cssi/SciSciGPT-SciSciNet huggingface dataset:
Optimal Resource Allocation for Cellular Networks with Virtual Cell Joint Decoding

This work presents a new resource allocation optimization framework for cellular networks using neighborhood-based optimization. Under this optimization framework resources are allocated within virtual cells encompassing several base-stations and the users within their coverage area. Incorporating the virtual cell concept enables the utilization of more sophisticated cooperative communication schemes such as coordinated multi-point decoding. We form the virtual cells using hierarchical clustering given a particular number of such cells. Once the virtual cells are formed, we consider a cooperative decoding scheme in which the base-stations in each virtual cell jointly decode the signals that they receive. We propose an iterative solution for the resource allocation problem resulting from the cooperative decoding within each virtual cell. Numerical results for the average system sum rate of our network design under hierarchical clustering are presented. These results indicate that virtual cells with neighborhood-based optimization leads to significant gains in sum rate over optimization within each cell, yet may also have a significant sum-rate penalty compared to fully-centralized optimization.
A note on mutually beneficial exchange and discrimination

We use Hammond and Axelrod’s (2006) agent-based model of ethnocentrism to show how people’s willingness to cooperate with outsiders is affected by mutually beneficial exchange. In environments where one person gains at the expense of others (‘charity economies’), we find discrimination to be a dominant behavior. However, non-discrimination becomes the dominant behavior when an environment contains productive investment and mutually beneficial exchange (‘investment economies’). These results point to market-improving policy prescriptions to reduce racial/ethnic tension and the conflict that arise from it.
Improving Robustness of ReRAM-based Spiking Neural Network Accelerator with Stochastic Spike-timing-dependent-plasticity

Spike-timing-dependent-plasticity (STDP) is an unsupervised learning algorithm for spiking neural network (SNN), which promises to achieve deeper understanding of human brain and more powerful artificial intelligence. While conventional computing system fails to simulate SNN efficiently, process-inmemory (PIM) based on devices such as ReRAM can be used in designing fast and efficient STDP based SNN accelerators, as it operates in high resemblance with biological neural network. However, the real-life implementation of such design still suffers from impact of input noise and device variation. In this work, we present a novel stochastic STDP algorithm that uses spiking frequency information to dynamically adjust synaptic behavior. The algorithm is tested in pattern recognition task with noisy input and shows accuracy improvement over deterministic STDP. In addition, we show that the new algorithm can be used for designing a robust ReRAM based SNN accelerator that has strong resilience to device variation.
The Framers’ Compromise

Michael Klarman argues in The Framers’ Coup that “[t]he compromises undertaken in Philadelphia . . . illustrate the extent to which the Constitution was a product of clashing interests rather than dispassionate political philosophizing.”1 Klarman’s history distinctively reaches beyond ratification of the United States Constitution to the compromises that delivered the Bill of Rights. And he does a particularly fine job of illuminating the subterranean interests that drove the compromises over accounting for slavery in the structure of the new system, showing where that institution lurked behind discourse and text that dared not speak its name. In the chapter he devotes to...
Hawkes Process for Understanding the Influence of Pathogenic Social Media Accounts

Over the past years, political events and public opinion on the Web have been allegedly manipulated by accounts dedicated to spreading disinformation and performing malicious activities on social media. These accounts hereafter referred to as "Pathogenic Social Media (PSM)" accounts, are often controlled by terrorist supporters, water armies or fake news writers and hence can pose threats to social media and general public. Understanding and analyzing PSMs could help social media firms devise sophisticated and automated techniques that could be deployed to stop them from reaching their audience and consequently reduce their threat. In this paper, we leverage the well-known statistical technique "Hawkes Process" to quantify the influence of PSM accounts on the dissemination of malicious information on social media platforms. Our findings on a real world ISIS-related dataset from Twitter indicate that PSMs are significantly different from regular users in making a message viral. Specifically, we observed that PSMs do not usually post URLs from mainstream news sources. Instead, their tweets usually receive large impact on audience, if contained URLs from Facebook and alternative news outlets. In contrary, tweets posted by regular users receive nearly equal impression regardless of the posted URLs and their sources. Our findings can further shed light on understanding and detecting PSM accounts.
Gaussian stochastic volatility models: Scaling regimes, large deviations, and moment explosions

Abstract In this paper, we establish sample path large and moderate deviation principles for log-price processes in Gaussian stochastic volatility models, and study the asymptotic behavior of exit probabilities, call pricing functions, and the implied volatility. In addition, we prove that if the volatility function in an uncorrelated Gaussian model grows faster than linearly, then, for the asset price process, all the moments of order greater than one are infinite. Similar moment explosion results are obtained for correlated models.
A BAYESIAN MARK INTERACTION MODEL FOR ANALYSIS OF TUMOR PATHOLOGY IMAGES.

With the advance of imaging technology, digital pathology imaging of tumor tissue slides is becoming a routine clinical procedure for cancer diagnosis. This process produces massive imaging data that capture histological details in high resolution. Recent developments in deep-learning methods have enabled us to identify and classify individual cells from digital pathology images at large scale. Reliable statistical approaches to model the spatial pattern of cells can provide new insight into tumor progression and shed light on the biological mechanisms of cancer. We consider the problem of modeling spatial correlations among three commonly seen cells observed in tumor pathology images. A novel geostatistical marking model with interpretable underlying parameters is proposed in a Bayesian framework. We use auxiliary variable MCMC algorithms to sample from the posterior distribution with an intractable normalizing constant. We demonstrate how this model-based analysis can lead to sharper inferences than ordinary exploratory analyses, by means of application to three benchmark datasets and a case study on the pathology images of $188$ lung cancer patients. The case study shows that the spatial correlation between tumor and stromal cells predicts patient prognosis. This statistical methodology not only presents a new model for characterizing spatial correlations in a multitype spatial point pattern conditioning on the locations of the points, but also provides a new perspective for understanding the role of cell–cell interactions in cancer progression.
Giving Time: Examining Sector Differences in Volunteering Intensity

Sector differences in prosocial motivations and behaviors among workers receives a great deal of attention in public administration scholarship. Extant literature consistently finds public sector workers are more likely to engage in prosocial behaviors, such as volunteering, than their peers in the private sector. Less attention has been paid to the sector gap in volunteerism along the intensive margin. Using time-diary data, which accounts for potential social desirability bias, from a nationally representative sample, this study investigates the gap between public sector workers and their private sector counterparts. The results suggest that public sector workers spend much more time, on an average day, volunteering than observably similar private sector peers, and the difference cannot be explained by other observable differences between public and private sector workers. The gap in volunteer intensity is largest at the local level and among teachers. The implications of these results for research and practice are discussed.
Paley type partial difference sets in abelian groups

Partial difference sets with parameters $(v,k,\lambda,\mu)=(v, (v-1)/2, (v-5)/4, (v-1)/4)$ are called Paley type partial difference sets. In this note we prove that if there exists a Paley type partial difference set in an abelian group $G$ of an order not a prime power, then $|G|=n^4$ or $9n^4$, where $n>1$ is an odd integer. In 2010, Polhill \cite{Polhill} constructed Paley type partial difference sets in abelian groups with those orders. Thus, combining with the constructions of Polhill and the classical Paley construction using non-zero squares of a finite field, we completely answer the following question: "For which odd positive integer $v > 1$, can we find a Paley type partial difference set in an abelian group of order $v$?"
The Liquid Metallic Hydrogen Model of the Sun and the Solar Atmosphere VI. Helium in the Chromosphere

Molecular hydrogen and hydrides have recently been advanced as vital agents in the generation of emission spectra in the chromosphere. This is a result of the role they play in the formation of condensed hydrogen structures (CHS) within the chromosphere (P.M. Robitaille. The Liquid Metallic Hydrogen Model of the Sun and the Solar Atmosphere IV. On the Nature of the Chromosphere. Progr. Phys., 2013, v. 3, 15‐21). Next to hydrogen, helium is perhaps the most intriguing component in this region of the Sun. Much like other elements, which combine with hydrogen to produce hydrides, helium can form the well-known helium hydride molecular ion, HeH + , and the excited neutral helium hydride molecule, HeH � . While HeH + is hypothesized to be a key cosmological molecule, its possible presence in the Sun, and that of its ex cited neutral counterpart, has not been considered. Still, these hydrides are likely to play a role in the synthesis of CHS, as the He I and He II emission lines strongly suggest. In this regard, the study of helium emission spectra can provide insight into the condensed nature of the Sun, especially when considering the 10830 A line associated with the 2 3 P!2 3 S triplet state transition. This line is strong in solar prominences and can be seen clearly on the disk. The excessive population of helium triplet states cannot be adequately explained using the gaseous models, since these states should be depopulated by collisional processes. Conversely, when He-based molecules are used to build CHS in a liquid metallic hydrogen model, an ever increasing population of the 2 3 S and 2 3 P states might be expected. The overpopulation of these triplet states leads to the conc lusion that these emission lines are unlikely to be produced through random collisional or photon excitation, as required by the gaseous models. This provides a significant h urdle for these models. Thus, the strong 2 3 P!2 3 S lines and the overpopulation of the helium triplet states p rovides the thirty-second line of evidence that the Sun is comprised of condensed matter.
Learning to Generate Music With Sentiment.

Deep Learning models have shown very promising results in automatically composing polyphonic music pieces. However, it is very hard to control such models in order to guide the compositions towards a desired goal. We are interested in controlling a model to automatically generate music with a given sentiment. This paper presents a generative Deep Learning model that can be directed to compose music with a given sentiment. Besides music generation, the same model can be used for sentiment analysis of symbolic music. We evaluate the accuracy of the model in classifying sentiment of symbolic music using a new dataset of video game soundtracks. Results show that our model is able to obtain good prediction accuracy. A user study shows that human subjects agreed that the generated music has the intended sentiment, however negative pieces can be ambiguous.
High signal-to-noise ratio ? The spectroscopic key to Algol systems

The usefulness of high signal-to-noise-ratio spectra for both radial-velocity and abundance studies of Algol systems is emphasised. It is shown that division by a hot star is a worthwhile step in pursuit of this objective. A preliminary analysis of high signalto-noise-ratio, red and near-infrared, Reticon observations of R CMa shows that its primary has solar CNO abundances within the 0.3 dex observational error. The low-mass (0.17 m⊙) secondary of this Algol system must have lost a large fraction of its original mass. Some of this material would have been extensively processed during the secondary's main-sequence lifetime and would therefore have had a highly non-solar CNO-abundance distribution. The lack of serious contamination of the primary's abundances is consistent with most, but not all, plausible mass-transfer scenarios.
Delta Scuti stars and Dwarf cepheids: Review and pulsation modes

Recent developments in the field of Delta Scuti stars are discussed. Considerable progress has been made in determining the multiple-period structure of individual variable stars and in identifying the excited radial and nonradial modes through a variety of methods. This allows an analysis of the change of excited modes as a function of the position in the instability strip.
Towards Attribute-Based Encryption for RAMs from LWE: Sub-linear Decryption, and More.

Attribute based encryption (ABE) is an advanced encryption system with a built-in mechanism to generate keys associated with functions which in turn provide restricted access to encrypted data. Most of the known candidates of attribute based encryption model the functions as circuits. This results in significant efficiency bottlenecks, especially in the setting where the function associated with the ABE key is represented by a random access machine (RAM) and a database, with the runtime of the RAM program being sublinear in the database size. In this work we study the notion of attribute based encryption for random access machines (RAMs), introduced in the work of Goldwasser, Kalai, Popa, Vaikuntanathan and Zeldovich (Crypto 2013). We present a construction of attribute based encryption for RAMs satisfying sublinear decryption complexity assuming learning with errors; this is the first construction based on standard assumptions. Previously, Goldwasser et al. achieved this result based on non-falsifiable knowledge assumptions. We also consider a dual notion of ABE for RAMs, where the database is in the ciphertext and we show how to achieve this dual notion, albeit with large attribute keys, also based on learning with errors.
Simulating Diffusion Properties of Solid-State Electrolytes via a Neural Network Potential: Performance and Training Scheme

The recently published DeePMD model, based on a deep neural network architecture, brings the hope of solving the time-scale issue which often prevents the application of first principle molecular dynamics to physical systems. With this contribution we assess the performance of the DeePMD potential on a real-life application and model diffusion of ions in solid-state electrolytes. We consider as test cases the well known Li10GeP2S12, Li7La3Zr2O12 and Na3Zr2Si2PO12. We develop and test a training protocol suitable for the computation of diffusion coefficients, which is one of the key properties to be optimized for battery applications, and we find good agreement with previous computations. Our results show that the DeePMD model may be a successful component of a framework to identify novel solid-state electrolytes.
Life-cycle mediated effects of urbanization on parasite communities in the estuarine fish, Fundulus heteroclitus

This study examined the relationship between urbanization and parasite community structure in the estuarine fish, Fundulus heteroclitus. We measured landscape and physicochemical factors associated with urbanization at 6 sites from 4 collection periods. Concurrently, we quantified the metazoan parasite community in F. heteroclitus collected at those sites, with 105 fish studied per site during the 4 collection periods. Parasite community composition differed among sites. Host size was the most important variable for direct life-cycle parasite assemblages and indirect life-cycle parasites at the individual fish level, while landscape and physicochemical factors determined the structure of indirect life-cycle parasite assemblages at the population scale. Variation in the prevalence and intensity of infection of two indirect life-cycle parasites, Lasiocotus minutus and Glossocercus caribaensis, were the primary parasites that drove differences across sites. Variation in the presence/absence of these indirect life-cycle parasite species was associated with sediment Ni concentrations, patch density, and marsh size. Our data support the hypothesis that urbanization, acting at both landscape and physicochemical scales, can have a significant impact on parasite community structure. This, however, varied by parasite life history: there was little effect of urbanization on the prevalence and intensity of direct life-cycle parasites, but significant variation was detected for indirect life-cycle parasites. This study demonstrates how anthropogenically driven landscape change influences fine-scale population dynamics of parasites.
Analysis of Structures by Combining Redundants

New method of analysis of elastic structures avoids solution of large systems of simultaneous equations; method is applicable to structures of any type or degree of indeterminacy; cases when reduction to well known elastic center and column analogy methods is possible, are analyzed.
Global Stability for a Class of Nonlinear PDE with Non-local Term

This paper is concerned with establishing global asymptotic stability results for a class of non-linear PDE, in which the nonlinearity enters as a scalar multiple which is non-local. The method of proof does not involve a Lyapounov function. It is shown that stability for the PDE is equivalent to stability for a differential delay equation. Stability for the delay equation is proven by exploiting certain monotonicity properties. These are established by using the methods of optimal control theory.
Spatiotemporal Analysis of DNA Integration during Natural Transformation Reveals a Mode of Nongenetic Inheritance in Bacteria

Summary Natural transformation (NT) is a major mechanism of horizontal gene transfer in microbial species that promotes the spread of antibiotic-resistance determinants and virulence factors. Here, we develop a cell biological approach to characterize the spatiotemporal dynamics of homologous recombination during NT in Vibrio cholerae. Our results directly demonstrate (1) that transforming DNA efficiently integrates into the genome as single-stranded DNA, (2) that the resulting heteroduplexes are resolved by chromosome replication and segregation, and (3) that integrated DNA is rapidly expressed prior to cell division. We show that the combination of these properties results in the nongenetic transfer of gene products within transformed populations, which can support phenotypic inheritance of antibiotic resistance in both V. cholerae and Streptococcus pneumoniae. Thus, beyond the genetic acquisition of novel DNA sequences, NT can also promote the nongenetic inheritance of traits during this conserved mechanism of horizontal gene transfer.
Extragalactic radio sources with hybrid morphology: implications for the Fanaroff-Riley dichotomy

We provide observational and theoretical perspec- tives on the currently much debated issue of the Fanaroff-Riley (FR) morphological dichotomy of extragalactic radio sources. In this context we introduce a new, albeit rare, class of double radio sources in which the two lobes exhibit clearly different FR mor- phologies. It is argued that such 'HYbrid MOrphology Radio Sources', or HYMORS, could be used to effectively constrain the theoretical mechanisms proposed for the FR dichotomy. Ba- sically, the existence of HYMORS supports explanations for the FR dichotomy based upon jet interaction with the medium exter- nal to the central engine, and appears quite difficult to reconcile with the class of explanations that posit fundamental differences in the central engine, such as black hole spin or jet composition, to be responsible for the two FR classes of double radio sources.
Contrastive Representation Distillation

Often we wish to transfer representational knowledge from one neural network to another. Examples include distilling a large network into a smaller one, transferring knowledge from one sensory modality to a second, or ensembling a collection of models into a single estimator. Knowledge distillation, the standard approach to these problems, minimizes the KL divergence between the probabilistic outputs of a teacher and student network. We demonstrate that this objective ignores important structural knowledge of the teacher network. This motivates an alternative objective by which we train a student to capture significantly more information in the teacher's representation of the data. We formulate this objective as contrastive learning. Experiments demonstrate that our resulting new objective outperforms knowledge distillation on a variety of knowledge transfer tasks, including single model compression, ensemble distillation, and cross-modal transfer. When combined with knowledge distillation, our method sets a state of the art in many transfer tasks, sometimes even outperforming the teacher network.
Amperometric measurements of cocaine cue and novel context-evoked glutamate and nitric oxide release in the nucleus accumbens core.

Cue-induced reinstatement of cocaine seeking after self-administration (SA) and extinction relies on glutamate release in the nucleus accumbens core (NAcore), which activates neuronal nitric oxide synthase interneurons. Nitric oxide (NO) is required for structural plasticity in NAcore medium spiny neurons, as well as cued cocaine seeking. However, NO release in the NAcore during reinstatement has yet to be directly measured. Furthermore, the temporal relationship between glutamate release and the induction of an NO response also remains unknown. Using wireless amperometric recordings in awake behaving rats, we quantified the magnitude and temporal dynamics of novel context- and cue-induced reinstatement-evoked glutamate and NO release in the NAcore. We found that re-exposure to cocaine-conditioned stimuli following SA and extinction increased extracellular glutamate, leading to release of NO in the NAcore. In contrast, exposing drug-naive rats to a novel context led to a lower magnitude rise in glutamate in the NAcore relative to cue-induced reinstatement. Interestingly, novel context exposure evoked a higher magnitude NO response relative to cue-induced reinstatement. Despite differences in magnitude, novel context evoked-NO release in the NAcore was also temporally delayed when compared to glutamate. These results demonstrate a dissociation between the magnitude of cocaine cue- and novel context-evoked glutamate and NO release in the NAcore, yet similarity in the temporal dynamics of their release. Together, these data contribute to a greater understanding of the relationship between glutamate and NO, two neurotransmitters implicated in encoding the valence of distinct contextual stimuli.
An Operational Approach to Information Leakage

Given two random variables $X$ and $Y$ , an operational approach is undertaken to quantify the “leakage” of information from $X$ to $Y$ . The resulting measure $\mathcal {L}\left ({X \!\! \to \!\! Y}\right)$ is called maximal leakage , and is defined as the multiplicative increase, upon observing $Y$ , of the probability of correctly guessing a randomized function of $X$ , maximized over all such randomized functions. A closed-form expression for $\mathcal {L}\left ({X \!\! \to \!\! Y}\right)$ is given for discrete $X$ and $Y$ , and it is subsequently generalized to handle a large class of random variables. The resulting properties are shown to be consistent with an axiomatic view of a leakage measure, and the definition is shown to be robust to variations in the setup. Moreover, a variant of the Shannon cipher system is studied, in which performance of an encryption scheme is measured using maximal leakage. A single-letter characterization of the optimal limit of (normalized) maximal leakage is derived and asymptotically-optimal encryption schemes are demonstrated. Furthermore, the sample complexity of estimating maximal leakage from data is characterized up to subpolynomial factors. Finally, the guessing framework used to define maximal leakage is used to give operational interpretations of commonly used leakage measures, such as Shannon capacity, maximal correlation, and local differential privacy.
Disentangling basal and accrued height-for-age for cross-population comparisons.

OBJECTIVES Current standards for comparing stunting across human populations assume a universal model of child growth. Such comparisons ignore population differences that are independent of deprivation and health outcomes. This article partitions variation in height-for-age that is specifically associated with deprivation and health outcomes to provide a basis for cross-population comparisons. MATERIALS AND METHODS Using a multilevel model with a sigmoid relationship of resources and growth, we partition variation in height-for-age z-scores (HAZ) from 1.5 million children across 70 countries into two components: (1) "accrued HAZ" shaped by environmental inputs (e.g., undernutrition, infectious disease, inadequate sanitation, poverty) and (2) a country-specific "basal HAZ" independent of such inputs. We validate these components against population-level infant mortality rates and assess how these basal differences may affect cross-population comparisons of stunting. RESULTS Basal HAZ differs reliably across countries (range of approximately 1.5 SD) and is independent of measures of infant mortality. By contrast, accrued HAZ captures stunting as impaired growth due to deprivation and is more closely associated with infant mortality than observed HAZ. Assessing stunting prevalence by accrued HAZ suggest that populations in West Africa and Haiti suffer much greater levels of stunting than indicated by observed HAZ. DISCUSSION Current universal standards may dramatically underestimate stunting in populations with taller basal HAZ. Relying on observed HAZ rather than accrued HAZ may also lead to inappropriate cross-population comparisons, such as concluding that Haitian children enjoy better conditions for growth than do Indian or Guatemalan children.
Building Calibrated Deep Models via Uncertainty Matching with Auxiliary Interval Predictors

With rapid adoption of deep learning in critical applications, the question of when and how much to trust these models often arises, which drives the need to quantify the inherent uncertainties. While identifying all sources that account for the stochasticity of models is challenging, it is common to augment predictions with confidence intervals to convey the expected variations in a model's behavior. We require prediction intervals to be well-calibrated, reflect the true uncertainties, and to be sharp. However, existing techniques for obtaining prediction intervals are known to produce unsatisfactory results in at least one of these criteria. To address this challenge, we develop a novel approach for building calibrated estimators. More specifically, we use separate models for prediction and interval estimation, and pose a bi-level optimization problem that allows the former to leverage estimates from the latter through an uncertainty matching strategy. Using experiments in regression, time-series forecasting, and object localization, we show that our approach achieves significant improvements over existing uncertainty quantification methods, both in terms of model fidelity and calibration error.
On the Number of Orthogonal Systems in Vector Spaces over Finite Fields

Iosevich and Senger (2008) showed that if a subset of the $d$-dimensional vector space over a finite field is large enough, then it contains many $k$-tuples of mutually orthogonal vectors. In this note, we provide a graph theoretic proof of this result.
IdeoTrace: a framework for ideology tracing with a case study on the 2016 U.S. presidential election

The 2016 United States presidential election has been characterized as a period of extreme divisiveness that was exacerbated on social media by the influence of fake news, trolls, and social bots. However, the extent to which the public became more polarized in response to these influences over the course of the election is not well understood. In this paper we propose IdeoTrace, a framework for (i) jointly estimating the ideology of social media users and news websites and (ii) tracing changes in user ideology over time. We apply this framework to the last two months of the election period for a group of 47508 Twitter users and demonstrate that both liberal and conservative users became more polarized over time.
ELSA: A Throughput-Optimized Design of an LSTM Accelerator for Energy-Constrained Devices

The next significant step in the evolution and proliferation of artificial intelligence technology will be the integration of neural network (NN) models within embedded and mobile systems. This calls for the design of compact, energy efficient NN models in silicon. In this article, we present a scalable application-specific integrated circuit (ASIC) design of an energy-efficient Long Short-Term Memory (LSTM) accelerator, named ELSA, which is suitable for energy-constrained devices. It includes several architectural innovations to achieve small area and high energy efficiency. To reduce the area and power consumption of the overall design, the compute-intensive units of ELSA employ approximate multiplications and still achieve high performance and accuracy. The performance is further improved through efficient synchronization of the elastic pipeline stages to maximize the utilization. The article also includes a performance model of ELSA, as a function of the hidden nodes and timesteps, permitting its use for the evaluation of any LSTM application. ELSA was implemented in register transfer level (RTL) and was synthesized and placed and routed in 65nm technology. Its functionality is demonstrated for language modeling—a common application of LSTM. ELSA is compared against a baseline implementation of an LSTM accelerator with standard functional units and without any of the architectural innovations of ELSA. The article demonstrates that ELSA can achieve significant improvements in power, area, and energy-efficiency when compared to the baseline design and several ASIC implementations reported in the literature, making it suitable for use in embedded systems and real-time applications.
Genetic Networks Underlying Natural Variation in Basal and Induced Activity Levels in Drosophila melanogaster .

Exercise is recommended by health professionals across the globe as part of a healthy lifestyle to prevent and/or treat the consequences of obesity. While overall, the health benefits of exercise and an active lifestyle are well understood, very little is known about how genetics impacts an individual’s inclination for and response to exercise. To address this knowledge gap, we investigated the genetic architecture underlying natural variation in activity levels in the model system Drosophila melanogaster. Activity levels were assayed in the Drosophila Genetics Reference Panel fly strains at baseline and in response to a gentle exercise treatment using the Rotational Exercise Quantification System. We found significant, sex-dependent variation in both activity measures and identified over 100 genes that contribute to basal and induced exercise activity levels. This gene set was enriched for genes with functions in the central nervous system and in neuromuscular junctions and included several candidate genes with known activity phenotypes such as flightlessness or uncoordinated movement. Interestingly, there were also several chromatin proteins among the candidate genes, two of which were validated and shown to impact activity levels. Thus, the study described here reveals the complex genetic architecture controlling basal and exercise-induced activity levels in D. melanogaster and provides a resource for exercise biologists.
Structure of the host cell recognition and penetration machinery of a Staphylococcus aureus bacteriophage.

Staphylococcus aureus is a common cause of infections in humans. The emergence of virulent, antibiotic-resistant strains of S. aureus is a significant public health concern. Most virulence and resistance factors in S. aureus are encoded by mobile genetic elements, and transduction by bacteriophages represents the main mechanism for horizontal gene transfer. The baseplate is a specialized structure at the tip of bacteriophage tails that plays key roles in host recognition, cell wall penetration, and DNA ejection. We have used high-resolution cryo-electron microscopy to determine the structure of the S. aureus bacteriophage 80α baseplate at 3.75 A resolution, allowing atomic models to be built for most of the major tail and baseplate proteins, including two tail fibers, the receptor binding protein, and part of the tape measure protein. Our structure provides a structural basis for understanding host recognition, cell wall penetration and DNA ejection in viruses infecting Gram-positive bacteria. Comparison to other phages demonstrates the modular design of baseplate proteins, and the adaptations to the host that take place during the evolution of staphylococci and other pathogens.
Screened expanding turning-vane concept

The paper presents the screened expanding corner vane concept, which can turn a channel flow by $$90^\circ$$, while simultaneously increasing the flow area by at least a factor of two and thus halving the mean velocity, all without incurring any significant flow separation. The concept is demonstrated experimentally, and investigated with analytical and computational models. One target application is an ultra-compact closed-circuit wind tunnel, whose overall length is roughly half that of a conventional wind tunnel with the same test section.
Adaptive Template Systems: Data-Driven Feature Selection for Learning with Persistence Diagrams

Feature extraction from persistence diagrams has received increasing attention in recent years, as a tool to enrich machine learning techniques. In this paper we explore an adaptive methodology to localize features in persistent diagrams, which are then used in learning tasks. Specifically, we investigate three algorithms, CDER, GMM and HDBSCAN, to obtain adaptive template functions/features. Said features are evaluated in three classification experiments with persistence diagrams. Namely, manifold, human shapes and protein classification. The main conclusion of our analysis is that adaptive template systems, as a feature extraction technique, yield competitive and often superior results in the studied examples. Moreover, from the adaptive algorithms here studied, CDER consistently provides the most reliable and robust adaptive featurization.
Separating Functions of the Phage-Encoded Quorum-Sensing-Activated Antirepressor Qtip.

Summary Quorum sensing is a process of chemical communication that bacteria use to track cell density and coordinate gene expression across a population. Bacteria-infecting viruses, called phages, can encode quorum-sensing components that enable them to integrate host cell density information into the lysis-lysogeny decision. Vibriophage VP882 is one such phage, and activation of its quorum-sensing pathway leads to the production of an antirepressor called Qtip. Qtip interferes with the prophage repressor (cIVP882), leading to host-cell lysis. Here, we show that Qtip interacts with the N terminus of cIVP882, inhibiting both cIVP882 DNA binding and cIVP882 autoproteolysis. Qtip also sequesters cIVP882, localizing it to the poles. Qtip can localize to the poles independently of cIVP882. Alanine-scanning mutagenesis of Qtip shows that its localization and interference with cIVP882 activities are separable. Comparison of Qtip to a canonical phage antirepressor reveals that despite both proteins interacting with their partner repressors, only Qtip drives polar localization.
TorsinB overexpression prevents abnormal twisting in DYT1 dystonia mouse models

Genetic redundancy can be exploited to identify therapeutic targets for inherited disorders. We explored this possibility in DYT1 dystonia, a neurodevelopmental movement disorder caused by a loss-of-function (LOF) mutation in the TOR1A gene encoding torsinA. Prior work demonstrates that torsinA and its paralog torsinB have conserved functions at the nuclear envelope. This work established that low neuronal levels of torsinB dictate the neuronal selective phenotype of nuclear membrane budding. Here, we examined whether torsinB expression levels impact the onset or severity of abnormal movements or neuropathological features in DYT1 mouse models. We demonstrate that torsinB levels bidirectionally regulate these phenotypes. Reducing torsinB levels causes a dose-dependent worsening whereas torsinB overexpression rescues torsinA LOF-mediated abnormal movements and neurodegeneration. These findings identify torsinB as a potent modifier of torsinA LOF phenotypes and suggest that augmentation of torsinB expression may retard or prevent symptom development in DYT1 dystonia.
Low-Rank Gradient Approximation for Memory-Efficient on-Device Training of Deep Neural Network

Training machine learning models on mobile devices has the potential of improving both privacy and accuracy of the models. However, one of the major obstacles to achieving this goal is the memory limitation of mobile devices. Reducing training memory enables models with high-dimensional weight matrices, like automatic speech recognition (ASR) models, to be trained on-device. In this paper, we propose approximating the gradient matrices of deep neural networks using a low-rank parameterization as an avenue to save training memory. The low-rank gradient approximation enables more advanced, memory-intensive optimization techniques to be run on device. Our experimental results show that we can reduce the training memory by about 33.0% for Adam optimization. It uses comparable memory to momentum optimization and achieves a 4.5% relative lower word error rate on an ASR personalization task.
Positive Peace, Paradox, and Contested Liberalisms

Particular conceptions of peace can be associated with logics of dominance and imposition, but also human flourishing and emancipation. These dynamics have been amply explored in debates about liberal peacebuilding over the past two decades. Despite a recent renaissance of attention, however, the concept of positive peace has only rarely been problematized to the same extent. That is unfortunate because it has the potential to operate at both ends of this imposition-emancipation spectrum. This paper revisits Galtung’s early (1960s) articulation of positive peace in order to resurface its fundamental radicalism, together with some of its inherent tensions, paradoxes and politics, particularly insofar as one might try to operationalize the theory. It then explores the linkages between the concept of positive peace and the prevailing peacebuilding paradigm of our day: liberal peacebuilding. I argue that our understanding of both positive peace and liberal peacebuilding can be clarified when viewed through the lens of contested liberalisms and conflicting liberal values. Advancing peace theory and praxis at this stage would benefit from an increasing willingness to openly confront some of these conflicts and a greater degree of transparency about our liberal commitments, including in the hard, “real world” cases where tensions seem irreconcilable.
Effect of a Single Session of Intermittent Hypoxia on Erythropoietin and Oxygen‐Carrying Capacity

Intermittent hypoxia, defined as alternating bouts of breathing hypoxic and normoxic air, has the potential to improve oxygen-carrying capacity through an erythropoietin-mediated increase in hemoglobin mass. The purpose of this study was to determine the effect of a single session of intermittent hypoxia on erythropoietin levels and hemoglobin mass in young healthy individuals. Nineteen participants were randomly assigned to an intermittent hypoxia group (Hyp, n = 10) or an intermittent normoxia group (Norm, n = 9). Intermittent hypoxia consisted of five 4-min hypoxic cycles at a targeted arterial oxygen saturation of 90% interspersed with 4-min normoxic cycles. Erythropoietin levels were measured before and two hours following completion of the protocol. Hemoglobin mass was assessed the day before and seven days after exposure to intermittent hypoxia or normoxia. As expected, the intermittent hypoxia group had a lower arterial oxygen saturation than the intermittent normoxia group during the intervention (Hyp: 89 ± 1 vs. Norm: 99 ± 1%, p < 0.01). Erythropoietin levels did not significantly increase following exposure to intermittent hypoxia (Hyp: 8.2 ± 4.5 to 9.0 ± 4.8, Norm: 8.9 ± 1.7 to 11.1 ± 2.1 mU·mL-1, p = 0.15). Hemoglobin mass did not change following exposure to intermittent hypoxia. This single session of intermittent hypoxia was not sufficient to elicit a significant rise in erythropoietin levels or hemoglobin mass in young healthy individuals.
Ranking State Environmental Outputs: Evidence from Panel Data

This paper uses state-level pollution data from 1986–1997 to construct two indices that rank U.S. states according to environmental outputs. A major finding is that marginal performers in other indices, such as Wyoming, garner top spots in these ranking systems. The paper also presents findings from fixed and random effects models of panel data that imply state income levels are positively associated with environmental outputs after a threshold level of income is obtained.
Deformation theories controlled by Hochschild cohomologies

We explore how the higher order Hochschild cohomology controls a deformation theory when the simplicial set models the 3-sphere. Besides generalizing to the d-sphere for any $$d\ge 1$$ , we also investigate a deformation theory corresponding to the tertiary Hochschild cohomology, which naturally reduces to those studied for the secondary and usual Hochschild cohomologies under certain conditions.
Fast Ionic Conduction in Solids

Publisher Summary This chapter reviews the fast ionic conduction in solids. In a few solids, ionic conduction may approach a value typical of a molten salt or of proton conduction in a strong acid. Ionic conduction is an essential process in many solid-state reactions, and fast ionic conduction at low temperatures may offer a low-temperature synthetic route to compounds that are either metastable or unstable at normal sintering temperatures. A normal ionic conductor may be intrinsic or extrinsic. Most stoichiometric compounds are intrinsic conductors at low temperature; the ions are ordered so that they just fill sets of crystallographically equivalent sites, and mobile ions are created by exciting ions across an energy gap into interstitial positions. Solid electrolytes are used as thin-membrane separators of two reactants or of two products. These membranes must be dense, inert in their operational environment, without chemically reactive inhomogeneities, resistant to cracking under thermal or mechanical shock, and easily fabricated into large area sample. It is found that the disorder of the mobile ions results in partial occupancy of a set of sites that are energetically equivalent.
9 – The Conic Sections

Publisher Summary This chapter focuses on four special types of graphs and their associated equations. The four types of graphs are called “conic sections” because each can be found by slicing a cone with a plane. There are many applications associated with conic sections. The planets orbit the sun in elliptical orbits. Many of the comets that come in contact with the gravitational field surrounding the earth travel in parabolic or hyperbolic paths. Flashlight and searchlight mirrors have elliptical or parabolic shapes because of the way surfaces with those shapes reflect light. The chapter presents graphs of ellipses and hyperbolas. The graphs that are centered about the origin have been considered. The most convenient method of solving a system that contains one or two second-degree equations is by substitution, although the addition method can be used at times.
22 – Air Pollution

Publisher Summary This chapter discusses the air pollutants that are toxic to trees. The air pollutants can occur in several forms: invisible gases, particulates, or aerosols. Most initial symptoms in plants from air pollutants appear on the leaves. Toxic gases enter the leaves through the stomata and react with the internal leaf tissues. The affected tissues are inhibited in their photosynthetic ability and are killed. Injury from air pollutants can be put into two major categories including chronic and acute. Chronic injury occurs from the cumulative effects of long exposure to toxic pollutants at low levels on sensitive species or at moderate to high levels on tolerant species. Acute injury occurs after short exposure to pollutants at high levels on tolerant species or at moderate to low levels on sensitive species. The specific effects of air pollutants on plant tissue vary with the pollutant, host, time of year, and numerous meteorological factors, such as temperature, relative humidity, wind, and solar radiation. Therefore, it is difficult to describe the typical symptoms of air pollution injury on trees or any other plants.
Creep Life of Ceramic Components Using a Finite-Element-Based Integrated Design Program (CARES/CREEP)

The desirable properties of ceramics at high temperatures have generated interest in their use for structural application such as in advanced turbine engine systems. Design lives for such systems can exceed 10,000 hours. The long life requirement necessitates subjecting the components to relatively low stresses. The combination of high temperatures and low stresses typically places failure for monolithic ceramics in the creep regime. The objective of this paper is to present a design methodology for predicting the lifetimes of structural components subjected to creep rupture conditions. This methodology utilizes commercially available finite element packages and takes into account the time-varying creep strain distributions (stress relaxation). The creep life, of a component is discretized into short time steps, during which the stress and strain distributions are assumed constant. The damage is calculated for each time step based on a modified Monkman-Grant creep rupture criterion. Failure is assumed to occur when the normalized accumulated damage at any point in the component is greater than or equal to unity. The corresponding time will be the creep rupture life for that component. Examples are chosen to demonstrate the Ceramics Analysis and Reliability Evaluation of Structures/CREEP (CARES/CREEP) integrated design program, which is written for the ANSYS finite element package. Depending on the component size and loading conditions, it was found that in real structures one of two competing failure modes (creep or slow crack growth) will dominate. Applications to benchmark problems and engine components are included.