cssi/SciSciGPT-SciSciNet huggingface dataset:
The Neglected History of Furū’ and the Premodern/Modern Binary

Marion Katz reflects on major developments in Islamic legal studies since the 1990’s, the decade that saw – as noted in the introduction to this Roundtable– expanded and diversified scholarly attention to Islamic legal studies. For her, it is puzzling then that outdated frameworks continue to percolate in the field, such as the crude “premodern / modern binary” and the continued neglect of what she calls fiqh studies. Katz urges scholars to pursue more nuanced approaches to deal with the sheer volume of the textual corpus and to fill in chasmic history of substantive law, namely: (1) the study of “core samples,” that is, the diachronic investigation of individual concepts and doctrines to document inflection points, and (2) the study of “transverse slices,” that is, the synchronic study of a wide range of material from a specific historical context that helps expose underlying and pervasive assumptions behind a broad area of law.
Dodrio: Exploring Transformer Models with Interactive Visualization

Why do large pre-trained transformer-based models perform so well across a wide variety of NLP tasks? Recent research suggests the key may lie in multi-headed attention mechanism’s ability to learn and represent linguistic information. Understanding how these models represent both syntactic and semantic knowledge is vital to investigate why they succeed and fail, what they have learned, and how they can improve. We present Dodrio, an open-source interactive visualization tool to help NLP researchers and practitioners analyze attention mechanisms in transformer-based models with linguistic knowledge. Dodrio tightly integrates an overview that summarizes the roles of different attention heads, and detailed views that help users compare attention weights with the syntactic structure and semantic information in the input text. To facilitate the visual comparison of attention weights and linguistic knowledge, Dodrio applies different graph visualization techniques to represent attention weights scalable to longer input text. Case studies highlight how Dodrio provides insights into understanding the attention mechanism in transformer-based models. Dodrio is available at https://poloclub.github.io/dodrio/.
VALUE PRESERVING WELFARE WEIGHTS FOR SOCIAL OPTIMIZATION PROBLEMS

We propose endogenous, value preserving (VP) welfare weights as an alternative to equal weights utilitarianism. VP weights require that the contribution of an agent's endowments to social welfare is proportional to the contribution of the final allocation to his welfare. Under three axioms, the per unit contribution of a good to welfare is shown to equal the gradient of the welfare function. VP weights coincide with Negishi weights in frictionless environments but prescribe redistribution toward a good's owner when the good's social value exceeds its market value. When ability is treated as an endowment, VP weights reward ability and effort.
Quantum Pseudorandomness and Classical Complexity

We construct a quantum oracle relative to which BQP = QMA but cryptographic pseudorandom quantum states and pseudorandom unitary transformations exist, a counterintuitive result in light of the fact that pseudorandom states can be "broken" by quantum Merlin-Arthur adversaries. We explain how this nuance arises as the result of a distinction between algorithms that operate on quantum and classical inputs. On the other hand, we show that some computational complexity assumption is needed to construct pseudorandom states, by proving that pseudorandom states do not exist if BQP = PP. We discuss implications of these results for cryptography, complexity theory, and quantum tomography.
Diet-dependent natriuretic peptide receptor C expression in adipose tissue is mediated by PPARγ via long-range distal enhancers.

The cardiac natriuretic peptides (NPs) are well established as regulators of blood pressure and fluid volume, but they also stimulate adipocyte lipolysis and control the gene program of nonshivering thermogenesis in brown adipose tissue. The NP "clearance" receptor C (NPRC) functions to clear NPs from the circulation via peptide internalization and degradation and thus is an important regulator of NP signaling and adipocyte metabolism. It is well known that the Nprc gene is highly expressed in adipose tissue and dynamically regulated upon nutrition and environmental changes. However, the molecular basis for how Nprc gene expression is regulated is still poorly understood. Here, we identified the nuclear receptor transcription factor peroxisome proliferator–activated receptor gamma (PPARγ) as a transcriptional regulator of Nprc expression in mouse adipocytes. During 3T3-L1 adipocyte differentiation, levels of Nprc expression increase in parallel with PPARγ induction. Rosiglitazone, a classic PPARγ agonist, increases, whereas siRNA knockdown of PPARγ reduces, Nprc expression in 3T3-L1 adipocytes. By using chromosome conformation capture and luciferase reporter assays, we demonstrate that PPARγ controls Nprc gene expression in adipocytes through its long-range distal enhancers. Furthermore, the induction of Nprc expression in adipose tissue during high-fat diet feeding is found to be associated with increased PPARγ enhancer activity. Our findings define PPARγ as a mediator of adipocyte Nprc gene expression and establish a new connection between PPARγ and the control of adipocyte NP signaling in obesity.
Assessment of Physical Properties of Water-Repellent Soils

AbstractThis note reports the physical and mechanical properties of two postwildfire sands collected in Southern California and chemically induced hydrophobic sand. Hydrophobicity decreases the att...
Vertex algebroids and Conformal vertex algebras associated with simple Leibniz algebras

Abstract We first investigate the algebraic structure of vertex algebroids B when B are simple Leibniz algebras. Next, we use these vertex algebroids B to construct indecomposable non-simple C 2 -cofinite N -graded vertex algebras V B ‾ . In addition, we classify N -graded irreducible V B ‾ -modules and examine conformal vectors of these N -graded vertex algebras V B ‾ .
Commentary Relative to the Seismic Structure of the Sun: Internal Rotation, Oblateness, and Solar Shape

Helioseismological studies have the ability to yield tremendous insight with respect to the internal structure and shape of the solar body. Such observations indicate that while the convection zone displays diff erential rotation, the core rotates as a rigid body. The latter is located below the tachocline layer, where powerful shear stresses are believed to occur. Beyond simple oblateness, seismological studies indicate that the Sun displays significant higher order shape terms (quadrupole, hexadecapole) which may, or may not, vary with the solar cycle. In this work, such seismological findings are briefly discussed with the intent of highlighting that 1) the diff erential rotation of the convection zone, 2) the rigid body rotation of the core, 3) the presence of the tachocline layer and 4) the appearance of higher order shape terms, all lend support to the idea that the solar body is composed of material in the condensed state. In this regard, the existence of the tachocline layer in the solar interior and the solid body rotation of the core constitute the nineteenth and twentieth lines of evidence that the Sun is condensed matter.
Non-Clairvoyant Dynamic Mechanism Design with Budget Constraints and Beyond

We provide a general design framework for dynamic mechanisms under complex environments, coined Lossless History Compression mechanisms. Lossless history compression mechanisms compress the history into a state carrying the least historical information without losing any generality in terms of either revenue or welfare. In particular, the characterization works for almost arbitrary constraints on the outcomes, and any objective function defined on the historical reports, allocations, and the cumulative payments. We then apply our framework to design a non-clairvoyant dynamic mechanism under budget and ex-post individual rationality constraints that is dynamic incentive-compatible and achieves non-trivial revenue performance, even without any knowledge about the future. In particular, our dynamic mechanism obtains a constant approximation to the optimal dynamic mechanism having access to all information in advance. To the best of our knowledge, this is the first dynamic mechanism that achieves a constant approximation and strictly respects dynamic incentive-compatibility and budget constraints without relying on any forecasts of the future.
Trust and local bias of individual investors

Abstract We examine the effects of social trust on the local bias of individual investors. Using the trading records of Chinese retail investors, we find that investors with higher trust exhibit lower local bias in their stock portfolios, and this pattern holds under various robustness tests. We further show that trust helps accomplish greater portfolio diversification and reduces familiarity-based investment bias in general. These findings suggest that trust plays an important role in individual investors’ financial decisions, especially when they face unfamiliar investment opportunities.
A Control Architecture for Provably-Correct Autonomous Driving

This paper presents a novel two-level control architecture for a fully autonomous vehicle in a deterministic environment, which can handle traffic rules as specifications and low-level vehicle control with real-time performance. At the top level, we use a simple representation of the environment and vehicle dynamics to formulate a linear Model Predictive Control (MPC) problem. We describe the traffic rules and safety constraints using Signal Temporal Logic (STL) formulas, which are mapped to mixed integer-linear constraints in the optimization problem. The solution obtained at the top level is used at the bottom-level to determine the best control command for satisfying the constraints in a more detailed framework. At the bottom-level, specification-based runtime monitoring techniques, together with detailed representations of the environment and vehicle dynamics, are used to compensate for the mismatch between the simple models used in the MPC and the real complex models. We obtain substantial improvements over existing approaches in the literature in the sense of runtime performance and we validate the effectiveness of our proposed control approach in the simulator CARLA.
Efficiency of Nonstandard and High Contact Ratio Involute Spur Gears

A power loss prediction was extended to include involute spur gears of nonstandard proportions. The method is used to analyze the effects of modified addendum, tooth thickness, and gear center distance in addition to the parameters previously considered which included gear diameter, pitch, pressure angle, face width, oil viscosity, speed, and torque. Particular emphasis was placed on high contact ratio gearing (contact ratios greater than two). Despite their higher sliding velocities, high contact ratio gears are designed to levels of efficiency comparable to those of conventional gears while retaining their advantages through proper selection of gear geometry.
Qubit-Based Clock Synchronization for QKD Systems Using a Bayesian Approach.

Quantum key distribution (QKD) systems provide a method for two users to exchange a provably secure key. Synchronizing the users’ clocks is an essential step before a secure key can be distilled. Qubit-based synchronization protocols directly use the transmitted quantum states to achieve synchronization and thus avoid the need for additional classical synchronization hardware. Previous qubit-based synchronization protocols sacrifice secure key either directly or indirectly, and all known qubit-based synchronization protocols do not efficiently use all publicly available information published by the users. Here, we introduce a Bayesian probabilistic algorithm that incorporates all published information to efficiently find the clock offset without sacrificing any secure key. Additionally, the output of the algorithm is a probability, which allows us to quantify our confidence in the synchronization. For demonstration purposes, we present a model system with accompanying simulations of an efficient three-state BB84 prepare-and-measure protocol with decoy states. We use our algorithm to exploit the correlations between Alice’s published basis and mean photon number choices and Bob’s measurement outcomes to probabilistically determine the most likely clock offset. We find that we can achieve a 95 percent synchronization confidence in only 4140 communication bin widths, meaning we can tolerate clock drift approaching 1 part in 4140 in this example when simulating this system with a dark count probability per communication bin width of 8×10−4 and a received mean photon number of 0.01.
A Methodology for Quantifying Flexibility in a fleet of Diverse DERs

This paper addresses the question: how many distributed energy resources (DERs) are needed to provide ± 1MW of flexibility over a number of hours? For this purpose, ametric based on an ISO’s own performance score is proposed. Then, a systematic procedure is presented and validated that makes use of either a simulator or the solution to an optimization problem based on a nominal analytical formulation to get flexibility in terms of kW-per-device. Furthermore, simulation-based analysis indicates that flexibility from different DER fleets adds linearly, that is, the total flexibility provided by a mixture of different DER types can be obtained as a convex combination of their individual kW-per-device flexibility. The proposed methodology is validated on (i) a centralized coordinator and (ii) a device driven DER coordination scheme called packetized energy management (PEM). Furthermore, the effect of heterogeneity as well as PEM specific parameters such as packet length and mean time-to-request on flexibility is also quantified.
Photoluminescence spectra of point defects in semiconductors: Validation of first-principles calculations

Optically and magnetically active point defects in semiconductors are interesting platforms for the development of solid-state quantum technologies. Their optical properties are usually probed by measuring photoluminescence spectra, which provide information on excitation energies and on the interaction of electrons with lattice vibrations. The authors present a detailed validation protocol of first principles calculations of photoluminescence spectra of defects in diamond and SiC, necessary for the interpretation of experiments and for robust predictions of the electronic properties of point defects in semiconductors.
Sparse Partial Least Squares for Coarse Noisy Graph Alignment

Graph signal processing (GSP) provides a powerful framework for analyzing signals arising in a variety of domains. In many applications of GSP, multiple network structures are available, each of which captures different aspects of the same underlying phenomenon. To integrate these different data sources, graph alignment techniques attempt to find the best correspondence between vertices of two graphs. We consider a generalization of this problem, where there is no natural one-to-one mapping between vertices, but where there is correspondence between the community structures of each graph. Because we seek to learn structure at this higher community level, we refer to this problem as "coarse" graph alignment. To this end, we propose a novel regularized partial least squares method which both incorporates the observed graph structures and imposes sparsity in order to reflect the underlying block community structure. We provide efficient algorithms for our method and demonstrate its effectiveness in simulations.
High efficiency end-fire 3-D optical phased array based on multi-layers SiN/SiO platform

OPA device has been widely studied for solid-state Lidar. We have investigated a multi-layers SiN/SiO structure to end-fire the beam with high efficiency. The advantage of the structure, simulation result and fabrication feasibility are discussed. In addition, the latest fabrication progress is also presented.
Non-injectivity of Nonzero Discriminant Polynomials and Applications to Packing Polynomials

We show that an integer-valued quadratic polynomial on \(\mathbb {R}^2\) can not be injective on the integer lattice points of any affine convex cone if its discriminant is nonzero. A consequence is the non-existence of quadratic packing polynomials on irrational sectors of \(\mathbb {R}^2\).
Low-bandwidth Modular Mathematical Modeling of DC Microgrid Systems for Control Development with Application to Shipboard Power Systems

In recent years, DC and AC microgrid (MG) systems have attracted a major attention due to various potential for integration of future technology into conventional systems and control. The integration of such technology requires appropriate tools for complex design, analysis and optimization. This paper presents a mathematical low-bandwidth modeling (LBM) approach that can be used for control development in DC and further be extended to AC MG systems. In this work, first a simplified mathematical model of a medium voltage DC (MVDC) shipboard MG system is presented, next, the overall system-level connection convention is presented to display the overall mathematical coupling of the individual subsystems, then, a simplified example of the control development is presented, and last, the overall system under a test scenario is implemented in Simulink Real-time.
Instability of LIFO queueing networks

Under the last-in, first-out (LIFO) discipline, jobs arriving later at a class always receive priority of service over earlier arrivals at any class belonging to the same station. Subcritical LIFO queueing networks with Poisson external arrivals are known to be stable, but an open problem has been whether this is also the case when external arrivals are given by renewal processes. Here, we show that this weaker assumption is not sufficient for stability by constructing a family of examples where the number of jobs in the network increases to infinity over time. This behavior contrasts with that for the other classical disciplines: processor sharing (PS), infinite server (IS) and first-in, first-out (FIFO), which are stable under general conditions on the renewals of external arrivals. Together with LIFO, PS and IS constitute the classical symmetric disciplines; with the inclusion of FIFO, these disciplines constitute the classical homogeneous disciplines. Our examples show that a general theory for stability of either family is doubtful.
The Impact of Distant Hurricane on Local Housing Markets

This study investigates whether the local housing markets can be affected by the occurrence of a large-scale but distant hurricane without direct impact. Using a dataset of residential transactions in Miami-Dade County, FL between 2005 and 2014, we find that the impact of a property’s flood risk exposure on its value varies over time, with a price penalty of 4% in hurricane striking period. By contrast, within a quiet hurricane period, properties within high flood hazard zones demand a price premium ranging roughly from 4% to 6%, which may represent the price impact of water-related amenities. The premium declined significantly upon Hurricane Sandy. Several possible explanations are provided and tested to explain the main findings. Our findings indicate that the occurrence of a disastrous hurricane which impacted faraway regions also raised local home buyers’ perception of flood risk, but for a short period of time (one quarter).
Bootstrap an end-to-end ASR system by multilingual training, transfer learning, text-to-text mapping and synthetic audio

Bootstrapping speech recognition on limited data resources has been an area of active research for long. The recent transition to all-neural models and end-to-end (E2E) training brought along particular challenges as these models are known to be data hungry, but also came with opportunities around language-agnostic representations derived from multilingual data as well as shared word-piece output representations across languages that share script and roots. We investigate here the effectiveness of different strategies to bootstrap an RNN-Transducer (RNN-T) based automatic speech recognition (ASR) system in the low resource regime, while exploiting the abundant resources available in other languages as well as the synthetic audio from a text-to-speech (TTS) engine. Our experiments demonstrate that transfer learning from a multilingual model, using a post-ASR text-to-text mapping and synthetic audio deliver additive improvements, allowing us to bootstrap a model for a new language with a fraction of the data that would otherwise be needed. The best system achieved a 46% relative word error rate (WER) reduction compared to the monolingual baseline, among which 25% relative WER improvement is attributed to the post-ASR text-to-text mappings and the TTS synthetic data.
Weight Pulling: A Novel Mouse Model of Human Progressive Resistance Exercise

This study describes a mouse model of progressive resistance exercise that utilizes a full-body/multi-joint exercise (weight pulling) along with a training protocol that mimics a traditional human paradigm (three training sessions per week, ~8–12 repetitions per set, 2 min of rest between sets, approximately two maximal-intensity sets per session, last set taken to failure, and a progressive increase in loading that is based on the individual’s performance). We demonstrate that weight pulling can induce an increase in the mass of numerous muscles throughout the body. The relative increase in muscle mass is similar to what has been observed in human studies, and is associated with the same type of long-term adaptations that occur in humans (e.g., fiber hypertrophy, myonuclear accretion, and, in some instances, a fast-to-slow transition in Type II fiber composition). Moreover, we demonstrate that weight pulling can induce the same type of acute responses that are thought to drive these long-term adaptations (e.g., the activation of signaling through mTORC1 and the induction of protein synthesis at 1 h post-exercise). Collectively, the results of this study indicate that weight pulling can serve as a highly translatable mouse model of progressive resistance exercise.
Activity-dependent Golgi satellite formation in dendrites reshapes the neuronal surface glycoproteome

Activity-driven changes in the neuronal surface glycoproteome are known to occur with synapse formation, plasticity, and related diseases, but their mechanistic basis and significance are unclear. Here, we observed that N-glycans on surface glycoproteins of dendrites shift from immature to mature forms containing sialic acid in response to increased neuronal activation. In exploring the basis of these N-glycosylation alterations, we discovered that they result from the growth and proliferation of Golgi satellites scattered throughout the dendrite. Golgi satellites that formed during neuronal excitation were in close association with endoplasmic reticulum (ER) exit sites and early endosomes and contained glycosylation machinery without the Golgi structural protein, GM130. They functioned as distal glycosylation stations in dendrites, terminally modifying sugars either on newly synthesized glycoproteins passing through the secretory pathway or on surface glycoproteins taken up from the endocytic pathway. These activities led to major changes in the dendritic surface of excited neurons, impacting binding and uptake of lectins, as well as causing functional changes in neurotransmitter receptors such as nicotinic acetylcholine receptors. Neural activity thus boosts the activity of the dendrite's satellite micro-secretory system by redistributing Golgi enzymes involved in glycan modifications into peripheral Golgi satellites. This remodeling of the neuronal surface has potential significance for synaptic plasticity, addiction, and disease.
Integrating Plug-in Electric Vehicles (PEVs) into Household Fleets - Factors Influencing Miles Traveled by PEV Owners in California

Abstract Household vehicle miles traveled (VMT) has traditionally been studied in the context of gasoline vehicles. In this study, we analyze VMT of Plug-in Electric Vehicles (PEVs) to understand PEV use and the factors that influence their use in a household. We use data from a unique repeat survey of PEV owners in California who were originally surveyed when they first bought their vehicle. Having two survey responses per household allows us to obtain more reliable VMT data and analyze the change in VMT over the vehicle ownership period. The results show that PEV VMT is correlated with traditional factors like population density, built environment, attitudes towards technology, and lifestyle preferences. Specific to PEVs, electric driving range and access to infrastructure, have a major influence on PEV VMT. Moreover, while lower electricity price at home may lead to a higher share of PEV VMT in total household VMT, we do not identify the presence of “rebound effect”. Overall, we observe that factors influencing PEV VMT are like those observed for conventional gasoline vehicles. We find that PEVs drive a similar amount as conventional vehicles, not less (due to range anxiety) as some have suggested. The results have implications for emissions impact assessments and travel demand models that depend on assumptions of average annual VMT for policymaking.
Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields

The rendering procedure used by neural radiance fields (NeRF) samples a scene with a single ray per pixel and may therefore produce renderings that are excessively blurred or aliased when training or testing images observe scene content at different resolutions. The straightforward solution of supersampling by rendering with multiple rays per pixel is impractical for NeRF, because rendering each ray requires querying a multilayer perceptron hundreds of times. Our solution, which we call "mip-NeRF" (a la "mipmap"), extends NeRF to represent the scene at a continuously-valued scale. By efficiently rendering anti-aliased conical frustums instead of rays, mip-NeRF reduces objectionable aliasing artifacts and significantly improves NeRF's ability to represent fine details, while also being 7% faster than NeRF and half the size. Compared to NeRF, mip-NeRF reduces average error rates by 17% on the dataset presented with NeRF and by 60% on a challenging multiscale variant of that dataset that we present. Mip-NeRF is also able to match the accuracy of a brute-force supersampled NeRF on our multiscale dataset while being 22x faster.
Rapid conversion of replicating and integrating Saccharomyces cerevisiae plasmid vectors via Cre recombinase.

Plasmid shuttle vectors capable of replication in both Saccharomyces cerevisiae and Escherichia coli and optimized for controlled modification in vitro and in vivo are a key resource supporting yeast as a premier system for genetics research and synthetic biology. We have engineered a series of yeast shuttle vectors optimized for efficient insertion, removal and substitution of plasmid yeast replication loci, allowing generation of a complete set of integrating, low copy and high copy plasmids via predictable operations as an alternative to traditional subcloning. We demonstrate the utility of this system through modification of replication loci via Cre recombinase, both in vitro and in vivo, and restriction endonuclease treatments.
Optimal transport and the Gauss curvature equation

In this short note, we consider the problem of prescribing the Gauss curvature and image of the Gauss map for the graph of a function over a domain in Euclidean space. The prescription of the image of the Gauss map turns this into a second boundary value problem. Our main observation is that this problem can be posed as an optimal transport problem where the target is a subset of the lower hemisphere of $\mathbb{S}^n$. As a result we obtain existence and regularity of solutions under mild assumptions on the curvature, as well as a quantitative version of a gradient blowup result due to Urbas, which turns out to fall within the optimal transport framework.
A distributed algorithm for high-dimension convex quadratically constrained quadratic programs

We propose a Jacobi-style distributed algorithm to solve convex, quadratically constrained quadratic programs (QCQPs), which arise from a broad range of applications. While small to medium-sized convex QCQPs can be solved efficiently by interior-point algorithms, high-dimension problems pose significant challenges to traditional algorithms that are mainly designed to be implemented on a single computing unit. The exploding volume of data (and hence, the problem size), however, may overwhelm any such units. In this paper, we propose a distributed algorithm for general, non-separable, high-dimension convex QCQPs, using a novel idea of predictor–corrector primal–dual update with an adaptive step size. The algorithm enables distributed storage of data as well as parallel, distributed computing. We establish the conditions for the proposed algorithm to converge to a global optimum, and implement our algorithm on a computer cluster with multiple nodes using message passing interface. The numerical experiments are conducted on data sets of various scales from different applications, and the results show that our algorithm exhibits favorable scalability for solving high-dimension problems.
A parity for 2-colourable links

We introduce the 2-parity colour. It is a theory of parity for a large class of virtual links, defined using the interaction between orientations of the link components and a certain type of colouring. The 2-colour parity is an extension of the Gaussian parity, to which it reduces on virtual knots. We show that the 2-colour parity descends to a parity on free links. We compare the 2-colour parity to other parity theories of virtual links, focusing on a theory due to Im and Park. The 2-colour parity yields a strictly stronger invariant than the Im-Park parity. We introduce an invariant, the 2-colour writhe, that takes the form of a string of integers. The 2-colour writhe is a concordance invariant, and so obstructs sliceness. It is also an obstruction to ($\pm$)-amphichirality and chequerboard colourability within a concordance class.
The conserved serine transporter SdaC moonlights to enable self recognition.

Cells can use self recognition to achieve cooperative behaviors. Self-recognition genes are thought to principally evolve in tandem with partner self-recognition alleles. However, other constraints on protein evolution could exist. Here, we have identified an interaction outside of self-recognition loci that could constrain the sequence variation of a self-recognition protein. We show that during collective swarm expansion in Proteus mirabilis, self-recognition signaling co-opts SdaC, a serine transporter. Serine uptake is crucial for bacterial survival and colonization. Single-residue variants of SdaC reveal that self recognition requires an open conformation of the protein; serine transport is dispensable. A distant ortholog from Escherichia coli is sufficient for self recognition; however, a paralogous serine transporter, YhaO, is not. Thus, SdaC couples self recognition and serine transport, likely through a shared molecular interface. Self recognition proteins may follow the framework of a complex interaction network rather than an isolated two-protein system. Understanding molecular and ecological constraints on self-recognition proteins lays the groundwork for insights into the evolution of self recognition and emergent collective behaviors. Importance Bacteria can receive secret messages from kin during migration. For Proteus mirabilis, these messages are necessary for virulence in multi-species infections. We show that a serine transporter-conserved among gamma-enterobacteria- enables self recognition. Molecular co-option of nutrient uptake could limit the sequence variation of these message proteins. SdaC is the primary transporter for L-serine, a vital metabolite for colonization during disease. Unlike many self-recognition receptors, SdaC is sufficiently conserved between species to achieve recognition. The predicted open conformation is shared by transport and recognition. SdaC reveals the interdependence of communication and nutrient acquisition. As the broader interactions of self-recognition proteins are studied, features shared among microbial self-recognition systems, such as Dictyostelium spp. and Neurospora spp., could emerge.
Abnormal Functional Network Topology and Its Dynamics during Sustained Attention Processing Significantly Implicate Post-TBI Attention Deficits in Children

Traumatic brain injury (TBI) is highly prevalent in children. Attention deficits are among the most common and persistent post-TBI cognitive and behavioral sequalae that can contribute to adverse outcomes. This study investigated the topological properties of the functional brain network for sustained attention processing and their dynamics in 42 children with severe post-TBI attention deficits (TBI-A) and 47 matched healthy controls. Functional MRI data during a block-designed sustained attention task was collected for each subject, with each full task block further divided into the pre-, early, late-, and post-stimulation stages. The task-related functional brain network was constructed using the graph theoretic technique. Then, the sliding-window-based method was utilized to assess the dynamics of the topological properties in each stimulation stage. Relative to the controls, the TBI-A group had significantly reduced nodal efficiency and/or degree of left postcentral, inferior parietal, inferior temporal, and fusiform gyri and their decreased stability during the early and late-stimulation stages. The left postcentral inferior parietal network anomalies were found to be significantly associated with elevated inattentive symptoms in children with TBI-A. These results suggest that abnormal functional network characteristics and their dynamics associated with the left parietal lobe may significantly link to the onset of the severe post-TBI attention deficits in children.
Mean-based Best Arm Identification in Stochastic Bandits under Reward Contamination

This paper investigates the problem of best arm identification in $\textit{contaminated}$ stochastic multi-arm bandits. In this setting, the rewards obtained from any arm are replaced by samples from an adversarial model with probability $\varepsilon$. A fixed confidence (infinite-horizon) setting is considered, where the goal of the learner is to identify the arm with the largest mean. Owing to the adversarial contamination of the rewards, each arm's mean is only partially identifiable. This paper proposes two algorithms, a gap-based algorithm and one based on the successive elimination, for best arm identification in sub-Gaussian bandits. These algorithms involve mean estimates that achieve the optimal error guarantee on the deviation of the true mean from the estimate asymptotically. Furthermore, these algorithms asymptotically achieve the optimal sample complexity. Specifically, for the gap-based algorithm, the sample complexity is asymptotically optimal up to constant factors, while for the successive elimination-based algorithm, it is optimal up to logarithmic factors. Finally, numerical experiments are provided to illustrate the gains of the algorithms compared to the existing baselines.
Data-to-text Generation by Splicing Together Nearest Neighbors

We propose to tackle data-to-text generation tasks by directly splicing together retrieved segments of text from “neighbor” source-target pairs. Unlike recent work that conditions on retrieved neighbors but generates text token-by-token, left-to-right, we learn a policy that directly manipulates segments of neighbor text, by inserting or replacing them in partially constructed generations. Standard techniques for training such a policy require an oracle derivation for each generation, and we prove that finding the shortest such derivation can be reduced to parsing under a particular weighted context-free grammar. We find that policies learned in this way perform on par with strong baselines in terms of automatic and human evaluation, but allow for more interpretable and controllable generation.
Efficient Neuromorphic Signal Processing with Loihi 2

The biologically inspired spiking neurons used in neuromorphic computing are nonlinear filters with dynamic state variables—very different from the stateless neuron models used in deep learning. The next version of Intel's neuromorphic research processor, Loihi 2, supports a wide range of stateful spiking neuron models with fully programmable dynamics. Here we showcase advanced spiking neuron models that can be used to efficiently process streaming data in simulation experiments on emulated Loihi 2 hardware. In one example, Resonate-and-Fire (RF) neurons are used to compute the Short Time Fourier Transform (STFT) with similar computational complexity but 47x less output bandwidth than the conventional STFT. In another example, we describe an algorithm for optical flow estimation using spatiotemporal RF neurons that requires over 90x fewer operations than a conventional DNN-based solution. We also demonstrate promising preliminary results using backpropagation to train RF neurons for audio classification tasks. Finally, we show that a cascade of Hopf resonators—a variant of the RF neuron—replicates novel properties of the cochlea and motivates an efficient spike-based spectrogram encoder.
Attack-resilient state estimation with intermittent data authentication

Abstract Network-based attacks on control systems may alter sensor data delivered to the controller, effectively causing degradation in control performance. As a result, having access to accurate state estimates, even in the presence of attacks on sensor measurements, is of critical importance. In this work, we analyze performance of resilient state estimators (RSEs) when any subset of sensors may be compromised by a stealthy attacker. Specifically, we consider systems with the well-known l 0 -based RSE and two commonly used sound intrusion detectors (IDs). For linear time-invariant plants with bounded noise, we define the notion of perfect attackability (PA) when attacks may result in unbounded estimation errors while remaining undetected by the employed ID (i.e., stealthy). We derive necessary and sufficient PA conditions, showing that a system can be perfectly attackable even if the plant is stable. While PA can be prevented with the use the standard cryptographic mechanisms (e.g., message authentication) that ensure data integrity under network-based attacks, their continuous use imposes significant communication and computational overhead. Consequently, we also study the impact that even intermittent use of data authentication has on RSE performance guarantees in the presence of stealthy attacks. We show that if messages from some of the sensors are even intermittently authenticated, stealthy attacks could not result in unbounded state estimation errors.
Altered temporal sequence of transcriptional regulators in the generation of human cerebellar granule cells

Brain development is regulated by conserved transcriptional programs across species, but little is known about divergent mechanisms that create species-specific characteristics. Among brain regions, human cerebellar histogenesis differs in complexity compared with non-human primates and rodents, making it important to develop methods to generate human cerebellar neurons that closely resemble those in the developing human cerebellum. We report a rapid protocol for the derivation of the human ATOH1 lineage, the precursor of excitatory cerebellar neurons, from human pluripotent stem cells (hPSC). Upon transplantation into juvenile mice, hPSC-derived cerebellar granule cells migrated along glial fibers and integrated into the cerebellar cortex. By Translational Ribosome Affinity Purification-seq, we identified an unexpected temporal shift in the expression of RBFOX3 (NeuN) and NEUROD1, which are classically associated with differentiated neurons, in the human outer external granule layer. This molecular divergence may enable the protracted development of the human cerebellum compared to mice.
A Transient Semantics for Typed Racket

Mixed-typed languages enable programmers to link typed and untyped components in various ways. Some offer rich type systems to facilitate the smooth migration of untyped code to the typed world; others merely provide a convenient form of type Dynamic together with a conventional structural type system. Orthogonal to this dimension, Natural systems ensure the integrity of types with a sophisticated contract system, while Transient systems insert simple first-order checks at strategic places within typed code. Furthermore, each method of ensuring type integrity comes with its own blame-assignment strategy. Typed Racket has a rich migratory type system and enforces the types with a Natural semantics. Reticulated Python has a simple structural type system extended with Dynamic and enforces types with a Transient semantics. While Typed Racket satisfies the most stringent gradual-type soundness properties at a significant performance cost, Reticulated Python seems to limit the performance penalty to a tolerable degree and is nevertheless type sound. This comparison raises the question of whether Transient checking is applicable to and beneficial for a rich migratory type system. This paper reports on the surprising difficulties of adapting the Transient semantics of Reticulated Python to the rich migratory type system of Typed Racket. The resulting implementation, Shallow Typed Racket, is faster than the standard Deep Typed Racket but only when the Transient blame assignment strategy is disabled. For language designers, this report provides valuable hints on how to equip an existing compiler to support a Transient semantics. For theoreticians, the negative experience with Transient blame calls for a thorough investigation of this strategy.
Configuring audience-oriented privacy policies

A variety of applications allow users to post and share large amounts of personal information. Yet users also sometimes unintentionally expose their data to wide audiences. Users struggle to manage the sharing and protection of their personal data with current privacy mechanisms. We propose AudienceView, an alternative privacy policy interface to provide a better mental model and visual feedback for privacy management on social network sites. In this paper, we present the prototype and evaluate its performance against an existing social network site privacy interface. The results demonstrate that with our prototype users can modify a privacy policy with greater confidence, in less time.
The composition of organocadmium compounds in diethyl ether solvent

Abstract The reactions of Grignard reagents with CdCl 2 , CdBr 2 and CdI 2 in 1/1 and 2/1 ratios in diethyl ether have been studied in an attempt to define the composition of the so called organocadmium reagent in solution. The studies involved elemental analyses and IR examination of both the soluble and insoluble reaction products. In 1/1 ratio the reaction proceeds as expected to yield magnesium halide and an equilibrium mixture of R 2 Cd and CdX 2 . The IR spectra support the conclusion that both CdX 2 and MgX 2 are coordinated with R 2 Cd in either a single or double halogen bridge arrangement. In 2/1 ratio the reaction between a Grignard reagent and CdX 2 produces only R 2 Cd and MgX 2 . Interaction between these two compounds was demonstrated from IR spectra and from enhanced solubility of the MgX 2 . Pure compounds of empirical formula RCdX · MgX 2 have been isolated when the Grignard reagent and CdX 2 were allowed to react in 1/1 ratio. Evidence for RCdX in solution is also presented.
Motivation for entry, occupational commitment and intent to remain: a survey regarding Registered Nurse retention.

gambino k.m. (2010) Motivation for entry, occupational commitment and intent to remain: a survey regarding Registered Nurse retention. Journal of Advanced Nursing 66(11), 2532–2541. Abstract Aim. This paper is a report of a study of the relationships between Registered Nurses’ motivation for entering the profession, occupational commitment and intent to remain with an employer until retirement. Background. Identifying and supporting nurses who are strongly committed to their profession may be the single most influential intervention in combating the nursing shortage. An understanding of the characteristics these individuals possess could lead to a decline in the high attrition rates plaguing the profession. Method. Using a survey design, Registered Nurses enrolled at the school of nursing and/or employed at the associated university medical centre of a large, not-for-profit state university were polled in 2008. Logistic regression analysis was used to determine how the variables of motivation for entry and occupational commitment could indicate intent to remain. Results. The strongest indicators of intent to remain were normative commitment and age, with a 70% average rate of correctly estimating retention. Exp(B) values for normative commitment (1·09) and age (1·07) indicated that for each one-point increase on the normative commitment scale or one-point increase in age, the odds of remaining with an employer until retirement increased by 1·1%. Conclusion. Transformational changes in healthcare environments and nursing schools must be made to encourage loyalty and obligation, the hallmarks of normative commitment. Retention strategies should accommodate mature nurses as well as promote normative commitment in younger nurses.
Fluorescent Imaging of Cancer in Zebrafish

Zebrafish are an ideal model organism to research cancer. Zebrafish embryos and larvae are optically translucent, which has made imaging multiple processes in development and disease possible. When coupled with fluorescent imaging techniques, zebrafish are fast becoming a model of choice for following tumor formation. This is highlighted by recent studies using fluorescent proteins to image xenograft transplantation, neovascularization, growth responses to drug treatments, and self-renewal. Fluorescent labeled tumors can be generated in zebrafish by multiple methods including chemical mutagenesis, oncogene expression by mosaic or stable transgenesis, or genetic mutations that are predisposing to cancer. In this chapter, we highlight the studies that have employed fluorescence to follow critical aspects of tumorigenesis, with particular focus on providing methods for labeling, isolating, transplanting, and imaging fluorescently labeled tumors in zebrafish.
Radiobiology of pulsed dose rates

Any departure from CLDR will yield an increased biological effect, which is greater for tissues with smaller /spl alpha///spl beta/ ratios and shorter T/sub 1/2/ of repair. The Relative Effectiveness (RE=1+g.d/(/spl beta///spl alpha/)) for late responding tissues will increase more than the RE for early responding tissues including tumors. This could obviously lead to a reduced therapeutic ratio. Fortunately the effect is small (less than 10%) unless doses per pulse greater than 1 Gy are used. Dose-per-pulse is paramount (together with total dose) in determining the biological effect. The effect of between-pulse interval is next in importance, (longer intervals helping therapeutic ratio but not if fewer and larger pulses are used). The influence of duration of pulses and dose-rate within each pulse is less. In tissues with the shortest half-times of repair (less than 0.5 hours) the biological effect will be greater for PDR than for CLDR. The authors show here that there are no consistent differences between half-times for tumors and for normal tissues. The effect of "office-hours" PDR is very close to that of the same size and number of pulses given with standard intervals continuously, because there will be only a few overlong (overnight) intervals compared with many more standard short intervals. Late complications will be ameliorated more than tumor cell kill by increasing the intervals between pulses without increasing their size. Probably the most valuable parts of this Handout are the Tables and the References.