cssi/SciSciGPT-SciSciNet huggingface dataset:
Family Matters: Education and the (Conditional) Effect of State Indoctrination in China

When and how does state indoctrination work? Building upon research on motivated reasoning and family socialization, I argue that only those individuals whose parents have connections to political patronage are subject to state indoctrination because their pro-regime biases transmitted from parents induce higher receptivity ex ante to government messages. Focusing on political education in China, I conduct a quasi-experimental analysis exploiting the sharp variation in textbook content generated by China's most recent curriculum reform. Results based on a national survey show that the new politics textbooks successfully affected only those individuals whose parents had worked for the government. The finding survives extensive robustness checks and falsification tests. I also consider several alternative explanations of the effects: preference falsification, selective attention, parental indoctrination, and educational quality. This paper not only highlights the role of intergenerational transmission in moderating the effectiveness of state indoctrination but also casts doubt on the actual degree to which regimes can change minds by changing educational content.
Beam-driven ECH waves: A parametric study

Electron cyclotron harmonic (ECH) waves play a significant role in driving the diffuse aurora, which constitutes more than 75% of the particle energy input into the ionosphere. ECH waves in magnetospheric plasmas have long been thought to be excited predominantly by the loss cone anisotropy (velocity–space gradients) that arises naturally in a planetary dipole field. Recent THEMIS observations, however, indicate that an electron beam can also excite such waves in Earth's magnetotail. The ambient and beam plasma conditions under which electron beam excitation can take place are unknown. Knowledge of such conditions would allow us to further explore the relative contribution of this excitation mechanism to ECH wave scattering of magnetospheric electrons at Earth and the outer planets. Using the hot plasma dispersion relation, we address the nature of beam-driven ECH waves and conduct a comprehensive parametric survey of this instability. We find that growth is provided by beam electron cyclotron resonances of both first and higher orders. We also find that these waves are unstable under a wide range of plasma conditions. The growth rate increases with beam density, beam velocity, and hot electron temperature; it decreases with increasing beam temperature and beam temperature anisotropy ( T ⊥ / T ∥), hot electron density, and cold electron density and temperature. Such conditions abound in Earth's magnetotail, where magnetospheric electrons heated by earthward convection and magnetic reconnection coexist with colder ionospheric electrons.
Active Attack Detection and Control in Constrained Cyber-Physical Systems Under Prevented Actuation Attack

This paper proposes an active attack detection scheme for constrained cyber-physical systems. Despite passive approaches where the detection is based on the analysis of the input-output data, active approaches interact with the system by designing the control input so to improve detection. This paper focuses on the prevented actuation attack, where the attacker prevents the exchange of information between the controller and actuators. The proposed scheme consists of two units: 1) detection, and 2) control. The detection unit includes a set of parallel detectors, which are designed based on the multiple-model adaptive estimation approach to detect the attack and to identify the attacked actuator(s). For what regards the control unit, a constrained optimization approach is developed to determine the control input such that the control and detection aims are achieved. In the formulation of the detection and control objective functions, a probabilistic approach is used to reap the benefits of the a priori information availability. The effectiveness of the proposed scheme is demonstrated through a simulation study on an irrigation channel.
An Integrated Mechanical Intelligence and Control Approach Towards Flight Control of Aerobat

Our goal in this work is to expand the theory and practice of robot locomotion by addressing critical challenges associated with the robotic biomimicry of bat aerial locomotion. Bats are known for their pronounced, fast wing articulations, e.g., bats can mobilize as many as forty joints during a single wingbeat, with some joints reaching to over one thousand degrees per second in angular speed. Copying bats flight is a significant ordeal, however, very rewarding. Aerial drones with morphing bodies similar to bats can be safer, agile and energy efficient owing to their articulated and soft wings. Current design paradigms have failed to copy bat flight because they assume only closed-loop feedback roles and ignore computational roles carried out by morphology. To respond to the urgency, a design framework called Morphing via Integrated Mechanical Intelligence and Control (MIMIC) is proposed. In this paper, using the dynamic model of Northeastern University's Aerobat, which is designed to test the effectiveness of the MIMIC framework, it will be shown that computational structures and closed-loop feedback can be successfully used to mimic bats stable flight apparatus.
Centrality in Epidemic Networks with Time-Delay: A Decision-Support Framework for Epidemic Containment

During an epidemic, infectious individuals might not be detectable until some time after becoming infected. The studies show that carriers with mild or no symptoms are the main contributors to the transmission of a virus within the population. The average time it takes to develop the symptoms causes a delay in the spread dynamics of the disease. When considering the influence of delay on the disease propagation in epidemic networks, depending on the value of the time-delay and the network topology, the peak of epidemic could be considerably different in time, duration, and intensity. Motivated by the recent worldwide outbreak of the COVID-19 virus and the topological extent in which this virus has spread over the course of a few months, this study aims to highlight the effect of time-delay in the progress of such infectious diseases in the meta-population networks rather than individuals or a single population. In this regard, the notions of epidemic network centrality in terms of the underlying interaction graph of the network, structure of the uncertainties, and symptom development duration are investigated to establish a centrality-based analysis of the disease evolution. A traffic volume convex optimization method is then developed to control the outbreak by identifying the sub-populations with the highest centrality and then isolating them while maintaining the same overall traffic volume (motivated by economic considerations) in the meta-population level. The numerical results, along with the theoretical expectations, highlight the impact of time-delay as well as the importance of considering the worst-case scenarios in investigating the most effective methods of epidemic containment.
Is OR "portable" in meta-analysis? Time to consider bivariate generalized linear mixed model.

Abstract Objectives A recent paper by Doi et al. advocated completely replacing the relative risk (RR) with the odds ratio (OR) as the effect measure in clinical trials and meta-analyses with binary outcomes. Besides some practical advantages of RR over OR, Doi et al.’s key assumption that the OR is “portable” in the meta-analysis, that is, study-specific ORs are likely not correlated with baseline risks, was not well justified. Study designs and settings We summarized Spearman's rank correlation coefficient between study-specific ORs and baseline risks in 40,243 meta-analyses from the Cochrane Database of Systematic Reviews. Results Study-specific ORs tend to be higher in studies with lower baseline risks of disease for most meta-analyses in Cochrane Database of Systematic Reviews. Using an actual meta-analysis example, we demonstrate that there is a strong negative correlation between OR (RR or RD) with the baseline risk and the conditional effects notably vary with baseline risks. Conclusions Replacing RR or RD with OR is currently unadvisable in clinical trials and meta-analyses. It is possible that no effect measure is “portable” in a meta-analysis. In addition to the overall (or marginal) effect, we suggest presenting the conditional effect based on the baseline risk using a bivariate generalized linear mixed model.
Causal Learning for Socially Responsible AI.

There have been increasing concerns about Artificial Intelligence (AI) due to its unfathomable potential power. To make AI address ethical challenges and shun undesirable outcomes, researchers proposed to develop socially responsible AI (SRAI). One of these approaches is causal learning (CL). We survey state-of-the-art methods of CL for SRAI. We begin by examining the seven CL tools to enhance the social responsibility of AI, then review how existing works have succeeded using these tools to tackle issues in developing SRAI such as fairness. The goal of this survey is to bring forefront the potentials and promises of CL for SRAI.
Genetic encoding of a highly photostable, long lifetime fluorescent amino acid for imaging in mammalian cells

Acridonylalanine (Acd) is a fluorescent amino acid that is highly photostable, with a high quantum yield and long fluorescence lifetime in water. These properties make it superior to existing genetically encodable fluorescent amino acids for monitoring protein interactions and conformational changes through fluorescence polarization or lifetime experiments, including fluorescence lifetime imaging microscopy (FLIM). Here, we report the genetic incorporation of Acd using engineered pyrrolysine tRNA synthetase (RS) mutants that allow for efficient Acd incorporation in both E. coli and mammalian cells. We compare protein yields and amino acid specificity for these Acd RSs to identify an optimal construct. We also demonstrate the use of Acd in FLIM, where its long lifetime provides strong contrast compared to endogenous fluorophores and engineered fluorescent proteins, which have lifetimes less than 5 ns.
Fluctuating paths of least time, Schrödinger equation, van der Waals torque and Casimir effect mechanism

The use of an infinity of fluctuating paths of least time that are compatible with the quantum mechanics indeterminacy provides a new interpretation in geometrical optic of the interference pattern...
Supersymmetric analysis of stochastic micro-bending in optical waveguides

Micro-bending attenuation in an optical waveguide can be modeled by a Fokker-Planck equation. It is shown that a supersymmetric transformation applied to the Fokker-Planck equation is equivalent to a change in the refractive index profile, resulting in a larger or smaller attenuation. For a broad class of monomial index profiles, it is always possible to obtain an index profile with a larger micro-bending attenuation using a supersymmetric transformation. However, obtaining a smaller attenuation is not always possible and is restricted to a subset of index profiles.
Academic bullying: How to be an ally.

Academic bullying and harassment are all too common, and institutional reactions are often inadequate. Although reports often focus on what the targetsof bullying can do to protect themselves, it is important to acknowledge that all members of the scientific community can and must address academic bullying. If witnesses to harassment, members of investigative committees, journal editors, other gatekeepers, and individuals at every level of the scientific community all leverage their knowledge and power to combat bullying, we can create a safer and more civil scientific environment.
Transitions in the Proteome and Phospho-Proteome During Xenopus laevis Development

Vertebrate development from an egg to a complex multi-tissue million-cell organism is supported by multiple phases of genome-scale remodeling of the repertoire of proteins and their post-translational modifications, yet so far we know little about these phases. In this paper we present comprehensive characterization of these processes reflected by eleven time points, approximately eleven thousand proteins, and six thousand phospho-forms in two replicates. We find that the most dramatic changes to the proteome occur during the transition to functional organ systems, which occurs as the embryo becomes a tadpole. At that time the absolute amount of non-yolk protein increases two-fold, and there is a shift in the balance of expression from proteins regulating gene expression to receptors, ligands, and proteins involved in cell-cell and cell-environment interactions. Between the early and late tadpole, the median increase of membrane and secreted proteins is substantially higher than that of nuclear proteins. For the first time, we have included quantitative phosphoproteomic data across the same developmental stages. In contrast to the significant protein changes that are concentrated at the end of the time series, the most significant phosphorylation changes are concentrated in the very early stages of development. A clear exception are phosphorylations of proteins involved in gene expression; these increase just after fertilization, with patterns that are highly correlated with the underlying protein changes. To improve our interpretation of this unique data set, we created a pipeline for identifying homologous human phosphorylations from the measured Xenopus phospho-proteome, and we estimated, where possible, the occupancy of phosphorylation sites. Overall, we detected many profound temporal transitions, which suggest concerted changes in developmental strategies in the embryo that are particularly pronounced once early patterning and specification are complete.
Personalized Speech Enhancement Through Self-Supervised Data Augmentation and Purification

Training personalized speech enhancement models is innately a no-shot learning problem due to privacy constraints and limited access to noise-free speech from the target user. If there is an abundance of unlabeled noisy speech from the test-time user, a personalized speech enhancement model can be trained using self-supervised learning. One straightforward approach to model personalization is to use the target speaker's noisy recordings as pseudo-sources. Then, a pseudo denoising model learns to remove injected training noises and recover the pseudo-sources. However, this approach is volatile as it depends on the quality of the pseudo-sources, which may be too noisy. As a remedy, we propose an improvement to the self-supervised approach through data purification. We first train an SNR predictor model to estimate the frame-by-frame SNR of the pseudo-sources. Then, the predictor's estimates are converted into weights which adjust the frame-by-frame contribution of the pseudo-sources towards training the personalized model. We empirically show that the proposed data purification step improves the usability of the speaker-specific noisy data in the context of personalized speech enhancement. Without relying on any clean speech recordings or speaker embeddings, our approach may be seen as privacy-preserving.
University Operations During a Pandemic: A Flexible Decision Analysis Toolkit

Modeling infection spread during pandemics is not new, with models using past data to tune simulation parameters for predictions. These help in understanding of the healthcare burden posed by a pan...
Bicausal Optimal Transport for Markov Chains via Dynamic Programming

In this paper we study the bicausal optimal transport problem for Markov chains, an optimal transport formulation suitable for stochastic processes which takes into consideration the accumulation of information as time evolves. Our analysis is based on a relation between the transport problem and the theory of Markov decision processes. This way we are able to derive necessary and sufficient conditions for optimality in the transport problem, as well as an iterative algorithm, namely the value iteration, for the calculation of the transportation cost. Additionally, we draw the connection with the classic theory on couplings for Markov chains, and in particular with the notion of faithful couplings.
Temporal Cluster Matching for Change Detection of Structures from Satellite Imagery

Longitudinal studies are vital to understanding dynamic changes of the planet, but labels (e.g., buildings, facilities, roads) are often available only for a single point in time. We propose a general model, Temporal Cluster Matching (TCM), for detecting building changes in time series of remotely sensed imagery when footprint labels are observed only once. The intuition behind the model is that the relationship between spectral values inside and outside of building’s footprint will change when a building is constructed (or demolished). For instance, in rural settings, the pre-construction area may look similar to the surrounding environment until the building is constructed. Similarly, in urban settings, the pre-construction areas will look different from the surrounding environment until construction. We further propose a heuristic method for selecting the parameters of our model which allows it to be applied in novel settings without requiring data labeling efforts (to fit the parameters). We apply our model over a dataset of poultry barns from 2016/2017 high-resolution aerial imagery in the Delmarva Peninsula and a dataset of solar farms from a 2020 mosaic of Sentinel 2 imagery in India. Our results show that our model performs as well when fit using the proposed heuristic as it does when fit with labeled data, and further, that supervised versions of our model perform the best among all the baselines we test against. Finally, we show that our proposed approach can act as an effective data augmentation strategy – it enables researchers to augment existing structure footprint labels along the time dimension and thus use imagery from multiple points in time to train deep learning models. We show that this improves the spatial generalization of such models when evaluated on the same change detection task.
Fake News Detection on News-Oriented Heterogeneous Information Networks through Hierarchical Graph Attention

The viral spread of fake news has caused great social harm, making fake news detection an urgent task. Current fake news detection methods rely heavily on text information by learning the extracted news content or writing style of internal knowledge. However, deliberate rumors can mask writing style, bypassing language models and invalidating simple text-based models. In fact, news articles and other related components (such as news creators and news topics) can be modeled as a heterogeneous information network (HIN for short). In this paper, we propose a novel fake news detection framework, namely Hierarchical Graph Attention Network (HGAT), which uses a novel hierarchical attention mechanism to perform node representation learning in HIN, and then detects fake news by classifying news article nodes. Experiments on two realworld fake news datasets show that HGAT can outperform text-based models and other network-based models. In addition, the experiments prove the expandability and generalizability of our for graph representation learning and other node classification related applications in heterogeneous graphs.
Discover the Unknown Biased Attribute of an Image Classifier

Recent works find that AI algorithms learn biases from data. Therefore, it is urgent and vital to identify biases in AI algorithms. However, the previous bias identification pipeline overly relies on human experts to conjecture potential biases (e.g., gender), which may neglect other underlying biases not realized by humans. To help human experts better find the AI algorithms' biases, we study a new problem in this work -- for a classifier that predicts a target attribute of the input image, discover its unknown biased attribute. To solve this challenging problem, we use a hyperplane in the generative model's latent space to represent an image attribute; thus, the original problem is transformed to optimizing the hyperplane's normal vector and offset. We propose a novel total-variation loss within this framework as the objective function and a new orthogonalization penalty as a constraint. The latter prevents trivial solutions in which the discovered biased attribute is identical with the target or one of the known-biased attributes. Extensive experiments on both disentanglement datasets and real-world datasets show that our method can discover biased attributes and achieve better disentanglement w.r.t. target attributes. Furthermore, the qualitative results show that our method can discover unnoticeable biased attributes for various object and scene classifiers, proving our method's generalizability for detecting biased attributes in diverse domains of images. The code is available at this https URL.
A Context-Enhanced De-identification System.

Many modern entity recognition systems, including the current state-of-the-art de-identification systems, are based on bidirectional long short-term memory (biLSTM) units augmented by a conditional...
Task Planning with a Weighted Functional Object-Oriented Network

In reality, there is still much to be done for robots to be able to perform manipulation actions with full autonomy. Complicated manipulation tasks, such as cooking, may still require a person to perform some actions that are very risky for a robot to perform. On the other hand, some other actions may be very risky for a human with physical disabilities to perform. Therefore, it is necessary to balance the workload of a robot and a human based on their limitations while minimizing the effort needed from a human in a collaborative robot (cobot) set-up. This paper proposes a new version of our functional object-oriented network (FOON) that integrates weights in its functional units to reflect a robot’s chance of successfully executing an action of that functional unit. The paper also presents a task planning algorithm for the weighted FOON to allocate manipulation action load to the robot and human to achieve optimal performance while minimizing human effort. Through a number of experiments, this paper shows several successful cases in which using the proposed weighted FOON and the task planning algorithm allow a robot and a human to successfully complete complicated tasks together with higher success rates than a robot doing them alone.
Low-bias ncRNA libraries using ordered two-template relay: Serial template jumping by a modified retroelement reverse transcriptase.

Selfish, non-long terminal repeat (non-LTR) retroelements and mobile group II introns encode reverse transcriptases (RTs) that can initiate DNA synthesis without substantial base pairing of primer and template. Biochemical characterization of these enzymes has been limited by recombinant expression challenges, hampering understanding of their properties and the possible exploitation of their properties for research and biotechnology. We investigated the activities of representative RTs using a modified non-LTR RT from Bombyx mori and a group II intron RT from Eubacterium rectale. Only the non-LTR RT supported robust and serial template jumping, producing one complementary DNA (cDNA) from several templates each copied end to end. We also discovered an unexpected terminal deoxynucleotidyl transferase activity of the RTs that adds nucleotide(s) of choice to 3′ ends of single- and/or double-stranded RNA or DNA. Combining these two types of activity with additional insights about nontemplated nucleotide additions to duplexed cDNA product, we developed a streamlined protocol for fusion of next-generation sequencing adaptors to both cDNA ends in a single RT reaction. When benchmarked using a reference pool of microRNAs (miRNAs), library production by Ordered Two-Template Relay (OTTR) using recombinant non-LTR retroelement RT outperformed all commercially available kits and rivaled the low bias of technically demanding home-brew protocols. We applied OTTR to inventory RNAs purified from extracellular vesicles, identifying miRNAs as well as myriad other noncoding RNAs (ncRNAs) and ncRNA fragments. Our results establish the utility of OTTR for automation-friendly, low-bias, end-to-end RNA sequence inventories of complex ncRNA samples.
Subgame-perfect equilibrium in games with almost perfect information: Dispensing with public randomization

Harris, Reny, and Robson (Econometrica, 1995) add a public randomization device to dynamic games with almost perfect information to ensure existence of subgame perfect equilibria (SPE). We show that when nature's moves are atomless in the original game, SPE obtained via this channel can be "de-correlated" to produce a payoff-equivalent SPE of the original game; thus, public randomization is in a sense without loss of generality. As a corollary, we provide an alternative route to a result of He and Sun (2016) on existence of SPE without public randomization, which in turn yields an equilibrium existence result for stochastic games with weakly continuous state transitions.
Nucleosynthetic signatures of primordial origin around supermassive black holes

If primordial black holes (PBHs) seeded the supermassive black holes (SMBHs) at the centers of high-redshift quasars, then the gas surrounding these black holes may reveal nucleosynthetic clues to their primordial origins. We present predictions of altered primordial abundances around PBHs massive enough to seed SMBHs at $z\ensuremath{\approx}6--7.5$. We find that if PBHs with initial masses of $\ensuremath{\sim}{10}^{5}\text{ }\text{ }{\mathrm{M}}_{\ensuremath{\bigodot}}$ are responsible for such SMBHs, they may produce primordial deuterium and Helium fractions enhanced by $\ensuremath{\ge}10%$, and lithium abundance depleted by $\ensuremath{\ge}10%$, at distances of up to $\ensuremath{\approx}$ a comoving kiloparsec away from the black hole after decoupling. We estimate that $\ensuremath{\sim}{10}^{8}\text{ }\text{ }{\mathrm{M}}_{\ensuremath{\bigodot}}$ of gas is enhanced (or depleted) by at least one percent. Evidence of these modified primordial deuterium, helium, and lithium abundances could still be present if this circum-PBH gas remains unaccreted by the SMBH and in or near the host galaxies of high-redshift quasars. Measuring the abundance anomalies will be challenging, but could offer a novel way to reveal the primordial origin of such SMBH seeds.
Synaptic Zn2+ potentiates the effects of cocaine on striatal dopamine neurotransmission and behavior.

Cocaine binds to the dopamine (DA) transporter (DAT) to regulate cocaine reward and seeking behavior. Zinc (Zn2+) also binds to the DAT, but the in vivo relevance of this interaction is unknown. We found that Zn2+ concentrations in postmortem brain (caudate) tissue from humans who died of cocaine overdose were significantly lower than in control subjects. Moreover, the level of striatal Zn2+ content in these subjects negatively correlated with plasma levels of benzoylecgonine, a cocaine metabolite indicative of recent use. In mice, repeated cocaine exposure increased synaptic Zn2+ concentrations in the caudate putamen (CPu) and nucleus accumbens (NAc). Cocaine-induced increases in Zn2+ were dependent on the Zn2+ transporter 3 (ZnT3), a neuronal Zn2+ transporter localized to synaptic vesicle membranes, as ZnT3 knockout (KO) mice were insensitive to cocaine-induced increases in striatal Zn2+. ZnT3 KO mice showed significantly lower electrically evoked DA release and greater DA clearance when exposed to cocaine compared to controls. ZnT3 KO mice also displayed significant reductions in cocaine locomotor sensitization, conditioned place preference (CPP), self-administration, and reinstatement compared to control mice and were insensitive to cocaine-induced increases in striatal DAT binding. Finally, dietary Zn2+ deficiency in mice resulted in decreased striatal Zn2+ content, cocaine locomotor sensitization, CPP, and striatal DAT binding. These results indicate that cocaine increases synaptic Zn2+ release and turnover/metabolism in the striatum, and that synaptically released Zn2+ potentiates the effects of cocaine on striatal DA neurotransmission and behavior and is required for cocaine-primed reinstatement. In sum, these findings reveal new insights into cocaine’s pharmacological mechanism of action and suggest that Zn2+ may serve as an environmentally derived regulator of DA neurotransmission, cocaine pharmacodynamics, and vulnerability to cocaine use disorders.
Beck-Type Identities for Euler Pairs of Order r

Partition identities are often statements asserting that the set \(\mathcal P_X\) of partitions of n subject to condition X is equinumerous to the set \(\mathcal P_Y\) of partitions of n subject to condition Y. A Beck-type identity is a companion identity to \(|\mathcal P_X|=|\mathcal P_Y|\) asserting that the difference b(n) between the number of parts in all partitions in \(\mathcal P_X\) and the number of parts in all partitions in \(\mathcal P_Y\) equals \(c|\mathcal P_{X'}|\) and also \(c|\mathcal P_{Y'}|\), where c is some constant related to the original identity, and \(X'\), respectively \(Y'\), is a condition on partitions that is a very slight relaxation of condition X, respectively Y. A second Beck-type identity involves the difference \(b'(n)\) between the total number of different parts in all partitions in \(\mathcal P_Y\) and the total number of different parts in all partitions in \(\mathcal P_X\). We extend these results to Beck-type identities accompanying all identities given by Euler pairs of order r (for any \(r\ge 2\)). As a consequence, we obtain many families of new Beck-type identities. We give analytic and bijective proofs of our results.
Newton-LESS: Sparsification without Trade-offs for the Sketched Newton Update

In second-order optimization, a potential bottleneck can be computing the Hessian matrix of the optimized function at every iteration. Randomized sketching has emerged as a powerful technique for constructing estimates of the Hessian which can be used to perform approximate Newton steps. This involves multiplication by a random sketching matrix, which introduces a trade-off between the computational cost of sketching and the convergence rate of the optimization algorithm. A theoretically desirable but practically much too expensive choice is to use a dense Gaussian sketching matrix, which produces unbiased estimates of the exact Newton step and which offers strong problem-independent convergence guarantees. We show that the Gaussian sketching matrix can be drastically sparsified, significantly reducing the computational cost of sketching, without substantially affecting its convergence properties. This approach, called Newton-LESS, is based on a recently introduced sketching technique: LEverage Score Sparsified (LESS) embeddings. We prove that Newton-LESS enjoys nearly the same problem-independent local convergence rate as Gaussian embeddings, not just up to constant factors but even down to lower order terms, for a large class of optimization tasks. In particular, this leads to a new state-of-the-art convergence result for an iterative least squares solver. Finally, we extend LESS embeddings to include uniformly sparsified random sign matrices which can be implemented efficiently and which perform well in numerical experiments.
Optimal Neural Program Synthesis from Multimodal Specifications

Multimodal program synthesis, which leverages different types of user input to synthesize a desired program, is an attractive way to scale program synthesis to challenging settings; however, it requires integrating noisy signals from the user (like natural language) with hard constraints on the program's behavior. This paper proposes an optimal neural synthesis approach where the goal is to find a program that satisfies user-provided constraints while also maximizing the program's score with respect to a neural model. Specifically, we focus on multimodal synthesis tasks in which the user intent is expressed using combination of natural language (NL) and input-output examples. At the core of our method is a top-down recurrent neural model that places distributions over abstract syntax trees conditioned on the NL input. This model not only allows for efficient search over the space of syntactically valid programs, but it allows us to leverage automated program analysis techniques for pruning the search space based on infeasibility of partial programs with respect to the user's constraints. The experimental results on a multimodal synthesis dataset (StructuredRegex) show that our method substantially outperforms prior state-of-the-art techniques in terms of accuracy and explores fewer states during search.
Exploring Post COVID-19 Outbreak Intradaily Mobility Pattern Change in College Students: A GPS-Focused Smartphone Sensing Study

With the outbreak of the COVID-19 pandemic in 2020, most colleges and universities move to restrict campus activities, reduce indoor gatherings and move instruction online. These changes required that students adapt and alter their daily routines accordingly. To investigate patterns associated with these behavioral changes, we collected smartphone sensing data using the Beiwe platform from two groups of undergraduate students at a major North American university, one from January to March of 2020 (74 participants), the other from May to August (52 participants), to observe the differences in students' daily life patterns before and after the start of the pandemic. In this paper, we focus on the mobility patterns evidenced by GPS signal tracking from the students' smartphones and report findings using several analytical methods including principal component analysis, circadian rhythm analysis, and predictive modeling of perceived sadness levels using mobility-based digital metrics. Our findings suggest that compared to the pre-COVID group, students in the mid-COVID group generally (1) registered a greater amount of midday movement than movement in the morning (8-10am) and in the evening (7-9pm), as opposed to the other way around; (2) exhibited significantly less intradaily variability in their daily movement; (3) visited less places and stayed at home more everyday, and; (4) had a significant lower correlation between their mobility patterns and negative mood.
Symbiotic Expression of Rhizobium meliloti Nitrogen Fixation Genes Is Regulated by Oxygen

Rhizobium meliloti elicits the development of specialized organs on the plant roots called nodules. How nitrogen fixation genes are coordinately expressed specifically within the nodule is an important question in understanding the plant-microbe symbiosis. Homologs of nif genes have been identified in R. meliloti, but inactivation of these genes by transposon mutagenesis does not affect symbiosis. The fact that FixL* retains oxygen-regulated activity in vitro demonstrates that the amino-terminal membrane attachment domain is not essential for FixL function. The net effect, then, is an increase in the level of phospho-FixJ. The development of an oxygen-regulated in vitro transcription system is an important step in the study of FixJ action at target promoters. Two-component response regulators that function as transcription factors can be divided into three subclasses based on sequence similarity outside the universally conserved N-terminal phosphoryl acceptor domain. In contrast with many other systems, the phosphorylated forms of both the sensor kinase (FixL) and the response regulator (FixJ) are very stable, greatly facilitating many biochemical approaches. Signal transduction can be reconstituted in vitro, from stimulus to gene expression, with a minimum of water-soluble components. The study of FixL/FixJ may have important consequences in understanding other oxygen-regulated biological processes. Oxygen is an important regulator of many processes in bacteria. In addition to the intermolecular interactions between the kinase (transmitter) and phosphoryl acceptor (receiver) domains, other important intermolecular interactions may be important, such as monomer-dimer transitions.
Abstract 443: Synergistic inhibition of HER2 positive breast cancer by triptolide and lapatinib

Proceedings: AACR Annual Meeting 2014; April 5-9, 2014; San Diego, CA HER2-positive breast cancers are poorly differentiated and have a higher potential for metastasis. Although trastuzumab and lapatinib have been used for the treatment of HER2-positive breast cancer, resistance to both drugs has dampened the long-term benefit of those drugs. As cancer has multiple genetic aberrations and can readily develop resistance to individual drugs with specific targets, we employed synergistic drug combination as a strategy to overcome drug resistance and to enhance the efficacy of existing drugs. From a screen of the Johns Hopkins Drug Library, we identified triptolide as a strong synergistic hit with lapatinib in HCC1954, a drug-resistant HER2-positive breast cancer cell line. The combination of lapatinib and nontoxic doses of triptolide synergistically inhibited the HER2 signaling pathway. We found that lapatinib increased both mRNA expression and the half-life of HER2 protein, whereas triptolide countered the effects of lapatinib by downregulating the expression of HER2, leading to synergistic inhibition of the HER2 signaling pathway. The synergy in cell proliferation was observed in all HER2-positive breast cancer cell lines regardless of drug resistance status, but was absent in HER2-negative ones. In mouse xenograft models, the combination of lapatinib and triptolide at their minimum effective doses almost completely inhibited the growth of the drug-resistant HER2-positive breast cancer cells. Together, these findings suggest that the combination of triptolide and lapatinib could be a promising new therapeutic regimen for drug-resistant HER2-positive breast cancer. Citation Format: Paweena Chalugun, Joong Sup Shim, Preethi C. Korangath, Saraswati Sukumar, Jun O. Liu. Synergistic inhibition of HER2 positive breast cancer by triptolide and lapatinib. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 443. doi:10.1158/1538-7445.AM2014-443
Exploiting Structure in Constraint Satisfaction Problems

Constraint satisfaction problems (CSPs) involve finding values for problem variables subject to restrictions on which combinations of values are allowed. Fig. 2.1a presents an example: color the graph shown such that no vertices joined by an edge have the same color. The small letters indicate the choice of colors available at each vertex (a stands for aquamarine if you like). Here the variables are the vertices, the values for each variable are the set of colors available at the vertex and the constraints all happen to be the same: “not same color”. This is a binary CSP because all constraints involve two variables. For simplicity, we will assume here that our problems are presented as binary CSPs.
A Critique of Stage Theories of Human Development

Behavior analysis differs from most psychological accounts of human development. Under developmental stage theories, what children are ‘likely to do’ at ‘approximately what developmental level or age’ are emphasized, but not ‘how or why they do it—the process—is undisclosed and largely unknown. Those who focus on the development of hypothetical structures often miss many of the observable behavioral phenomena that are of importance in development. Pseudo-explanations like stage and critical periods do not lead to better prediction or control of the behaviors or mental capacities being studied. Further, such theories often invoke internal processes as causal factors, advancing hypotheses difficult to confirm/disprove with empirical research. Behavioral-analytic theory conceives development to be progressive behavior changes shaped by consequences. These behaviors in turn become “hierarchically organized.’ Behavior analysis is both a methodology for studying human development and a proven technology for increasing intellectual abilities and teaching useful skills. In this chapter we attempt to show that planned interventions based on this empirical behavioral foundation should be more effective and have more durable outcomes. Keywords: critique of stage theories; behavior analysis; child development; critical periods; reinforcement theory
God's Income Tax: What Jewish Tithing Practices Can Teach Us About Tax Reform

Tax reform and religion were two of the hot button issues during the last election. While at first glance these issues seem unrelated, a number of scholars have argued that religious values should guide our decisions regarding tax reform. This article posits that the relationship between religion and taxes is even stronger than has previously been suggested. People have been tithing for thousands of years. When they determine the amount of the tithe based on their income, the practice amounts to a religious income tax, or, more precisely, God's income tax. This article explores the Jewish traditions of tithing, looking at both agricultural tithing, which is described in the Bible, and the practice of maaser kesafim, which involves tithing from all types of income, a practice that derives from the Bible but which developed more recently. Over the centuries, religious authorities have developed a sophisticated tax jurisprudence that holds lessons for us as we struggle with the questions of whether to reform our current income tax or replace it entirely with some form of consumption tax. In comparing Jewish tithing traditions and Federal income tax, this article (1) tests claims that Judeo-Christian values require a progressive tax system; (2) examines the ways in which culture and context affect income definition (and are likely to do so in the future); and (3) explores whether a flat-rate income tax or consumption tax will be less complex than our current tax system, as proponents for such systems claim.
Theoretical Electron-Atom Elastic Scattering Cross Sections: Selected Elements, 1 keV to 256 keV

Abstract Calculated differential elastic electron scattering cross sections for 24 selected elements are presented in tabular form in the energy range 1 to 256 keV at values of 2n keV. The total elastic and momentum-transfer cross sections are tabulated also. Parameters for 12-parameter analytic fits to the differential values are given for 80 elements in a second table. The calculations were done in the relativistic static approximation with relativistic atomic wavefunctions for the heavier elements (Z > 35).
Analysis of adaptive CMOS down conversion mixers

Analysis of CMOS direct conversion architecture with adaptive DC offset compensation is presented. Due to process mismatches and local oscillator (LO) crosstalk, DC offsets up to 30 mV are observed at the mixer output. For a practical direct conversion or zero-IF down-conversion system, the incoming RF signal can be as low as -100 dBm or few microvolts at this stage and any LO coupling will cause a DC offset orders of magnitude larger than the received signal. The DC offset needs to be effectively reduced to prevent the consecutive gain stages from entering saturation and destroying the RF signal. To achieve this, an adaptive DC shifting circuit is presented. Adding a tunable DC offset on the LO signal can effectively counteract the output DC offset by exploiting the quadratic LO dependence of the process mismatch induced offsets. In addition to that, DSP approaches for adaptively generating the control signals for the DC shifting circuitry are investigated.
Effect of heat transfer on melt/solid interface shape and solute segregation in edge-defined film-fed growth: Finite element analysis

The effects of the heat transfer environment in Edge-Defined Film-Fed Growth on melt-solid interface shape and lateral dopant segregation are studied by finite-element analysis of two-dimensional models for heat and mass transfer. Heat transfer configurations are studied that correspond to the uniform surroundings assumed in previous models and to lowand high-speed growth systems. The maximum growth rate for a silicon sheet is calculated and the range of validity of one-dimensional heat transfer models is established. The lateral segregation that results from curvature of the solidification interface is calculated for two solutes, boron and aluminum. In this way, heat transfer is linked directly to the uniformity of the product crystal.
Regularity of Solutions to Nonlinear Dispersive Equations

On considere le probleme de Cauchy pour les equations dispersives non lineaires de la forme ∂ t u+iP(D)u=F(u), x∈R n , −∞<t<∞, u(x,0)=u 0 (x)
Entropy-driven DNA denaturation.

We present a nonlinear dynamical model for DNA denaturation which includes cooperativity effects through anharmonic nearest-neighbor stacking interactions. Transfer-integral calculations show that this one-dimensional model, without including long-range interactions, exhibits a sharp denaturation reminiscent of a first-order transition at a temperature lower than a similar model with harmonic coupling. Self-consistent phonon calculations point out the essential role of nonlinear effects in the mechanism.
Divergent β‐tubulins from foraminifera: Implications for microtubule assembly

Foraminifera utilize an unusual and very rapid microtubule assembly/disassembly system: a state transition between microtubules and helical filaments. The helical filament is an assembly polymorph that forms when the microtubule lattice uncoils into a ribbon of tubulin dimers connected only by lateral subunit/subunit bonds. An unusual β-tubulin sequence, which may be implicated in this process, has previously been reported from the foraminiferan Reticulomyxa filosa. In order to determine the taxonomic distribution and possible significance of this tubulin isoform, we sequenced and analyzed β-tubulin genes from a broad taxonomic range of foraminifera. These genes contain a large number of substitutions, conserved within the group, which may alter the biochemical properties of β-tubulin, especially in regions involved in subunit/subunit binding between α- and β-tubulin in the microtubule lattice. In particular, the M-loop, which is intimately involved in lateral subunit binding, shows only 30% homology to the consensus sequence for eukaryotic β-tubulins. Multiple regions identified to be involved in longitudinal contacts with α-tubulin are also highly substituted. In contrast, the foraminiferal α-tubulin is well conserved with its homologs in other organisms. We suggest that these alterations to the consensus eukaryotic β-tubulin sequence may help to explain the unusual assembly properties of foraminiferal microtubules. This work was supported by NSF OPP0003639.
Tethered spinal cord in association with diastematomyelia

Abstract There is no good evidence that division of the filum terminale with simultaneous removal of the central spur in patients with diastematomyelia improves their eventual neurological status. We describe 5 patients in whom the filum was not divided and who were later reoperated upon because of lack of improvement or because of increasing neurological deficit. In 3 patients the division of a congenitally short filum terminale resulted in no improvement; in the other 2, symptoms were relieved following the division of adhesions at the site of previous surgery.