, and 2006 and exogenous variation from the city's division and reunification. We estimate substantial and highly localized production and residential externalities. We show that the model with the estimated agglomeration parameters can account both qualitatively and quantitatively for the observed changes in city structure. We show how our quantitative framework can be used to undertake counterfactuals for changes in the organization of economic activity within cities in response, for example, to changes in the transport network.
Factors That Fit the Time Series and Cross-Section of Stock Returns

We propose a new method for estimating latent asset pricing factors that fit the time-series and cross-section of expected returns. Our estimator generalizes Principal Component Analysis (PCA) by including a penalty on the pricing error in expected returns. We show that our estimator strongly dominates PCA and finds weak factors with high Sharpe-ratios that PCA cannot detect. Studying a large number of characteristic sorted portfolios we find that five latent factors with economic meaning explain well the cross-section and time-series of returns. We show that out-of-sample the maximum Sharpe-ratio of our five factors is more than twice as large as with PCA with significantly smaller pricing errors. Our factors are based on only a subset of the stock characteristics implying that a significant amount of characteristic information is redundant.
Nanophotonics for Information Systems

The field of photonics finds applications in information technology, health care, lighting, and sensing. This paper explores the role of nanotechnology with focus on nanophotonics in dielectric and inhomogeneous metamaterials for optical communications, information and signal processing in data centers.
Distinct Transcriptomic Cell Types and Neural Circuits of the Subiculum and Prosubiculum along the Dorsal-Ventral Axis

Summary Subicular regions play important roles in spatial processing and many cognitive functions, and these are mainly attributed to the subiculum (Sub) rather than the prosubiculum (PS). Using single-cell RNA sequencing, we identify 27 transcriptomic cell types residing in sub-domains of the Sub and PS. Based on in situ expression of reliable transcriptomic markers, the precise boundaries of the Sub and PS are consistently defined along the dorsoventral axis. Using these borders to evaluate Cre-line specificity and tracer injections, we find bona fide Sub projections topographically to structures important for spatial processing and navigation. In contrast, the PS sends its outputs to widespread brain regions crucial for motivation, emotion, reward, stress, anxiety, and fear. The Sub and PS, respectively, dominate dorsal and ventral subicular regions and receive different afferents. These results reveal two molecularly and anatomically distinct circuits centered in the Sub and PS, respectively, providing a consistent explanation for historical data and a clearer foundation for future studies.
Expected Constant Round Byzantine Broadcast Under Dishonest Majority

Byzantine Broadcast (BB) is a central question in distributed systems, and an important challenge is to understand its round complexity. Under the honest majority setting, it is long known that there exist randomized protocols that can achieve BB in expected constant rounds, regardless of the number of nodes n. However, whether we can match the expected constant round complexity in the corrupt majority setting—or more precisely, when \(f \ge n/2 + \omega (1)\)—remains unknown, where f denotes the number of corrupt nodes. In this paper, we are the first to resolve this long-standing question. We show how to achieve BB in expected \(O((n/(n-f))^2)\) rounds. Our results hold under a weakly adaptive adversary who cannot perform “after-the-fact removal” of messages already sent by a node before it becomes corrupt. We also assume trusted setup and the Decision Linear (DLIN) assumption in bilinear groups.
On Faithfulness and Factuality in Abstractive Summarization

It is well known that the standard likelihood training and approximate decoding objectives in neural text generation models lead to less human-like responses for open-ended tasks such as language modeling and story generation. In this paper we have analyzed limitations of these models for abstractive document summarization and found that these models are highly prone to hallucinate content that is unfaithful to the input document. We conducted a large scale human evaluation of several neural abstractive summarization systems to better understand the types of hallucinations they produce. Our human annotators found substantial amounts of hallucinated content in all model generated summaries. However, our analysis does show that pretrained models are better summarizers not only in terms of raw metrics, i.e., ROUGE, but also in generating faithful and factual summaries as evaluated by humans. Furthermore, we show that textual entailment measures better correlate with faithfulness than standard metrics, potentially leading the way to automatic evaluation metrics as well as training and decoding criteria.
The Summary Loop: Learning to Write Abstractive Summaries Without Examples.

This work presents a new approach to unsupervised abstractive summarization based on maximizing a combination of coverage and fluency for a given length constraint. It introduces a novel method that encourages the inclusion of key terms from the original document into the summary: key terms are masked out of the original document and must be filled in by a coverage model using the current generated summary. A novel unsupervised training procedure leverages this coverage model along with a fluency model to generate and score summaries. When tested on popular news summarization datasets, the method outperforms previous unsupervised methods by more than 2 R-1 points, and approaches results of competitive supervised methods. Our model attains higher levels of abstraction with copied passages roughly two times shorter than prior work, and learns to compress and merge sentences without supervision.
Positive semidefinite programming: mixed, parallel, and width-independent

We give the first approximation algorithm for mixed packing and covering semidefinite programs (SDPs) with polylogarithmic dependence on width. Mixed packing and covering SDPs constitute a fundamental algorithmic primitive with applications in combinatorial optimization, robust learning, and quantum complexity. The current approximate solvers for positive semidefinite programming can handle only pure packing instances, and technical hurdles prevent their generalization to a wider class of positive instances. For a given multiplicative accuracy of є, our algorithm takes O(log3(ndρ) · є−3) parallelizable iterations, where n, d are the problem dimensions and ρ is a width parameter of the instance, generalizing or improving all previous parallel algorithms in the positive linear and semidefinite programming literature. When specialized to pure packing SDPs, our algorithm’s iteration complexity is O(log2 (nd) · є−2), a slight improvement and derandomization of the state-of-the-art due to Allen-Zhu et. al. ’16, Peng et. al. ’16, and Wang et. al. ’15. For several common structured instances, the iterations of our algorithm run in nearly-linear time. In doing so, we give matrix analytic techniques to overcome obstacles that have stymied prior approaches to this problem, as stated in Peng et. al. ’16 and Mahoney et. al. ’16. Crucial to our analysis are a simplification of existing algorithms for mixed positive linear programs, achieved by removing an asymmetry from modifying covering constraints, and a suite of matrix inequalities with proofs based on analyzing the Schur complements of matrices in a higher dimension. We hope that our algorithm and techniques open the door to improved solvers for positive semidefinite programming and its applications.
Rethinking Classification and Localization for Object Detection

Two head structures (i.e. fully connected head and convolution head) have been widely used in R-CNN based detectors for classification and localization tasks. However, there is a lack of understanding of how does these two head structures work for these two tasks. To address this issue, we perform a thorough analysis and find an interesting fact that the two head structures have opposite preferences towards the two tasks. Specifically, the fully connected head (fc-head) is more suitable for the classification task, while the convolution head (conv-head) is more suitable for the localization task. Furthermore, we examine the output feature maps of both heads and find that fc-head has more spatial sensitivity than conv-head. Thus, fc-head has more capability to distinguish a complete object from part of an object, but is not robust to regress the whole object. Based upon these findings, we propose a Double-Head method, which has a fully connected head focusing on classification and a convolution head for bounding box regression. Without bells and whistles, our method gains +3.5 and +2.8 AP on MS COCO dataset from Feature Pyramid Network (FPN) baselines with ResNet-50 and ResNet-101 backbones, respectively.
Income-driven Labor Market Polarization

We propose a mechanism for labor-market polarization based on the nonhomotheticity of demand that we call the income-driven channel. Our mechanism builds on a novel empirical fact: expenditure elasticities and production intensities in low- and high-skill occupations are positively correlated across sectors. Thus, as income grows, demand shifts towards expenditure-elastic sectors, and the relative demand for low- and high-skill occupations increases, causing labor-market polarization. A calibrated general-equilibrium model suggests this mechanism accounts for 90% and 35% of the increase in the wage-bill share of low- and high-skill occupations observed in the US during 1980-2016, and for 64% and 28% of the rise in the employment shares of low- and high-skill occupations. This mechanism is similarly important for the polarization of labor markets in Western Europe during 1980-2016, as well as in the US during earlier decades and, possibly, the near future.
Quality-Adjusted House Price Indexes

The constant-quality assumption in repeat-sales house price indexes (HPIs) introduces a significant time-varying attribute bias. The direction, magnitude, and source of the bias varies throughout the market cycle and across metropolitan statistical areas (MSAs). We mitigate the bias using a data-driven textual analysis approach that identifies and includes salient text from real estate agent remarks in the repeat-sales estimation. Absent the text, MSA-level HPIs are biased downward by as much as 7 percent during the financial crisis and upward by as much as 20 percent after the crisis. The geographic concentration of the bias magnifies its effect on local HPIs.
Bayesian modelling of high-throughput sequencing assays with malacoda

NGS studies have uncovered an ever-growing catalog of human variation while leaving an enormous gap between observed variation and experimental characterization of variant function. High-throughput screens powered by NGS have greatly increased the rate of variant functionalization, but the development of comprehensive statistical methods to analyze screen data has lagged. In the massively parallel reporter assay (MPRA), short barcodes are counted by sequencing DNA libraries transfected into cells and the cell's output RNA in order to simultaneously measure the shifts in transcription induced by thousands of genetic variants. These counts present many statistical challenges, including overdispersion, depth dependence, and uncertain DNA concentrations. So far, the statistical methods used have been rudimentary, employing transformations on count level data and disregarding experimental and technical structure while failing to quantify uncertainty in the statistical model. We have developed an extensive framework for the analysis of NGS functionalization screens available as an R package called malacoda (available from github.com/andrewGhazi/malacoda). Our software implements a probabilistic, fully Bayesian model of screen data. The model uses the negative binomial distribution with gamma priors to model sequencing counts while accounting for effects from input library preparation and sequencing depth. The method leverages the high-throughput nature of the assay to estimate the priors empirically. External annotations such as ENCODE data or DeepSea predictions can also be incorporated to obtain more informative priors-a transformative capability for data integration. The package also includes quality control and utility functions, including automated barcode counting and visualization methods. To validate our method, we analyzed several datasets using malacoda and alternative MPRA analysis methods. These data include experiments from the literature, simulated assays, and primary MPRA data. We also used luciferase assays to experimentally validate several hits from our primary data, as well as variants for which the various methods disagree and variants detectable only with the aid of external annotations.
The Tradeoff Between Coverage and Computation in Wireless Networks

We consider a distributed edge computing scenario consisting of several wireless nodes that are located over an area of interest. Specifically, some of the “master” nodes are tasked to sense the environment (e.g., by acquiring images or videos via cameras) and process the corresponding sensory data, while the other nodes are assigned as “workers” to help the computationally-intensive processing tasks of the masters. A new tradeoff that has not been previously explored in the existing literature arises in such a formulation: On one hand, one wishes to allocate as many master nodes as possible to cover a large area for accurate monitoring. On the other hand, one also wishes to allocate as many worker nodes as possible to maximize the computation rate of the sensed data. It is in the context of this tradeoff that this work is presented. By utilizing the basic physical layer principles of wireless communication systems, we formulate and analyze the tradeoff between the coverage and computation performance of spatial networks. We also present an algorithm to find the optimal tradeoff and demonstrate its performance through numerical simulations.
Deep Generalization of Structured Low-Rank Algorithms (Deep-SLR)

Structured low-rank (SLR) algorithms, which exploit annihilation relations between the Fourier samples of a signal resulting from different properties, is a powerful image reconstruction framework in several applications. This scheme relies on low-rank matrix completion to estimate the annihilation relations from the measurements. The main challenge with this strategy is the high computational complexity of matrix completion. We introduce a deep learning (DL) approach to significantly reduce the computational complexity. Specifically, we use a convolutional neural network (CNN)-based filterbank that is trained to estimate the annihilation relations from imperfect (under-sampled and noisy) k-space measurements of Magnetic Resonance Imaging (MRI). The main reason for the computational efficiency is the pre-learning of the parameters of the non-linear CNN from exemplar data, compared to SLR schemes that learn the linear filterbank parameters from the dataset itself. Experimental comparisons show that the proposed scheme can enable calibration-less parallel MRI; it can offer performance similar to SLR schemes while reducing the runtime by around three orders of magnitude. Unlike pre-calibrated and self-calibrated approaches, the proposed uncalibrated approach is insensitive to motion errors and affords higher acceleration. The proposed scheme also incorporates image domain priors that are complementary, thus significantly improving the performance over that of SLR schemes.
Efficiency of Harmonic Quantum Otto Engines at Maximal Power

Recent experimental breakthroughs produced the first nano heat engines that have the potential to harness quantum resources. An instrumental question is how their performance measures up against the efficiency of classical engines. For single ion engines undergoing quantum Otto cycles it has been found that the efficiency at maximal power is given by the Curzon-Ahlborn efficiency. This is rather remarkable as the Curzon-Alhbron efficiency was originally derived for endoreversible Carnot cycles. Here, we analyze two examples of endoreversible Otto engines within the same conceptual framework as Curzon and Ahlborn's original treatment. We find that for endoreversible Otto cycles in classical harmonic oscillators the efficiency at maximal power is, indeed, given by the Curzon-Ahlborn efficiency. However, we also find that the efficiency of Otto engines made of quantum harmonic oscillators is significantly larger.
Extreme Software Defined Radio – GHz in Real Time

Software defined radio is a widely accepted paradigm for design of reconfigurable modems. The continuing march of Moore's law makes real-time signal processing on general purpose processors feasible for a large set of waveforms. Data rates in the low Mbps can be processed on low-power ARM processors, and much higher data rates can be supported on large x86 processors. The advantages of all-software development (vs. FPGA/DSP/GPU) are compelling – much wider pool of talent, lower development time and cost, and easier maintenance and porting. However, very high-rate systems (above 100 Mbps) are still firmly in the domain of custom and semi-custom hardware (mostly FPGAs). In this paper we describe an architecture and testbed for an SDR that can be easily scaled to support over 3 GHz of bandwidth and data rate up to 10 Gbps. The paper covers a novel technique to parallelize typically serial algorithms for phase and symbol tracking, followed by a discussion of data distribution for a massively parallel architecture. We provide a brief description of a mixed-signal front end and conclude with measurement results. To the best of the author's knowledge, the system described in this paper is an order of magnitude faster than any prior published result.
Off-Policy Estimation of Long-Term Average Outcomes with Applications to Mobile Health.

Due to the recent advancements in wearables and sensing technology, health scientists are increasingly developing mobile health (mHealth) interventions. In mHealth interventions, mobile devices are...
Stochasticity and robustness in spiking neural networks

Abstract Despite drawing inspiration from biological systems which are inherently noisy and variable, artificial neural networks have been shown to require precise weights to carry out the task which they are trained to accomplish. This creates a challenge when adapting these artificial networks to specialized execution platforms which may encode weights in a manner which restricts their accuracy and/or precision. Reflecting back on the non-idealities which are observed in biological systems, we investigated the effect these properties have on the robustness of spiking neural networks under perturbations to weights. First, we examined techniques extant in conventional neural networks which resemble noisy processes, and postulated they may produce similar beneficial effects in spiking neural networks. Second, we evolved a set of spiking neural networks utilizing biological non-idealities to solve a pole-balancing task, and estimated their robustness. We showed it is higher in networks using noisy neurons, and demonstrated that one of these networks can perform well under the variance expected when a hafnium-oxide based resistive memory is used to encode synaptic weights. Lastly, we trained a series of networks using a surrogate gradient method on the MNIST classification task. We confirmed that these networks demonstrate similar trends in robustness to the evolved networks. We discuss these results and argue that they display empirical evidence supporting the role of noise as a regularizer which can increase network robustness.
Attractor-state itinerancy in neural circuits with synaptic depression

Neural populations with strong excitatory recurrent connections can support bistable states in their mean firing rates. Multiple fixed points in a network of such bistable units can be used to model memory retrieval and pattern separation. The stability of fixed points may change on a slower timescale than that of the dynamics due to short-term synaptic depression, leading to transitions between quasi-stable point attractor states in a sequence that depends on the history of stimuli. To better understand these behaviors, we study a minimal model, which characterizes multiple fixed points and transitions between them in response to stimuli with diverse time- and amplitude-dependencies. The interplay between the fast dynamics of firing rate and synaptic responses and the slower timescale of synaptic depression makes the neural activity sensitive to the amplitude and duration of square-pulse stimuli in a nontrivial, history-dependent manner. Weak cross-couplings further deform the basins of attraction for different fixed points into intricate shapes. We find that while short-term synaptic depression can reduce the total number of stable fixed points in a network, it tends to strongly increase the number of fixed points visited upon repetitions of fixed stimuli. Our analysis provides a natural explanation for the system’s rich responses to stimuli of different durations and amplitudes while demonstrating the encoding capability of bistable neural populations for dynamical features of incoming stimuli.
Fisher Information and Logarithmic Sobolev Inequality for Matrix-Valued Functions

We prove a version of Talagrand’s concentration inequality for subordinated sub-Laplacians on a compact Riemannian manifold using tools from noncommutative geometry. As an application, motivated by quantum information theory, we show that on a finite-dimensional matrix algebra the set of self-adjoint generators satisfying a tensor stable modified logarithmic Sobolev inequality is dense.
Exploring Help Facilities in Game-Making Software

Help facilities have been crucial in helping users learn about software for decades. But despite widespread prevalence of game engines and game editors that ship with many of today’s most popular games, there is a lack of empirical evidence on how help facilities impact game-making. For instance, certain types of help facilities may help users more than others. To better understand help facilities, we created game-making software that allowed us to systematically vary the type of help available. We then ran a study of 1646 participants that compared six help facility conditions: 1) Text Help, 2) Interactive Help, 3) Intelligent Agent Help, 4) Video Help, 5) All Help, and 6) No Help. Each participant created their own first-person shooter game level using our game-making software with a randomly assigned help facility condition. Results indicate that Interactive Help has a greater positive impact on time spent, controls learnability, learning motivation, total editor activity, and game level quality. Video Help is a close second across these same measures.
Targeted Forgetting and False Memory Formation in Continual Learners through Adversarial Backdoor Attacks

Artificial neural networks are well-known to be susceptible to catastrophic forgetting when continually learning from sequences of tasks. Various continual (or "incremental") learning approaches have been proposed to avoid catastrophic forgetting, but they are typically adversary agnostic, i.e., they do not consider the possibility of a malicious attack. In this effort, we explore the vulnerability of Elastic Weight Consolidation (EWC), a popular continual learning algorithm for avoiding catastrophic forgetting. We show that an intelligent adversary can take advantage of EWC’s continual learning capabilities to cause gradual and deliberate forgetting by introducing small amounts of misinformation to the model during training. We demonstrate such an adversary’s ability to assume control of the model via injection of backdoor attack samples on both permuted and split benchmark variants of the MNIST dataset. Importantly, once the model has learned the adversarial misinformation, the adversary can then control the amount of forgetting of any task. Equivalently, the malicious actor can create a "false memory" about any task by inserting carefully-designed backdoor samples to any fraction of the test instances of that task. Perhaps most damaging, we show this vulnerability to be very acute; the model memory can be easily compromised with the addition of backdoor samples into as little as 1% of the training data of even a single task.
Privacy-Preserving Deep Action Recognition: An Adversarial Learning Framework and A New Dataset.

We investigate privacy-preserving action recognition in deep learning, a problem with growing importance in smart camera applications. A novel adversarial training framework is formulated to learn an anonymization transform for input videos such that the trade-off between target utility task performance and the associated privacy budgets is explicitly optimized on the anonymized videos. Notably, the privacy budget, often defined and measured in task-driven contexts, cannot be reliably indicated using any single model performance, because strong protection of privacy should sustain against any malicious model that tries to steal private information. To tackle this problem, we propose two new optimization strategies of model restarting and model ensemble to achieve stronger universal privacy protection against any attacker models. Extensive experiments have been carried out and analyzed. On the other hand, given few public datasets available with both utility and privacy labels, the data-driven (supervised) learning cannot exert its full power on this task. To further address this dataset challenge, we have constructed a new dataset, termed PA-HMDB51, with both target task labels (action) and selected privacy attributes (gender, age, race, nudity, and relationship) annotated on a per-frame basis. This first-of-its-kind video dataset and evaluation protocol can greatly facilitate visual privacy research.
PolymoRF: polymorphic wireless receivers through physical-layer deep learning

Today's wireless technologies are largely based on inflexible designs, which makes them inefficient and prone to a variety of wireless attacks. To address this key issue, wireless receivers will need to (i) infer on-the-fly the physical-layer parameters currently used by transmitters; and if needed, (ii) change their hardware and software structures to demodulate the incoming waveform. In this paper, we introduce PolymoRF, a deep learning-based polymorphic receiver able to reconfigure itself in real time based on the inferred waveform parameters. Our key technical innovations are (i) a novel embedded deep learning architecture, called RFNet, which enables the solution of key waveform inference problems; (ii) a generalized hardware/software architecture that integrates RFNet with radio components and signal processing. We prototype PolymoRF on a custom software-defined radio platform, and show through extensive over-the-air experiments that PolymoRF achieves throughput within 87% of a perfect-knowledge Oracle system, thus demonstrating for the first time that polymorphic receivers are feasible.
Look Locally Infer Globally: A Generalizable Face Anti-Spoofing Approach

State-of-the-art presentation attack detection approaches tend to overfit to the presentation attack instruments seen during training and fail to generalize to unknown presentation attack instruments. Given that face presentation attack detection is inherently a local task, we propose a face presentation attack detection framework, namely Self-Supervised Regional Fully Convolutional Network ( SSR-FCN ), that is trained to learn local discriminative cues from a face image in a self-supervised manner. The proposed framework (i) improves generalizability while maintaining the computational efficiency of holistic face presentation attack detection approaches ( SSR-FCN can achieve TDR = 65% @ 2.0% FDR when evaluated on a dataset, SiW-M, comprising of 13 different presentation attack instruments under unknown attacks while achieving competitive performances under standard benchmark datasets (Oulu-NPU, CASIA-MFSD, and Replay-Attack).
Enhanced Detection of Fetal Pose in 3D MRI by Deep Reinforcement Learning with Physical Structure Priors on Anatomy

Fetal MRI is heavily constrained by unpredictable and substantial fetal motion that causes image artifacts and limits the set of viable diagnostic image contrasts. Current mitigation of motion artifacts is predominantly performed by fast, single-shot MRI and retrospective motion correction. Estimation of fetal pose in real time during MRI stands to benefit prospective methods to detect and mitigate fetal motion artifacts where inferred fetal motion is combined with online slice prescription with low-latency decision making. Current developments of deep reinforcement learning (DRL), offer a novel approach for fetal landmarks detection. In this task 15 agents are deployed to detect 15 landmarks simultaneously by DRL. The optimization is challenging, and here we propose an improved DRL that incorporates priors on physical structure of the fetal body. First, we use graph communication layers to improve the communication among agents based on a graph where each node represents a fetal-body landmark. Further, additional reward based on the distance between agents and physical structures such as the fetal limbs is used to fully exploit physical structure. Evaluation of this method on a repository of 3-mm resolution in vivo data demonstrates a mean accuracy of landmark estimation 10 mm of ground truth as 87.3%, and a mean error of 6.9 mm. The proposed DRL for fetal pose landmark search demonstrates a potential clinical utility for online detection of fetal motion that guides real-time mitigation of motion artifacts as well as health diagnosis during MRI of the pregnant mother.