cssi/SciSciGPT-SciSciNet huggingface dataset:
Functional Context Affects Scene Processing.

Rapid visual perception is often viewed as a bottom-up process. Category-preferred neural regions are often characterized as automatic, default processing mechanisms for visual inputs of their categorical preference. To explore the sensitivity of such regions to top-down information, we examined three scene-preferring brain regions, the occipital place area (OPA), the parahippocampal place area (PPA), and the retrosplenial complex (RSC) and tested whether the processing of outdoor scenes is influenced by the functional contexts in which they are seen. Context was manipulated by presenting real-world landscape images as if being viewed through a window or within a picture frame-manipulations that do not affect scene content but do affect one's functional knowledge regarding the scene. This manipulation influences neural scene processing (as measured by fMRI): The OPA and the PPA exhibited greater neural activity when participants viewed images as if through a window as compared with within a picture frame, whereas the RSC did not show this difference. In a separate behavioral experiment, functional context affected scene memory in predictable directions (boundary extension). Our interpretation is that the window context denotes three dimensionality, therefore rendering the perceptual experience of viewing landscapes as more realistic. Conversely, the frame context denotes a 2-D image. As such, more spatially biased scene representations in the OPA and the PPA are influenced by differences in top-down, perceptual expectations generated from context. In contrast, more semantically biased scene representations in the RSC are likely to be less affected by top-down signals that carry information about the physical layout of a scene.
A Fresh Start: The Evolving Use of Juvenile Records in College Admissions

Questions about criminal and juvenile records in the college application process are common and frequently fail to account for the unique characteristics of juvenile justice systems. The ways in which colleges and universities ask about juvenile records often encourage applicants to disclose information in spite of statutory protections and fly in the face of the public policy underlying a range of legal safeguards that are intended to help individuals with records from juvenile systems in moving forward and receiving a second chance. In recent years, a series of legislative and institutional changes have begun to restrict how colleges and universities may ask about criminal and juvenile records. Four states have passed laws limiting how criminal history may be used in the admissions process. The Common Application has moved to make asking about criminal history optional, and it now gives institutions more flexibility in deciding how to phrase criminal history questions. This Article presents a first-of-its-kind empirical analysis of how the more than 800 schools that use the Common Application, and schools in the first states to restrict use of criminal history, have responded to these changes. While these reforms have affected how frequently colleges and universities ask about criminal history, they continue to leave the door open for some postsecondary institutions to push applicants to disclose juvenile records. The growing movement to restrict use of criminal history in the college admissions process presents a critical opportunity to reconsider the role that postsecondary systems should play in supporting the rehabilitative goals of juvenile justice systems. To that end, this Article concludes by providing recommendations for legislative and institutional language that can more effectively ensure that individuals with juvenile records are given a true second chance and a meaningful opportunity to earn postsecondary degrees.
Parylene photonics: a flexible, broadband optical waveguide platform with integrated micromirrors for biointerfaces

Targeted light delivery into biological tissue is needed in applications such as optogenetic stimulation of the brain and in vivo functional or structural imaging of tissue. These applications require very compact, soft, and flexible implants that minimize damage to the tissue. Here, we demonstrate a novel implantable photonic platform based on a high-density, flexible array of ultracompact (30 μm × 5 μm), low-loss (3.2 dB/cm at λ = 680 nm, 4.1 dB/cm at λ = 633 nm, 4.9 dB/cm at λ = 532 nm, 6.1 dB/cm at λ = 450 nm) optical waveguides composed of biocompatible polymers Parylene C and polydimethylsiloxane (PDMS). This photonic platform features unique embedded input/output micromirrors that redirect light from the waveguides perpendicularly to the surface of the array for localized, patterned illumination in tissue. This architecture enables the design of a fully flexible, compact integrated photonic system for applications such as in vivo chronic optogenetic stimulation of brain activity. A flexible and biocompatible optical platform is demonstrated that can be used for biophotonic applications. Delivering light into tissue is important for biological imaging and manipulation, such as for medical treatments or monitoring brain activity. However, traditional light delivery systems are composed of rigid materials, which can damage biological tissue. Here, a team from Carnegie Mellon University led by Maysamreza Chamanzar demonstrates a photonic platform composed of biocompatible and mechanically flexible polymeric optical waveguides, termed Parylene photonics. This platform features embedded micromirrors that enable targeted light delivery to specific locations within the tissue. They show that this system can efficiently deliver light with a soft and flexible device, demonstrating its potential as a platform for biointerfacing.
Hindsight logging for model training

In modern Machine Learning, model training is an iterative, experimental process that can consume enormous computation resources and developer time. To aid in that process, experienced model develo...
Attitudes towards involving children in decision-making surrounding lung transplantation.

BACKGROUND Medical care has shifted from a paternalistic model towards one centered around patient autonomy and shared decision-making (SDM), yet the role of the pediatric patient in decision-making is unclear. Studies suggest that many children with chronic disease are capable of making medical decisions at a young age, yet no standardized approaches have been developed for involving children in these decisions. METHODS This is a single-center survey study investigating the attitudes of pediatric pulmonologists towards involvement of children in decisions regarding lung transplantation, utilizing a hypothetical case scenario with systematic manipulation of age and maturity level. We evaluated physician belief regarding ultimate decision-making authority, reconciliation of parent-child discordance, and utility of ethics and psychiatry consultation services. RESULTS The majority of pediatric pulmonologists at this center believe decision-making authority rests with the parents. The effects of age and maturity are unclear. In instances of parent-child disagreement, physicians are more likely to try to convince parents to defer to the child if the child is both older and more mature. Physicians are divided on the utility of ethics and psychiatry consultations. CONCLUSION Involvement of children with cystic fibrosis in SDM is broadly supported but inconsistently implemented. Despite evidence that children with chronic disease may have decisional capacity at a young age, the majority of physicians still grant decisional authority to parents. There are numerous barriers to involving children in decisions, including legal considerations. The role of age and maturity level in influencing these decisions appears small and warrants further investigation.
Ribovore: ribosomal RNA sequence analysis for GenBank submissions and database curation.

Background The DNA sequences encoding ribosomal RNA genes (rRNAs) are commonly used as markers to identify species, including in metagenomics samples that may combine many organismal communities. The 16S small subunit ribosomal RNA (SSU rRNA) gene is typically used to identify bacterial and archaeal species. The nuclear 18S SSU rRNA gene, and 28S large subunit (LSU) rRNA gene have been used as DNA barcodes and for phylogenetic studies in different eukaryote taxonomic groups. Because of their popularity, the National Center for Biotechnology Information (NCBI) receives a disproportionate number of rRNA sequence submissions and BLAST queries. These sequences vary in quality, length, origin (nuclear, mitochondria, plastid), and organism source and can represent any region of the ribosomal cistron. Results To improve the timely verification of quality, origin and loci boundaries, we developed Ribovore, a software package for sequence analysis of rRNA sequences. The ribotyper and ribosensor programs are used to validate incoming sequences of bacterial and archaeal SSU rRNA. The ribodbmaker program is used to create high-quality datasets of rRNAs from different taxonomic groups. Key algorithmic steps include comparing candidate sequences against rRNA sequence profile hidden Markov models (HMMs) and covariance models of rRNA sequence and secondary-structure conservation, as well as other tests. Nine freely available blastn rRNA databases created and maintained with Ribovore are used for checking incoming GenBank submissions and used by the blastn browser interface at NCBI. Since 2018, Ribovore has been used to analyze more than 50 million prokaryotic SSU rRNA sequences submitted to GenBank, and to select at least 10,435 fungal rRNA RefSeq records from type material of 8350 taxa. Conclusion Ribovore combines single-sequence and profile-based methods to improve GenBank processing and analysis of rRNA sequences. It is a standalone, portable, and extensible software package for the alignment, classification and validation of rRNA sequences. Researchers planning on submitting SSU rRNA sequences to GenBank are encouraged to download and use Ribovore to analyze their sequences prior to submission to determine which sequences are likely to be automatically accepted into GenBank.
Dopamine-Related Striatal Neurophysiology is Associated with Specialization of Frontostriatal Reward Circuitry Through Adolescence

Characterizing developmental changes in frontostriatal circuitry is critical to our understanding of adolescent development and can clarify neurobiological mechanisms underlying increased reward sensitivity and sensation seeking, and the emergence of psychopathology during this period. However, the role of striatal neurobiology in the development of frontostriatal circuitry through human adolescence remains largely unknown. We combine longitudinal MR-based assessments of striatal tissue-iron as a correlate of dopamine-related neurobiology with functional magnetic resonance imaging indices of resting-state and reward-state connectivity to investigate the contribution of dopaminergic processes to developmental changes in frontostriatal circuitry. Connectivity between the nucleus accumbens and ventral anterior cingulate, subgenual cingulate, and orbitofrontal cortices decreased through adolescence into adulthood. Nucleus accumbens tissue-iron mediated age-related changes and was associated with variability in connectivity. Our results provide evidence that developmental changes in dopamine-related striatal properties contribute to specialization of frontostriatal circuitry, potentially underlying changes in sensation seeking and reward sensitivity into adulthood.
The Effects of Absenteeism on Academic and Social-Emotional Outcomes: Lessons for COVID-19

In March 2020, most schools in the United States transitioned to distance learning in an effort to contain COVID-19. A significant number of students did not fully engage in remote learning opportu...
On Rich 2-to-1 Games

We propose a variant of the 2-to-1 Games Conjecture that we call the Rich 2-to-1 Games Conjecture and show that it is equivalent to the Unique Games Conjecture. We are motivated by two considerations. Firstly, in light of the recent proof of the 2-to-1 Games Conjecture [Subhash Khot et al., 2017; Irit Dinur et al., 2018; Irit Dinur et al., 2018; Subhash Khot et al., 2018], we hope to understand how one might make further progress towards a proof of the Unique Games Conjecture. Secondly, the new variant along with perfect completeness in addition, might imply hardness of approximation results that necessarily require perfect completeness and (hence) are not implied by the Unique Games Conjecture.
Machine learning optimization of peptides for presentation by class II MHCs.

T cells play a critical role in cellular immune responses to pathogens and cancer and can be activated and expanded by MHC-presented antigens contained in peptide vaccines. We present a machine learning method to optimize the presentation of peptides by class II MHCs by modifying their anchor residues. Our method first learns a model of peptide affinity for a class II MHC using an ensemble of deep residual networks, and then uses the model to propose anchor residue changes to improve peptide affinity. We use a high throughput yeast display assay to show that anchor residue optimization improves peptide binding. Supplementary information: Supplementary data are available at Bioinformatics online.
Factors of sex and age dictate the regulation of GABAergic activity by corticotropin-releasing factor receptor 1 in the medial sub-nucleus of the central amygdala.

Abstract Adolescents are phenotypically characterized with hyper-sensitivity to stress and inappropriate response to stress-inducing events. Despite behavioral distinctions from adults, investigations of developmental shifts in the function of stress peptide corticotropin-releasing factor (CRF) are generally limited. Rodent models have determined that CRF receptor 1 (CRFR1) activation within the central amygdala is associated with a stress response and induces increased GABAergic synaptic neurotransmission within adult males. To investigate age- and sex-specific function of this system, we performed whole-cell patch clamp electrophysiology in brain slices from naive adolescent (postnatal days (P) 40–49) and adult (>P70) male and female Sprague Dawley rats to assess GABAergic activity in the medial central amygdala (CeM). Our results indicate a dynamic influence of age and sex on neuronal excitability within this region, as well as basal spontaneous and miniature (m) inhibitory post-synaptic currents (IPSCs) in the CeM. In addition to replicating prior findings of CRFR1-regulated increases in mIPSC frequency in adult males, we found that the selective CRFR1 agonist, Stressin-1, attenuated mIPSC frequency in adolescent males, at a concentration that did not produce an effect in adult males. Importantly, this age-specific distinction was absent in females, as Stressin-1 attenuated mIPSC frequency in both adolescent and adult females. Finally, an increase in mIPSC frequency in response to the CRF1R antagonist, NBI 35965, was observed only in the CeM of adult males. Together, these data emphasize the robust influence of age and sex on neurophysiological function of a brain region involved in the production of the stress response.
Extreme black hole anabasis

We study the SL(2) transformation properties of spherically symmetric perturbations of the Bertotti-Robinson universe and identify an invariant μ that characterizes the backreaction of these linear solutions. The only backreaction allowed by Birkhoff’s theorem is one that destroys the AdS2 × S2 boundary and builds the exterior of an asymptotically flat Reissner-Nordstrom black hole with $$ Q=M\sqrt{1-\mu /4} $$ . We call such backreaction with boundary condition change an anabasis. We show that the addition of linear anabasis perturbations to Bertotti-Robinson may be thought of as a boundary condition that defines a connected AdS2 ×S2. The connected AdS2 is a nearly-AdS2 with its SL(2) broken appropriately for it to maintain connection to the asymptotically flat region of Reissner-Nordstrom. We perform a backreaction calculation with matter in the connected AdS2 × S2 and show that it correctly captures the dynamics of the asymptotically flat black hole.
How much is too much: the difficulties of social media content moderation

Recent events, including the 2020 Presidential Election and the Insurrection of the U.S. Capitol, have shown us that social media can be used for darker purposes. Hate speech, fake news, and conten...
The Alpha Beta Gamma of the Labor Market

Based on patterns of employment transitions, we identify three different types of workers in the US labor market: α’s β’s and γ’s. Workers of type α make up over half of all workers, are most likely to remain on the same job for more than 2 years and, when they become unemployed, typically find a new job within 1 quarter. Workers of type γ comprise less than one-fifth of workers, have a low probability of staying on the same job for more than 2 years and, when they become unemployed, face a high probability of remaining jobless for more than 1 year. Workers of type β are in between αs and γ’s. The earnings losses caused by displacement are relatively small and transitory for α-workers, while they are large and persistent for γ-workers. During the Great Recession, excess unemployment for α-workers rose by little and was reabsorbed quickly; unemployment for γ-workers rose by 20 percentage points and was not reabsorbed 4 years after its peak. We use a search-theoretic model of the labor market to rationalize the different patterns of employment transitions across types. The model naturally explains both the variation in the consequences of job displacement across types, and the variation in the dynamics of unemployment during the Great Recession. Our view is that several puzzling micro and macro phenomena about the labor market are driven by the behavior of the small group of γ-workers.
Arf1 directly recruits the Pik1-Frq1 PI4K complex to regulate the final stages of Golgi maturation

Proper Golgi complex function depends on the activity of Arf1, a GTPase whose effectors assemble and transport outgoing vesicles. Phosphatidylinositol 4-phosphate (PI4P) generated at the Golgi by the conserved PI 4-kinase Pik1 (PI4KIIIβ) is also essential for Golgi function, although its precise roles in vesicle formation are less clear. Arf1 has been reported to regulate PI4P production, but whether Pik1 is a direct Arf1 effector is not established. Using a combination of live-cell time-lapse imaging analyses, acute PI4P depletion experiments, and in vitro protein-protein interaction assays on Golgi-mimetic membranes, we present evidence for a model in which Arf1 initiates the final stages of Golgi maturation by tightly controlling PI4P production through direct recruitment of the Pik1-Frq1 PI4-kinase complex. This PI4P serves as a critical signal for AP-1 and secretory vesicle formation, the final events at maturing Golgi compartments. This work therefore establishes the regulatory and temporal context surrounding Golgi PI4P production and its precise roles in Golgi maturation.
A Unified Feature Representation for Lexical Connotations

Ideological attitudes and stance are often expressed through subtle meanings of words and phrases. Understanding these connotations is critical to recognizing the cultural and emotional perspectives of the speaker. In this paper, we use distant labeling to create a new lexical resource representing connotation aspects for nouns and adjectives. Our analysis shows that it aligns well with human judgments. Additionally, we present a method for creating lexical representations that capture connotations within the embedding space and show that using the embeddings provides a statistically significant improvement on the task of stance detection when data is limited.
Koopman Operator Methods for Global Phase Space Exploration of Equivariant Dynamical Systems

Abstract In this paper, we develop the Koopman operator theory for dynamical systems with symmetry. In particular, we investigate how the Koopman operator and eigenfunctions behave under the action of the symmetry group of the underlying dynamical system. Further, exploring the underlying symmetry, we propose an algorithm to construct a global Koopman operator from local Koopman operators. In particular, we show, by exploiting the symmetry, data from all the invariant sets are not required for constructing the global Koopman operator; that is, local knowledge of the system is enough to infer the global dynamics.
Extensions of bundles of C*-algebras

Bundles of C*-algebras can be used to represent limits of physical theories whose algebraic structure depends on the value of a parameter. The primary example is the ℏ → 0 limit of the C*-algebras ...
Post-acute COVID-19 sequelae in cases managed in the community or hospital in the UK: a population based study

ObjectiveTo compare post-COVID-19 sequelae between hospitalised and non-hospitalised individuals DesignPopulation-based cohort study Setting1,383 general practices in England contributing to Clinical Practice Research Database Aurum Participants46,687 COVID-19 cases diagnosed between 1st August to 17th October 2020 (45.4% male; mean age 40), either hospitalised within two weeks of diagnosis or non-hospitalised, and followed-up for a maximum of three months. Main outcome measuresEvent rates of new symptoms, diseases, prescriptions and healthcare utilisation in hospitalised and non-hospitalised individuals, with between-group comparison using Cox regression. Outcomes compared at 6 and 12 months prior to index date, equating to first UK wave and pre-pandemic. Non-hospitalised group outcomes stratified by age and sex. Results45,272 of 46,687 people were non-hospitalised; 1,415 hospitalised. Hospitalised patients had higher rates of 13/26 symptoms and 11/19 diseases post-COVID-19 than the community group, received more prescriptions and utilised more healthcare. The largest differences were noted for rates per 100,000 person-weeks [95%CI] of breathlessness: 536 [432 to 663] v. 85 [77 to 93]; joint pain: 295 [221 to 392] v. 168 [158 to 179]; diabetes: 303 [225 to 416] v. 36 [32 to 42], hypertension: 244 [178 to 344] v. 47 [41 to 53]. Although low, rates of chest tightness, tinnitus and lung fibrosis were higher in the community group. 4.2% (1882/45,272) of the community group had a post-acute burden, most frequently reporting anxiety, breathlessness, chest pain and fatigue. In those non-hospitalised, age and sex differences existed in outcome rates. Healthcare utilisation in the community group increased 28.5% post-COVID-19 relative to pre-pandemic. ConclusionsPost-COVID-19 sequelae differ between hospitalised and non-hospitalised individuals, with age and sex-specific differences in those non-hospitalised. Most COVID-19 cases managed in the community do not report ongoing issues to healthcare professionals. Post-COVID-19 follow-up and management strategies need to be tailored to specific groups.
Density of States Estimation for Out of Distribution Detection

Perhaps surprisingly, recent studies have shown probabilistic model likelihoods have poor specificity for out-of-distribution (OOD) detection and often assign higher likelihoods to OOD data than in-distribution data. To ameliorate this issue we propose DoSE, the density of states estimator. Drawing on the statistical physics notion of ``density of states,'' the DoSE decision rule avoids direct comparison of model probabilities, and instead utilizes the ``probability of the model probability,'' or indeed the frequency of any reasonable statistic. The frequency is calculated using nonparametric density estimators (e.g., KDE and one-class SVM) which measure the typicality of various model statistics given the training data and from which we can flag test points with low typicality as anomalous. Unlike many other methods, DoSE requires neither labeled data nor OOD examples. DoSE is modular and can be trivially applied to any existing, trained model. We demonstrate DoSE's state-of-the-art performance against other unsupervised OOD detectors on previously established ``hard'' benchmarks.
Chord Embeddings: Analyzing What They Capture and Their Role for Next Chord Prediction and Artist Attribute Prediction

Natural language processing methods have been applied in a variety of music studies, drawing the connection between music and language. In this paper, we expand those approaches by investigating chord embeddings, which we apply in two case studies to address two key questions: (1) what musical information do chord embeddings capture?; and (2) how might musical applications benefit from them? In our analysis, we show that they capture similarities between chords that adhere to important relationships described in music theory. In the first case study, we demonstrate that using chord embeddings in a next chord prediction task yields predictions that more closely match those by experienced musicians. In the second case study, we show the potential benefits of using the representations in tasks related to musical stylometrics.
A note on Lie algebra cohomology

Given a finite dimensional Lie algebra L, let I be the augmentation ideal in the universal enveloping algebra U(L). We study the conditions on L under which the Ext-groups Ext(k,k) for the trivial L-module k are the same when computed in the category of all U(L)-modules or in the category of I-torsion U(L)-modules. An application to cohomology of equivariant sheaves is given.
On the Quantum Width of a Black Hole Horizon

The many low energy modes near a black hole horizon give the thermal atmosphere a divergent entropy which becomes of order $A/4G$ with a Planck scale cut-off. However, Sorkin has given a Newtonian argument for 3+1 Schwarzschild black holes to the effect that fluctuations of such modes provide the horizon with a non-zero quantum mechanical width. This width then effectively enforces a cut-off at much larger distances so that the entropy of the thermal atmosphere is negligible in comparison with $A/4G$ for large black holes. We generalize and improve this result by giving a relativistic argument valid for any spherical black hole in any dimension. The result is again a cut-off $L_c$ at a geometric mean of the Planck scale and the black hole radius; in particular, $L_c^d \sim \frac{R}{T_H} \ell_p^{d-2}$. With this cut-off, the entropy of the thermal atmosphere is again parametrically small in comparison with the Bekenstein-Hawking entropy of the black hole. The effect of a large number $N$ of fundamental fields and the discrepancies from naive predictions of a stretched horizon model are also discussed.
A Hybrid DC Fault Primary Protection Algorithm for Multi-Terminal HVdc Systems

Protection against dc faults is one of the main technical hurdles faced when operating converter-based HVdc systems. Protection becomes even more challenging for multi-terminal dc (MTdc) systems with more than two terminals/converter stations. In this paper, a hybrid primary fault detection algorithm for MTdc systems is proposed to detect a broad range of failures. Sensor measurements, i.e., line currents and dc reactor voltages measured at local terminals, are first processed by a top-level context clustering algorithm. For each cluster, the best fault detector is selected among a detector pool according to a rule resulting from a learning algorithm. The detector pool consists of existing detection algorithms, each performing differently across fault scenarios. The proposed detection algorithm: i) offers superior performance compared to an individual detector through a data-driven approach; ii) detects all major fault types including pole-to-pole (P2P), pole-to-ground (P2G), and external dc faults; iii) identifies faults with various fault locations and impedances; iv) is more robust to noisy sensor measurements compared to existing methods; v) does not require exhaustive simulation and sampling for training the model. Performance and effectiveness of the proposed algorithm are evaluated and verified based on time-domain simulations in the PSCAD/EMTDC software environment.
Structure vs. randomness for bilinear maps

We prove that the slice rank of a 3-tensor (a combinatorial notion introduced by Tao in the context of the cap-set problem), the analytic rank (a Fourier-theoretic notion introduced by Gowers and Wolf), and the geometric rank (a recently introduced algebro-geometric notion) are all equivalent up to an absolute constant. As a corollary, we obtain strong trade-offs on the arithmetic complexity of a biased bililnear map, and on the separation between computing a bilinear map exactly and on average. Our result settles open questions of Haramaty and Shpilka [STOC 2010], and of Lovett [Discrete Anal., 2019] for 3-tensors.
A genome-first approach to rare variants in hypertrophic cardiomyopathy genes MYBPC3 and MYH7 in a medical biobank

'Genome-first' approaches to analyzing rare variants can reveal new insights into human biology and disease. Because pathogenic variants are often rare, new discovery requires aggregating rare coding variants into 'gene burdens' for sufficient power. However, a major challenge is deciding which variants to include in gene burden tests. Pathogenic variants in MYBPC3 and MYH7 are well-known causes of hypertrophic cardiomyopathy (HCM), and focusing on these 'positive control' genes in a genome-first approach could help inform variant selection methods and gene burdening strategies for other genes and diseases. Integrating exome sequences with electronic health records among 41 759 participants in the Penn Medicine BioBank, we evaluated the performance of aggregating predicted loss-of-function (pLOF) and/or predicted deleterious missense (pDM) variants in MYBPC3 and MYH7 for gene burden phenome-wide association studies (PheWAS). The approach to grouping rare variants for these two genes produced very different results: pLOFs but not pDM variants in MYBPC3 were strongly associated with HCM, whereas the opposite was true for MYH7. Detailed review of clinical charts revealed that only 38.5% of patients with HCM diagnoses carrying an HCM-associated variant in MYBPC3 or MYH7 had a clinical genetic test result. Additionally, 26.7% of MYBPC3 pLOF carriers without HCM diagnoses had clear evidence of left atrial enlargement and/or septal/LV hypertrophy on echocardiography. Our study shows the importance of evaluating both pLOF and pDM variants for gene burden testing in future studies to uncover novel gene-disease relationships and identify new pathogenic loss-of-function variants across the human genome through genome-first analyses of healthcare-based populations.
On the welfare effects of phasing out paper currency

Abstract We quantify the welfare effects of cash suppression policies within a general equilibrium model where cash reduces transactions costs and aids tax evasion in underground markets. In the model, currency suppression increases transactions costs and raises effective tax rates, but shifts resources out of costly underground markets and relaxes the government budget. When coupled with a reduction in distortionary taxes on consumption or factor inputs to ensure budget neutrality, cash suppression policies increase welfare in our baseline representative agent model. In a model with individual heterogeneity in cash use, suppression increases welfare for all, but by less for cash-intensive users.
Monogenic Cyclotomic compositions

Let $m$ and $n$ be positive integers, and let $p$ be a prime. Let $T(x)=\Phi_{p^m}(\Phi_{2^n}(x))$, where $\Phi_k(x)$ is the cyclotomic polynomial of index $k$. In this article, we prove that $T(x)$ is irreducible over $\mathbf Q$ and that $\left\{1,\theta,\theta^2,\ldots,\theta^{2^{n-1}p^{m-1}(p-1)-1}\right\}$ is a basis for the ring of integers of $\mathbf Q (\theta)$, where $T(\theta)= 0$.
The rôle of Coulomb branches in 2D gauge theory

I give a simple construction of certain Coulomb branches $C_{3,4}(G;E)$ of gauge theory in 3 and 4 dimensions defined by Nakajima et al. for a compact Lie group $G$ and a polarisable quaternionic representation $E$. The manifolds $C(G; 0)$ are abelian group schemes (over the bases of regular adjoint $G_c$-orbits, respectively conjugacy classes), and $C(G;E)$ is glued together from two copies of $C(G;0)$ shifted by a rational Lagrangian section $\varepsilon_V$, the Euler class of the index bundle of a polarisation $V$ of $E$. Extending the interpretation of $C_3(G;0)$ as "classifying space" for topological 2D gauge theories, I characterise functions on $C_3(G;E)$ as operators on the equivariant quantum cohomologies of $M\times V$, for all compact symplectic $G$-manifolds $M$. The non-commutative version has an analogous description in terms of the $\Gamma$-function of $V$, appearing to play the role of Fourier transformed J-function of the gauged linear Sigma-model $V/G$.
Learning to Price Against a Moving Target

In the Learning to Price setting, a seller posts prices over time with the goal of maximizing revenue while learning the buyer's valuation. This problem is very well understood when values are stationary (fixed or iid). Here we study the problem where the buyer's value is a moving target, i.e., they change over time either by a stochastic process or adversarially with bounded variation. In either case, we provide matching upper and lower bounds on the optimal revenue loss. Since the target is moving, any information learned soon becomes out-dated, which forces the algorithms to keep switching between exploring and exploiting phases.
Computing $L$-polynomials of Picard curves from Cartier-Manin matrices

We study the sequence of zeta functions $Z(C_p,T)$ of a generic Picard curve $C:y^3=f(x)$ defined over $\mathbb{Q}$ at primes $p$ of good reduction for $C$. We define a degree 9 polynomial $\psi_f\in \mathbb{Q}[x]$ such that the splitting field of $\psi_f(x^3/2)$ is the $2$-torsion field of the Jacobian of $C$. We prove that, for all but a density zero subset of primes, the zeta function $Z(C_p,T)$ is uniquely determined by the Cartier-Manin matrix $A_p$ of $C$ modulo $p$ and the splitting behavior modulo $p$ of $f$ and $\psi_f$; we also show that for primes $\equiv 1 \pmod{3}$ the matrix $A_p$ suffices and that for primes $\equiv 2 \pmod{3}$ the genericity assumption on $C$ is unnecessary. An element of the proof, which may be of independent interest, is the determination of the density of the set of primes of ordinary reduction for a generic Picard curve. By combining this with recent work of Sutherland, we obtain a practical deterministic algorithm that computes $Z(C_p,T)$ for almost all primes $p \le N$ using $N\log(N)^{3+o(1)}$ bit operations. This is the first practical result of this type for curves of genus greater than 2.
A Low-Loss 1-4 GHz Optically-Controlled Silicon Plasma Switch

This paper presents a low-loss optically-controlled inline RF switch suitable for L- and S-band applications. Under 1.5 W laser power, the switch exhibits a measured ON-state insertion loss of less than 0.33 dB and return loss better than 20 dB across the band. The measured OFF-state isolation ranges from 27 dB at 1 GHz to 17 dB at 4 GHz. The switch comprises a single silicon chiplet excited by a 915-nm laser fiber which creates electron-hole pairs, thereby exciting the ON-state silicon plasma. An optical fiber is guided through the bottom of the RF substrate to illuminate the chiplet, which bridges a 1.075-mm microstrip line gap. To the best of our knowledge, this is the lowest-loss silicon plasma switch demonstrated today.
Incentivized Bandit Learning with Self-Reinforcing User Preferences

In this paper, we investigate a new multi-armed bandit (MAB) online learning model that considers real-world phenomena in many recommender systems: (i) the learning agent cannot pull the arms by itself and thus has to offer rewards to users to incentivize arm-pulling indirectly; and (ii) if users with specific arm preferences are well rewarded, they induce a "self-reinforcing" effect in the sense that they will attract more users of similar arm preferences. Besides addressing the tradeoff of exploration and exploitation, another key feature of this new MAB model is to balance reward and incentivizing payment. The goal of the agent is to maximize the total reward over a fixed time horizon $T$ with a low total payment. Our contributions in this paper are two-fold: (i) We propose a new MAB model with random arm selection that considers the relationship of users' self-reinforcing preferences and incentives; and (ii) We leverage the properties of a multi-color Polya urn with nonlinear feedback model to propose two MAB policies termed "At-Least-$n$ Explore-Then-Commit" and "UCB-List". We prove that both policies achieve $O(log T)$ expected regret with $O(log T)$ expected payment over a time horizon $T$. We conduct numerical simulations to demonstrate and verify the performances of these two policies and study their robustness under various settings.
Learning Interaction Kernels for Agent Systems on Riemannian Manifolds

Interacting agent and particle systems are extensively used to model complex phenomena in science and engineering. We consider the problem of learning interaction kernels in these dynamical systems constrained to evolve on Riemannian manifolds from given trajectory data. The models we consider are based on interaction kernels depending on pairwise Riemannian distances between agents, with agents interacting locally along the direction of the shortest geodesic connecting them. We show that our estimators converge at a rate that is independent of the dimension of the state space, and derive bounds on the trajectory estimation error, on the manifold, between the observed and estimated dynamics. We demonstrate the performance of our estimator on two classical first order interacting systems: Opinion Dynamics and a Predator-Swarm system, with each system constrained on two prototypical manifolds, the $2$-dimensional sphere and the Poincare disk model of hyperbolic space.
Writing by Memorizing: Hierarchical Retrieval-based Medical Report Generation

Medical report generation is one of the most challenging tasks in medical image analysis. Although existing approaches have achieved promising results, they either require a predefined template database in order to retrieve sentences or ignore the hierarchical nature of medical report generation. To address these issues, we propose MedWriter that incorporates a novel hierarchical retrieval mechanism to automatically extract both report and sentence-level templates for clinically accurate report generation. MedWriter first employs the Visual-Language Retrieval (VLR) module to retrieve the most relevant reports for the given images. To guarantee the logical coherence between generated sentences, the Language-Language Retrieval (LLR) module is introduced to retrieve relevant sentences based on the previous generated description. At last, a language decoder fuses image features and features from retrieved reports and sentences to generate meaningful medical reports. We verified the effectiveness of our model by automatic evaluation and human evaluation on two datasets, i.e., Open-I and MIMIC-CXR.
Serological Response in Lung Transplant Recipients after Two Doses of SARS-CoV-2 mRNA Vaccines.

Background: Lung-transplant (LT) recipients are at high risk for COVID-19 due to immunosuppression and respiratory tropism of SARS-CoV-2. The information on the effect of COVID-19 mRNA vaccines to elicit immunogenic responses after a two-dose (2D) regimen in LT recipients is sparse. Thus, we assessed the effect of Pfizer-BioNTech and Moderna mRNA vaccines’ 2D regimen on anti-spike responses in immunocompromised LT recipients. Methods: We utilized serum samples from LT recipients vaccinated for SARS-CoV-2 with 2D of either the Pfizer-BioNTech or Moderna vaccines and 2D-vaccinated naive (non-transplanted and non-exposed to COVID-19) group. Antibody responses were assessed using the FDA-approved SARS-CoV-2 anti-nucleocapsid protein IgG assay (IgGNC), the SARS-CoV-2 anti-spike protein IgM assay (IgMSP), and the SARS-CoV-2 anti-spike protein IgG II assay (IgGSP). CD4+ T-cell activity was assessed as a marker of immune competence using the ImmuKnow® assay. Results: About 25% (18/73) of SARS-CoV-2 uninfected-LT patients generated a positive spike-IgG response following 2D of vaccines, with 36% (9/25) in the Moderna cohort and only 19% (9/48) in the Pfizer cohort. 2D in LT patients elicited a significantly lesser median IgGSP response (1.7 AU/mL, 95% CI: 0.6–7.5 AU/mL) compared to non-transplanted, uninfected naive subjects (14,209 AU/mL, 95% CI: 11,261–18,836 AU/mL; p < 0.0001). In LT patients, the Moderna-evoked seropositivity trend was higher than Pfizer. Conclusion: 2D COVID-19 vaccination elicits a dampened serological response in LT patients. Whether assessing other arms of host immunity combined with a higher vaccine dose can better capture and elicit improved immunogenicity in this immunocompromised population warrants investigation.
Transfer Learning for Efficient Iterative Safety Validation.

Safety validation is important during the development of safety-critical autonomous systems but can require significant computational effort. Existing algorithms often start from scratch each time the system under test changes. We apply transfer learning to improve the efficiency of reinforcement learning based safety validation algorithms when applied to related systems. Knowledge from previous safety validation tasks is encoded through the action value function and transferred to future tasks with a learned set of attention weights. Including a learned state and action value transformation for each source task can improve performance even when systems have substantially different failure modes. We conduct experiments on safety validation tasks in gridworld and autonomous driving scenarios. We show that transfer learning can improve the initial and final performance of validation algorithms and reduce the number of training steps.
Global constraints within the developmental program of the Drosophila wing

Organismal development is a complex process, involving a vast number of molecular constituents interacting on multiple spatio-temporal scales in the formation of intricate body structures. Despite this complexity, development is remarkably reproducible and displays tolerance to both genetic and environmental perturbations. This robustness implies the existence of hidden simplicities in developmental programs. Here, using the Drosophila wing as a model system, we develop a new quantitative strategy that enables a robust description of biologically salient phenotypic variation. Analyzing natural phenotypic variation across a highly outbred population, and variation generated by weak perturbations in genetic and environmental conditions, we observe a highly constrained set of wing phenotypes. Remarkably, the phenotypic variants can be described by a single integrated mode that corresponds to a non-intuitive combination of structural variations across the wing. This work demonstrates the presence of constraints that funnel environmental inputs and genetic variation into phenotypes stretched along a single axis in morphological space. Our results provide quantitative insights into the nature of robustness in complex forms while yet accommodating the potential for evolutionary variations. Methodologically, we introduce a general strategy for finding such invariances in other developmental contexts.
Bandit Linear Optimization for Sequential Decision Making and Extensive-Form Games

Tree-form sequential decision making (TFSDM) extends classical one-shot decision making by modeling tree-form interactions between an agent and a potentially adversarial environment. It captures the online decision-making problems that each player faces in an extensive-form game, as well as Markov decision processes and partially-observable Markov decision processes where the agent conditions on observed history. Over the past decade, there has been considerable effort into designing online optimization methods for TFSDM. Virtually all of that work has been in the full-feedback setting, where the agent has access to counterfactuals, that is, information on what would have happened had the agent chosen a different action at any decision node. Little is known about the bandit setting, where that assumption is reversed (no counterfactual information is available), despite this latter setting being well understood for almost 20 years in one-shot decision making. In this paper, we give the first algorithm for the bandit linear optimization problem for TFSDM that offers both (i) linear-time iterations (in the size of the decision tree) and (ii) O(sqrt(T)) cumulative regret in expectation compared to any fixed strategy, at all times T. This is made possible by new results that we derive, which may have independent uses as well: 1) geometry of the dilated entropy regularizer, 2) autocorrelation matrix of the natural sampling scheme for sequence-form strategies, 3) construction of an unbiased estimator for linear losses for sequence-form strategies, and 4) a refined regret analysis for mirror descent when using the dilated entropy regularizer.
Performance of the Rotor Systems Research Aircraft calibrated rotor loads measurement system

The compound Rotor Systems Research Aircraft (RSRA) is designed to make high accuracy, simultaneous measurements of all rotor forces and moments in flight. Physical calibration of the rotor force- and moment-measurement system when installed in the aircraft is required to account for any errors and to ensure that measurement system accuracy is traceable to the National Bureau of Standards. The first static calibration and associated analysis has been completed with good results. Hysteresis was a potential cause of static calibration errors, but was found to be negligible in flight, and analytical methods have been devised to eliminate its effects on calibration data. Flight tests confirmed that the calibrated rotor loads measurement system performs as expected in flight, and that it can dependably make direct measurements of fuselage vertical drag in hover.
Properties and unbiased estimation of F- and D-statistics in samples containing related and inbred individuals.

The Patterson F- and D-statistics are commonly-used measures for quantifying population relationships and for testing hypotheses about demographic history. These statistics make use of allele frequency information across populations to infer different aspects of population history, such as population structure and introgression events. Inclusion of related or inbred individuals can bias such statistics, which may often lead to the filtering of such individuals. Here we derive statistical properties of the F- and D-statistics, including their biases due to the inclusion of related or inbred individuals, their variances, and their corresponding mean squared errors. Moreover, for those statistics that are biased, we develop unbiased estimators and evaluate the variances of these new quantities. Comparisons of the new unbiased statistics to the originals demonstrates that our newly-derived statistics often have lower error across a wide population parameter space. Furthermore, we apply these unbiased estimators using several global human populations with the inclusion of related individuals to highlight their application on an empirical dataset. Finally, we implement these unbiased estimators in open-source software package funbiased for easy application by the scientific community. .
Inverse Design of Plasma Metamaterial Devices for Optical Computing

We apply inverse-design methods to produce two-dimensional plasma metamaterial (PMM) devices. Backpropagated finite-difference frequency-domain (FDFD) simulations are used to design waveguides and demultiplexers operating under both TE and TM modes. Demultiplexing and waveguiding are demonstrated for devices composed of plasma elements with reasonable plasma densities of approximately $7$ GHz, allowing for future in situ training and experimental realization of these designs. We also explore the possible applicability of PMMs to nonlinear boolean operations for use in optical computing. Functionally complete logical connectives (or and and) are achieved in the TM mode.