cssi/SciSciGPT-SciSciNet huggingface dataset:
A Ternary Based Bit Scalable, 8.80 TOPS/W CNN accelerator with Many-core Processing-in-memory Architecture with 896K synapses/mm 2

A Processing-In-Memory (PIM) accelerator with ternary SRAM is proposed for low-power, large-scale deep neural network (DNN) processing. The accelerator consists of Ternary Neural Arithmetic Memory (TNAM) which is capable of bit-scalable MAC (multiply and accumulation) operation in accordance with target accuracy and power limit. An ADC less readout circuits to reduce analog-digital conversion power and a system-level variation avoidance technique utilizing features of TNAM are also proposed. A test chip with large-scale PIM is fabricated and successfully operate convolutional neural networks (CNNs) with 8.8TOPS/W and highest accuracy and area density among recent SRAM-type PIMs are obtained.
Fracture Patterns Differ Between Osteogenesis Imperfecta and Routine Pediatric Fractures

Purpose: Physicians are sometimes called upon to estimate the likelihood that an individual fracture may be due to Osteogenesis Imperfecta (OI), especially the least severe type I OI. Additionally, parents often question whether their child’s fracture may represent abnormal bone quality. We sought differences between OI and routine pediatric fractures for diagnostic purposes. Methods: A retrospective review of patients with OI and without OI presenting between January 1, 2003 and December 31, 2015 at a single institution was performed. Closed fractures occurring between 0 and 21 years that did not result from motor vehicle accidents and non-accidental trauma were included. Fractures in the non-OI group presumed to be pathologic were excluded. Data collected were age …
Prescriptive Cluster-Dependent Support Vector Machines with an Application to Reducing Hospital Readmissions *

We augment linear Support Vector Machine (SVM) classifiers by adding three important features: (i) we introduce a regularization constraint to induce a sparse classifier; (ii) we devise a method that partitions the positive class into clusters and selects a sparse SVM classifier for each cluster; and (iii) we develop a method to optimize the values of controllable variables in order to reduce the number of data points which are predicted to have an undesirable outcome, which, in our setting, coincides with being in the positive class. The latter feature leads to personalized prescriptions/recommendations. We apply our methods to the problem of predicting and preventing hospital readmissions within 30-days from discharge for patients that underwent a general surgical procedure. To that end, we leverage a large dataset containing over 2.28 million patients who had surgeries in the period 2011–2014 in the U.S. The dataset has been collected as part of the American College of Surgeons National Surgical Quality Improvement Program (NSOIP).
Understanding the Role of Momentum in Stochastic Gradient Methods

The use of momentum in stochastic gradient methods has become a widespread practice in machine learning. Different variants of momentum, including heavy-ball momentum, Nesterov's accelerated gradient (NAG), and quasi-hyperbolic momentum (QHM), have demonstrated success on various tasks. Despite these empirical successes, there is a lack of clear understanding of how the momentum parameters affect convergence and various performance measures of different algorithms. In this paper, we use the general formulation of QHM to give a unified analysis of several popular algorithms, covering their asymptotic convergence conditions, stability regions, and properties of their stationary distributions. In addition, by combining the results on convergence rates and stationary distributions, we obtain sometimes counter-intuitive practical guidelines for setting the learning rate and momentum parameters.
Why physics in medicine

Despite its crucial role in the development of new medical imaging technologies, in clinical practice, physics has primarily been involved in technical evaluation of technologies. However, this narrow role is no longer adequate. New trajectories in medicine call for a stronger role for physics in the clinic. The movement towards evidence-based, quantitative, and value-based medicine requires physicists to play a more integral role in delivering innovative precision care through the intentional clinical application of physical sciences. There are three aspects of this clinical role: technology assessment based on metrics as they relate to expected clinical performance, optimized use of technologies for patient-centered clinical outcomes, and retrospective analysis of imaging operations to ensure attainment of expectations in terms of quality and variability. These tasks fuel the drive towards high-quality, consistent practice of medical imaging that is patient-centered, evidence-based, and safe. While this particular article focuses on imaging, this trajectory and paradigm is equally applicable to the multitudes of the applications of physics in medicine.
Alcohol-preferring P rats exhibit aversion-resistant drinking of alcohol adulterated with quinine.

Abstract Understanding why some people continue to drink alcohol despite negative consequences and others do not is a central problem in the study of alcohol use disorder (AUD). In this study, we used alcohol-preferring P rats (a strain bred to prefer to drink alcohol, a model for genetic risk for AUD) and Wistar rats (control) to examine drinking despite negative consequences in the form of an aversive bitter taste stimulus produced by quinine. Animals were trained to consume 10% ethanol in a simple Pavlovian conditioning task that paired alcohol access with an auditory stimulus. When the alcohol was adulterated with quinine (0.1 g/L), P rats continued to consume alcohol + quinine at the same rate as unadulterated alcohol, despite a demonstrated aversion to quinine-adulterated alcohol when given a choice between adulterated and unadulterated alcohol in the home cage. Conversely, Wistar rats decreased consumption of quinine-adulterated alcohol in the task, but continued to try the alcohol + quinine solution at similar rates to unadulterated alcohol. These results indicate that following about 8 weeks of alcohol consumption, P rats exhibit aversion-resistant drinking. This model could be used in future work to explore how the biological basis of alcohol consumption and genetic risk for excessive drinking lead to drinking that is resistant to devaluation.
Functionally Distinct Gamma Range Activity Revealed by Stimulus Tuning in Human Visual Cortex.

Summary Neocortical gamma activity has long been hypothesized as a mechanism for synchronizing brain regions to support visual perception and cognition more broadly. Although early studies focused on narrowband gamma oscillations (∼20–60 Hz), recent work has emphasized a more broadband “high-gamma” response (∼70–150+ Hz). These responses are often conceptually or analytically treated as synonymous markers of gamma activity. Using high-density intracranial recordings from the human visual cortex, we challenge this view by showing distinct spectral, temporal, and functional properties of narrow and broadband gamma. Across four experiments, narrowband gamma was strongly selective for gratings and long-wavelength colors, displaying a delayed response onset, sustained temporal profile, and contrast-dependent peak frequency. In addition, induced narrowband gamma oscillations lacked phase consistency across stimulus repetitions and displayed highly focal inter-site synchronization. In contrast, broadband gamma was consistently observed for all presented stimuli, displaying a rapid response onset, transient temporal profile, and invariant spectral properties. We exploited stimulus tuning to highlight the functional dissociation of these distinct signals, reconciling prior inconsistencies across species and stimuli regarding the ubiquity of visual gamma oscillations during natural vision. The occurrence of visual narrowband gamma oscillations, unlike broadband high gamma, appears contingent on specific structural and chromatic stimulus attributes intersecting with the receptive field. Together, these findings have important implications for the study, analysis, and functional interpretation of neocortical gamma-range activity.
Active non-linear noise control with non-linearities in the secondary path

The filtered-X LMS algorithm is successfully applied in linear active noise control and linear active vibration control. However, when it comes to non-linear noise or vibration control, or when the secondary path has a non-linearity, this algorithm must be modified to ensure that it converges and that it is unbiased. Some modified algorithms have been proposed for active control of nonlinear noise with a linear secondary path [L.Z. Tan and J. Jiang, Aug. 2001] or a weakly non-linear secondary path [V. DeBrunner and D. Zhou, 2002]. In this paper, we extend 'a non-linear filtered-X LMS' algorithm to solve the active non-linear noise control (ANNC) and active non-linear vibration control (ANVC) with non-linearities in the secondary path using an adaptive Volterra filter for the first time. Two improved versions of the non-linear adaptive filters that improve the stability and reduce the computational complexity are proposed and discussed in detail. Simulations show their effectiveness.
Blt Azumaya algebras and moduli of maximal orders

We study moduli spaces of maximal orders in a ramified division algebra over the function field of a smooth projective surface. As in the case of moduli of stable commutative surfaces, we show that there is a Kollar-type condition giving a better moduli problem with the same geometric points: the stack of blt Azumaya algebras. One virtue of this refined moduli problem is that it admits a compactification with a virtual fundamental class.
How Austrians can contribute to constitutional political economy (and why they should)

Will a society’s political agents provide good governance? An answer must be sought first and foremost at the constitutional level. While Austrians have made important contributions to constitutional political economy (CPE), they have often avoided interesting and important questions regarding today’s constitutional realities. This is particularly true when it comes to de jure constitutional design. Why do de jure constitutions matter? Can they be designed such that they are robust? When they are not robust, does constitutional drift lead to a better or worse governance environment? In discussing these questions, I attempt to point Austrian scholars towards some potentially fruitful CPE research avenues.
Worst-case complexity of cyclic coordinate descent: $$O(n^2)$$ gap with randomized version

This paper concerns the worst-case complexity of cyclic coordinate descent (C-CD) for minimizing a convex quadratic function, which is equivalent to Gauss–Seidel method, Kaczmarz method and projection onto convex sets (POCS) in this simple setting. We observe that the known provable complexity of C-CD can be $$\mathcal {O}(n^2)$$ times slower than randomized coordinate descent (R-CD), but no example was proven to exhibit such a large gap. In this paper we show that the gap indeed exists. We prove that there exists an example for which C-CD takes at least $$\mathcal {O}(n^4 \kappa _{\text {CD}} \log \frac{1}{\epsilon })$$ operations, where $$\kappa _{\text {CD}}$$ is related to Demmel’s condition number and it determines the convergence rate of R-CD. It implies that in the worst case C-CD can indeed be $$\mathcal {O}(n^2)$$ times slower than R-CD, which has complexity $$\mathcal {O}( n^2 \kappa _{\text {CD}} \log \frac{1}{\epsilon })$$ . Note that for this example, the gap exists for any fixed update order, not just a particular order. An immediate consequence is that for Gauss–Seidel method, Kaczmarz method and POCS, there is also an $$\mathcal {O}(n^2) $$ gap between the cyclic versions and randomized versions (for solving linear systems). One difficulty with the analysis is that the spectral radius of a non-symmetric iteration matrix does not necessarily constitute a lower bound for the convergence rate. Finally, we design some numerical experiments to show that the size of the off-diagonal entries is an important indicator of the practical performance of C-CD.
Denoising of Aligned Genomic Data

Noise in genomic sequencing data is known to have effects on various stages of genomic data analysis pipelines. Variant identification is an important step of many of these pipelines, and is increasingly being used in clinical settings to aid medical practices. We propose a denoising method, dubbed SAMDUDE, which operates on aligned genomic data in order to improve variant calling performance. Denoising human data with SAMDUDE resulted in improved variant identification in both individual chromosome as well as whole genome sequencing (WGS) data sets. In the WGS data set, denoising led to identification of almost 2,000 additional true variants, and elimination of over 1,500 erroneously identified variants. In contrast, we found that denoising with other state-of-the-art denoisers significantly worsens variant calling performance. SAMDUDE is written in Python and is freely available at https://github.com/ihwang/SAMDUDE .
RF FDSOI Technology and Modelling

The combination of high performance mmWave FET transistors, low voltage logic, and low complexity mask build makes FDSOI ideal for many RF mm Wave applications including IOT, 5G, and Radar. This paper will focus on the technology attributes of 22FDX FDSOI technology.
Capital inflows, sustained investment surges and the role of external economies of scale in a developing economy

Standard open economy macro models with unemployment predict a contractionary short-run effect of international capital inflows. Empirical evidence, on the other hand, often associates such inflows with short-term booms, and developing country policy makers frequently go out of their way to welcome foreign capital. Employing a portfolio balance framework, this paper distinguishes between international financial (i.e., bond) and "real" (i.e., equity) flows to explore the different consequences for capital accumulation that may follow over the medium run. The presence of external economies of scale generates multiple equilibria, and different kinds of capital flows may push investment in one direction or the other for sustained periods of time.
Adaptive control systems with L/sup 1+/spl alpha// tracking

A redesign of model reference adaptive control is proposed using a non-quadratic Lyapunov function to derive a new adaptive law. For systems with relative degree one such a redesign ensures a tracking error which belongs to L/sup 1+/spl alpha//. A choice of 0</spl alpha/<1 leads to a generically faster convergence of the tracking error to zero than the standard adaptive law with /spl alpha/=1. Simulations indicate that such a redesign improves system tracking performance.
Asymptotic Analysis for Greedy Initialization of Threshold-Based Distributed Optimization of Persistent Monitoring on Graphs

Abstract We consider the optimal multi-agent persistent monitoring problem defined for a team of agents on a set of nodes (targets) interconnected according to a fixed graph topology. The objective is to minimize a measure of mean overall node state uncertainty evaluated over a finite time interval. In prior work, a class of distributed threshold-based parametric controllers has been proposed where agent dwell times at nodes and transitions from one node to the next are controlled by enforcing thresholds on the respective node uncertainties. Under such a threshold policy, on-line gradient-based techniques are then used to determine optimal threshold values. However, due to the non-convexity of the problem, this approach leads to often poor local optima highly dependent on the initial thresholds used. To overcome this initialization challenge, in this paper, the asymptotic steady-state behavior of the agent-target system is extensively analyzed for a single-agent system and dense graphs. Based on the obtained theoretical results, a computationally efficient off-line greedy technique is developed to systematically generate initial thresholds. Extensive numerical results show that the initial thresholds obtained lead to significantly better results than the locally optimal solutions known to date.
Understanding Pregnant Women with Opioid Use Disorder

Background: Pregnant women with opioid use disorder are a major concern to the healthcare industry. They are not only one of the most stigmatized groups but are in need of outreach. Women who are afflicted by opioid addiction are a highly vulnerable group who are at risk of adverse pregnancy outcomes and perinatal complications. The purpose of this study was to do a meta-synthesis of the literature to gain a better understanding of the women’s perspective who are addicted to opioid during pregnancy. Subjects and Method: We conducted an exhaustive literature search for qualitative studies that focused on women with opioid used disorder during pregnancy and postpartum. After conducting a rigorous methodological protocol for meta-synthesis described by Joanna Briggs Institute, ten studies met the criteria for inclusion into this study. We used the Qualitative Assessment and Review Instrument (QARI) from the Joanna Briggs Institute to assist with data management. We extracted the representative quotations and categorized them into like themes. Results: There were three themes formulated from this meta-synthesis: overarching fear, trust, and educational deficits. This meta-synthesis of qualitative studies leads to a greater understanding of how pregnant women with opioid use disorder perceive the consequences of their actions. Conclusion: Implications from this meta-synthesis will inform those working with opioid-addicted pregnant women and aid in establishing best practices for them and their infants by revealing their addiction experiences and narratives. Keywords: opioid use disorder, pregnancy, stigma Correspondence: Deborah Stiffler. Indiana University School of Nursing, Indianapolis, IN, USA. Email: dstiffle­@iu.edu. Journal of Maternal and Child Health (2019), 4(6): 461-474 https://doi.org/10.26911/thejmch.2019.04.06.07
Lithium alters expression of RNAs in a type-specific manner in differentiated human neuroblastoma neuronal cultures, including specific genes involved in Alzheimer’s disease

Lithium (Li) is a medication long-used to treat bipolar disorder. It is currently under investigation for multiple nervous system disorders, including Alzheimer's disease (AD). While perturbation of RNA levels by Li has been previously reported, its effects on the whole transcriptome has been given little attention. We, therefore, sought to determine comprehensive effects of Li treatment on RNA levels. We cultured and differentiated human neuroblastoma (SK-N-SH) cells to neuronal cells with all-trans retinoic acid (ATRA). We exposed cultures for one week to lithium chloride or distilled water, extracted total RNA, depleted ribosomal RNA and performed whole-transcriptome RT-sequencing. We analyzed results by RNA length and type. We further analyzed expression and protein interaction networks between selected Li-altered protein-coding RNAs and common AD-associated gene products. Lithium changed expression of RNAs in both non-specific (inverse to sequence length) and specific (according to RNA type) fashions. The non-coding small nucleolar RNAs (snoRNAs) were subject to the greatest length-adjusted Li influence. When RNA length effects were taken into account, microRNAs as a group were significantly less likely to have had levels altered by Li treatment. Notably, several Li-influenced protein-coding RNAs were co-expressed or produced proteins that interacted with several common AD-associated genes and proteins. Lithium's modification of RNA levels depends on both RNA length and type. Li activity on snoRNA levels may pertain to bipolar disorders while Li modification of protein coding RNAs may be relevant to AD.
EEG Signal Dimensionality Reduction and Classification using Tensor Decomposition and Deep Convolutional Neural Networks

A new deep learning-based electroencephalography (EEG) signal analysis framework is proposed. While deep neural networks, specifically convolutional neural networks (CNNs), have gained remarkable attention recently, they still suffer from high dimensionality of the training data. Two-dimensional input images of CNNs are more vulnerable to be redundant versus one-dimensional input time-series of conventional neural networks. In this study, we propose a new dimensionality reduction framework for reducing the dimension of CNN inputs based on the tensor decomposition of the time-frequency representation of EEG signals. The proposed tensor decomposition-based dimensionality reduction algorithm transforms a large set of slices of the input tensor to a concise set of slices which are called super-slices. Employing super-slices not only handles the artifacts and redundancies of the EEG data but also reduces the dimension of the CNNs training inputs. We also consider different time-frequency representation methods for EEG image generation and provide a comprehensive comparison among them. We test our proposed framework on HCB-MIT data and as results show our approach outperforms other previous studies.
MPC-based Precision Cooling Strategy (PCS) for Efficient Thermal Management of Automotive Air Conditioning System

In this paper, we propose an MPC-based precision cooling strategy (PCS) for energy efficient thermal management of an automotive air conditioning (A/C) system. The proposed PCS is able to provide precise tracking of the time-varying cooling power trajectory, which is assumed to meet the passenger comfort requirements. In addition, by leveraging the emerging connected and automated vehicles (CAVs) technology, vehicle speed preview can be incorporated in our A/C thermal management strategy for further energy efficiency improvement. This proposed A/C thermal management strategy is developed and evaluated based on a physics-based A/C system model (ACSim) from Ford Motor Company for the vehicles with electrified powertrains. Over SC03 cycle, for tracking the same cooling power trajectory, the proposed PCS provides 4.9% energy saving at the cost of slight increase in the cabin temperature (less than 1°C), compared with Ford benchmark case. It is also demonstrated that by coordinating with future vehicle speed and shifting the A/C power load, the A/C energy consumption can be further reduced.
Pass-through of commodity price shocks in distribution channels with risk-averse agents

Abstract We apply variance analysis for studying the risk sharing mechanism in distribution channels, in which the risk-averse buyer and supplier are suffered from commodity price shocks. We obtain the closed-form optimal pass-through rate that minimizes the total channel price/cost risk and maximizes the channel throughput for a Stackelberg leadership game and a Nash bargaining solution. Using the commodity price data in the steel industry, we demonstrate that the pass-through rate should be set between 40 and 80% for major metal alloys.
JUUL and Combusted Cigarettes Comparably Impair Endothelial Function.

Author(s): Rao, Poonam; Liu, Jiangtao; Springer, Matthew | Abstract: ABSTRACT Objectives JUUL and earlier generation electronic cigarettes (e-cigs) are promoted as being less hazardous than cigarettes. While JUUL Labs, in particular, claims that switching from smoking to vaping has beneficial impacts, the health effects of such products are not well understood. We investigated whether exposure to JUUL and previous generation e-cig aerosol impairs endothelial function comparably to cigarette smoke. Methods We exposed rats to aerosol from Virginia Tobacco flavor JUUL, an e-cig tank system using unflavored freebase nicotine e-liquid, Marlboro Red combustible tobacco cigarettes, or clean air for 10 cycles of 2 second inhalation over 5 minutes. Endothelial function (FMD) was measured pre- and post-exposure. Blood was collected 20 mins post-exposure for serum nicotine analysis. Results Aerosol/smoke from JUUL, previous generation e-cigs, and cigarettes all impaired FMD. The extent of impairment ranged from 34%-58%, although the differences between groups were insignificant. Nicotine was highest in serum from the JUUL group; for the other e-cig and cigarette groups, nicotine levels were lower and comparable to each other. Conclusions Aerosol from JUUL and previous generation e-cigs impairs endothelial function in rats, comparable to impairment by cigarette smoke.
On the Complexity of Sequence-to-Graph Alignment

Availability of extensive genetics data across multiple individuals and populations is driving the growing importance of graph based reference representations. Aligning sequences to graphs is a fundamental operation on several types of sequence graphs (variation graphs, assembly graphs, pan-genomes, etc.) and their biological applications. Though research on sequence to graph alignments is nascent, it can draw from related work on pattern matching in hypertext. In this paper, we study sequence to graph alignment problems under Hamming and edit distance models, and linear and affine gap penalty functions, for multiple variants of the problem that allow changes in query alone, graph alone, or in both. We prove that when changes are permitted in graphs either standalone or in conjunction with changes in the query, the sequence to graph alignment problem is \(\mathcal {NP}\)-complete under both Hamming and edit distance models for alphabets of size \({\ge }2\). For the case where only changes to the sequence are permitted, we present an \(O(|V|+m|E|)\) time algorithm, where m denotes the query size, and V and E denote the vertex and edge sets of the graph, respectively. Our result is generalizable to both linear and affine gap penalty functions, and improves upon the run-time complexity of existing algorithms.
Heuristic recurrent algorithms for photonic Ising machines

The inability of conventional electronic architectures to efficiently solve large combinatorial problems motivates the development of novel computational hardware. There has been much effort toward developing application-specific hardware across many different fields of engineering, such as integrated circuits, memristors, and photonics. However, unleashing the potential of such architectures requires the development of algorithms which optimally exploit their fundamental properties. Here, we present the Photonic Recurrent Ising Sampler (PRIS), a heuristic method tailored for parallel architectures allowing fast and efficient sampling from distributions of arbitrary Ising problems. Since the PRIS relies on vector-to-fixed matrix multiplications, we suggest the implementation of the PRIS in photonic parallel networks, which realize these operations at an unprecedented speed. The PRIS provides sample solutions to the ground state of Ising models, by converging in probability to their associated Gibbs distribution. The PRIS also relies on intrinsic dynamic noise and eigenvalue dropout to find ground states more efficiently. Our work suggests speedups in heuristic methods via photonic implementations of the PRIS.
optimization Model for Planning Precision Grasps with Multi-Fingered Hands

Precision grasps with multi-fingered hands are important for precise placement and in-hand manipulation tasks. Searching precision grasps on the object represented by point cloud, is challenging due to the complex object shape, high-dimensionality, collision and undesired properties of the sensing and positioning. This paper proposes an optimization model to search for precision grasps with multi-fingered hands. The model takes noisy point cloud of the object as input and optimizes the grasp quality by iteratively searching for the palm pose and finger joints positions. The collision between the hand and the object is approximated and penalized by a series of least-squares. The collision approximation is able to handle the point cloud representation of the objects with complex shapes. The proposed optimization model is able to locate collision-free optimal precision grasps efficiently. The average computation time is 0.50 sec/grasp. The searching is robust to the incompleteness and noise of the point cloud. The effectiveness of the algorithm is demonstrated by experiments.
Influence of Substrate Concentration on the Culturability of Heterotrophic Soil Microbes Isolated by High-Throughput Dilution-to-Extinction Cultivation.

ABSTRACT The vast majority of microbes inhabiting oligotrophic shallow subsurface soil environments have not been isolated or studied under controlled laboratory conditions. In part, the challenges associated with isolating shallow subsurface microbes may persist because microbes in deeper soils are adapted to low nutrient availability or quality. Here, we use high-throughput dilution-to-extinction culturing to isolate shallow subsurface microbes from a conifer forest in Arizona, USA. We hypothesized that the concentration of heterotrophic substrates in microbiological growth medium would affect which microbial taxa were culturable from these soils. To test this, we diluted cells extracted from soil into one of two custom-designed defined growth media that differed by 100-fold in the concentration of amino acids and organic carbon. Across the two media, we isolated a total of 133 pure cultures, all of which were classified as Actinobacteria or Alphaproteobacteria. The substrate availability dictated which actinobacterial phylotypes were culturable but had no significant effect on the culturability of Alphaproteobacteria. We isolated cultures that were representative of the most abundant phylotype in the soil microbial community (Bradyrhizobium spp.) and representatives of five of the top 10 most abundant Actinobacteria phylotypes, including Nocardioides spp., Mycobacterium spp., and several other phylogenetically divergent lineages. Flow cytometry of nucleic acid-stained cells showed that cultures isolated on low-substrate medium had significantly lower nucleic acid fluorescence than those isolated on high-substrate medium. These results show that dilution-to-extinction is an effective method to isolate abundant soil microbes and that the concentration of substrates in culture medium influences the culturability of specific microbial lineages. IMPORTANCE Isolating environmental microbes and studying their physiology under controlled conditions are essential aspects of understanding their ecology. Subsurface ecosystems are typically nutrient-poor environments that harbor diverse microbial communities—the majority of which are thus far uncultured. In this study, we use modified high-throughput cultivation methods to isolate subsurface soil microbes. We show that a component of whether a microbe is culturable from subsurface soils is the concentration of growth substrates in the culture medium. Our results offer new insight into technical approaches and growth medium design that can be used to access the uncultured diversity of soil microbes.
Repetitive Readout Enhanced by Machine Learning

Single-shot readout is a key component for scalable quantum information processing. However, many solid-state qubits with favorable properties lack the single-shot readout capability. One solution is to use the repetitive quantum-non-demolition readout technique, where the qubit is correlated with an ancilla, which is subsequently read out. The readout fidelity is therefore limited by the back-action on the qubit from the measurement. Traditionally, a threshold method is taken, where only the total photon count is used to discriminate qubit state, discarding all the information of the back-action hidden in the time trace of repetitive readout measurement. Here we show by using machine learning (ML), one obtains higher readout fidelity by taking advantage of the time trace data. ML is able to identify when back-action happened, and correctly read out the original state. Since the information is already recorded (but usually discarded), this improvement in fidelity does not consume additional experimental time, and could be directly applied to preparation-by-measurement and quantum metrology applications involving repetitive readout.
Gnomonious projections for bend-free textures: thoughts on the splay-twist phase

The Hopf fibration has inspired any number of geometric structures in physical systems, in particular, in chiral liquid crystalline materials. Because the Hopf fibration lives on the three sphere, ...
Loss of cerebellar function selectively affects intrinsic rhythmicity of eupneic breathing

Respiration is controlled by central pattern generating circuits in the brain stem, whose activity can be modulated by inputs from other brain areas to adapt respiration to autonomic and behavioral demands. The cerebellum is known to be part of the neuronal circuitry activated during respiratory challenges, such as hunger for air, but has not been found to be involved in the control of spontaneous, unobstructed breathing (eupnea). Here we applied a measure of intrinsic rhythmicity, the CV2, which evaluates the similarity of subsequent intervals and is thus sensitive to changes in rhythmicity at the temporal resolution of individual respiratory intervals. The variability of intrinsic respiratory rhythmicity was reduced in a mouse model of cerebellar ataxia compared to their healthy littermates. Irrespective of that difference, the average respiratory rate and the average coefficient of variation (CV) were comparable between healthy and ataxic mice. We argue that these findings are consistent with a proposed role of the cerebellum in modulating the duration of individual respiratory intervals, which could serve the purpose of coordinating respiration with other rhythmic orofacial movements, such as fluid licking and swallowing.
Epipelagic Langlands parameters and L-packets for unitary groups

Reeder and Yu have recently given a new construction of a class of supercuspidal representations called epipelagic representations [M. Reeder and J.-K. Yu, Epipelagic representations and invariant ...
Dynamics of Planetary Systems within Star Clusters: Aspects of the Solar System’s Early Evolution

Most planetary systems—including our own—are born within stellar clusters, where interactions with neighboring stars can help shape the system architecture. This paper develops an orbit-averaged formalism to characterize the cluster's mean-field effects, as well as the physics of long-period stellar encounters. Our secular approach allows for an analytic description of the dynamical consequences of the cluster environment on its constituent planetary systems. We analyze special cases of the resulting Hamiltonian, corresponding to eccentricity evolution driven by planar encounters, as well as hyperbolic perturbations upon dissipative disks. We subsequently apply our results to the early evolution of our solar system, where the cluster's collective potential perturbs the solar system's plane, and stellar encounters act to increase the velocity dispersion of the Kuiper Belt. Our results are twofold. First, we find that cluster effects can alter the mean plane of the solar system by ≾1° and are thus insufficient to explain the ψ ≈ 6° obliquity of the Sun. Second, we delineate the extent to which stellar flybys excite the orbital dispersion of the cold classical Kuiper Belt and show that while stellar flybys may grow the cold belt's inclination by the observed amount, the resulting distribution is incompatible with the data. Correspondingly, our calculations place an upper limit on the product of the stellar number density and residence time of the Sun in its birth cluster, η τ ≾ 2 × 10⁴ Myr pc⁻³.
Genetics and public health

In order to draw attention to the need for public health action in genetics in Latin America, the author begins by giving a brief review of congenital anomalies, including hereditary diseases and chromosomal anomalies. He notes that these defects affect at least 5% of live births in the different regions of the world, regardless of the development status or ethnic make-up of their populations. In the Region of the Americas, birth defects rank somewhere between second and fifth place among causes of death in children under 1 year of age, and account for 2% to 27% of infant mortality. It is logical to expect that these disorders will take on more relative importance as the general indicators of child health improve, as has been the case in industrialized countries. The fact that pathologies of genetic origin affect a wide range of organs and systems, are chronic, and require expensive therapy and rehabilitation means that they demand services that countries must be prepared to provide. The author proposes three general objectives for health activities regarding genetics: to minimize clinical manifestations in individuals who are born with congenital anomalies by means of adequate care at all service levels; to improve the quality of life for those individuals and their families by helping them to become involved in the normal life of their communities; and to ensure that people at high risk of conceiving children with genetic diseases receive counseling and support services so that they can exercise their right to informed reproduction. Finally, he recommends eight strategies for setting up genetic health programs with the resources available in each country.
OCTEN: Online Compression-Based Tensor Decomposition

Tensor decompositions are powerful tools for large data analytics, as they jointly model multiple aspects of data into one framework and enable the discovery of the latent structures and higher-order correlations within the data. One of the most widely studied and used decompositions, especially in data mining and machine learning, is the Canonical Polyadic or PARAFAC decomposition. However, today's datasets are not static and often grow and change over time. To operate on such large dynamic data, we present OCTEN, the first ever compression-based online parallel implementation for the CP/PARAFAC decomposition. We conduct an extensive empirical analysis of the algorithms in terms of fitness, memory used and CPU time and in order to demonstrate the compression and scalability of the method, we apply OCTEN to big tensor data. Indicatively, OCTEN performs on-par or better than state-of-the-art online and offline methods in terms of decomposition accuracy and efficiency, while achieving memory savings ranging in 40-200%.
Annealing for Distributed Global Optimization

The paper proves convergence to global optima for a class of distributed algorithms for nonconvex optimization in network-based multi-agent settings. Agents are permitted to communicate over a time-varying undirected graph. Each agent is assumed to possess a local objective function (assumed to be smooth, but possibly nonconvex). The paper considers algorithms for optimizing the sum function. A distributed algorithm of the consensus + innovations type is proposed which relies on first-order information at the agent level. Under appropriate conditions on network connectivity and the cost objective, convergence to the set of global optima is achieved by an annealing-type approach, with decaying Gaussian noise independently added into each agent’s update step. It is shown that the proposed algorithm converges in probability to the set of global minima of the sum function.
Testing a thermodynamic approach to collective animal behavior in laboratory fish schools.

Collective behaviors displayed by groups of social animals are observed frequently in nature. Understanding and predicting the behavior of complex biological systems is dependent on developing effective descriptions and models. While collective animal systems are characteristically nonequilibrium, we can employ concepts from equilibrium statistical mechanics to motivate the measurement of material-like properties in laboratory animal aggregates. Here, we present results from a new set of experiments that utilize high speed footage of two-dimensional schooling events, particle tracking, and projected static and dynamic light fields to observe and control the behavior of negatively phototaxic fish schools (Hemigrammus bleheri). First, we use static light fields consisting of dark circular regions to produce visual stimuli that confine the schools to a range of areas. We find that schools have a maximum density which is independent of group size, and that a swim pressurelike quantity, $\mathrm{\ensuremath{\Pi}}$ increases linearly with number density, suggesting that unperturbed schools exist on an isotherm. Next, we use dynamic light fields where the radius of the dark region shrinks linearly with time to compress the schools. We find that an effective temperature parameter depends on the compression time and our results are thus consistent with the school having a constant heat flux. These findings further evidence the utility of effective thermodynamic descriptions of nonequilibrium systems in collective animal behavior.
LSF-Join: Locality Sensitive Filtering for Distributed All-Pairs Set Similarity Under Skew

All-pairs set similarity is a widely used data mining task, even for large and high-dimensional datasets. Traditionally, similarity search has focused on discovering very similar pairs, for which a variety of efficient algorithms are known. However, recent work has highlighted the importance of discovering pairs of sets with relatively small intersection sizes. For example, in a recommender system, two users may be alike even though their interests only overlap on a small percentage of items. In such systems, it is also common that some dimensions are highly-skewed, because they are very popular. Together, these two properties render previous approaches infeasible for large input sizes. To address this problem, we present a new distributed algorithm, LSF-Join, for approximate all-pairs set similarity. The core of our algorithm is a randomized selection procedure based on Locality Sensitive Filtering. In particular, our method deviates from prior approximate algorithms, which are based on Locality Sensitive Hashing. Theoretically, we show that LSF-Join efficiently finds most close pairs, even for small similarity thresholds and for skewed input sets. We prove guarantees on the communication, work, and maximum load of LSF-Join, and we also experimentally demonstrate its accuracy on multiple graphs.
Spectral Sparsification via Bounded-Independence Sampling

We give a deterministic, nearly logarithmic-space algorithm for mild spectral sparsification of undirected graphs. Given a weighted, undirected graph G on n vertices described by a binary string of length N, an integer k ≤ log n and an error parameter e > 0, our algorithm runs in space O(k log(N w_max/w_min)) where w_max and w_min are the maximum and minimum edge weights in G, and produces a weighted graph H with O(n^(1+2/k)/e²) edges that spectrally approximates G, in the sense of Spielmen and Teng [Spielman and Teng, 2004], up to an error of e. Our algorithm is based on a new bounded-independence analysis of Spielman and Srivastava’s effective resistance based edge sampling algorithm [Spielman and Srivastava, 2011] and uses results from recent work on space-bounded Laplacian solvers [Jack Murtagh et al., 2017]. In particular, we demonstrate an inherent tradeoff (via upper and lower bounds) between the amount of (bounded) independence used in the edge sampling algorithm, denoted by k above, and the resulting sparsity that can be achieved.
When Are Fuzzy Extractors Possible

Fuzzy extractors (Dodis et al., SIAM J. Computing 2008) convert repeated noisy readings of a high-entropy secret into the same uniformly distributed key. A minimum condition for the security of the key is the hardness of guessing a value that is similar to the secret, because the fuzzy extractor converts such a guess to the key. We quantify this property in a new notion called fuzzy min-entropy . We ask: is fuzzy min-entropy sufficient to build fuzzy extractors? We provide two answers for different settings. 1) If the construction is provided a description of the probability distribution $W$ that defines the noisy source then fuzzy min-entropy is a sufficient condition for information-theoretic key extraction from $W$ . 2) A more ambitious goal is to design a single extractor that works for all possible sources. This more ambitious goal is impossible: there is a family of sources with high fuzzy min-entropy for which no single fuzzy extractor is secure. This is true in three settings: a) for standard fuzzy extractors, b) for fuzzy extractors that are allowed to sometimes be wrong, c) and for secure sketches, which are the main ingredient of most fuzzy extractor constructions.
The Effects of Client Status and the Auditor's Presentation of Multiple Estimation Alternatives on Client Financial Reporting Aggressiveness

Accounting estimates are highly subjective and multiple estimation alternatives often exist for a single account. When addressing audit-related adjustments with clients, auditors must decide whethe...
Adversarial Impacts on Autonomous Decentralized Lightweight Swarms

The decreased size and cost of Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) has enabled the use of swarms of unmanned autonomous vehicles to accomplish a variety of tasks. By utilizing swarming behaviors, it is possible to efficiently accomplish coordinated tasks while minimizing per-drone computational requirements. Some drones rely on decentralized protocols that exhibit emergent behavior across the swarm. While fully decentralized algorithms remove obvious attack vectors their susceptibility to external influence is less understood. This work investigates the influences that can compromise the functionality of an autonomous swarm leading to hazardous situations and cascading vulnerabilities. When a swarm is tasked with missions involving the safety or health of humans, external influences could have serious consequences. The adversarial swarm in this work utilizes an attack vector embedded within the decentralized movement algorithm of a previously defined autonomous swarm designed to create a perimeter sentry swarm. Various simulations confirm the adversarial swarm’s ability to capture significant portions (6-23%) of the perimeter.
The entry price threshold in EU agriculture: Deterrent or barrier?

Abstract The paper investigates the effects of the entry price scheme (EPS) for a specific sector of agriculture: fresh fruit and vegetables. The analysis is conducted on the EU prices of tomatoes, lemons and apples for some of the main competing countries on the EU domestic markets: Morocco, Argentina, Turkey and China. The econometric analysis is based on testing and estimating a switching vector autoregressive model with endogenous threshold entry price level. The model shows the isolation effects and the accumulation of Standard Import Values (SIVs) above the trigger entry price. This paper contributes to clarify the role played by the EPS in avoiding or deterring low priced imports from main EU partner countries.
9 – Standard costing

Publisher Summary This chapter discusses standard costing. Standard costing is a technique that uses standards for costs and revenues for the purpose of control through variance analysis. A standard cost is a predetermined calculation of how much costs should be under specified working conditions. A standard cost is built up from an assessment of the value of cost elements and correlates technical specifications and the quantification of materials, labor, and other costs to the prices and/or wage rates expected to apply during this period in which the standard cost is expected to be used. Such standards can be used as established control tools through variance analysis, inventory valuation, and even for fixing prices. Standards can be set for direct materials, both price and usage, for direct labor, and for indirect fixed or variable conversion expenses. Within the context of the operating of standard costing, the difference between the goal set and the results achieved can be quantified, analyzed, and unambiguously communicated. The control process envisaged by standard costing is an open loop system. It is classified as an open system because remedial or corrective action is not automatic, but depends upon action from outside.