cssi/SciSciGPT-SciSciNet huggingface dataset:
Modeling of High-Precision Neutron Nonelastic Cross Sections

-A new method has been applied to the determination of neutron nonelastic cross sections for iron 56 Fe and lead 208 Pb for energies between 5 and 26 MeV. These data have estimated errors of only a few percent and do not suffer from the ambiguities encountered in earlier nonelastic data. We attempt to fit these high-precision data using both a semiclassical single phase shift model (nuclear Ramsauer model) as well as a recent global optical model that well reproduces a wide body of neutron scattering observables. At the 5% uncertainty level, both models produce satisfactory fits. However, neither model gives satisfactory fits to these new precise data. We conclude that fitting precise data, i.e., data with errors of ∼2% or less, may require a nuclear mass dependence of radii that reflects structure effects such as shell closures.
DoubletDecon: Deconvoluting Doublets from Single-Cell RNA-Sequencing Data

Methods for single-cell RNA sequencing (scRNA-seq) have greatly advanced in recent years. While droplet- and well-based methods have increased the capture frequency of cells for scRNA-seq, these technologies readily produce technical artifacts, such as doublet cell captures. Doublets occurring between distinct cell types can appear as hybrid scRNA-seq profiles, but do not have distinct transcriptomes from individual cell states. We introduce DoubletDecon, an approach that detects doublets with a combination of deconvolution analyses and the identification of unique cell-state gene expression. We demonstrate the ability of DoubletDecon to identify synthetic, mixed-species, genetic, and cell-hashing cell doublets from scRNA-seq datasets of varying cellular complexity with a high sensitivity relative to alternative approaches. Importantly, this algorithm prevents the prediction of valid mixed-lineage and transitional cell states as doublets by considering their unique gene expression. DoubletDecon has an easy-to-use graphical user interface and is compatible with diverse species and unsupervised population detection algorithms.
Post-Disruption Performance Recovery to Enhance Resilience of Interconnected Network Systems

Mitigating the effect of disruptive events at the operating phase of complex systems therefore improving the systems’ resilience is an important yet challenging task. To improve the resilience, one...
Drawbacks and Proposed Solutions for Real-time Processing on Existing State-of-the-art Locality Sensitive Hashing Techniques

Nearest-neighbor query processing is a fundamental operation for many image retrieval applications. Often, images are stored and represented by high-dimensional vectors that are generated by feature-extraction algorithms. Since tree-based index structures are shown to be ineffective for high dimensional processing due to the well-known “Curse of Dimensionality”, approximate nearest neighbor techniques are used for faster query processing. Locality Sensitive Hashing (LSH) is a very popular and efficient approximate nearest neighbor technique that is known for its sublinear query processing complexity and theoretical guarantees. Nowadays, with the emergence of technology, several diverse application domains require real-time high-dimensional data storing and processing capacity. Existing LSH techniques are not suitable to handle real-time data and queries. In this paper, we discuss the challenges and drawbacks of existing LSH techniques for processing real-time highdimensional image data. Additionally, through experimental analysis, we propose improvements for existing state-of-the-art LSH techniques for efficient processing of highdimensional image data.
Comparing Rewinding and Fine-tuning in Neural Network Pruning.

Many neural network pruning algorithms proceed in three steps: train the network to completion, remove unwanted structure to compress the network, and retrain the remaining structure to recover lost accuracy. The standard retraining technique, fine-tuning, trains the unpruned weights from their final trained values using a small fixed learning rate. In this paper, we compare fine-tuning to alternative retraining techniques. Weight rewinding (as proposed by Frankle et al., (2019)), rewinds unpruned weights to their values from earlier in training and retrains them from there using the original training schedule. Learning rate rewinding (which we propose) trains the unpruned weights from their final values using the same learning rate schedule as weight rewinding. Both rewinding techniques outperform fine-tuning, forming the basis of a network-agnostic pruning algorithm that matches the accuracy and compression ratios of several more network-specific state-of-the-art techniques.
Protein Network Structure Enables Switching between Liquid and Gel States.

Biomolecular condensates are emerging as an important organizational principle within living cells. These condensed states are formed by phase separation, yet little is known about how material pro...
Recursion relation for general 3d blocks

We derive closed-form expressions for all ingredients of the Zamolodchikov-like recursion relation for general spinning conformal blocks in 3-dimensional conformal field theory. This result opens a path to efficient automatic generation of conformal block tables, which has immediate applications in numerical conformal bootstrap program. Our derivation is based on an understanding of null states and conformally-invariant differential operators in momentum space, combined with a careful choice of the relevant tensor structures bases. This derivation generalizes straightforwardly to higher spacetime dimensions d, provided the relevant Clebsch-Gordan coefficients of Spin (d) are known.
Workload-Aware Opportunistic Energy Efficiency in Multi-FPGA Platforms

The continuous growth of big data applications with high computational and scalability demands has resulted in increasing popularity of cloud computing. Optimizing the performance and power consumption of cloud resources is therefore crucial to relieve the costs of data centers. In recent years, multi-FPGA platforms have gained traction in data centers as low-cost yet high-performance solutions particularly as acceleration engines, thanks to the high degree of parallelism they provide. Nonetheless, the size of data centers workloads varies during service time, leading to significant underutilization of computing resources while consuming a large amount of power, which turns out as a key factor of data center inefficiency, regardless of the underlying hardware structure. In this paper, we propose an efficient framework to throttle the power consumption of multi-FPGA platforms by dynamically scaling the voltage and hereby frequency during runtime according to prediction of, and adjustment to the workload level, while maintaining the desired Quality of Service (QoS). This is in contrast to, and more efficient than, conventional approaches that merely scale (i.e., power-gate) the computing nodes or frequency. The proposed framework carefully exploits a pre-characterized library of delay-voltage, and power-voltage information of FPGA resources, which we show is indispensable to obtain the efficient operating point due to the different sensitivity of resources w.r.t. voltage scaling, particularly considering multiple power rails residing in these devices. Our evaluations by implementing state-of-the-art deep neural network accelerators revealed that, providing an average power reduction of 4.0×, the proposed framework surpasses the previous works by 33.6% (up to 83%).
A Comparison of Mining Incomplete and Inconsistent Data

We present experimental results on a comparison of incompleteness and inconsistency. Our experiments were conducted on 141 data sets, including 71 incomplete data and 62 inconsistent, created from eight original numerical data sets. We used the Modified Learning from Examples Module version 2 (MLEM2) rule induction algorithm for data mining. Among eight types of data sets combined with three kinds of probabilistic approximations used in experiments, in 12 out of 24 combinations the error rate, computed as a result of ten-fold cross validation, was smaller for inconsistent data (two-tailed test, 5 % significance level). For one data set, combined with all three probabilistic approximations, the error rate was smaller for incomplete data. For remaining nine combinations the difference in performance was statistically insignificant. Thus, we may claim that there is some experimental evidence that incompleteness is generally worse than inconsistency for data mining.
Mechanism of Dissociation Kinetics in Polyelectrolyte Complex Micelles

Polyelectrolyte-based nanoscale self-assemblies, such as micelles, possess diverse desirable attributes such as the capability for sequestering and protecting biomacromolecules against inhospitable...
Analysis of NIF scaling using physics informed machine learning

Over 120 DT ice layer thermonuclear (TN) ignition experiments in inertial confinement fusion (ICF) were conducted on the National Ignition Facility (NIF) in the last eight years. None of the experiments achieved ignition. In fact, the measured neutron outputs from the experiments were well below what was expected. Although experiments to fine-tune the target designs are the focus of the national ICF program, insightful analysis of the existing data is a pressing need. In highly integrated ignition experiments, it is impossible to vary only one design parameter without perturbing all the other implosion variables. Thus, to determine the nonlinear relationships between the design parameters and performance from the data, a multivariate analysis based on physics models is necessary. To this end, we apply machine learning and deep learning methods to the existing NIF experimental data to uncover the patterns and physics scaling laws in TN ignition. In this study, we focus on the scaling laws between the implosion parameters and neutron yield using different supervised learning methods. Descriptions, comparisons, and contrasts between the methods are presented. Our results show that these models are able to infer a relationship between the observed stagnation conditions and neutron yields. This exploratory study will help build new capabilities to evaluate capsule designs and provide suggestions for new designs.
A novel approach for detection and ranking of trendy and emerging cyber threat events in Twitter streams

We present a new machine learning and text information extraction approach to detection of cyber threat events in Twitter that are novel (previously non-extant) and developing (marked by significance with respect to similarity with a previously detected event). While some existing approaches to event detection measure novelty and trendiness, typically as independent criteria and occasionally as a holistic measure, this work focuses on detecting both novel and developing events using an unsupervised machine learning approach. Furthermore, our proposed approach enables the ranking of cyber threat events based on an importance score by extracting the tweet terms that are characterized as named entities, keywords, or both. We also impute influence to users in order to assign a weighted score to noun phrases in proportion to user influence and the corresponding event scores for named entities and keywords. To evaluate the performance of our proposed approach, we measure the efficiency and detection error rate for events over a specified time interval, relative to human annotator ground truth.
On Constructing a Knowledge Base of Chinese Criminal Cases.

We are developing a knowledge base over Chinese judicial decision documents to facilitate landscape analyses of Chinese Criminal Cases. We view judicial decision documents as a mixed-granularity semi-structured text where different levels of the text carry different semantic constructs and entailments. We use a combination of context-sensitive grammar, dependency parsing and discourse analysis to extract a formal and interpretable representation of these documents. Our knowledge base is developed by constructing associations between different elements of these documents. The interpretability is contributed in part by our formal representation of the Chinese criminal laws, also as semi-structured documents. The landscape analyses utilize these two representations and enable a law researcher to ask legal pattern analysis queries.
A Deep Learning Approach to Grasping the Invisible

We study an emerging problem named “grasping the invisible” in robotic manipulation, in which a robot is tasked to grasp an initially invisible target object via a sequence of pushing and grasping actions. In this problem, pushes are needed to search for the target and rearrange cluttered objects around it to enable effective grasps. We propose to solve the problem by formulating a deep learning approach in a critic-policy format. The target-oriented motion critic, which maps both visual observations and target information to the expected future rewards of pushing and grasping motion primitives, is learned via deep Q-learning. We divide the problem into two subtasks, and two policies are proposed to tackle each of them, by combining the critic predictions and relevant domain knowledge. A Bayesian-based policy accounting for past action experience performs pushing to search for the target; once the target is found, a classifier-based policy coordinates target-oriented pushing and grasping to grasp the target in clutter. The motion critic and the classifier are trained in a self-supervised manner through robot-environment interactions. Our system achieves a 93% and 87% task success rate on each of the two subtasks in simulation and an 85% task success rate in real robot experiments on the whole problem, which outperforms several baselines by large margins. Supplementary material is available at https://sites.google.com/umn.edu/grasping-invisible .
Linear operators with infinite entropy

Abstract We examine the chaotic behavior of certain continuous linear operators on infinite-dimensional Banach spaces, and provide several equivalent characterizations of when these operators have infinite topological entropy. For example, it is shown that infinite topological entropy is equivalent to non-zero topological entropy for translation operators on weighted Lebesgue function spaces. In particular, finite non-zero entropy is impossible for this class of operators, which answers a question raised by Yin and Wei.
Orbit lifetime characteristics for Space Station

The factors that influence the orbital lifetime characteristics of the NASA Space Station are discussed. These include altitude, attitude, launch date, ballistic coefficient, and the presence of large articulating solar arrays. Examples from previous program systems studies are presented that illustrate how each factor affects Station orbit lifetime. The effect of atmospheric density models on orbit lifetime predictions is addressed along with the uncertainty of these predictions using current trajectory analysis of the Long Duration Exposure Facility spacecraft. Finally, nominal reboost altitude profiles and fuel requirement considerations are presented for implementing a reboost strategy based on planned Shuttle Orbiter rendezvous strategy and contingency considerations.
Twisted sequences of extensions

Gabber and Joseph introduced a ladder diagram between two natural sequences of extensions. Their diagram is used to produce a ‘twisted’ sequence that is applied to old and new results on extension ...
Decision making in dynamic and interactive environments based on cognitive hierarchy theory, Bayesian inference, and predictive control

In this paper, we describe an integrated framework for autonomous decision making in a dynamic and interactive environment. We model the interactions between the ego agent and its operating environment as a two-player dynamic game, and integrate cognitive behavioral models, Bayesian inference, and receding-horizon optimal control to define a dynamically-evolving decision strategy for the ego agent. Simulation examples representing autonomous vehicle control in three traffic scenarios where the autonomous ego vehicle interacts with a human-driven vehicle are reported.
Protein Structure Prediction and Design in a Biologically Realistic Implicit Membrane

Protein design is a powerful tool for elucidating mechanisms of function and engineering new therapeutics and nanotechnologies. Although soluble protein design has advanced, membrane protein design remains challenging because of difficulties in modeling the lipid bilayer. In this work, we developed an implicit approach that captures the anisotropic structure, shape of water-filled pores, and nanoscale dimensions of membranes with different lipid compositions. The model improves performance in computational benchmarks against experimental targets, including prediction of protein orientations in the bilayer, ΔΔG calculations, native structure discrimination, and native sequence recovery. When applied to de novo protein design, this approach designs sequences with an amino acid distribution near the native amino acid distribution in membrane proteins, overcoming a critical flaw in previous membrane models that were prone to generating leucine-rich designs. Furthermore, the proteins designed in the new membrane model exhibit native-like features including interfacial aromatic side chains, hydrophobic lengths compatible with bilayer thickness, and polar pores. Our method advances high-resolution membrane protein structure prediction and design toward tackling key biological questions and engineering challenges.
Spline Based Intrusion Detection in Vehicular Ad Hoc Networks (VANET)

Intrusion detection systems (IDSs) play a crucial role in the identification and mitigation for attacks on host systems. Of these systems, vehicular ad hoc networks (VANETs) are particularly difficult to protect due to the dynamic nature of their clients and their necessity for constant interaction with their respective cyber-physical systems. Currently, there is a need for a VANET-specific IDS that can satisfy these requirements. Spline function-based IDSs have shown to be effective in traditional network settings. By examining the various construction of splines and testing their robustness, the viability for a spline-based IDS can be determined.
Safety Control with Preview Automaton

This paper considers the problem of safety controller synthesis for systems equipped with sensor modalities that can provide preview information. We consider switched systems where switching mode is an external signal for which preview information is available. In particular, it is assumed that the sensors can notify the controller about an upcoming mode switch before the switch occurs. We propose preview automaton, a mathematical construct that captures both the preview information and the possible constraints on switching signals. Then, we study safety control synthesis problem with preview information. An algorithm that computes the maximal invariant set in a given mode-dependent safe set is developed. These ideas are demonstrated on two case studies from autonomous driving domain.
Feedback Control of a Two-Component Signaling System by an Fe-S-Binding Receiver Domain

Two-component signaling systems (TCSs) function to detect environmental cues and transduce this information into a change in transcription. In its simplest form, TCS-dependent regulation of transcription entails phosphoryl-transfer from a sensory histidine kinase to its cognate DNA-binding receiver protein. However, in certain cases, auxiliary proteins may modulate TCSs in response to secondary environmental cues. Caulobacter crescentus FixT is one such auxiliary regulator. FixT is composed of a single receiver domain and functions as a feedback inhibitor of the FixL-FixJ (FixLJ) TCS, which regulates the transcription of genes involved in adaptation to microaerobiosis. We sought to define the impact of fixT on Caulobacter cell physiology and to understand the molecular mechanism by which FixT represses FixLJ signaling. fixT deletion results in excess production of porphyrins and premature entry into stationary phase, demonstrating the importance of feedback inhibition of the FixLJ signaling system. Although FixT is a receiver domain, it does not affect dephosphorylation of the oxygen sensor kinase FixL or phosphoryl-transfer from FixL to its cognate receiver FixJ. Rather, FixT represses FixLJ signaling by inhibiting the FixL autophosphorylation reaction. We have further identified a 4-cysteine motif in Caulobacter FixT that binds an Fe-S cluster and protects the protein from degradation by the Lon protease. Our data support a model in which the oxidation of this Fe-S cluster promotes the degradation of FixT in vivo This proteolytic mechanism facilitates clearance of the FixT feedback inhibitor from the cell under normoxia and resets the FixLJ system for a future microaerobic signaling event.IMPORTANCE Two-component signal transduction systems (TCSs) are broadly conserved in the bacterial kingdom and generally contain two molecular components, a sensor histidine kinase and a receiver protein. Sensor histidine kinases alter their phosphorylation state in direct response to a physical or chemical cue, whereas receiver proteins "receive" the phosphoryl group from the kinase to regulate a change in cell physiology. We have discovered that a single-domain receiver protein, FixT, binds an Fe-S cluster and controls Caulobacter heme homeostasis though its function as a negative-feedback regulator of the oxygen sensor kinase FixL. We provide evidence that the Fe-S cluster protects FixT from Lon-dependent proteolysis in the cell and endows FixT with the ability to function as a second, autonomous oxygen/redox sensor in the FixL-FixJ signaling pathway. This study introduces a novel mechanism of regulated TCS feedback control by an Fe-S-binding receiver domain.
Mode Clustering for Markov Jump Systems

In this work, we consider the problem of mode clustering in Markov jump models. This model class consists of multiple dynamical modes with a switching sequence that determines how the system switches between them over time. Under different active modes, the observations can have different characteristics. Given the observations only and without knowing the mode sequence, the goal is to cluster the modes based on their transition distributions in the Markov chain to find a reduced-rank Markov matrix that is embedded in the original Markov chain. Our approach involves mode sequence estimation, mode clustering and reduced-rank model estimation, where mode clustering is achieved by applying the singular value decomposition and k-means. We show that, under certain conditions, the clustering error can be bounded, and the reduced-rank Markov chain is a good approximation to the original Markov chain. Through simulations, we show the efficacy of our approach and the application of our approach to real world scenarios.
On Testing Prebond Dies with Incomplete Clock Networks in a 3D IC Using DLLs

3D integration of ICs is an emerging technology where multiple silicon dies are stacked vertically. The manufacturing itself is based on wafer-to-wafer bonding, die-to-wafer bonding or die-to-die bonding. Wafer-to-wafer bonding has the lowest yield as a good die may be stacked against a bad die, resulting in a wasted good die. Thus the latter two options are preferred to keep yield high and manufacturing costs low. However, these methods require dies to be tested separately before they are stacked. A problem with testing dies separately is that the clock network of a prebond die may be incomplete before stacking. In this paper we present a solution to address this problem. The solution is based on on-die Delay Lock Loop (DLL) implementations that are only activated during testing prebond unstacked dies to synchronize disconnected clock regions. A problem with using DLLs in testing is that they cannot be turned on or off within a single cycle. Since scan-based testing requires that test patterns be scanned in at a slow clock frequency before fast capture clocks are applied, On-Product Clock Generation (OPCG) must be used. The proposed solution addresses the above problems and allows a prebond with an incomplete clock network to be tested with low skew.
Global DNA Hypomethylation in Epithelial Ovarian Cancer: Passive Demethylation and Association with Genomic Instability

A hallmark of human cancer is global DNA hypomethylation (GDHO), but the mechanisms accounting for this defect and its pathological consequences have not been investigated in human epithelial ovarian cancer (EOC). In EOC, GDHO was associated with advanced disease and reduced overall and disease-free survival. GDHO (+) EOC tumors displayed a proliferative gene expression signature, including FOXM1 and CCNE1 overexpression. Furthermore, DNA hypomethylation in these tumors was enriched within genomic blocks (hypomethylated blocks) that overlapped late-replicating regions, lamina-associated domains, PRC2 binding sites, and the H3K27me3 histone mark. Increased proliferation coupled with hypomethylated blocks at late-replicating regions suggests a passive hypomethylation mechanism. This hypothesis was further supported by our observation that cytosine DNA methyltransferases (DNMTs) and UHRF1 showed significantly reduced expression in GDHO (+) EOC after normalization to canonical proliferation markers, including MKI67. Finally, GDHO (+) EOC tumors had elevated chromosomal instability (CIN), and copy number alterations (CNA) were enriched at the DNA hypomethylated blocks. Together, these findings implicate a passive DNA demethylation mechanism in ovarian cancer that is associated with genomic instability and poor prognosis.
Nuclear Star Clusters in Edge-on Galaxies

From observations of edge-on, late-type galaxies, we present morphological evidence that some nuclear star clusters have experienced in situ star formation. We find three nuclear clusters that, viewed from the edge-on perspective, have both a compact disk-like component and a spheroidal component. In each cluster, the disk components are closely aligned with the major axis of the host galaxy and have bluer colors than the spheroidal components. We spectroscopically verify that one of the observed multiple component clusters has multiple generations of stars. These observations lead us to suggest a formation mechanism for nuclear star clusters, in which stars episodically form in compact nuclear disks, and then lose angular momentum, eventually forming an older spheroid.
Systematic detection of horizontal gene transfer across genera among multidrug-resistant bacteria in a single hospital.

Bacteria are able to pass each other genes that make them invulnerable to antibiotics. This exchange of genetic material, also called horizontal gene transfer, can turn otherwise harmless bacteria into drug-resistant ‘superbugs’. This is particularly problematic in hospitals, where bacteria use horizontal gene transfer to become resistant to several antibiotics and disinfectants at once, leading to serious infections that are difficult to treat. How can scientists stop bacteria from sharing genes with one another? To answer this question, first it is important to understand how horizontal gene transfer happens in the bacteria that cause infections in hospitals. To this end, Evans et al. examined the genomes of over 2000 different bacteria, collected from a hospital over 18 months, for signs of horizontal transfer. First the experiments identified the genetic material that had potentially been transferred between bacteria, also known as ‘mobile genetic elements’. Next, Evans et al. examined the data of patients who had been infected with the bacteria carrying these mobile genetic elements to see whether horizontal transfer might have happened in the hospital. By combining genomics with patient data, it was determined that many of the mobile genetic elements identified were likely being shared among hospital bacteria. One of the mobile genetic elements identified was able to provide resistance to several drugs, and appeared to have been horizontally transferred between bacteria infecting two separate patients. The findings of Evans et al. show that the horizontal transfer of mobile genetic elements in hospital settings is likely frequent, but complex and difficult to study with current methods. The results of this study show how these events can now be tracked and analyzed, which may lead to new strategies for controlling the spread of antibiotic resistance.
Weighted Gradient Coding with Leverage Score Sampling

A major hurdle in machine learning is scalability to massive datasets. Approaches to overcome this hurdle include compression of the data matrix and distributing the computations. Leverage score sampling provides a compressed approximation of a data matrix using an importance weighted subset. Gradient coding has been recently proposed in distributed optimization to compute the gradient using multiple unreliable worker nodes. By designing coding matrices, gradient coded computations can be made resilient to stragglers, which are nodes in a distributed network that degrade system performance. We present a novel weighted leverage score approach, that achieves improved performance for distributed gradient coding by utilizing an importance sampling.
Well-Posedness of Weinberger’s Center of Mass by Euclidean Energy Minimization

The center of mass of a finite measure with respect to a radially increasing weight is shown to exist, be unique, and depend continuously on the measure.
Branch point control at malonyl-CoA node: A computational framework to uncover the design principles of an ideal genetic-metabolic switch.

Dr. Xu would like to acknowledge the Cellular & Biochem Engineering Program of the National Science Foundation under grant no.1805139 for funding support. Dr. Xu would also like to acknowledge the discussion of this project with the ENCH482/682 and ENCH640 students at the University of Maryland Baltimore County in the Fall 2018 and Spring 2019.
An immersed finite element method for elliptic interface problems in three dimensions

Abstract This article presents an immersed finite element (IFE) method for solving the typical three-dimensional second order elliptic interface problem with an interface-independent Cartesian mesh. The local IFE space on each interface element consists of piecewise trilinear polynomials constructed by extending polynomials from one subelement to the whole element according to the jump conditions of the interface problem. In this space, the IFE shape functions with the Lagrange degrees of freedom can always be constructed regardless of interface location and discontinuous coefficients. The proposed IFE space is proven to have the optimal approximation capabilities to the functions satisfying the jump conditions. A group of numerical examples with representative interface geometries are presented to demonstrate features of the proposed IFE method.
Public Administration Reform in Bulgaria: Top-down and Externally-driven Approach

The article examines public administration reform (PAR) in Bulgaria and the main factors that shaped the reform agenda and dynamics. PAR is examined along five key dimensions – transparency and accountability, civil service and human resources management (HRM), public service delivery and digitalisation, organisation and management of government, and policy-making coordination and implementation. The article argues that there are four main factors influencing reform dynamics and determining policy outcomes in the Bulgarian case: the specific political choices made by government elites, external influence of the EU and of past national legacies, and the importance of institutions and reform mechanisms. To illustrate these factors at work, the article examines three policy initiatives, i.e. e-government, the reduction of administrative burden, and civil service reform. The article presents a longitudinal analysis and a qualitative case-study approach, utilising Annual Reports on the Status of the Public Administration 2001–2018, mapping European Semester Documents 2011–2017, an inventory of PAR initiatives 2005–2018, and interviews of public officials. The pushes for reform have been top-down, externally-driven, and stop-and-go in nature. The results confirm previous findings that Bulgaria is among the EU countries with the poorest record in PAR, struggling to overcome communist legacies and high levels of corruption and politicisation. The Bulgarian case highlights several important lessons: the importance of political will and political dynamics for the outcome of reform efforts; the importance of external pressure and financing; the difficulty of uprooting long-standing legacies in administrative traditions; and the limitations of the top-down approach as an obstacle to the sustainability of reform efforts.
Developing postharvest disinfestation treatments for legumes using radio frequency energy

There is an urgent need to develop technically effective and environmentally sound phytosanitary and quarantine treatments for the legume industry to replace chemical fumigation. The goal of this study was to develop practical non-chemical treatments for postharvest disinfestations of legumes using radio frequency (RF) energy. A pilot-scale 27 MHz, 6 kW RF unit was used to investigate RF heating and consequent quality attributes in treated chickpea, green pea, and lentil samples. Only 5–7 min were needed to raise the central temperature of 3 kg legume samples to 60 °C using RF energy, compared to more than 275 min when using forced hot air at 60 °C. RF heating uniformity in legume samples was improved by adding forced hot air, and back and forth movements on the conveyor at 0.56 m min−1. The final temperatures exceeded 55.8 °C in the interior of the sample container and 57.3 °C on the surface for all three legumes, resulting in low uniformity index values of 0.014–0.016 (ratio of standard deviation to the average temperature rise) for the interior temperature distributions and 0.061–0.078 for surface temperature distributions. RF treatments combined with forced hot air at 60 °C to maintain the target treatment temperature for 10 min followed by forced room air cooling through a 1 cm product layer provided good product quality. No significant differences in weight loss, moisture content, colour or germination were observed between RF treatments and unheated controls.
Robust model reference adaptive control for multivariable plants

A complete procedure for generating and analysing robust model reference adaptive control schemes for multivariable plants is developed. The procedure consists of two parts: the first part involves the characterization of the integral structure of the modelled part of the plant, and the associated parametrization of the controller structure; and the second part involves the development of a general robust adaptive law for adjusting the controller parameters so that the closed-loop plant is globally stable despite the presence of unmodelled dynamics and bounded disturbances. The use of dominantly rich signals for improving convergence and the bounds for the tracking and parameter error is also analysed.
Discrete Convex Analysis and Its Applications in Operations: A Survey

Discrete convexity, in particular, L-natural-convexity and M-natural-convexity, provides a critical opening to attack several classical problems in inventory theory, as well as many other operations problems that arise from more recent practices, for instance, appointment scheduling and bike-sharing. As a powerful framework, discrete convex analysis is becoming increasingly popular in the literature. This review will survey the landscape of the approach. We start by introducing several key concepts, namely, L-natural-convexity and M-natural-convexity and their variants, followed by a discussion of some fundamental properties that are most useful for studying operations models. We then illustrate various applications of these concepts and properties. Examples include network flow problem, stochastic inventory control, appointment scheduling, game theory, portfolio contract, discrete choice model, and bike-sharing. We focus our discussion on demonstrating how discrete convex analysis can shed new insights on existing problems, and/or bring about much simpler analyses and algorithm developments than previous methods in the literature. We also present several results and analyses that are new to the literature.
Designing stellarators using perpendicular permanent magnets

We have developed a fast method to design perpendicular permanent magnets for simplifying stellarator coils based on existing codes. Coil complexity is one of the main challenges for stellarators. To date, only electromagnetic coils have been used to generate 3D fields for stellarators. Permanent magnets provide an alternative way to produce the desired magnetic field for optimized stellarators. In this paper, we revisit the concept of representing surface current using magnetic dipoles and carry out numerical validations. A surface magnetization is proven to be equivalent to the surface current that can be linearly solved by existing coil design codes. An incremental multi-layer method has been developed to obtain a practical solution that is attainable with present permanent magnets. With this method, we can reproduce a half-Tesla NCSX configuration using specially designed neodymium magnets together with simple planar coils. It shows that stellarator coils could be substantially simplified by adopting permanent magnets.
Synapse-specific expression of mu opioid receptor long-term depression in the dorsomedial striatum

The dorsal striatum is a brain region involved in action control, with dorsomedial striatum (DMS) mediating goal-directed actions and dorsolateral striatum (DLS) mediating habitual actions. Presynaptic long-term synaptic depression (LTD) plasticity at glutamatergic inputs to dorsal striatum mediates many dorsal striatum-dependent behaviors and disruption of LTD influences action control. Our previous work identified mu opioid receptors (MORs) as mediators of synapse-specific forms of synaptic depression at a number of different DLS synapses. We demonstrated that anterior insular cortex inputs are the sole inputs that express alcohol-sensitive MOR-mediated LTD (mOP-LTD) in DLS. Here, we explore mOP-LTD in DMS using mouse brain slice electrophysiology. We found that contrary to DLS, DMS mOP-LTD is induced by activation of MORs at inputs from both anterior cingulate and medial prefrontal cortices as well as at basolateral amygdala inputs and striatal cholinergic interneuron synapses on to DMS medium spiny neurons, suggesting that MOR synaptic plasticity in DMS is less synapse-specific than in DLS. Furthermore, only mOP-LTD at cortical inputs was sensitive to alcohol's deleterious effects. These results suggest that alcohol-induced neuroadaptations are differentially expressed in a synapse-specific manner and could be playing a role in alterations of goal-directed and habitual behaviors.
Comparison of Cepheid Xpert Xpress and Abbott ID Now to Roche cobas for the Rapid Detection of SARS-CoV-2.

Abstract Background The SARS-CoV-2 pandemic has created an urgent and unprecedented need for rapid large-scale diagnostic testing to inform timely patient management. However, robust data are lacking on the relative performance of available rapid molecular tests across a full range of viral concentrations. Objective This study aimed to compare two recently-authorized rapid tests, Cepheid Xpert Xpress SARS-CoV-2 and Abbott ID Now SARS-CoV-2, to the Roche cobas SARS-CoV-2 assay for samples with low, medium, and high viral concentrations. Study design A total of 113 nasopharyngeal swabs from remnant patient samples were tested, including 88 positives spanning the full range of observed Ct values on the cobas assay. Results Compared to cobas, the overall positive agreement was 73.9% with ID Now and 98.9% with Xpert. Negative agreement was 100% and 92.0% for ID Now and Xpert, respectively. Both ID Now and Xpert showed 100% positive agreement for medium and high viral concentrations (Ct value 30, positive agreement was 34.3% for ID Now and 97.1% for Xpert. Conclusions While Xpert showed high agreement with cobas across a wide range of viral concentrations, this study highlights an important limitation of ID Now for specimens collected in viral or universal transport media with low viral concentrations. Further studies are needed to evaluate the performance of ID Now for direct swabs
Enhancement of shock-capturing methods via machine learning

In recent years, machine learning has been used to create data-driven solutions to problems for which an algorithmic solution is intractable, as well as fine-tuning existing algorithms. This research applies machine learning to the development of an improved finite-volume method for simulating PDEs with discontinuous solutions. Shock-capturing methods make use of nonlinear switching functions that are not guaranteed to be optimal. Because data can be used to learn nonlinear relationships, we train a neural network to improve the results of a fifth-order WENO method. We post-process the outputs of the neural network to guarantee that the method is consistent. The training data consist of the exact mapping between cell averages and interpolated values for a set of integrable functions that represent waveforms we would expect to see while simulating a PDE. We demonstrate our method on linear advection of a discontinuous function, the inviscid Burgers’ equation, and the 1-D Euler equations. For the latter, we examine the Shu–Osher model problem for turbulence–shock wave interactions. We find that our method outperforms WENO in simulations where the numerical solution becomes overly diffused due to numerical viscosity.
Voices from the Newspaper Club: Patient Life at a State Psychiatric Hospital (1988-1992)

The authors conducted a qualitative analysis of thirty-seven issues of The DDU Review, a newsletter produced by residents of the Dual Diagnosis Unit, a residential unit for people who had diagnoses of developmental disability and serious mental illness in the Central State Hospital (Indiana, USA). The analysis of the newsletters produced between September 1988 and June 1992 revealed three major themes: 1) the mundane; 2) good behavior; and 3) advocacy. Contrary to the authors’ expectations, the discourse of medicalization—such as relations with physicians, diagnoses, and medications—receive little attention. Instead, the patient-journalists focus on prosaic aspects of institutional life. The patients used their writing as a form self-definition and advocacy. The authors argue that even though it is tempting to consider the patients’ emphasis on good behavior as evidence of institutional control, internalized discipline, and medicalization, a more nuanced interpretation, which focuses on how the patients’ understood their own experiences, is warranted. Researchers must also recognize the ways in which The DDU Review reveals the patient-journalists’ experience of an institutional life that includes non-medical staff (attendants, secretaries, and therapists), varied social relationships among patients, and negotiated freedoms.
Development of a Coarse-Grained Water Forcefield via Multistate Iterative Boltzmann Inversion

A coarse-grained water model is developed using multistate iterative Boltzmann inversion. Following previous work, the k-means algorithm is used to dynamically map multiple water molecules to a single coarse-grained bead, allowing the use of structure-based coarse-graining methods. The model is derived to match the bulk and interfacial properties of liquid water and improves upon previous work that used single state iterative Boltzmann inversion. The model accurately reproduces the density and structural correlations of water at 305 K and 1.0 atm, stability of a liquid droplet at 305 K, and shows little tendency to crystallize at physiological conditions. This work also illustrates several advantages of using multistate iterative Boltzmann inversion for deriving generally applicable coarse-grained forcefields.
Machine-learning-assisted metasurface design for high-efficiency thermal emitter optimization

Nanophotonic devices can provide solutions to challenges in energy conversion, information technologies, chemical or biological sensing, quantum computing, and secure communications. The realization of practical optical structures and devices is a complex problem due to the multitude of constraints on their optical performance, materials, scalability, and experimental tolerances, all of which are requirements implying large optimization spaces. However, despite the complexity of the process, to date, almost all nanophotonic structures are designed either intuitively or based on a priori selected topologies, and by adjusting a limited number of parameters. These intuition-based models are limited to ad hoc needs and have narrow applicability and predictive power, with the exhaustive parameter searches often performed manually. Since the comprehensive search in hyper-dimensional design space is highly resource-heavy, multi-objective optimization has so far been almost impossible. Humans' restrained capacity to think hyper-dimensionally also limits the perception of multivariate optimization models, and, therefore, advanced machinery is needed to manage the multi-domain, hyper-dimensional design parameter space. In this work, we merge the topology optimization method with deep learning algorithms, such as adversarial autoencoders, and show substantial improvement of the optimization process in terms of computational time (4900 times faster) and final devices efficiencies (∼98%) by providing unparalleled control of the compact design space representations. By enabling efficient, global optimization searches within complex landscapes, the proposed compact hyperparametric representations could become crucial for multi-constrained problems. The proposed approach could enable a much broader scope of the optimal designs and data-driven materials synthesis that goes beyond photonic and optoelectronic applications.
Universal lex ideal approximations of extended Hilbert functions and Hamilton numbers

Abstract Let R ( h ) denote the polynomial ring in variables x 1 , … , x h over a specified field K. We consider all of these rings simultaneously, and in each use lexicographic (lex) monomial order with x 1 > ⋯ > x h . Given a fixed homogeneous ideal I in R ( h ) , for each d there is unique lex ideal generated in degree at most d whose Hilbert function agrees with the Hilbert function of I up to degree d. When we consider I R ( N ) for N ≥ h , the set B d ( I , N ) of minimal generators for this lex ideal in degree at most d may change, but B d ( I , N ) is constant for all N ≫ 0 . We let B d ( I ) denote the set of generators one obtains for all N ≫ 0 , and we let b d = b d ( I ) be its cardinality. The sequences b 1 , … , b d , … obtained in this way may grow very fast. Remarkably, even when I = ( x 1 2 , x 2 2 ) , one obtains a very interesting sequence, 0 , 2 , 3 , 4 , 6 , 12 , 924 , 409620 , … . This sequence is the same as H d − 1 + 1 for d ≥ 2 , where H d is the dth Hamilton number. The Hamilton numbers were studied by Hamilton and by Hammond and Sylvester because of their occurrence in a counting problem connected with the use of Tschirnhaus transformations in manipulating polynomial equations.