cssi/SciSciGPT-SciSciNet huggingface dataset:
Blue Light-Induced Gene Expression Alterations in Cultured Neurons Are the Result of Phototoxic Interactions with Neuronal Culture Media

Blue wavelength light is used as an optical actuator in numerous optogenetic technologies employed in neuronal systems. However, the potential side effects of blue light in neurons has not been thoroughly explored, and recent reports suggest that neuronal exposure to blue light can induce transcriptional alterations in vitro and in vivo Here, we examined the effects of blue wavelength light in cultured primary rat cortical cells. Exposure to blue light (470 nm) resulted in upregulation of several immediate early genes (IEGs) traditionally used as markers of neuronal activity, including Fos and Fosb, but did not alter the expression of circadian clock genes Bmal1, Cry1, Cry2, Clock, or Per2 IEG expression was increased following 4 h of 5% duty cycle light exposure, and IEG induction was not dependent on light pulse width. Elevated levels of blue light exposure induced a loss of cell viability in vitro, suggestive of overt phototoxicity. Induction of IEGs by blue light was maintained in cortical cultures treated with AraC to block glial proliferation, indicating that induction occurred selectively in postmitotic neurons. Importantly, changes in gene expression induced by blue wavelength light were prevented when cultures were maintained in a photoinert media supplemented with a photostable neuronal supplement instead of commonly utilized neuronal culture media and supplements. Together, these findings suggest that light-induced gene expression alterations observed in vitro stem from a phototoxic interaction between commonly used media and neurons, and offer a solution to prevent this toxicity when using photoactivatable technology in vitro.
The Polarization of Reality

Americans are polarized not only in their views on policy issues and attitudes toward government and society but also in their perceptions of the same factual reality. We conceptualize how to think about the "polarization of reality" and review recent papers that show that Republicans and Democrats view the same reality through a different lens. Perhaps as a result, they hold different views about policies and what should be done to address economic and social issues. We also show that providing information leads to different reassessments of reality and different responses along the policy support margin, depending on one's political leaning.
DC electrical degradation of YSZ: Voltage-controlled electrical metallization of a fast ion conducting insulator

DC electrical degradation as a form of dielectric and resistance breakdown is a common phenomenon in thin-film devices including resistance-switching memory. To obtain design data and to probe the degradation mechanism, highly accelerated lifetime tests (HALT) are often conducted at higher temperatures with thicker samples. While the mechanism is well established in semiconducting oxides such as perovskite titanates, it is not in stabilized zirconia and other fast oxygen-ion conductors that have little electronic conductivity. Here we model the mechanism by an oxygen-driven, transport-limited, metal-insulator transition, which finds support in rich experimental observations - including in situ videos and variable temperature studies - of yttria-stabilized zirconia. They are contrasted with the findings in semiconducting titanates and resistance memory, and provide new insight into ceramic processing with extremely rapid heating and cooling such as flash sintering and melt processing.
The birth environment of the solar system constrained by the relative abundances of the solar radionuclides

The relative abundances of the radionuclides in the solar system at the time of its birth are crucial arbiters for competing hypotheses regarding the birth environment of the Sun. The presence of short-lived radionuclides, as evidenced by their decay products in meteorites, has been used to suggest that particular, sometimes exotic, stellar sources were proximal to the Sun’s birth environment. The recent confirmation of neutron star - neutron star (NS-NS) mergers and associated kilonovae as potentially dominant sources of r-process nuclides can be tested in the case of the solar birth environment using the relative abundances of the longer-lived nuclides. Critical analysis of the 15 radionuclides and their stable partners for which abundances and production ratios are well known suggests that the Sun formed in a typical massive star-forming region (SFR). The apparent overabundances of short-lived radionuclides (e.g. 26Al, 41Ca, 36Cl) in the early solar system appears to be an artifact of a heretofore under-appreciation for the important influences of enrichment by Wolf-Rayet winds in SFRs. The long-lived nuclides (e.g. 238U, 244Pu, 247Cr, 129I) are consistent with an average time interval between production events of 108 years, seemingly too short to be the products of NS-NS mergers alone. The relative abundances of all of these nuclides can be explained by their mean decay lifetimes and an average residence time in the ISM of ∼200 Myr. This residence time evidenced by the radionuclides is consistent with the average lifetime of dust in the ISM and the timescale for converting molecular cloud mass to stars.
List decodable learning via sum of squares

In the list-decodable learning setup, an overwhelming majority (say a 1 − β-fraction) of the input data consists of outliers and the goal of an algorithm is to output a small list L of hypotheses such that one of them agrees with inliers. We devise list decodable learning algorithms for the problem of linear regression using the sum of squares SDP hierarchy. In the list-decodable linear regression problem, we are given labelled examples {(Xi, yi)}i∈[N] containing a subset S of βN inliers {Xi}i∈S that are drawn i.i.d. from standard Gaussian distribution N(0, I) in Rd, where the corresponding labels yi are well-approximated by a linear function [MATH HERE]. We devise an algorithm that outputs a list L of linear functions such that there exists some l ∈ L that is close to [MATH HERE]. This yields the first algorithm for linear regression in a list-decodable setting. Our results hold for a general distribution of examples whose concentration and anti-concentration properties can be certified by low degree sum-of-squares proofs. In an independent and concurrent work, Karmalkar et al. [KKK19] also obtain an algorithm for list-decodable linear regression using the Sum-of-Squares SDP hierarchy.
Smart Building Energy Management using Nonlinear Economic Model Predictive Control

Owing to the call for energy efficiency, the need to optimize the energy consumption of commercial buildings–responsible for over 40% of US energy consumption–has recently gained significant attention. Moreover, the ability to participate in the retail electricity markets through proactive demand-side participation has recently led to development of economic model predictive control (EMPC) for building's Heating, Ventilation, and Air Conditioning (HVAC) system. The objective of this paper is to develop a price-sensitive operational model for building's HVAC systems while considering inflexible loads and other distributed energy resources (DERs) such as photovoltaic (PV) generation and battery storage for the buildings. A Nonlinear Economic Model Predictive Controller (NL-EMPC) is presented to minimize the net cost of energy usage by building's HVAC system while satisfying the comfort-level of building's occupants. The efficiency of the proposed NL-EMPC controller is evaluated using several simulation case studies.
The Joint Effects of Supervisor Pay Transparency and Vertical Pay Dispersion on Reporting Honesty

We experimentally investigate how supervisor pay transparency interacts with vertical pay dispersion to affect subordinates' reporting honesty in a budgeting setting. We find that the effect of sup...
Network Estimation via Graphon With Node Features

One popular model for network analysis is the exchangeable graph model (ExGM), which is characterized by a two-dimensional function known as a graphon . Estimating an underlying graphon becomes the key of such analysis. Several nonparametric estimation methods have been proposed, and some are provably consistent. However, if certain useful features of the nodes (e.g., age and schools in a social network context) are available, none of these methods were designed to incorporate this source of information to help with the estimation. This paper develops a consistent graphon estimation method that integrates information from both the adjacency matrix itself and node features. We show that properly leveraging the features can improve the estimation. A cross-validation method is proposed to automatically select the tuning parameter of the method.
A Finite-Length Construction of Irregular Spatially-Coupled Codes

Spatially-coupled (SC) LDPC codes have recently emerged as an excellent choice for error correction in modern data storage and communication systems due to their outstanding performance. It has long been known that irregular graph codes offer performance advantage over their regular counterparts. In this paper, we present a novel combinatorial framework for designing finite-length irregular SC LDPC codes. Our irregular SC codes have the desirable properties of regular SC codes thanks to their structure while offering significant performance benefits that come with the node degree irregularity. Coding constructions proposed in this work contribute to the existing portfolio of finite-length graph code designs.
Incorporating Uncertainty into USDA Commodity Price Forecasts

From 1977 through April 2019, USDA published monthly season‐average price (SAP) forecasts for key agricultural commodities in the form of intervals meant to indicate forecasters' uncertainty but without attaching a confidence level. In May 2019, USDA eliminated the intervals and began publishing a single point estimate—a value that has a very low probability of being realized. We demonstrate how a density forecasting format can improve the usefulness of USDA price forecasts and explain how such a methodology can be implemented. We simulate 21 years of out‐of‐sample density‐based SAP forecasts using historical data, with forward‐looking, backward‐looking, and composite methods, and we evaluate them based on commonly‐accepted criteria. Each of these approaches would offer USDA the ability to portray richer and more accurate price forecasts than its old intervals or its current single point estimates. Backward‐looking methods require little data and provide significant improvements. For commodities with active derivatives markets, option‐implied volatilities (IVs) can be used to generate forward‐looking and composite models that reflect (and adjust dynamically to) market sentiment about uncertainty—a feature that is not possible using backward‐looking data alone. At certain forecast steps, a composite method that combines forward‐ and backward‐looking information provides useful information regarding farm‐level prices beyond that contained in IVs.
High-Performance Megahertz-Frequency Resonant DC–DC Converter for Automotive LED Driver Applications

This paper introduces a megahertz-frequency resonant dc–dc converter that inherently achieves a load-independent output current while maintaining high efficiency across a wide output voltage range. These properties make the proposed converter well-suited for automotive light-emitting diode (LED) driver applications, where a varying number of LEDs need to be driven with a constant current. The proposed converter achieves load-independent output current by utilizing an LCL -T resonant network, and achieves high efficiency using a comprehensive design optimization methodology. This LCL -T resonant converter is also capable of regulating its output current at any desired value by utilizing phase-shift control. The performance of the LCL -T resonant converter is theoretically and experimentally compared with an LC 3 L and an LCLC resonant converter. For the experimental comparison, prototypes LCL -T, LC 3 L , and LCLC resonant converters are designed to operate at 2 MHz and across an output voltage range of 3.3–49.5 V while supplying a constant 0.5 A output current to the LEDs. The LCL -T resonant converter prototype achieves a peak efficiency of 91.1%, which is 0.6% and 1.8% higher than the peak efficiency of the LC 3 L and the LCLC converter prototypes, respectively. Furthermore, the LCL -T converter prototype maintains 0.8% and 1.6% higher average efficiency over its 15:1 output voltage range relative to the LC 3 L and the LCLC converter prototypes, respectively.
VLBI Imaging of Luminous Infrared Galaxies: Starbursts & AGN

As described by Colin Lonsdale in this Workshop, VLBI observations reveal thepresence of both AGN and Starburst activity in Luminous Infrared Galaxies.We highlight new results on two classical LIGs which span the range of LIGradio activity: Arp 220, which reveals luminous radio supernovae (RSN)produced in an intense starburst, and Mrk231, which has a powerful compact,AGN radio core. Second epoch observations of the compact radio sources inArp 220 confirm their nature as luminous Radio Supernovae, but indicate alower luminous RSN frequency (νLRSN ≲ 0.3 yr-1) andconsequent slower decay rate than previously suggested. We interpret thisas due to the dense starburst medium into which the supernovaedetonate. The compact radio morphology of Mrk231 places it among theCompact Symmetric Objects (CSOs) which are suggested to be young radio sources in which asymmetric lobes or hotspots reveal theworking surface of a relativistic jet upon the ambient medium. Assumingthat the lobes in Mrk 231 are confined by ram pressure, we estimate an agefor the jet/compact source, τ ≲ 106yr. We interpret Mrk 231as a newly formed QSO emerging from a starburst.
Actor Conditioned Attention Maps for Video Action Detection

While observing complex events with multiple actors, humans do not assess each actor separately, but infer from the context. The surrounding context provides essential information for understanding actions. To this end, we propose to replace region of interest(RoI) pooling with an attention module, which ranks each spatio-temporal region’s relevance to a detected actor instead of cropping. We refer to these as Actor-Conditioned Attention Maps (ACAM), which amplify/dampen the features extracted from the entire scene. The resulting actor-conditioned features focus the model on regions that are relevant to the conditioned actor. For actor localization, we leverage pre-trained object detectors, which transfer better. The proposed model is efficient and our action detection pipeline achieves near real-time performance. Experimental results on AVA 2.1 and JHMDB demonstrate the effectiveness of attention maps, with improvements of 7 mAP on AVA and 4 mAP on JHMDB.
Exponential Automatic Amortized Resource Analysis.

Automatic amortized resource analysis (AARA) is a type-based technique for inferring concrete (non-asymptotic) bounds on a program’s resource usage. Existing work on AARA has focused on bounds that are polynomial in the sizes of the inputs. This paper presents and extension of AARA to exponential bounds that preserves the benefits of the technique, such as compositionality and efficient type inference based on linear constraint solving. A key idea is the use of the Stirling numbers of the second kind as the basis of potential functions, which play the same role as the binomial coefficients in polynomial AARA. To formalize the similarities with the existing analyses, the paper presents a general methodology for AARA that is instantiated to the polynomial version, the exponential version, and a combined system with potential functions that are formed by products of Stirling numbers and binomial coefficients. The soundness of exponential AARA is proved with respect to an operational cost semantics and the analysis of representative example programs demonstrates the effectiveness of the new analysis.
RESTORE: Robust intEnSiTy nORmalization mEthod for multiplexed imaging

Recent advances in multiplexed imaging technologies promise to improve the understanding of the functional states of individual cells and the interactions between the cells in tissues. This often requires compilation of results from multiple samples. However, quantitative integration of information between samples is complicated by variations in staining intensity and background fluorescence that obscure biological variations. Failure to remove these unwanted artifacts will complicate downstream analysis and diminish the value of multiplexed imaging for clinical applications. Here, to compensate for unwanted variations, we automatically identify negative control cells for each marker within the same tissue and use their expression levels to infer background signal level. The intensity profile is normalized by the inferred level of the negative control cells to remove between-sample variation. Using a tissue microarray data and a pair of longitudinal biopsy samples, we demonstrated that the proposed approach can remove unwanted variations effectively and shows robust performance. Chang et al. develop an analytical method called RESTORE to control for variations due to technical artifacts in multiplexed imaging. They test their method on a CycIF stained tissue microarray dataset and biopsies processed at different times. Their method can improve the applicability of imaging techniques in diagnostics and inference using unbiased clustering methods.
Finding the limbs and cusps of generalized cylinders

This paper addresses the problem of finding analytically the limbs and cusps of generalized cylinders. Orthographic projections of generalized cylinders whose axis is straight and whose axis is an arbitrary 3D curve are considered in turn. In both cases, the general equations of the limbs and cusps are given. They are solved for three classes of generalized cylinders: solids of revolution, straight homogeneous generalized cylinders whose scaling sweeping rule is a polynomial of degree less than or equal to 5 and generalized cylinders whose axis is an arbitrary 3D curve but the cross section is circular and constant. Examples of limbs and cusps found for each class are given. Applications and extensions to perspective projection and completely general straight generalized cylinders are discussed.
Learning to Grasp Without Seeing

Can a robot grasp an unknown object without seeing it? In this paper, we present a tactile-sensing based approach to this challenging problem of grasping novel objects without prior knowledge of their location or physical properties. Our key idea is to combine touch based object localization with tactile based re-grasping. To train our learning models, we created a large-scale grasping dataset, including more than 30K RGB frames and over 2.8 million tactile samples from 7800 grasp interactions of 52 objects. Moreover, we propose an unsupervised auto-encoding scheme to learn a representation of tactile signals. This learned representation shows a significant improvement of 4–9% over prior works on a variety of tactile perception tasks. Our system consists of two steps. First, our touch localization model sequentially “touch-scans” the workspace and uses a particle filter to aggregate beliefs from multiple hits of the target. It outputs an estimate of the object’s location, from which an initial grasp is established. Next, our re-grasping model learns to progressively improve grasps with tactile feedback based on the learned features. This network learns to estimate grasp stability and predict adjustment for the next grasp. Re-grasping thus is performed iteratively until our model identifies a stable grasp. Finally, we demonstrate extensive experimental results on grasping a large set of novel objects using tactile sensing alone. Furthermore, when applied on top of a vision-based policy, our re-grasping model significantly boosts the overall accuracy by \(10.6\%\). We believe this is the first attempt at learning to grasp with only tactile sensing and without any prior object knowledge. For supplementary video and dataset see: cs.cmu.edu/GraspingWithoutSeeing.
Environmental sustainability practices and exports: The interplay of strategy and institutions in Latin America

Abstract We integrate institutional economics with stakeholder theory to examine the antecedents and outcomes of Environmental Sustainability Practices (ESP) for small and medium enterprises in Latin America. We find that these firms primarily engage in ESP to succeed in export markets. We also find that this is especially true when firms demonstrate organizational agency by adopting complementary strategies such as obtaining international quality certifications to develop credibility among stakeholders. Lastly, their cause receives a further boost when they are based in institutional environments that are known for strong democratic voice and accountability mechanisms. In elucidating the interplay of strategy and institutions, we suggest the need to better integrate organizational agency into institutional theory and institutional environment into stakeholder theory.
Deep learning with sentence embeddings pre-trained on biomedical corpora improves the performance of finding similar sentences in electronic medical records

Capturing sentence semantics plays a vital role in a range of text mining applications. Despite continuous efforts on the development of related datasets and models in the general domain, both datasets and models are limited in biomedical and clinical domains. The BioCreative/OHNLP2018 organizers have made the first attempt to annotate 1068 sentence pairs from clinical notes and have called for a community effort to tackle the Semantic Textual Similarity (BioCreative/OHNLP STS) challenge. We developed models using traditional machine learning and deep learning approaches. For the post challenge, we focused on two models: the Random Forest and the Encoder Network. We applied sentence embeddings pre-trained on PubMed abstracts and MIMIC-III clinical notes and updated the Random Forest and the Encoder Network accordingly. The official results demonstrated our best submission was the ensemble of eight models. It achieved a Person correlation coefficient of 0.8328 – the highest performance among 13 submissions from 4 teams. For the post challenge, the performance of both Random Forest and the Encoder Network was improved; in particular, the correlation of the Encoder Network was improved by ~ 13%. During the challenge task, no end-to-end deep learning models had better performance than machine learning models that take manually-crafted features. In contrast, with the sentence embeddings pre-trained on biomedical corpora, the Encoder Network now achieves a correlation of ~ 0.84, which is higher than the original best model. The ensembled model taking the improved versions of the Random Forest and Encoder Network as inputs further increased performance to 0.8528. Deep learning models with sentence embeddings pre-trained on biomedical corpora achieve the highest performance on the test set. Through error analysis, we find that end-to-end deep learning models and traditional machine learning models with manually-crafted features complement each other by finding different types of sentences. We suggest a combination of these models can better find similar sentences in practice.
Stability and Bayesian Consistency in Two-Sided Markets

We propose a criterion of stability for two-sided markets with asymmetric information. A central idea is to formulate off-path beliefs conditional on counterfactual pairwise deviations and on-path beliefs in the absence of such deviations. A matching-belief configuration is stable if the matching is individually rational with respect to the system of on-path beliefs and is not blocked with respect to the system of off-path beliefs. The formulation provides a language for assessing matching outcomes with respect to their supporting beliefs and opens the door to further belief-based refinements. The main refinement analyzed in the paper requires the Bayesian consistency of on-path and off-path beliefs with prior beliefs. We define concepts of Bayesian efficiency, the rational expectations competitive equilibrium, and the core. Their contrast with pairwise stability manifests the role of information asymmetry in matching formation.
12 – Wilt Diseases

Publisher Summary This chapter discusses wilt diseases. The diseases that affect the vascular system of a plant are called “wilt diseases.” Wilt diseases can kill a large and healthy tree during a single growing season. The attack on the vascular tissue by the fungus causes moisture stress that eventually leads to wilting. The vascular system performs the vital function of transport within the plant; therefore, the diseases of the vascular system can cause rapid killing of large branches and even entire trees. Wilt diseases can be separated into two major groups: those that begin in the branches as a result of leaf or bark feeding by pathogen-infested insects and those that begin in the roots as a result of wounding or direct root penetration by the fungus. Wilt disease fungi are facultative parasites that can live on the host after it has died. The dead host remains a source of inoculum for insect vectors or a center for the spread of soil pathogens for several years. Wilt fungi in the trunk and branches can be carried to new hosts by insect vectors. These fungi can also invade the healthy roots of the adjacent trees of the same species through root grafts.
Expression of Fractals Through Neural Network Functions

To help understand the underlying mechanisms of neural networks (NNs), several groups have studied the number of linear regions $\ell $ of piecewise linear (PwL) functions, generated by deep neural networks (DNN). In particular, they showed that $\ell $ can grow exponentially with the number of network parameters $p$ , a property often used to explain the advantages of deep over shallow NNs. Nonetheless, a dimension argument shows that DNNs cannot generate all PwL functions with $\ell $ linear regions when $\ell > p$ . It is thus natural to seek to characterize specific families of functions with $\ell > p$ linear regions that can be constructed by DNNs. Iterated Function Systems (IFS) recursively construct a sequence of PwL functions $F_{k}$ with a number of linear regions which is exponential in $k$ . We show that $F_{k}$ can be generated by a NN using only $\mathcal {O}(k)$ parameters. IFS are used extensively to generate natural-looking landscape textures in artificial images as well as for compression of natural images. The surprisingly good performance of this compression suggests that human visual system may lock in on self-similarities. The combination of this phenomenon with the capacity of DNNs to efficiently approximate IFS may contribute to the success of DNNs, particularly striking for image processing tasks.
Rosenberg’s “learning by using” and technology diffusion

Abstract This paper formulates Rosenberg’s [Rosenberg, N., 1982. Inside the Black Box: Technology and Economics. Cambridge University Press, Cambridge] “learning by using” as a stochastic process. The producer of machines learns from the experience of users. Due to this learning, the quality of machines improves over time. It turns out that the process of this improvement takes an approximately exponential form. This improvement process, combined with the growth of demand due to the improvement, can produce an S-shape diffusion curve of machines. Strong demand and advancement of communication technology increase the diffusion speed. The distributional property of the stochastic process and the implications for inequality across machine users are also explored.
CHAPTER 12 – Androgens

Publisher Summary The endocrine physiology of the male and the interplay of the several hormones associated with sex determination, fetal development, and, following birth, growth and sexual maturation. The hormones of masculinity are comprised of the gonadotropins, luteinizing hormone and follicle-stimulating hormone, produced by the anterior pituitary and the androgenic steroid hormones, testosterone, androstenedione, dehydroepiandrosterone, and 5α-dihydrotestosterone produced by the gonads. The female steroid hormones, namely, estradiol and estrone, play an important role in the male in certain selected circumstances. The male testes have the dual function of being responsible for both the production and release of the germ cell, the spermatozoan, as well as the biosynthesis and secretion of the key androgenic steroid hormone testosterone. In adult males, the two seminal vesicles are 10–20 cm in length and lie distal to the ejaculatory duct. Under the actions of androgens, they produce the mucoid secretions that constitute the major volume of the ejaculate.
Program design and student outcomes in graduate education

Abstract Doctoral programs in the humanities and related social sciences are characterized by high attrition and long times to degree. In 1991 the Andrew W. Mellon Foundation launched the Graduate Education Initiative (GEI) to improve the quality of graduate programs and in turn reduce attrition and shorten time-to-degree. Over a 10-year period, the Foundation provided approximately $58 million to 54 departments at 10 major research universities. We estimate the impact of the GEI on attrition rates and time-to-degree using competing-risk duration models and student-level data. The data span the start of the GEI and include information for students at a set of control departments. We estimate that the GEI had modest impacts on student outcomes in the expected directions: reducing attrition rates, reducing time-to-degree, and increasing completion rates. The impacts of the GEI appear to have been driven in part by reductions in entering cohort size, increases in financial support, and increases in student quality.
Correlating Patient Anxiety with Urinary Symptoms Before and After Holmium Laser Enucleation of the Prostate

Introduction and Objective: Preoperative anxiety has been shown to correlate with certain urinary symptoms in patients with certain types of voiding dysfunction, particularly overactive bladder. Th...
Semantic Scaffolds for Pseudocode-to-Code Generation

We propose a method for program generation based on semantic scaffolds, lightweight structures representing the high-level semantic and syntactic composition of a program. By first searching over plausible scaffolds then using these as constraints for a beam search over programs, we achieve better coverage of the search space when compared with existing techniques. We apply our hierarchical search method to the SPoC dataset for pseudocode-to-code generation, in which we are given line-level natural language pseudocode annotations and aim to produce a program satisfying execution-based test cases. By using semantic scaffolds during inference, we achieve a 10% absolute improvement in top-100 accuracy over the previous state-of-the-art. Additionally, we require only 11 candidates to reach the top-3000 performance of the previous best approach when tested against unseen problems, demonstrating a substantial improvement in efficiency.
Reconstruction of an Non-Monochromatically Illuminated Object Imaged through an Electron Microscope with a Fluctuating Electromagnetic Field

It is shown that a weak phase object imaged by an electron microscope within the presence of instabilities of the lense currents and the acceleration voltage, fluctuating electromagnetic field, can be reconstructed from the intensity distribution in the image plane. Perfectly incoherent illumination of the object is assumed and the influence of the energy spread of the illuminating wave on the point spread function of the microscope is rigorously taken into account.
Lethe: A Tunable Delete-Aware LSM Engine

Data-intensive applications fueled the evolution of log structured merge (LSM) based key-value engines that employ the out-of-place paradigm to support high ingestion rates with low read/write interference. These benefits, however, come at the cost of treating deletes as a second-class citizen. A delete inserts a tombstone that invalidates older instances of the deleted key. State-of-the-art LSM engines do not provide guarantees as to how fast a tombstone will propagate to persist the deletion. Further, LSM engines only support deletion on the sort key. To delete on another attribute (e.g., timestamp), the entire tree is read and re-written. We highlight that fast persistent deletion without affecting read performance is key to support: (i) streaming systems operating on a window of data, (ii) privacy with latency guarantees on the right-to-be-forgotten, and (iii) en masse cloud deployment of data systems that makes storage a precious resource. To address these challenges, in this paper, we build a new key-value storage engine, Lethe, that uses a very small amount of additional metadata, a set of new delete-aware compaction policies, and a new physical data layout that weaves the sort and the delete key order. We show that Lethe supports any user-defined threshold for the delete persistence latency offering higher read throughput (1.17-1.4x) and lower space amplification (2.1-9.8x), with a modest increase in write amplification (between 4% and 25%). In addition, Lethe supports efficient range deletes on a secondary delete key by dropping entire data pages without sacrificing read performance nor employing a costly full tree merge.
A new regularization approach for numerical differentiation

It is well known that the problem of numerical differentiation is an ill-posed problem and one requires regularization methods to approximate the solution. The commonly practiced regularization met...
Optimizing Black-box Metrics with Adaptive Surrogates

We address the problem of training models with black-box and hard-to-optimize metrics by expressing the metric as a monotonic function of a small number of easy-to-optimize surrogates. We pose the training problem as an optimization over a relaxed surrogate space, which we solve by estimating local gradients for the metric and performing inexact convex projections. We analyze gradient estimates based on finite differences and local linear interpolations, and show convergence of our approach under smoothness assumptions with respect to the surrogates. Experimental results on classification and ranking problems verify the proposal performs on par with methods that know the mathematical formulation, and adds notable value when the form of the metric is unknown.
Spectral Embedding Norm: Looking Deep into the Spectrum of the Graph Laplacian

The extraction of clusters from a dataset which includes multiple clusters and a significant background component is a nontrivial task of practical importance. In image analysis this manifests for ...
Ethnicity and acculturation: Asian American substance use from early adolescence to mature adulthood

Research on Asian American substance use has, to date, been limited by monolithic conceptions of Asian identity, inadequate attention to acculturative processes, and a dearth of longitudinal analys...
Menu-size Complexity and Revenue Continuity of Buy-many Mechanisms

We study the multi-item mechanism design problem where a monopolist sells n heterogeneous items to a single buyer. In recent work, Chawla et al. [2] advocated studying revenue maximization of multi-item mechanisms under the so-called "buy-many" constraint. Informally, a mechanism is buy-many if the buyer is allowed to participate in the mechanism any number of times. For example, a buyer interested in purchasing a subset of items may purchase the components of this subset individually. Viewing the mechanism as a function that assigns prices to allocations, the buy-many constraint is essentially equivalent to a subadditivity constraint over the prices. The buy-many constraint is a natural property that most real-world mechanisms satisfy. All of the simple classes of mechanisms studied in the literature such as item pricing, grand bundle pricing, two part tariffs, etc. also satisfy this property. As such, buy-many mechanisms are a worthy object of study. Chawla et al. asked whether buy-many mechanisms exhibit structural properties that arbitrary mechanisms do not. In this paper we study two such properties: menu-size complexity and revenue continuity. We discuss these two properties, their significance, and our results.
Capillary surfaces in a wedge: Differing contact angles

The possible zero-gravity equilibrium configurations of capillary surfaces u(x, y) in cylindrical containers whose sections are (wedge) domains with corners are investigated mathematically, for the case in which the contact angles on the two sides of the wedge may differ. In such a situation the behavior can depart in significant qualitative ways from that for which the contact angles on the two sides are the same. Conditions are described under which such qualitative changes must occur. Numerically computed surfaces are depicted to indicate the behavior.
LOCALIZATION OF THE IgG EFFECTOR SITE FOR MONOCYTE RECEPTORS

Publisher Summary This chapter discusses the localization of the IgG effector site for monocyte receptors based on a study. It reviews a study in which papain digestion of human IgG followed by cleavage with CNBr of the isolated Fc fragments yielded dialyzable peptides estimated to consist of 4–7% of the original amount of IgG. Three different preparations of peptides at a concentration of 2.5 mg/ml inhibited rosette formation of antibody coated ORh+ erythrocytes with human monocytes to the extent of 64%, 70%, and 78%. After separation of the peptides into seven fractions by descending paper chromatography, the inhibiting activity was present only in two fractions showing mobility in the RF ranges of 0. 35–0.50 and 0.50–0. 70, respectively. Both fractions were labeled with iodinated 3-(4-hydroxy) phenylpropionic acid N-hydroxysuccinimide ester and allowed to absorb on monocyte monolayers. Adherent radiolabeled peptides were eluted by incubation with IgG and subsequently purified by chromatography on paper and silicic acid. A single radioactive spot with an RF of 0.8, coinciding with the presence of a peptide, was eluted from the silicic acid chromatograms of both fractions. Samples were analyzed for amino acid composition and tested for rosette inhibition. Amino acid analysis revealed 10 amino acid residues on the following composition: Tyr, Ser2, Lys2, Leu, Thr, Val, Asp, and Arg.A concentration of O.6 nmol of peptide per monolayer of 8 × 105 monocytes inhibited 88–90% rosette formation. On the basis of observation that a sequence of this composition comprises residues 407–416 of the Eu myeloma gamma chain, the effector site of IgG for monocyte binding was thus localized in the CH3 region of the gamma chain.
Hippocampal Efferents to Retrosplenial Cortex and Lateral Septum Are Required for Memory Acquisition.

Learning and memory involves a large neural network of many brain regions, including the notable hippocampus along with the retrosplenial cortex (RSC) and lateral septum (LS). Previous studies have established that the dorsal hippocampus (dHPC) plays a critical role during the acquisition and retrieval/expression of episodic memories. However, the role of downstream circuitry from the dHPC, including the dHPC-to-RSC and dHPC-to-LS pathways, has come under scrutiny only recently. Here, we used an optogenetic approach with contextual fear conditioning in mice to determine whether the above two pathways are involved in acquisition and expression of contextual fear memory. We found that a selective inhibition of the dHPC neuronal terminals in either the RSC or LS during acquisition impaired subsequent memory performance, suggesting that both the dHPC-to-RSC and dHPC-to-LS pathways play a critical role in memory acquisition. We also selectively inhibited the two dHPC efferent pathways during memory retrieval and found a differential effect on memory performance. These results indicate the intricacies of memory processing and that hippocampal efferents to cortical and subcortical regions may be differentially involved in aspects of physiological and cognitive memory processes.
Human Rights and Technology: New Challenges for Justice and Accountability

This review surveys contemporary challenges in the field of technology and human rights. The increased use of artificial intelligence (AI) in decision making in the public and private sectors—e.g.,...
Competing for Jurisdiction: Practical Legitimation and the Persistence of Informal Recycling in Urban India

How did informal garbage collectors, who had long provided the only door-to-door and recycling services in Delhi, manage to survive the introduction of formal garbage collection trucks? This question raises the larger problem of informal institutions-well-organized and socially recognized, but legally unauthorized and unregulated platforms for political and economic organization-have proven so persistent. I draw on evidence collected during 20 months of ethnographic research in Delhi, focusing on participant observation with informal collectors during their neighborhood routes and interviews with 50 informal collectors. Bringing together political and urban sociology, postcolonial urban studies, and institutional theory, the paper frames competition over city garbage and recycling as a relational matter. I argue that informal workers preserved their jurisdiction through practical legitimation, depending on everyday actions and social expectations rather than explicit laws or beliefs to secure legitimacy. I demonstrate how status-based relations, here based on caste and labor migration, can confer legitimacy and provide a source of regulation, as actors set out and meet implicit expectations for appropriate actions, relationships, and social boundaries.
Compassionate Release as a "Right" in the Age of COVID-19.

In their article “Vexing, Veiled, and Inequitable: Social Distancing and the “Rights” Divide in the Age of COVID-19,” Fairchild et al. (2020) argue that social distancing is a right to be “extended...
The density of complex zeros of random sums

Abstract Let { η j } j = 0 N be a sequence of independent and identically distributed random complex Gaussian variables, and let { f j ( z ) } j = 0 N be a sequence of given analytic functions that are real-valued on the real line. We prove an exact formula for the expected density of the distribution of complex zeros of the random equation ∑ j = 0 N η j f j ( z ) = K , where K ∈ C . The method of proof employs a formula for the expected absolute value of quadratic forms of Gaussian random variables. We also obtain the limiting behaviour of the density function as N tends to infinity and provide numerical computations for the density function and empirical distributions for random sums with various choices of the functions f j ( z ) . Finally, we study the cases when the functions f j ( z ) are polynomials orthogonal on the real line and the unit circle.
Optimal assignment of collaborating agents in multi-body asset-guarding games

We study a multi-body asset-guarding game in missile defense where teams of interceptor missiles collaborate to defend a non-manuevering asset against a group of threat missiles. We approach the problem in two steps. We first formulate an assignment problem where we optimally assign subsets of collaborating interceptors to each threat so that all threats are intercepted as far away from the asset as possible. We assume that each interceptor is controlled by a collaborative guidance law derived from linear quadratic dynamic games. Our results include a 6-DOF simulation of a 5-interceptor versus 3-threat missile engagement where each agent is modeled as a missile airframe controlled by an autopilot. Despite the assumption of linear dynamics in our collaborative guidance law and the unmodeled dynamics in the simulation environment (e.g., varying density and gravity), we show that the simulated trajectories match well with those predicted by our approach. Furthermore, we show that a more agile threat, with greater speed and acceleration, can be intercepted by inferior interceptors when they collaborate. We believe the concepts introduced in this paper may be applied in asymmetric missile defense scenarios, including defense against advanced cruise missiles and hypersonic vehicles.
Extended interaction oscillator/amplifier modulator technology

Extended Interaction Oscillators and Amplifiers (EIO/A) have proven to be reliable high power sources of millimeter waves.1 Their unique capabilities have been demonstrated in various modulator configurations. Proper selection and tailoring of the modulator will define the capability of the EIO/A to meet the requirements of the radar system. This paper describes the basic characteristics of the EIO/A, the typical modulator configurations developed to drive the EIO/A, and comparative results attained by each method.
Abelian extensions in dynamical Galois theory

We propose a conjectural characterization of when the dynamical Galois group associated to a polynomial is abelian, and we prove our conjecture in several cases, including the stable quadratic case over ℚ. In the postcritically infinite case, the proof uses algebraic techniques, including a result concerning ramification in towers of cyclic p-extensions. In the postcritically finite case, the proof uses the theory of heights together with results of Amoroso and Zannier and Amoroso and Dvornicich, as well as properties of the Arakelov–Zhang pairing.
The optimal discovery procedure for significance analysis of general gene expression studies.

MOTIVATION Analysis of biological data often involves the simultaneous testing of thousands of genes. This requires two key steps: the ranking of genes and the selection of important genes based on a significance threshold. One such testing procedure, called the optimal discovery procedure (ODP), leverages information across different tests to provide an optimal ranking of genes. This approach can lead to substantial improvements in statistical power compared to other methods. However, current applications of the ODP have only been established for simple study designs using microarray technology. Here, we extend this work to the analysis of complex study designs and RNA-sequencing studies. RESULTS We apply our extended framework to a static RNA-sequencing study, a longitudinal study, an independent sampling time-series study,and an independent sampling dose-response study. Our method shows improved performance compared to other testing procedures, finding more differentially expressed genes and increasing power for enrichment analysis. Thus, the extended ODP enables a favorable significance analysis of genome-wide gene expression studies. AVAILABILITY AND IMPLEMENTATION The algorithm is implemented in our freely available R package called edge and can be downloaded at https://www.bioconductor.org/packages/release/bioc/html/edge.html. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.