cssi/SciSciGPT-SciSciNet huggingface dataset:
Total Synthesis of (+)-Discodermolide

The total synthesis of (+)-discodermolide is described. The approach involves assemblage of three key stereotriad subunits through addition of nonracemic allenyltin, -indium, and -zinc reagents to (S)-3-silyloxy-2-methylpropanal derivatives, followed by reduction of the resulting anti,syn- or syn,syn-homopropargylic alcohol adducts to the (E)-homoallylic alcohols and subsequent Sharpless epoxidation. Addition of methyl cuprate reagents or Red-Al to the resultant epoxy alcohols yielded the key precursors, alkyne 4, aldehyde 9, and alcohol 24. Addition of alkyne 4 (as the lithio species 10) to aldehyde 9 afforded the propargylic alcohol 11 as the major stereoisomer. Lindlar hydrogenation and installation of appropriate protecting groups led to aldehyde 17. This was converted to the (Z)-vinylic iodide 18 upon treatment with α-iodoethylidene triphenylphosphorane. Suzuki coupling of this vinylic iodide with a boranate derived from iodide 25 led to the coupled product 27 with the complete carbon backbone of (+)...
RG-inspired machine learning for lattice field theory

Machine learning has been a fast growing field of research in several areas dealing with large datasets. We report recent attempts to use renormalization group (RG) ideas in the context of machine learning. We examine coarse graining procedures for perceptron models designed to identify the digits of the MNIST data. We discuss the correspondence between principal components analysis (PCA) and RG flows across the transition for worm configurations of the 2D Ising model. Preliminary results regarding the logarithmic divergence of the leading PCA eigenvalue were presented at the conference. More generally, we discuss the relationship between PCA and observables in Monte Carlo simulations and the possibility of reducing the number of learning parameters in supervised learning based on RG inspired hierarchical ansatzes.
Representation of k-mer Sets Using Spectrum-Preserving String Sets.

Given the popularity and elegance of \(k\)-mer based tools, finding a space-efficient way to represent a set of \(k\)-mers is important for improving the scalability of bioinformatics analyses. One popular approach is to convert the set of \(k\)-mers into the more compact set of unitigs. We generalize this approach and formulate it as the problem of finding a smallest spectrum-preserving string set (SPSS) representation. We show that this problem is equivalent to finding a smallest path cover in a compacted de Bruijn graph. Using this reduction, we prove a lower bound on the size of the optimal SPSS and propose a greedy method called UST that results in a smaller representation than unitigs and is nearly optimal with respect to our lower bound. We demonstrate the usefulness of the SPSS formulation with two applications of UST. The first one is a compression algorithm, UST-Compress, which we show can store a set of \(k\)-mers using an order-of-magnitude less disk space than other lossless compression tools. The second one is an exact static \(k\)-mer membership index, UST-FM, which we show improves index size by 10–44% compared to other state-of-the-art low memory indices. Our tool is publicly available at: https://github.com/medvedevgroup/UST/.
Refractory Pain, Existential Suffering, and Palliative Care: Releasing an Unbearable Lightness of Being

Since the beginning of the hospice movement in 1967, "total pain management" has been the declared goal of hospice care. Palliating the whole person's physical, psychosocial, and spiritual states or conditions is central to managing the pain that induces suffering. At the end-stage of life, an inextricable component of the ethics of adjusted care requires recognition of a fundamental right to avoid cruel and unusual suffering from terminal illness. This Article urges wider consideration and use of terminal sedation, or sedation until death, as an efficacious palliative treatment and as a reasonable medical procedure in order to safeguard the "right" to a dignified death. Once the state establishes a human right to avoid refractory pain of whatever nature in end-stage illness, a coordinate responsibility must be assumed by health care providers to make medical judgments consistent with preserving the best interests of a patient's quality of life by alleviating suffering. The principle of medical futility is the preferred construct for implementing this professional responsibility. Rather than continue to be mired in the vexatious quagmire of the doctrine of double effect--all in an effort to "test" whether end-stage decisions by health care providers are licit or illicit--a relatively simple test of proportionality, or cost-benefit analysis, is proffered. Imbedded, necessarily, in this equation is the humane virtue of compassion, charity, mercy or agape. Assertions of state interest in safeguarding public morality by restricting intimate associational freedoms to accelerate death in a terminal illness are suspicious, if, indeed, not invalid. No terminally ill individual suffering from either intractable somatic or non-somatic pain, or both, should be forced to continue living.
Zero-cycles on a product of elliptic curves over a $p$-adic field

We consider a product $X=E_1\times\cdots\times E_d$ of elliptic curves over a finite extension $K$ of $\mathbb{Q}_p$ with a combination of good or split multiplicative reduction. We assume that at most one of the elliptic curves has supersingular reduction. Under these assumptions, we prove that the Albanese kernel of $X$ is the direct sum of a finite group and a divisible group, extending work of Raskind and Spiess to cases that include supersingular phenomena. Our method involves studying the kernel of the cycle map $CH_0(X)/p^n\rightarrow H^{2d}_{\text{et}}(X, \mu_{p^n}^{\otimes d})$. We give specific criteria that guarantee this map is injective for every $n\geq 1$. When all curves have good ordinary reduction, we show that it suffices to extend to a specific finite extension $L$ of $K$ for these criteria to be satisfied. This extends previous work of Yamazaki and Hiranouchi.
Network-based Virus-Host Interaction Prediction with Application to SARS-CoV-2.

COVID-19, caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), has quickly become a global health crisis since the first report of infection in December of 2019. However, the infection spectrum of SARS-CoV-2 and its comprehensive protein-level interactions with hosts remain unclear. There is a massive amount of under-utilized data and knowledge about RNA viruses highly relevant to SARS-CoV-2 and proteins of their hosts. More in-depth and more comprehensive analyses of that knowledge and data can shed new insight into the molecular mechanisms underlying the COVID-19 pandemic and reveal potential risks. In this work, we constructed a multi-layer virus-host interaction network to incorporate these data and knowledge. We developed a machine learning-based method to predict virus-host interactions at both protein and organism levels. Our approach revealed five potential infection targets of SARS-CoV-2 and 19 highly possible interactions between SARS-CoV-2 proteins and human proteins in the innate immune pathway.
Wasserstein autoregressive models for density time series

Data consisting of time-indexed distributions of cross-sectional or intraday returns have been extensively studied in finance, and provide one example in which the data atoms consist of serially dependent probability distributions. Motivated by such data, we propose an autoregressive model for density time series by exploiting the tangent space structure on the space of distributions that is induced by the Wasserstein metric. The densities themselves are not assumed to have any specific parametric form, leading to flexible forecasting of future unobserved densities. The main estimation targets in the order-$p$ Wasserstein autoregressive model are Wasserstein autocorrelations and the vector-valued autoregressive parameter. We propose suitable estimators and establish their asymptotic normality, which is verified in a simulation study. The new order-$p$ Wasserstein autoregressive model leads to a prediction algorithm, which includes a data driven order selection procedure. Its performance is compared to existing prediction procedures via application to four financial return data sets, where a variety of metrics are used to quantify forecasting accuracy. For most metrics, the proposed model outperforms existing methods in two of the data sets, while the best empirical performance in the other two data sets is attained by existing methods based on functional transformations of the densities.
Examining the empirical redundancy of organizational justice constructs

Abstract Justice scholars have raised concerns about the potential redundancy of organizational justice constructs as their measures were often found to be highly correlated with one another. However, investigation into the problem has been difficult because measurement artifacts attenuated correlations between measures, masking the true extent of construct overlaps. Applying a recent methodological advance that allows correcting for the biasing effect of measurement artifacts, we conducted two studies to examine the question of empirical redundancy of major justice constructs: distributive justice, procedural justice, interactional justice, and overall justice. The studies yielded very similar results: the constructs underlying typical measures of organizational justice were highly correlated, with construct-level correlations ranging from 0.83 to 0.94. They also had the same pattern of relationships with other constructs in their nomological networks. Using Fairness Heuristic Theory (Lind, 2001; Van den Bos, 2001), we explained how the current results can be reconciled with past research regarding the dimensionality of organizational justice. We further discussed how the results can bring in new perspective to interpret past findings in organizational justice research and offer practical implications to organizations.
More style, less work: card-style data decrease risk-limiting audit sample sizes

U.S. elections rely heavily on computers which introduce digital threats to election outcomes. Risk-limiting audits (RLAs) mitigate threats to some of these systems by manually inspecting random samples of ballot cards. RLAs have a large chance of correcting wrong outcomes (by conducting a full manual tabulation of a trustworthy record of the votes), but can save labor when reported outcomes are correct. This efficiency is eroded when sampling cannot be targeted to ballot cards that contain the contest(s) under audit. States that conduct RLAs of contests on multi-card ballots or of small contests can dramatically reduce sample sizes by using information about which ballot cards contain which contests---by keeping track of card-style data (CSD). For instance, CSD reduces the expected number of draws needed to audit a single countywide contest on a 4-card ballot by 75%. Similarly, CSD reduces the expected number of draws by 95% or more for an audit of two contests with the same margin on a 4-card ballot if one contest is on every ballot and the other is on 10% of ballots. In realistic examples, the savings can be several orders of magnitude.
Controlling the Risk of Conversational Search via Reinforcement Learning

Users often formulate their search queries and questions with immature language without well-developed keywords and complete structures. Such queries are likely to fail to express their true information needs and raise ambiguity as fragmental language often yield various interpretations and aspects. This gives search engines a hard time processing and understanding the query, and eventually leads to unsatisfactory retrieval results. An alternative approach to direct answer while facing an ambiguous query is to proactively ask clarifying questions to the user. Recent years have seen many works and shared tasks from both NLP and IR community about identifying the need for asking clarifying question and methodology to generate them. An often neglected fact by these works is that although sometimes the need for clarifying questions is correctly recognized, the clarifying questions these system generate are still off-topic and dissatisfaction provoking to users and may just cause users to leave the conversation. In this work, we propose a risk-aware conversational search agent model to balance the risk of answering user’s query and asking clarifying questions. The agent is fully aware that asking clarifying questions can potentially collect more information from user, but it will compare all the choices it has and evaluate the risks. Only after then, it will make decision between answering or asking. To demonstrate that our system is able to retrieve better answers, we conduct experiments on the MSDialog dataset which contains real-world customer service conversations from Microsoft products community. We also purpose a reinforcement learning strategy which allows us to train our model on the original dataset directly and saves us from any further data annotation efforts. Our experiment results show that our risk-aware conversational search agent is able to significantly outperform strong non-risk-aware baselines.
Inhibition of Candida albicans and Mixed Salivary Bacterial Biofilms on Antimicrobial Loaded Phosphated Poly(methyl methacrylate)

Biofilms play a crucial role in the development of Candida-associated denture stomatitis. Inhibition of microbial adhesion to poly(methyl methacrylate) (PMMA) and phosphate containing PMMA has been examined in this work. C. albicans and mixed salivary microbial biofilms were compared on naked and salivary pre-conditioned PMMA surfaces in the presence or absence of antimicrobials (Cetylpyridinium chloride [CPC], KSL-W, Histatin 5 [His 5]). Polymers with varying amounts of phosphate (0–25%) were tested using four C. albicans oral isolates as well as mixed salivary bacteria and 24 h biofilms were assessed for metabolic activity and confirmed using Live/Dead staining and confocal microscopy. Biofilm metabolism was reduced as phosphate density increased (15%: p = 0.004; 25%: p = 0.001). Loading of CPC on 15% phosphated disks showed a substantial decrease (p = 0.001) in biofilm metabolism in the presence or absence of a salivary pellicle. Salivary pellicle on uncharged PMMA enhanced the antimicrobial activity of CPC only. CPC also demonstrated remarkable antimicrobial activity on mixed salivary bacterial biofilms under different conditions displaying the potent efficacy of CPC (350 µg/mL) when combined with an artificial protein pellicle (Biotene half strength).
Structural Refinement of the Auditory Brainstem Neurons in Baboons During Perinatal Development

Children born prematurely suffer from learning disabilities and exhibit reading, speech, and cognitive difficulties, which are associated with an auditory processing disorder. However, it is unknown whether gestational age at delivery and the unnatural auditory environment in neonatal intensive care units (NICU) collectively affect proper auditory development and neuronal circuitry in premature newborns. We morphologically characterized fetal development of the medial superior olivary nucleus (MSO), an area important for binaural hearing and sound localization, in the auditory brainstem of baboon neonates at different gestational ages. Axonal and synaptic structures and the tonotopic differentiation of ion channels in the MSO underwent profound refinements after hearing onset in the uterus. These developmental refinements of the MSO were significantly altered in preterm baboon neonates in the NICU. Thus, the maternal environment in uterus is critical for auditory nervous system development during the last trimester of pregnancy and critically affects the anatomic and functional formation of synapses and neural circuitry in the preterm newborn brain.
Ambient lighting, use of outdoor spaces and perceptions of public safety: evidence from a survey experiment

Observational evidence suggests that better ambient lighting leads people to feel safer when spending time outdoors in their community. We subject this finding to greater scrutiny and elaborate on the extent to which improvements in street lighting affect routine activities during nighttime hours. We report evidence from a survey experiment that examines individuals’ perceptions of safety under two different intensities of nighttime ambient lighting. Brighter street lighting leads individuals to feel safer and over half of survey respondents are willing to pay an additional $400 per year in taxes in order to finance a hypothetical program which would replace dim yellow street lights with brighter LED lights. However, poor lighting does not change people’s willingness to spend time outdoors or to engage in behaviors which mitigate risk. Results suggest that street lighting is a means through which policymakers can both control crime and improve community well-being.
Towards Si-based electrically injected group-IV lasers

Approaches towards Si-based lasers are discussed using lattice-matched group-IV heterostructures that can be deposited strain free on Si substrates with either Ge or GeSn buffer layers. Several strain-free designs are presented that include Ge/GeSiSn quantum cascade structure, GeSn/GeSiSn double heterostructure, and GeSn/GeSiSn multiple quantum wells.
Prophylactic laser versus continued surveillance after initial bevacizumab treatment for retinopathy of prematurity

Guidelines on managing infants after initial anti-vascular endothelial growth factor (VEGF) treatment (eg, bevacizumab) for retinopathy of prematurity (ROP) are limited. We found that following initial bevacizumab treatment for ROP, prophylactically lasering infant eyes before hospital discharge reduced the number and duration of outpatient ROP screening examinations compared with following infants, per current United States (US) ROP screening guidelines.
Real-Time Loss Minimizing Control of Induction Machines for Dynamic Load Profiles Under Deadbeat-Direct Torque and Flux Control

This article develops a real-time loss minimizing control technique on induction machines for dynamic load profiles. Steady-state loss minimization has been well addressed. However, when the steady-state optimal control is applied to a highly dynamic load profile, more losses are induced during transients due to the slow rotor flux dynamics. Several heuristic methods have been developed for real-time loss reduction, but they are not designed for minimum losses. Loss-minimizing flux trajectories have been generated by dynamic programming but require prior information of load profiles. In this article, a real-time loss minimizing control technique is developed for applications without prior information of load profiles. It resembles the loss-minimizing stator flux trajectory generated by a modified dynamic programming method, which generates the best practical solution for unknown load profiles. Both simulation and experimental results demonstrate the energy saving capability of the proposed method. Significant energy savings have been demonstrated on traction applications.
Exploring the Role of Common Model of Cognition in Designing Adaptive Coaching Interactions for Health Behavior Change

Our research aims to develop intelligent collaborative agents that are human-aware: They can model, learn, and reason about their human partner’s physiological, cognitive, and affective states. In this article, we study how adaptive coaching interactions can be designed to help people develop sustainable healthy behaviors. We leverage the common model of cognition (CMC) [31] as a framework for unifying several behavior change theories that are known to be useful in human–human coaching. We motivate a set of interactive system desiderata based on the CMC-based view of behavior change. Then, we propose PARCoach, an interactive system that addresses the desiderata. PARCoach helps a trainee pick a relevant health goal, set an implementation intention, and track their behavior. During this process, the trainee identifies a specific goal-directed behavior as well as the situational context in which they will perform it. PARCCoach uses this information to send notifications to the trainee, reminding them of their chosen behavior and the context. We report the results from a 4-week deployment with 60 participants. Our results support the CMC-based view of behavior change and demonstrate that the desiderata for proposed interactive system design is useful in producing behavior change.
Closed-loop Neurofeedback of Alpha Synchrony during Goal-directed Attention

Alpha oscillations in sensory cortex, under frontal control, desynchronize during attentive preparation. Here, in a selective attention study with simultaneous EEG in humans of either sex, we first demonstrate that diminished anticipatory alpha synchrony between the mid-frontal region of the dorsal attention network and ventral visual sensory cortex (frontal-sensory synchrony (FSS)) significantly correlates with greater task performance. Then, in a double-blind, randomized controlled study in healthy adults, we implement closed-loop neurofeedback of the anticipatory alpha FSS signal over ten days of training. We refer to this closed-loop experimental approach of rapid neurofeedback (NF) integrated within a cognitive task as cognitive neurofeedback (cNF). We show that cNF results in significant trial-by-trial modulation of the anticipatory alpha FSS measure during training, concomitant plasticity of stimulus-evoked alpha/theta responses, as well as transfer of benefits to response time improvements on a standard test of sustained attention. In a third study, we implement cNF training in children with ADHD, replicating trial-by-trial modulation of the anticipatory alpha FSS signal as well as significant improvement of sustained attention response times. These first findings demonstrate the basic mechanisms and translational utility of rapid cognitive-task-integrated neurofeedback. SIGNIFICANCE STATEMENT When humans prepare to attend to incoming sensory information, neural oscillations in the alpha band (8-14 Hz) undergo desynchronization under the control of prefrontal cortex. Here, in an attention study with electroencephalography, we first show that frontal-sensory synchrony (FSS) of alpha oscillations during attentive preparation significantly correlates with task performance. Then, in a randomized controlled study in healthy adults, we show that neurofeedback training of this alpha FSS signal within the attention task is feasible. We show that this rapid cognitive neurofeedback (cNF) approach engenders plasticity of stimulus-evoked neural responses, and improves performance on a standard test of sustained attention. In a final study, we implement cNF in children with ADHD, replicating the improvement of sustained attention found in adults.
Constraints and enablers for increasing carbon storage in the terrestrial biosphere

Harnessing nature-based climate solutions (NbCS) to help simultaneously achieve climate and conservation goals is an attractive win-win. The contribution of NbCS to climate action relies on both biogeochemical potential and the ability to overcome environmental, economic and governance constraints for implementation. As such, estimates of additional NbCS-related terrestrial biosphere storage potential range from less than 100 GtCO2 to more than 800 GtCO2. In this Review, we assess the negative emissions contributions of NbCS — including reforestation, improved forest management and soil carbon sequestration — alongside their environmental, social and governance constraints. Given near-term implementation challenges and long-term biogeochemical constraints, a reasonable value for the expected impact of NbCS is up to 100–200 GtCO2 in negative emissions for the remainder of the twenty-first century. To sustainably reach this level, focus should be on projects with clear co-benefits, and must not come at the expense of a reduction in emissions from deforestation and forest degradation, rapid decarbonization and innovation from alternative negative emissions technologies. Nature-based climate solutions can help meet climate mitigation goals, but estimates of their carbon storage potential vary. This Review discusses the constraints and potential contributions of increasing carbon storage in the terrestrial biosphere, suggesting a conservative estimate of 100–200 GtCO2 in negative emissions to 2100.
A deep learning-based framework for segmenting invisible clinical target volumes with estimated uncertainties for post-operative prostate cancer radiotherapy

In post-operative radiotherapy for prostate cancer, precisely contouring the clinical target volume (CTV) to be irradiated is challenging, because the cancerous prostate gland has been surgically removed, so the CTV encompasses the microscopic spread of tumor cells, which cannot be visualized in clinical images like computed tomography or magnetic resonance imaging. In current clinical practice, physicians' segment CTVs manually based on their relationship with nearby organs and other clinical information, but this allows large inter-physician variability. Automating post-operative prostate CTV segmentation with traditional image segmentation methods has yielded suboptimal results. We propose using deep learning to accurately segment post-operative prostate CTVs. The model proposed is trained using labels that were clinically approved and used for patient treatment. To segment the CTV, we segment nearby organs first, then use their relationship with the CTV to assist CTV segmentation. To ease the encoding of distance-based features, which are important for learning both the CTV contours' overlap with the surrounding OARs and the distance from their borders, we add distance prediction as an auxiliary task to the CTV network. To make the DL model practical for clinical use, we use Monte Carlo dropout (MCDO) to estimate model uncertainty. Using MCDO, we estimate and visualize the 95% upper and lower confidence bounds for each prediction which informs the physicians of areas that might require correction. The model proposed achieves an average Dice similarity coefficient (DSC) of 0.87 on a holdout test dataset, much better than established methods, such as atlas-based methods (DSC<0.7). The predicted contours agree with physician contours better than medical resident contours do. A reader study showed that the clinical acceptability of the automatically segmented CTV contours is equal to that of approved clinical contours manually drawn by physicians. Our deep learning model can accurately segment CTVs with the help of surrounding organ masks. Because the DL framework can outperform residents, it can be implemented practically in a clinical workflow to generate initial CTV contours or to guide residents in generating these contours for physicians to review and revise. Providing physicians with the 95% confidence bounds could streamline the review process for an efficient clinical workflow as this would enable physicians to concentrate their inspecting and editing efforts on the large uncertain areas.
Elastodynamic transformation cloaking for non-centrosymmetric gradient solids

In this paper, we investigate the possibility of elastodynamic transformation cloaking in bodies made of non-centrosymmetric gradient solids. The goal of transformation cloaking is to hide a hole from elastic disturbances in the sense that the mechanical response of a homogeneous and isotropic body with a hole covered by a cloak would be identical to that of the corresponding homogeneous and isotropic body outside the cloak. It is known that in the case of centrosymmetric gradient solids exact transformation cloaking is not possible; the balance of angular momentum is the obstruction to transformation cloaking. We will show that this no-go theorem holds for non-centrosymmetric gradient solids as well.
A GPU-Accelerated Mixed-Precision WENO Method for Extremal Black Hole and Gravitational Wave Physics Computations

We develop and use a novel mixed-precision weighted essentially non-oscillatory (WENO) method for solving the Teukolsky equation, which arises when modeling perturbations of Kerr black holes. We show that WENO methods outperform higher-order finite-difference methods, standard in the discretization of the Teukolsky equation, due to the need to add dissipation for stability purposes in the latter. In particular, as the WENO scheme uses no additional dissipation, it is well suited for scenarios requiring long-time evolution such as the study of price tails and gravitational wave emission from extreme mass ratio binaries. In the mixed-precision approach, the expensive computation of the WENO weights is performed in reduced floating-point precision that results in a significant speedup factor of $$\approx 3.3$$ . In addition, we use state-of-the-art Nvidia general-purpose graphics processing units and cluster parallelism to further accelerate the WENO computations. Our optimized WENO solver can be used to quickly generate accurate results of significance in the field of black hole and gravitational wave physics. We apply our solver to study the behavior of the Aretakis charge—a conserved quantity, that if detected by a gravitational wave observatory like LIGO/Virgo would prove the existence of extremal black holes.
Direct Imaging of Exoplanets beyond the Radial Velocity Limit: Application to the HD 134987 System

Future direct imaging missions will primarily observe planets that have been previously detected, mostly via the radial velocity (RV) technique, to characterize planetary atmospheres. In the meantime, direct imaging may discover new planets within existing planetary systems that have bright enough reflected flux, yet with insufficient signals for other methods to detect. Here, we investigate the parameter space within which planets are unlikely to be detected by RV in the near future due to precision limitations, but could be discovered through reflected light with future direct imaging missions. We use the HD 134987 system as a working example, combine RV and direct imaging detection limit curves in the same parameter space through various assumptions, and insert a fictitious planet into the system while ensuring it lies between the RV and imaging detection limits. Planet validity tested through dynamical simulations and retrieval tests revealed that the planet could indeed be detected by imaging while remaining hidden from RV surveys. Direct imaging retrieval was carried out using starshade simulations for two mission concepts: the Starshade Rendezvous Probe that could be coupled with the Nancy Grace Roman Space Telescope, and the Habitable Exoplanet Observatory. This method is applicable to any other systems and high contrast direct imaging instruments, and could help inform future imaging observations and data analysis on the discovery of new exoplanets.
How to Learn when Data Reacts to Your Model: Performative Gradient Descent

Performative distribution shift captures the setting where the choice of which ML model is deployed changes the data distribution. For example, a bank which uses the number of open credit lines to determine a customer's risk of default on a loan may induce customers to open more credit lines in order to improve their chances of being approved. Because of the interactions between the model and data distribution, finding the optimal model parameters is challenging. Works in this area have focused on finding stable points, which can be far from optimal. Here we introduce performative gradient descent (PerfGD), which is the first algorithm which provably converges to the performatively optimal point. PerfGD explicitly captures how changes in the model affects the data distribution and is simple to use. We support our findings with theory and experiments.
The $P=W$ identity for cluster varieties

We find new examples of the $P=W$ identity of de Cataldo-Hausel-Migliorini by studying cluster varieties. We prove that the weight filtration of 2D cluster varieties correspond to the perverse filtration of elliptic fibrations which are deformation equivalent to the elliptic fibrations of types $I_b$ or $2I_b$. These examples do not arise from character varieties or moduli of Higgs bundles.
ENTRUST: Argument Reframing with Language Models and Entailment

Framing involves the positive or negative presentation of an argument or issue depending on the audience and goal of the speaker. Differences in lexical framing, the focus of our work, can have large effects on peoples’ opinions and beliefs. To make progress towards reframing arguments for positive effects, we create a dataset and method for this task. We use a lexical resource for “connotations” to create a parallel corpus and propose a method for argument reframing that combines controllable text generation (positive connotation) with a post-decoding entailment component (same denotation). Our results show that our method is effective compared to strong baselines along the dimensions of fluency, meaning, and trustworthiness/reduction of fear.
A Graph-Constrained Changepoint Learning Approach for Automatic QRS-Complex Detection

This study presents a new viewpoint on ECG signal analysis by applying a graph-based changepoint detection model to locate R-peak positions. This model is based on a new graph learning algorithm to learn the constraint graph given the labeled ECG data. The proposed learning algorithm starts with a simple initial graph and iteratively edits the graph so that the final graph has the maximum accuracy in R-peak detection. We evaluate the performance of the algorithm on the MIT-BIH Arrhythmia Database. The evaluation results demonstrate that the proposed method can obtain comparable results to other state-of-the-art approaches. The proposed method achieves the overall sensitivity of Sen = 99.64%, positive predictivity of PPR = 99.71%, and detection error rate of DER = 0.19.
Reconstruction of functions from prescribed proximal points

Abstract Under investigation is the problem of finding the best approximation of a function in a Hilbert space subject to convex constraints and prescribed nonlinear transformations. We show that in many instances these prescriptions can be represented using firmly nonexpansive operators, even when the original observation process is discontinuous. The proposed framework thus captures a large body of classical and contemporary best approximation problems arising in areas such as harmonic analysis, statistics, interpolation theory, and signal processing. The resulting problem is recast in terms of a common fixed point problem and solved with a new block-iterative algorithm that features approximate projections onto the individual sets as well as an extrapolated relaxation scheme that exploits the possible presence of affine constraints. A numerical application to signal recovery is demonstrated.
Multiphase AGN Winds from X-Ray-irradiated Disk Atmospheres

The mechanism of thermal driving for launching mass outflows is interconnected with classical thermal instability (TI). In a recent paper, we demonstrated that as a result of this interconnectedness, radial wind solutions of X-ray heated flows are prone to becoming clumpy. In this paper, we first show that the Bernoulli function determines whether or not the entropy mode can grow due to TI in dynamical flows. Based on this finding, we identify a critical `unbound' radius beyond which TI should accompany thermal driving. Our numerical disk wind simulations support this result and reveal that clumpiness is a consequence of buoyancy disrupting the stratified structure of steady state solutions. Namely, instead of a smooth transition layer separating the highly ionized disk wind from the cold phase atmosphere below, hot bubbles formed from TI rise up and fragment the atmosphere. These bubbles first appear within large scale vortices that form below the transition layer, and they result in the episodic production of distinctive cold phase structures referred to as irradiated atmospheric fragments (IAFs). Upon interacting with the wind, IAFs advect outward and develop extended crests. The subsequent disintegration of the IAFs takes place within a turbulent wake that reaches high elevations above the disk. We show that this dynamics has the following observational implications: dips in the absorption measure distribution are no longer expected within TI zones and there can be a less sudden desaturation of X-ray absorption lines such as \OVIII as well as multiple absorption troughs in \FeXXVK.
Implicit Bias of Linear RNNs

Contemporary wisdom based on empirical studies suggests that standard recurrent neural networks (RNNs) do not perform well on tasks requiring long-term memory. However, precise reasoning for this behavior is still unknown. This paper provides a rigorous explanation of this property in the special case of linear RNNs. Although this work is limited to linear RNNs, even these systems have traditionally been difficult to analyze due to their non-linear parameterization. Using recently-developed kernel regime analysis, our main result shows that linear RNNs learned from random initializations are functionally equivalent to a certain weighted 1D-convolutional network. Importantly, the weightings in the equivalent model cause an implicit bias to elements with smaller time lags in the convolution and hence, shorter memory. The degree of this bias depends on the variance of the transition kernel matrix at initialization and is related to the classic exploding and vanishing gradients problem. The theory is validated in both synthetic and real data experiments.
Optimal Consumption Under a Habit-Formation Constraint

We propose a new optimal consumption model in which the degree of addictiveness of habit formation is directly controlled through a constraint on admissible consumption. In particular, we assume that the individual is unwilling to consume at a rate below a certain proportion 0<α≤1 of her consumption habit, which is the exponentially-weighted average of past consumption rates. α=1 prohibits the habit process to decrease and corresponds to the completely addictive model. α=0 makes the habit-formation constraint moot and corresponds to the non-addictive model. 0<α<1 leads to partially addictive models, with the level of addictiveness increasing with α. In contrast to the existing habit-formation literature, our constraint cannot be incorporated in the objective function through infinite marginal utility, and alters the analysis of the corresponding control problem. Assuming that the individual invests in a risk-free market, we formulate and solve an infinite-horizon, deterministic control problem to maximize the discounted CRRA utility of the individual's consumption-to-habit process subject to the said habit-formation constraint. We derive the optimal consumption policies explicitly in terms of the solution of a nonlinear free-boundary problem, which we analyze in detail. Impatient individuals (or, equivalently, those with more addictive habits) always consume above the minimum rate; thus, they eventually attain the minimum wealth-to-habit ratio. Patient individuals (or, equivalently, those with less addictive habits) consume at the minimum rate if their wealth-to-habit ratio is below a threshold, and above it otherwise. By consuming patiently, these individuals maintain a wealth-to-habit ratio that is greater than the minimum acceptable level. Additionally, we prove that the optimal consumption path is hump-shaped if the initial wealth-to-habit ratio is either: (1) larger than a high threshold; or (2) below a low threshold and the agent is more risk seeking (that is, less risk averse). Thus, we provide a simple explanation for the consumption hump observed by various empirical studies.
The Scourge of Online Solutions and an Academic Hertzsprung–Russell Diagram

We report on preliminary results of a statistical study of student performance in more than a decade of calculus-based introductory physics courses. Treating average homework and test grades as pro...
Named Entity Recognition with Small Strongly Labeled and Large Weakly Labeled Data

Weak supervision has shown promising results in many natural language processing tasks, such as Named Entity Recognition (NER). Existing work mainly focuses on learning deep NER models only with weak supervision, i.e., without any human annotation, and shows that by merely using weakly labeled data, one can achieve good performance, though still underperforms fully supervised NER with manually/strongly labeled data. In this paper, we consider a more practical scenario, where we have both a small amount of strongly labeled data and a large amount of weakly labeled data. Unfortunately, we observe that weakly labeled data does not necessarily improve, or even deteriorate the model performance (due to the extensive noise in the weak labels) when we train deep NER models over a simple or weighted combination of the strongly labeled and weakly labeled data. To address this issue, we propose a new multi-stage computational framework – NEEDLE with three essential ingredients: (1) weak label completion, (2) noise-aware loss function, and (3) final fine-tuning over the strongly labeled data. Through experiments on E-commerce query NER and Biomedical NER, we demonstrate that NEEDLE can effectively suppress the noise of the weak labels and outperforms existing methods. In particular, we achieve new SOTA F1-scores on 3 Biomedical NER datasets: BC5CDR-chem 93.74, BC5CDR-disease 90.69, NCBI-disease 92.28.
MicroRNA‐135a Regulates NHE9 To Inhibit Proliferation and Migration of Glioblastoma Cells

Glioblastoma multiformae (GBM) is the most aggressive type of malignant brain tumor with complex molecular profile. Overexpression of Na+/H+ Exchanger isoform 9 (NHE9) promotes tumor progression and correlates positively with insensitivity to radiochemotherapy and poor prognosis. However, molecular mechanisms responsible for increase in NHE9 levels beyond a critical threshold have not been identified. Bioinformatics analysis, luciferase reporter assays, real-time PCR and western blotting were conducted to examine the expression profiles and identify microRNAs (miRNA) that target NHE9. Cell proliferation and migration assays were conducted in U87 glioblastoma cells to determine the consequence of miRNA mediated targeting of NHE9. Endosomal pH measurements, immunofluorescence microscopy and surface biotinylation experiments were conducted to characterize the mechanistic basis of regulation. We show that microRNA 135a (miR-135a) targets NHE9 to downregulate its expression in U87 cells. MiR-135a levels are significantly lower in glioblastoma cells compared to normal brain tissue. Downregulation of NHE9 expression by miR-135a affects proliferative and migratory capacity of U87 cells. Selectively increasing NHE9 expression in these cells restored their ability to proliferate and migrate. We demonstrate that miR-135a takes a two-pronged approach affecting epidermal growth factor receptors (EGFRs) to suppress tumor cell growth and migration. EGFR activity is a potent stimulator of oncogenic signaling. While miR-135a targets EGFR transcripts to decrease the total number of receptors made, by targeting NHE9 it routes the few EGFRs made away from the plasma membrane to dampen oncogenic signaling. NHE9 is localized to sorting endosomes in glioblastoma cells where it alkalinizes the endosome lumen by leaking protons. Downregulation of NHE9 expression by miR-135a acidifies sorting endosomes limiting EGFR trafficking to the glioblastoma cell membrane. We propose downregulation of miR-135a as a potential mechanism underlying the high NHE9 expression observed in subset of glioblastomas. Future studies should explore miR-135a as a potential therapeutic for glioblastomas with NHE9 overexpression.
Equivariant Spherical Deconvolution: Learning Sparse Orientation Distribution Functions from Spherical Data

We present a rotation-equivariant self-supervised learning framework for the sparse deconvolution of non-negative scalar fields on the unit sphere. Spherical signals with multiple peaks naturally arise in Diffusion MRI (dMRI), where each voxel consists of one or more signal sources corresponding to anisotropic tissue structure such as white matter. Due to spatial and spectral partial voluming, clinically-feasible dMRI struggles to resolve crossing-fiber white matter configurations, leading to extensive development in spherical deconvolution methodology to recover underlying fiber directions. However, these methods are typically linear and struggle with small crossing-angles and partial volume fraction estimation. In this work, we improve on current methodologies by nonlinearly estimating fiber structures via self-supervised spherical convolutional networks with guaranteed equivariance to spherical rotation. We perform validation via extensive single and multi-shell synthetic benchmarks demonstrating competitive performance against common baselines. We further show improved downstream performance on fiber tractography measures on the Tractometer benchmark dataset. Finally, we show downstream improvements in terms of tractography and partial volume estimation on a multi-shell dataset of human subjects.
Average-Case Rigidity Lower Bounds

It is shown that there exists \(f : \{0,1\}^{n/2} \times \{0,1\}^{n/2} \rightarrow \{0,1\}\) in E\(^\mathbf {NP}\) such that for every \(2^{n/2} \times 2^{n/2}\) matrix M of rank \(\le \rho \) we have \(\mathbb {P}_{x,y}[f(x,y)\ne M_{x,y}] \ge 1/2-2^{-\varOmega (k)}\), whenever \(\log \rho \le \delta n/k(\log n + k)\) for a sufficiently small \(\delta > 0\), and n is large enough. This generalizes recent results which bound below the probability by \(1/2-\varOmega (1)\) or apply to constant-depth circuits.
Galois criterion for torsion points of Drinfeld modules

In this paper, we formulate the Drinfeld module analogue of a question raised by Lang and studied by Katz on the existence of rational points on abelian varieties over number fields. Given a maxima...
Multi-resolution characterization of molecular taxonomies in bulk and single-cell transcriptomics data

As high-throughput genomics assays become more efficient and cost effective, their utilization has become standard in large-scale biomedical projects. These studies are often explorative, in that relationships between samples are not explicitly defined a priori, but rather emerge from data-driven discovery and annotation of molecular subtypes, thereby informing hypotheses and independent evaluation. Here, we present K2Taxonomer, a novel unsupervised recursive partitioning algorithm and associated R package that utilize ensemble learning to identify robust subgroups in a ‘taxonomy-like’ structure. K2Taxonomer was devised to accommodate different data paradigms, and is suitable for the analysis of both bulk and single-cell transcriptomics, and other ‘-omics’, data. For each of these data types, we demonstrate the power of K2Taxonomer to discover known relationships in both simulated and human tissue data. We conclude with a practical application on breast cancer tumor infiltrating lymphocyte (TIL) single-cell profiles, in which we identified co-expression of translational machinery genes as a dominant transcriptional program shared by T cells subtypes, associated with better prognosis in breast cancer tissue bulk expression data.
Coulomb scattering in the Born approximation and the use of generalized functions

We discuss three approaches that have been used in obtaining the Born approximation for Coulomb scattering: the standard approach, making use of a convergence factor (“screening”), Oppenheimer's approach using cylindrical (instead of spherical) coordinates, and finally the Landau and Lifshitz approach. We simplify and clarify Oppenheimer's calculation, which has been rarely used because of its complications, and make the Landau and Lifshitz method rigorous. Although the latter method does require some background from the theory of generalized functions, it is nevertheless a very instructive and important technique deserving more exposure to instructors and theoretical physicists in general.