cssi/SciSciGPT-SciSciNet huggingface dataset:
American Legal Realism

This essay sets out the main elements of the revisionary and philosophical interpretation of the jurisprudence of American Legal Realism that I have developed in a series of articles over the last decade. This reading emphasizes the commitment of all the Realists to a core descriptive claim about adjudication (judges respond primarily to the underlying facts of the cases, rather than to legal rules and reasons); shows how the Realists divide in to two camps over the correct interpretation of this "core" claim (the Idiosncyrasy Wing of Frank, and the Sociological Wing of Llewellyn, Oliphant, Moore, Green, and the vast majority of Realists); demonstrates the connection of the Sociological Wing of Realism to the Realist project of law reform, including the work of the American Law Institute; examines and distinguishes the Realist arguments for the indeterminacy of law from Critical Legal Studies arguments; and shows how the Realists lay the foundation for the program of a "naturalized" jurisprudence, in opposition to the dominant "conceptual" jurisprudence of Anglophone legal philosophy. The revisionary reading also debunks certain popular myths about Legal Realism, like the following: the Realists believed "what the judge ate for breakfast determines the decision"; a critique of the public/private distinction was a central part of Realist jurisprudence; and the Realists were committed to an incoherent form of rule-skepticism.
Using Selection by Nonantibiotic Stressors to Sensitize Bacteria to Antibiotics

Evolutionary adaptation of bacteria to nonantibiotic selective forces, such as osmotic stress, has been previously associated with increased antibiotic resistance, but much less is known about potentially sensitizing effects of nonantibiotic stressors. In this study, we use laboratory evolution to investigate adaptation of Enterococcus faecalis, an opportunistic bacterial pathogen, to a broad collection of environmental agents, ranging from antibiotics and biocides to extreme pH and osmotic stress. We find that nonantibiotic selection frequently leads to increased sensitivity to other conditions, including multiple antibiotics. Using population sequencing and whole-genome sequencing of single isolates from the evolved populations, we identify multiple mutations in genes previously linked with resistance to the selecting conditions, including genes corresponding to known drug targets or multidrug efflux systems previously tied to collateral sensitivity. Finally, we hypothesized based on the measured sensitivity profiles that sequential rounds of antibiotic and nonantibiotic selection may lead to hypersensitive populations by harnessing the orthogonal collateral effects of particular pairs of selective forces. To test this hypothesis, we show experimentally that populations evolved to a sequence of linezolid (an oxazolidinone antibiotic) and sodium benzoate (a common preservative) exhibit increased sensitivity to more stressors than adaptation to either condition alone. The results demonstrate how sequential adaptation to drug and nondrug environments can be used to sensitize bacteria to antibiotics and highlight new potential strategies for exploiting shared constraints governing adaptation to diverse environmental challenges.
Massive accretion disks

Recent high resolution near infrared (HST-NICMOS) and mm-interferometric imaging have revealed dense gas and dust accretion disks in nearby ultra-luminous galactic nuclei. In the best studied ultraluminousIR galaxy, Arp 220, the 2μm imaging shows dust disks in both of the merging galactic nuclei and mm-CO line imaging indicates molecular gasmasses ∼ 109M⊙ for each disk. The two gas disks in Arp 220 are counterrotating and their dynamical masses are ∼ 2×109M⊙, that is, only slightly largerthan the gas masses. These disks have radii ∼100 pc and thickness 10-50 pc. The high brightness temperatures of the CO lines indicatethat the gas in the disks has area filling factors ∼25-50% and mean densitiesof ≥ 104 cm-3. Within these nuclear disks, the rate of massive star formation is undoubtedly prodigious and, given the high viscosity of the gas, there will also be high radial accretion rates, perhaps ≥ 10 M⊙ yr -1. If this inflow persists to very small radii, it is enough to feed even the highest luminosity AGNs.
Development of the mandibular curve of spee and maxillary compensating curve: A finite element model

The curved planes of the human dentition seen in the sagittal view, the mandibular curve of Spee and the maxillary compensating curve, have clinical importance to modern dentistry and potential relevance to the craniofacial evolution of hominins. However, the mechanism providing the formation of these curved planes is poorly understood. To explore this further, we use a simplified finite element model, consisting of maxillary and mandibular “blocks”, developed to simulate tooth eruption, and forces opposing eruption, during simplified masticatory function. We test our hypothesis that curved occlusal planes develop from interplay between tooth eruption, occlusal load, and mandibular movement. Our results indicate that our simulation of rhythmic chewing movement, tooth eruption, and tooth eruption inhibition, applied concurrently, results in a transformation of the contacting maxillary and mandibular block surfaces from flat to curved. The depth of the curvature appears to be dependent on the radius length of the rotating (chewing) movement of the mandibular block. Our results suggest mandibular function and maxillo-mandibular spatial relationship may contribute to the development of human occlusal curvature.
Optimal quantization for nonuniform Cantor distributions

AbstractLet P be a Borel probability measure on ℝ such that where S1 and S2 are two similarity mappings on ℝ such that for all x ∈ ℝ. Such a probability measure P has support the Cantor set generat...
Effects of initial microbial biomass abundance on respiration during pine litter decomposition.

Microbial biomass is increasingly used to predict respiration in soil organic carbon (SOC) models. Its increased use combined with the difficulty of accurately measuring this variable points a need to directly assess the importance of microbial biomass abundance for carbon (C) cycling. To test the hypothesis that the initial microbial biomass abundance (i.e. biomass abundance on new plant litter) is a strong driver of plant litter C cycling, we manipulated biomass abundance by 10 and 100-fold dilution and composition using 12 source communities on sterile pine litter and measured respiration in microcosms for 30 days. In the first two days of microbial growth on fresh litter, a 100-fold difference in initial biomass abundance caused an average difference in respiration of nearly 300%, but the effect rapidly declined to less than 30% in 10 days and to 14% in 30 days. Parallel simulations with a soil carbon model, SOMIC 1.0, also predicted a 14% difference over 30 days, consistent with the experimental results. Model simulations predicted convergence of cumulative CO2 to within 10% in three months and within 4% in three years. Rapid microbial growth, evidenced by appearance of visible microbial mats on the litter during the first week of incubation, likely attenuates the effects of large initial differences in biomass abundance. In contrast, the persistence of source community as an explanatory factor in driving differences in respiration across microcosms supports the importance of microbial composition in C cycling. Overall, the results suggest that the initial abundance of microbial biomass on litter is a weak driver of C flux from litter decomposition over long timescales (months to years) when litter communities have equal nutrient availability. By extension, slight variation in the timing of microbial dispersal to fresh litter is likely to be a minor factor in long-term C flux. Importance Microbial biomass is one of the most common microbial parameters used in land carbon (C) cycle models, however, it is notoriously difficult to measure accurately. To understand the consequences of mismeasurement, as well as the broader importance of microbial biomass abundance as a direct driver of ecological phenomena, greater quantitative understanding of the role of microbial biomass abundance in environmental processes is needed. Using microcosms, we manipulated the initial biomass of numerous microbial communities across a 100-fold range and measured effects on CO2 production during plant litter decomposition. We found that the effects of initial biomass abundance on CO2 production was largely attenuated within a week, while the effects of community type remained significant over the course of the experiment. Overall, our results suggest that initial microbial biomass abundance in litter decomposition within an ecosystem is a weak driver of long-term C cycling dynamics.
Swoosh! Rattle! Thump! - Actions that Sound

Truly intelligent agents need to capture the interplay of all their senses to build a rich physical understanding of their world. In robotics, we have seen tremendous progress in using visual and tactile perception; however, we have often ignored a key sense: sound. This is primarily due to the lack of data that captures the interplay of action and sound. In this work, we perform the first large-scale study of the interactions between sound and robotic action. To do this, we create the largest available sound-action-vision dataset with 15,000 interactions on 60 objects using our robotic platform Tilt-Bot. By tilting objects and allowing them to crash into the walls of a robotic tray, we collect rich four-channel audio information. Using this data, we explore the synergies between sound and action and present three key insights. First, sound is indicative of fine-grained object class information, e.g., sound can differentiate a metal screwdriver from a metal wrench. Second, sound also contains information about the causal effects of an action, i.e. given the sound produced, we can predict what action was applied to the object. Finally, object representations derived from audio embeddings are indicative of implicit physical properties. We demonstrate that on previously unseen objects, audio embeddings generated through interactions can predict forward models 24% better than passive visual embeddings. Project videos and data are at this https URL
Joint Scheduling of URLLC and eMBB Traffic in 5G Wireless Networks

Emerging 5G systems will need to efficiently support both enhanced mobile broadband traffic (eMBB) and ultra-low-latency communications (URLLC) traffic. In these systems, time is divided into slots which are further sub-divided into minislots. From a scheduling perspective, eMBB resource allocations occur at slot boundaries, whereas to reduce latency URLLC traffic is pre-emptively overlapped at the minislot timescale, resulting in selective superposition/puncturing of eMBB allocations. This approach enables minimal URLLC latency at a potential rate loss to eMBB traffic.We study joint eMBB and URLLC schedulers for such systems, with the dual objectives of maximizing utility for eMBB traffic while immediately satisfying URLLC demands. For a linear rate loss model (loss to eMBB is linear in the amount of URLLC superposition/puncturing), we derive an optimal joint scheduler. Somewhat counter-intuitively, our results show that our dual objectives can be met by an iterative gradient scheduler for eMBB traffic that anticipates the expected loss from URLLC traffic, along with an URLLC demand scheduler that is oblivious to eMBB channel states, utility functions and allocation decisions of the eMBB scheduler. Next we consider a more general class of (convex/threshold) loss models and study optimal online joint eMBB/URLLC schedulers within the broad class of channel state dependent but minislot-homogeneous policies. A key observation is that unlike the linear rate loss model, for the convex and threshold rate loss models, optimal eMBB and URLLC scheduling decisions do not de-couple and joint optimization is necessary to satisfy the dual objectives. We validate the characteristics and benefits of our schedulers via simulation.
Ocular Point-of-Care Ultrasonography to Diagnose Posterior Chamber Abnormalities: A Systematic Review and Meta-analysis

Importance Diagnosing posterior chamber ocular abnormalities typically requires specialist assessment. Point-of-care ultrasonography (POCUS) performed by nonspecialists, if accurate, could negate the need for urgent ophthalmologist evaluation. Objective This meta-analysis sought to define the diagnostic test characteristics of emergency practitioner–performed ocular POCUS to diagnose multiple posterior chamber abnormalities in adults. Data Sources PubMed (OVID), MEDLINE, EMBASE, Cochrane, CINAHL, and SCOPUS were searched from inception through June 2019 without restrictions. Conference abstracts and trial registries were also searched. Bibliographies of included studies and relevant reviews were manually searched, and experts in the field were queried. Study Selection Included studies compared ocular POCUS performed by emergency practitioners with a reference standard of ophthalmologist evaluation. Pediatric studies were excluded. All 116 studies identified during abstract screening as potentially relevant underwent full-text review by multiple authors, and 9 studies were included. Data Extraction and Synthesis In accordance with PRISMA guidelines, multiple authors extracted data from included studies. Results were meta-analyzed for each diagnosis using a bivariate random-effects model. Data analysis was performed in July 2019. Main Outcomes and Measures The outcomes of interest were diagnostic test characteristics of ocular POCUS for the following diagnoses: retinal detachment, vitreous hemorrhage, vitreous detachment, intraocular foreign body, globe rupture, and lens dislocation. Results Nine studies (1189 eyes) were included. All studies evaluated retinal detachment, but up to 5 studies assessed each of the other diagnoses of interest. For retinal detachment, sensitivity was 0.94 (95% CI, 0.88-0.97) and specificity was 0.94 (95% CI, 0.85-0.98). Sensitivity and specificity were 0.90 (95% CI, 0.65-0.98) and 0.92 (95% CI, 0.75-0.98), respectively, for vitreous hemorrhage and were 0.67 (95% CI, 0.51-0.81) and 0.89 (95% CI, 0.53-0.98), respectively, for vitreous detachment. Sensitivity and specificity were high for lens dislocation (0.97 [95% CI, 0.83-0.99] and 0.99 [95% CI, 0.97-1.00]), intraocular foreign body (1.00 [95% CI, 0.81-1.00] and 0.99 [95% CI, 0.99-1.00]), and globe rupture (1.00 [95% CI, 0.63-1.00] and 0.99 [95% CI, 0.99-1.00]). Results were generally unchanged in sensitivity analyses of studies with low risk of bias. Conclusions and Relevance This study suggests that emergency practitioner–performed ocular POCUS is an accurate test to assess for retinal detachment in adults. Its utility in diagnosing other posterior chamber abnormalities is promising but needs further study.
A Descriptive Appraisal, 1935–1980

The writer evaluates the responses he received to causes, ideas and programs he advocated over his 45 year career as a Pastoral Theologian. He contsructs a four-fold typology of responses and discusses the concerns which have characterized his professional life, e.g. older persons, sexuality, ethics in pastoral care, theology in CPE, institutional chaplaincy, interprofessional cooperation, case study method, graduate programs in Religion and Personality, and Pastoral Theology. An evaluation of progress in these areas is followed by a concluding statement on his concerns.
Trajectory Optimization for Rotary-Wing UAVs in Wireless Networks with Random Requests

This paper studies the trajectory optimization problem in a scenario where a single rotary-wing UAV acts as a relay of data payloads for downlink transmission requests generated randomly by two ground nodes (GNs) in a wireless network. The goal is to optimize the UAV trajectory in order to minimize the expected average communication delay to serve these random requests. It is shown that the problem can be cast as a semi-Markov decision process (SMDP), and the resulting minimization problem is solved via multi- chain policy iteration. The optimality of a two-scale optimization approach is proved: the optimal trajectory in the communication phase greedily minimizes the communication delay of the current request while moving between the current start position and a target end position (inner optimization); the end positions are selected to minimize the expected average long-term delay in the SMDP (outer optimization). Numerical simulations show that the expected average delay is minimized when the UAV moves towards the geometric center of the GNs during phases in which it is not actively servicing transmission requests, and demonstrate significant improvements over sensible heuristics. Finally, it is revealed that the optimal end positions of communication phases become increasingly independent of the data payload, for large data payload values.
Covert Millimeter-Wave Communication via a Dual-Beam Transmitter

In this paper, we investigate covert communication over millimeter-wave (mmWave) frequencies. In particular, a dual-beam mmWave transmitter, comprised of two independent antenna arrays, attempts to reliably communicate to a receiver Bob when hiding the existence of transmission from a warden Willie. In this regard, operating over mmWave bands not only increases the covertness thanks to directional beams, but also increases the transmission data rates given much more available bandwidths and enables ultra-low form factor transceivers due to the lower wavelengths used compared to the conventional radio frequency (RF) counterpart. We assume that the transmitter Alice employs one of its antenna arrays to form a directive beam for transmission to Bob. The other antenna array is used by Alice to generate another beam toward Willie as a jamming signal with its transmit power changing independently from a transmission block to another block. We characterize Willie's detection performance with the optimal detector and the closed-form of its expected value from Alice's perspective. We further derive the closed-form expression for the outage probability of the Alice-Bob link, which enables characterizing the optimal covert rate that can be achieved using the proposed setup. Our results demonstrate the superiority of mmWave covert communication, in terms of covertness and rate, compared to the RF counterpart.
Event-Driven Network Model for Space Mission Optimization with High-Thrust and Low-Thrust Spacecraft

Numerous high-thrust and low-thrust space propulsion technologies have been developed in the recent years with the goal of expanding space exploration capabilities; however, designing and optimizin...
Multi-Rank Sparse and Functional PCA Manifold Optimization and Iterative Deflation Techniques

We consider the problem of estimating multiple principal components using the recently-proposed Sparse and Functional Principal Components Analysis (SFPCA) estimator. We first propose an extension of SFPCA which estimates several principal components simultaneously using manifold optimization techniques to enforce orthogonality constraints. While effective, this approach is computationally burdensome so we also consider iterative deflation approaches which take advantage of existing fast algorithms for rank-one SFPCA. We show that alternative deflation schemes can more efficiently extract signal from the data, in turn improving estimation of subsequent components. Finally, we compare the performance of our manifold optimization and deflation techniques in a scenario where orthogonality does not hold and find that they still lead to significantly improved performance.
Nested Distributed Gradient Methods with Adaptive Quantized Communication

In this paper, we consider minimizing a sum of local convex objective functions in a distributed setting, where communication can be costly. We propose and analyze a class of nested distributed gradient methods with adaptive quantized communication (NEAR-DGD+Q). We show the effect of performing multiple quantized communication steps on the rate of convergence and on the size of the neighborhood of convergence, and prove R-Linear convergence to the exact solution with increasing number of consensus steps and adaptive quantization. We test the performance of the method, as well as some practical variants, on quadratic functions, and show the effects of multiple quantized communication steps in terms of iterations/gradient evaluations, communication and cost.
Co-expression clustering across flower development identifies modules for diverse floral forms in Achimenes (Gesneriaceae).

Background Genetic pathways involved with flower color and shape are thought to play an important role in the development of flowers associated with different pollination syndromes, such as those associated with bee, butterfly, or hummingbird pollination. Because pollination syndromes are complex traits that are orchestrated by multiple genes and pathways, the gene regulatory networks have not been explored. Gene co-expression networks provide a systems level approach to identify important contributors to floral diversification. Methods RNA-sequencing was used to assay gene expression across two stages of flower development (an early bud and an intermediate stage) in 10 species of Achimenes (Gesneriaceae). Two stage-specific co-expression networks were created from 9,503 orthologs and analyzed to identify module hubs and the network periphery. Module association with bee, butterfly, and hummingbird pollination syndromes was tested using phylogenetic mixed models. The relationship between network connectivity and evolutionary rates (d N/d S) was tested using linear models. Results Networks contained 65 and 62 modules that were largely preserved between developmental stages and contained few stage-specific modules. Over a third of the modules in both networks were associated with flower color, shape, and pollination syndrome. Within these modules, several hub nodes were identified that related to the production of anthocyanin and carotenoid pigments and the development of flower shape. Evolutionary rates were decreased in highly connected genes and elevated in peripheral genes. Discussion This study aids in the understanding of the genetic architecture and network properties underlying the development of floral form and provides valuable candidate modules and genes for future studies.
Quantum Tunneling Hygrometer with Temperature Stabilized Nanometer Gap

We present the design, fabrication and response of a humidity sensor based on electrical tunneling through temperature-stabilized nanometer gaps. The sensor consists of two stacked metal electrodes separated by ~2.5 nm of vertical air gap. Upper and lower electrodes rest on separate 1.5 μm thick polyimide patches with nearly identical thermal expansion but different gas absorption characteristics. When exposed to a humidity change, the patch under the bottom electrode swells but the patch under the top electrode does not, as it is covered with a water-vapor diffusion barrier ~8 nm of Al2O3. The air gap thus decreases leading to increase in the tunneling current across the junction. The gap however is independent of temperature fluctuations as both patches expand or contract by near equal amounts. Humidity sensor action demonstrates an unassisted reversible resistance reduction Rmax/Rmin ~105 when the device is exposed to 20–90 RH% at a standby DC power consumption of ~0.4 pW. The observed resistance change when subject to a temperature sweep of 25–60 ° C @24% RH was ~0.0025% of the full device output range.
Single-Cell Analysis of the Muscle Stem Cell Hierarchy Identifies Heterotypic Communication Signals Involved in Skeletal Muscle Regeneration

Summary Muscle regeneration relies on the regulation of muscle stem cells (MuSCs) through paracrine signaling interactions. We analyzed muscle regeneration in mice using single-cell RNA sequencing (scRNA-seq) and generated over 34,000 single-cell transcriptomes spanning four time-points. We identified 15 distinct cell types including heterogenous populations of muscle stem and progenitor cells. We resolved a hierarchical map of these myogenic cells by trajectory inference and observed stage-specific regulatory programs within this continuum. Through ligand-receptor interaction analysis, we identified over 100 candidate regeneration-associated paracrine communication pairs between MuSCs and non-myogenic cells. We show that myogenic stem/progenitor cells exhibit heterogeneous expression of multiple Syndecan proteins in cycling myogenic cells, suggesting that Syndecans may coordinate myogenic fate regulation. We performed ligand stimulation in vitro and confirmed that three paracrine factors (FGF2, TGFβ1, and RSPO3) regulate myogenic cell proliferation in a Syndecan-dependent manner. Our study provides a scRNA-seq reference resource to investigate cell communication interactions in muscle regeneration.
WhatsGNU: a tool for identifying proteomic novelty

To understand diversity in enormous collections of genome sequences, we need computationally scalable tools that can quickly contextualize individual genomes based on their similarities and identify features of each genome that make them unique. We present WhatsGNU, a tool based on exact match proteomic compression that, in seconds, classifies any new genome and provides a detailed report of protein alleles that may have novel functional differences. We use this technique to characterize the total allelic diversity (panallelome) of Salmonella enterica, Mycobacterium tuberculosis, Pseudomonas aeruginosa, and Staphylococcus aureus. It could be extended to others. WhatsGNU is available from https://github.com/ahmedmagds/WhatsGNU.
PIE-FLIM Measurements of Two Different FRET-Based Biosensor Activities in the Same Living Cells

Abstract We report the use of pulsed interleaved excitation (PIE)-fluorescence lifetime imaging microscopy (FLIM) to measure the activities of two different biosensor probes simultaneously in single living cells. Many genetically encoded biosensors rely on the measurement of Forster resonance energy transfer (FRET) to detect changes in biosensor conformation that accompany the targeted cell signaling event. One of the most robust ways of quantifying FRET is to measure changes in the fluorescence lifetime of the donor fluorophore using FLIM. The study of complex signaling networks in living cells demands the ability to track more than one of these cellular events at the same time. Here, we demonstrate how PIE-FLIM can separate and quantify the signals from different FRET-based biosensors to simultaneously measure changes in the activity of two cell signaling pathways in the same living cells in tissues. The imaging system described here uses selectable laser wavelengths and synchronized detection gating that can be tailored and optimized for each FRET pair. Proof-of-principle studies showing simultaneous measurement of cytosolic calcium and protein kinase A activity are shown, but the PIE-FLIM approach is broadly applicable to other signaling pathways.
Phosphorylated Lamin A/C in the Nuclear Interior Binds Active Enhancers Associated with Abnormal Transcription in Progeria

LMNA encodes nuclear Lamin A/C that tethers lamina-associated domains (LADs) to the nuclear periphery. Mutations in LMNA cause degenerative disorders including the premature aging disorder Hutchinson-Gilford progeria, but the mechanisms are unknown. We report that Ser22-phosphorylated (pS22) Lamin A/C was localized to the nuclear interior in human fibroblasts throughout the cell cycle. pS22-Lamin A/C interacted with a subset of putative active enhancers, not LADs, at locations co-bound by the transcriptional activator c-Jun. In progeria-patient fibroblasts, a subset of pS22-Lamin A/C-binding sites were lost, whereas new pS22-Lamin A/C-binding sites emerged in normally quiescent loci. New pS22-Lamin A/C binding was accompanied by increased histone acetylation, increased c-Jun binding, and upregulation of nearby genes implicated in progeria pathophysiology. These results suggest that Lamin A/C regulates gene expression by enhancer binding. Disruption of the gene regulatory rather than LAD tethering function of Lamin A/C may underlie the pathogenesis of disorders caused by LMNA mutations.
LanCe: A Comprehensive and Lightweight CNN Defense Methodology against Physical Adversarial Attacks on Embedded Multimedia Applications

Recently, adversarial attacks can be applied to the physical world, causing practical issues to various Convolutional Neural Networks (CNNs) powered applications. Most existing physical adversarial attack defense works only focus on eliminating explicit perturbation patterns from inputs, ignoring interpretation to CNN’s intrinsic vulnerability. Therefore, they lack expected versatility to different attacks and thereby depend on considerable data processing costs. In this paper, we propose LanCe – a comprehensive and lightweight CNN defense methodology against different physical adversarial attacks. By interpreting CNN’s vulnerability, we find that non-semantic adversarial perturbations can activate CNN with significantly abnormal activations and even overwhelm other semantic input patterns’ activations. We improve the CNN recognition process by adding a self-verification stage to detect the potential adversarial input with only one CNN inference cost. Based on the detection result, we further propose a data recovery methodology to defend the physical adversarial attacks. We apply such defense methodology into both image and audio CNN recognition scenarios and analyze the computational complexity for each scenario, respectively. Experiments show that our methodology can achieve an average 91% successful rate for attack detection and 89% accuracy recovery. Moreover, it is at most 3× faster compared with the state-of-the-art defense methods, making it feasible to resource-constrained embedded systems, such as mobile devices.
Network Analysis of Murine Cortical Dynamics Implicates Untuned Neurons in Visual Stimulus Coding.

Summary Unbiased and dense sampling of large populations of layer 2/3 pyramidal neurons in mouse primary visual cortex (V1) reveals two functional sub-populations: neurons tuned and untuned to drifting gratings. Whether functional interactions between these two groups contribute to the representation of visual stimuli is unclear. To examine these interactions, we summarize the population partial pairwise correlation structure as a directed and weighted graph. We find that tuned and untuned neurons have distinct topological properties, with untuned neurons occupying central positions in functional networks (FNs). Implementation of a decoder that utilizes the topology of these FNs yields accurate decoding of visual stimuli. We further show that decoding performance degrades comparably following manipulations of either tuned or untuned neurons. Our results demonstrate that untuned neurons are an integral component of V1 FNs and suggest that network interactions contain information about the stimulus that is accessible to downstream elements.
FairPrep: Promoting Data to a First-Class Citizen in Studies on Fairness-Enhancing Interventions.

The importance of incorporating ethics and legal compliance into machine-assisted decision-making is broadly recognized. Further, several lines of recent work have argued that critical opportunities for improving data quality and representativeness, controlling for bias, and allowing humans to oversee and impact computational processes are missed if we do not consider the lifecycle stages upstream from model training and deployment. Yet, very little has been done to date to provide system-level support to data scientists who wish to develop and deploy responsible machine learning methods. We aim to fill this gap and present FairPrep, a design and evaluation framework for fairness-enhancing interventions. FairPrep is based on a developer-centered design, and helps data scientists follow best practices in software engineering and machine learning. As part of our contribution, we identify shortcomings in existing empirical studies for analyzing fairness-enhancing interventions. We then show how FairPrep can be used to measure the impact of sound best practices, such as hyperparameter tuning and feature scaling. In particular, our results suggest that the high variability of the outcomes of fairness-enhancing interventions observed in previous studies is often an artifact of a lack of hyperparameter tuning. Further, we show that the choice of a data cleaning method can impact the effectiveness of fairness-enhancing interventions.
Reframing SU(1,1) Interferometry

SU(1,1) interferometry, proposed in a classic 1986 paper by Yurke, McCall, and Klauder [Phys. Rev. A 33, 4033 (1986)], involves squeezing, displacing, and then unsqueezing two bosonic modes. It has, over the past decade, been implemented in a variety of experiments. Here I take SU(1,1) interferometry apart, to see how and why it ticks. SU(1,1) interferometry arises naturally as the two-mode version of active-squeezing-enhanced, back-action-evading measurements aimed at detecting the phase-space displacement of a harmonic oscillator subjected to a classical force. Truncating an SU(1,1) interferometer, by omitting the second two-mode squeezer, leaves a prototype that uses the entanglement of two-mode squeezing to detect and characterize a disturbance on one of the two modes from measurement statistics gathered from both modes.
Dynamic changes in the regulatory T-cell heterogeneity and function by murine IL-2 mutein.

The therapeutic expansion of Foxp3+ regulatory T cells (Tregs) shows promise for treating autoimmune and inflammatory disorders. Yet, how this treatment affects the heterogeneity and function of Tregs is not clear. Using single-cell RNA-seq analysis, we characterized 31,908 Tregs from the mice treated with a half-life extended mutant form of murine IL-2 (IL-2 mutein, IL-2M) that preferentially expanded Tregs, or mouse IgG Fc as a control. Cell clustering analysis revealed that IL-2M specifically expands multiple sub-states of Tregs with distinct expression profiles. TCR profiling with single-cell analysis uncovered Treg migration across tissues and transcriptional changes between clonally related Tregs after IL-2M treatment. Finally, we identified IL-2M–expanded Tnfrsf9+Il1rl1+ Tregs with superior suppressive function, highlighting the potential of IL-2M to expand highly suppressive Foxp3+ Tregs.
Specific histone modifications associate with alternative exon selection during mammalian development

Alternative splicing (AS) is frequent during early mouse embryonic development. Specific histone post-translational modifications (hPTMs) have been shown to regulate exon splicing by either directly recruiting splice machinery or indirectly modulating transcriptional elongation. In this study, we hypothesized that hPTMs regulate expression of alternatively spliced genes for specific processes during differentiation. To address this notion, we applied an innovative machine learning approach to relate global hPTM enrichment to AS regulation during mammalian tissue development. We found that specific hPTMs, H3K36me3 and H3K4me1, play a role in skipped exon selection among all the tissues and developmental time points examined. In addition, we used iterative random forest model and found that interactions of multiple hPTMs most strongly predicted splicing when they included H3K36me3 and H3K4me1. Collectively, our data demonstrated a link between hPTMs and alternative splicing which will drive further experimental studies on the functional relevance of these modifications to alternative splicing.
Horizontal mergers and innovation in concentrated industries

It is an open question in antitrust economics whether allowing dominant firms to acquire smaller rivals is ultimately helpful or harmful to the long run rate of innovation and therefore long-term consumer welfare. I develop a framework to study this question in a dynamic oligopoly model where firms endogenously engage in investment, entry, exit and mergers. Firms produce vertically differentiated goods, compete by innovating on product quality, and can acquire rival firms to gain market power. In a benchmark model, mergers are modeled to be exclusively harmful to consumers in the short run by reducing competition and increasing prices. Despite this, under standard industry settings it is possible to show that the prospect of a buyout creates a powerful incentive for firms to preemptively enter the industry and invest to make themselves an attractive merger partner. The result is significantly higher rate of innovation with mergers than without and significantly higher long-run consumer welfare as well. Further results explore the circumstances under which this result is likely to hold. In order for the long run increase in innovation to outweigh the short run harm to consumers caused by mergers, entry costs must be low, entrants and incumbents must both have the ability to innovate rapidly, and the degree of horizontal product differentiation must be low. Alternatively, when dominant firms can directly incorporate the acquired firm’s innovation into their own product, mergers will typically benefit consumers in both the short run and long run.
Star Gels: New Hybrid Network Materials from Polyfunctional Single Component Precursors

New molecular precursors to inorganic/organic hybrid network materials have been designed and synthesized. The molecules comprise multiple trialkoxysilane-terminated arms linked to an organic core. When hydrolyzed, these materials form network structures whose network elements are interconnected with flexible crosslinks. Clear, compliant glasses and thick films have been generated from a number of these precursors. They are significantly tougher and less brittle than purely inorganic glasses or conventional purely organic thermosetting materials such as epoxy resins. The gels typically lose all surface-connected porosity during drying. Gelation rates for pure “star gel” precursors can be extremely high; the stars can also greatly enhance condensation rates for conventional sol-gel systems based on tetraalkoxysilanes.
Do Volatile Firms Pay Volatile Earnings? Evidence Using Linked Worker-Firm Data

ABSTRACTDespite the importance of earnings instability, little is known about its correlates or causes. This article seeks to better understand earnings instability by studying whether volatile firms pay volatile earnings and is the first to directly test the relationship using US linked employer–employee data. The article finds a positive and statistically significant relationship using within-firm variation. In addition, this article finds that lower earning workers are passed significantly more volatility from their employing firms than are higher earning workers.
Sequential tests of multiple hypotheses controlling false discovery and nondiscovery rates

We propose a general and flexible procedure for testing multiple hypotheses about sequential (or streaming) data that simultaneously controls both the false discovery rate (FDR) and false nondiscov...
Predicting the Path Loss of Wireless Channel Models Using Machine Learning Techniques in MmWave Urban Communications

The classic wireless communication channel modeling is performed using Deterministic and Stochastic channel methodologies. Machine learning (ML) emerges to revolutionize system design for 5G and beyond. ML techniques such as supervise leaning methods will be used to predict the wireless channel path loss of a variate of environments base on a certain dataset. The propagation signal of communication systems fundamentals is focusing on channel modeling particularly for new frequency bands such as MmWave. Machine learning can facilitate rapid channel modeling for 5G and beyond wireless communication systems due to the availability of partially relevant channel measurement data and model. When irregularity of the wireless channels leads to a complex methodology to achieve accurate models, appropriate machine learning methodology explores to reduce the complexity and increase the accuracy. In this paper, we demonstrate alternative procedures beyond traditional channel modeling to enhance the path loss models using machine learning techniques, to alleviate the dilemma of channel complexity and time consuming process that the measurements take. This demonstrated regression uses the measurement data of a certain scenario to successfully assist the prediction of path loss model of a different operating environment.
Semi-parametric Robust Event Detection for Massive Time-Domain Databases

The detection and analysis of events within massive collections of time-series has become an extremely important task for time-domain astronomy. In particular, many scientific investigations (e.g. the analysis of microlensing and other transients) begin with the detection of isolated events in irregularly-sampled series with both non-linear trends and non-Gaussian noise. We outline a semi-parametric, robust, parallel method for identifying variability and isolated events at multiple scales in the presence of the above complications. This approach harnesses the power of Bayesian modeling while maintaining much of the speed and scalability of more ad-hoc machine learning approaches. We also contrast this work with event detection methods from other fields, highlighting the unique challenges posed by astronomical surveys. Finally, we present results from the application of this method to 87.2 million EROS-2 sources, where we have obtained a greater than 100-fold reduction in candidates for certain types of phenomena while creating high-quality features for subsequent analyses.
Combining Machine Learning Models using combo Library

Model combination, often regarded as a key sub-field of ensemble learning, has been widely used in both academic research and industry applications. To facilitate this process, we propose and implement an easy-to-use Python toolkit, combo, to aggregate models and scores under various scenarios, including classification, clustering, and anomaly detection. In a nutshell, combo provides a unified and consistent way to combine both raw and pretrained models from popular machine learning libraries, e.g., scikit-learn, XGBoost, and LightGBM. With accessibility and robustness in mind, combo is designed with detailed documentation, interactive examples, continuous integration, code coverage, and maintainability check; it can be installed easily through Python Package Index (PyPI) or {https://github.com/yzhao062/combo}.
Task-Driven Perception and Manipulation for Constrained Placement of Unknown Objects

Recent progress in robotic manipulation has dealt with the case of previously unknown objects in the context of relatively simple tasks, such as bin-picking. Existing methods for more constrained problems, however, such as deliberate placement in a tight region, depend more critically on shape information to achieve safe execution. This work deals with pick-and-constrained placement of objects without access to geometric models. The objective is to pick an object and place it safely inside a desired goal region without any collisions, while minimizing the time and the sensing operations required to complete the task. An algorithmic framework is proposed for this purpose, which performs manipulation planning simultaneously over a conservative and an optimistic estimate of the object's volume. The conservative estimate ensures that the manipulation is safe while the optimistic estimate guides the sensor-based manipulation process when no solution can be found for the conservative estimate. To maintain these estimates and dynamically update them during manipulation, objects are represented by a simple volumetric representation, which stores sets of occupied and unseen voxels. The effectiveness of the proposed approach is demonstrated by developing a robotic system that picks a previously unseen object from a table-top and places it in a constrained space. The system comprises of a dual-arm manipulator with heterogeneous end-effectors and leverages hand-offs as a re-grasping strategy. Real-world experiments show that straightforward pick-sense-and-place alternatives frequently fail to solve pick-and-constrained placement problems. The proposed pipeline, however, achieves more than 95% success rate and faster execution times as evaluated over multiple physical experiments.
Explication of CB1 receptor contributions to the hypothermic effects of Δ9-tetrahydrocannabinol (THC) when delivered by vapor inhalation or parenteral injection in rats.

Abstract The use of Δ9-tetrahydrocannabinol (THC) by inhalation using e-cigarette technology grows increasingly popular for medical and recreational purposes. This has led to development of e-cigarette based techniques to study the delivery of THC by inhalation in laboratory rodents. Inhaled THC reliably produces hypothermic and antinociceptive effects in rats, similar to effects of parenteral injection of THC. This study was conducted to determine the extent to which the hypothermic response depends on interactions with the CB1 receptor, using pharmacological antagonist (SR141716, AM-251) approaches. Groups of rats were implanted with radiotelemetry devices capable of reporting activity and body temperature, which were assessed after THC inhalation or injection. SR141716 (4 mg/kg, i.p.) blocked or attenuated antinociceptive effects of acute THC inhalation in male and female rats. SR141716 was unable to block the initial hypothermia caused by THC inhalation, but temperature was restored to normal more quickly. Alterations in antagonist pre-treatment time, dose and the use of a rat strain with less sensitivity to THC-induced hypothermia did not change this pattern. Pre-treatment with SR141716 (4 mg/kg, i.p.) blocked hypothermia induced by i.v. THC and reversed hypothermia when administered 45 or 90 min after THC (i.p.). SR141716 and AM-251 (4 mg/kg, i.p.) sped recovery from, but did not block, hypothermia caused by vapor THC in female rats made tolerant by prior repeated THC vapor inhalation. The CB2 antagonist AM-630, had no effect. These results suggest that hypothermia consequent to THC inhalation is induced by other mechanisms in addition to CB1 receptor activation.
Pulsed Third-Spin-Assisted Recoupling NMR for Obtaining Long-Range 13C-13C and 15N-13C Distance Restraints.

We present a class of pulsed third-spin-assisted recoupling (P-TSAR) magic-angle-spinning solid-state NMR techniques that achieve efficient polarization transfer over long distances to provide important restraints for structure determination. These experiments utilize second-order cross terms between strong 1H-13C and 1H-15N dipolar couplings to achieve 13C-13C and 15N-13C polarization transfer, similar to the principle of continuous-wave (CW) TSAR experiments. However, in contrast to the CW-TSAR experiments, these P-TSAR experiments require much less radiofrequency (rf) energy and allow a much simpler routine for optimizing the rf field strength. We call the technique PULSAR (pulsed proton-assisted recoupling) for homonuclear spin pairs. For heteronuclear spin pairs, we improve the recently introduced PERSPIRATIONCP (proton-enhanced rotor-echo short pulse irradiation cross-polarization) experiment by shifting the pulse positions and removing the z-filters, which significantly broaden the bandwidth and increase the efficiency of polarization transfer. We demonstrate the PULSAR and PERSPIRATIONCP techniques on the model protein GB1 and found cross peaks for distances as long as 10 and 8 A for 13C-13C and 15N-13C spin pairs, respectively. We then apply these methods to the amyloid fibrils formed by the peptide hormone glucagon and show that long-range correlation peaks are readily observed to constrain intermolecular packing in this cross-β fibril. We provide an analytical model for the PULSAR and PERSPIRATIONCP experiments to explain the measured and simulated chemical shift dependence and pulse flip angle dependence of polarization transfer. These two techniques are useful for measuring long-range distance restraints to determine the three-dimensional structures of proteins and other biological macromolecules.