cssi/SciSciGPT-SciSciNet huggingface dataset:
Mites alight! Sunflower crop area and pollen supplementation enhance honey bee resistance to Varroa destructor

ABSTRACT Anthropogenic landscape changes can affect parasite epidemiology in wild and agricultural animals. Honey bees are agricultural animals whose services are threatened by loss of floral resources and by parasites, most notably the invasive mite Varroa destructor. Existing mite control strategies rely heavily on chemical treatments that can adversely affect bees. Alternative, pesticide-free control methods are urgently needed to maintain effective pollination services. Many flowering plants provide nectar and pollen that enhance resistance to parasites in animals. Enrichment of landscapes with antiparasitic floral resources could therefore provide a sustainable means of parasite control in pollinators. Floral rewards of Asteraceae plants have been shown to reduce parasitic infection in diverse bee species, including honey and bumble bees. Here, we tested the effects of sunflower (Helianthus annuus) cropland and pollen supplementation on honey bee resistance to macro- and microparasites. Our results show that each doubling of sunflower crop area is associated with a 28% reduction in mite infestation intensity. Late-summer supplementation of colonies with sunflower pollen reduced mite infestation by 2.75-fold relative to an artificial pollen. Our findings suggest the potential for sunflower plantings or pollen supplements to counteract a main driver of honey bee losses worldwide.
Wear Mechanisms in Electron Sources for Ion Propulsion, 1: Neutralizer Hollow Cathode

Upon the completion of two long-duration life tests of a 30-cm ion engine, the orifice channel of the neutralizer hollow cathode was eroded away to as much as twice its original diameter. Whereas the neutralizer cathode orifice opened significantly, no noticeable erosion of the discharge cathode orifice was observed. Noquantitative explanation of these erosion trends has been established since the completion of the two life tests. A two-dimensional model of the partially ionized gas inside these devices has been developed and applied to the neutralizer hollow cathode. The numerical simulations show that the main mechanism responsible for the channel erosion is sputtering by Xe+. These ions are accelerated by the sheath along the channel and bombard the surface with kinetic energy/charge of about 17 V at the beginning of cathode life. The density of the ions inside the neutralizer orifice is computed to be as high as 2.1 x 10(sup 22) m(sup -3). Because of the 3.5-times larger diameter of the discharge cathode orifice, the ion density inside the orifice is more than 40 times lower and the sheath drop 7 V lower compared with the values in the neutralizer. At these conditions, Xe+ can cause no significant sputtering of the surface.
Ciliary and extraciliary Gpr161 pools repress hedgehog signaling in a tissue-specific manner

The role of compartmentalized signaling in primary cilia during tissue morphogenesis is not well understood. The cilia localized G protein-coupled receptor, Gpr161, represses hedgehog pathway via cAMP signaling. We engineered a knock-in at the Gpr161 locus in mice to generate a variant (Gpr161mut1), which was ciliary localization defective but cAMP signaling competent. Tissue phenotypes from hedgehog signaling depend on downstream bifunctional Gli transcriptional factors functioning as activators or repressors. Compared to knockout (ko), Gpr161mut1/ko had delayed embryonic lethality, moderately increased hedgehog targets, and partially down-regulated Gli3 repressor. Unlike ko, the Gpr161mut1/ko neural tube did not show Gli2 activator-dependent expansion of ventral-most progenitors. Instead, the intermediate neural tube showed progenitor expansion that depends on loss of Gli3 repressor. Increased extraciliary receptor levels in Gpr161mut1/mut1 prevented ventralization. Morphogenesis in limb buds and midface requires Gli repressor; these tissues in Gpr161mut1/mut1 manifested hedgehog hyperactivation phenotypes-polydactyly and midfacial widening. Thus, ciliary and extraciliary Gpr161 pools likely establish tissue-specific Gli repressor thresholds in determining morpho-phenotypic outcomes.
Relational Analysis of Sensor Attacks on Cyber-Physical Systems

Cyber-physical systems, such as self-driving cars or autonomous aircraft, must defend against attacks that target sensor hardware. Analyzing system design can help engineers understand how a compromised sensor could impact the system’s behavior; however, designing security analyses for cyber-physical systems is difficult due to their combination of discrete dynamics, continuous dynamics, and nondeterminism.This paper contributes a framework for modeling and analyzing sensor attacks on cyber-physical systems, using the formalism of hybrid programs. We formalize and analyze two relational properties of a system’s robustness. These relational properties respectively express (1) whether a system’s safety property can be influenced by sensor attacks, and (2) whether a system’s high-integrity state can be affected by sensor attacks. We characterize these relational properties by defining an equivalence relation between a system under attack and the original unattacked system. That is, the system satisfies the robustness properties if executions of the attacked system are appropriately related to executions of the unattacked system.We present two techniques for reasoning about the equivalence relation and thus proving the relational properties for a system. One proof technique decomposes large proof obligations to smaller proof obligations. The other proof technique adapts the self-composition technique from the literature on secure information-flow, allowing us to reduce reasoning about the equivalence of two systems to reasoning about properties of a single system. This technique allows us to reuse existing tools for reasoning about properties of hybrid programs, but is challenging due to the combination of discrete dynamics, continuous dynamics, and nondeterminism.To validate the usefulness of our relational properties and proof techniques, we present three case studies motivated by real design flaws in existing cyber-physical systems.
Is Ice Formation by Sea Spray Particles at Cirrus Temperatures Controlled by Crystalline Salts

The ice nucleating ability of sea spray aerosols (SSA) has been explored in recent years due to the abundance of SSA in the atmosphere. The role of SSA in ice nucleation extends to cirrus clouds, due to processes that loft SSA to the upper troposphere. This is of special relevance because of the frequent occurrence of cirrus in the atmosphere, their role in the Earth’s radiative balance, and uncertainties regarding how aerosols may affect their formation and evolution. In this study, a continuous flow diffusion chamber (CFDC) is used to investigate the ice nucleating ability of size-selected particle distributions of SSA and its primary constituent sodium chloride (NaCl) at temperatures < 235 K. Results show that above ~220 K, the majority of SSA and NaCl particles fully deliquesce and freeze via homogeneous nucleation at or below water relative humidities, RHw, of ~ 95%. However, below 220 K, the onset RHw of freezing for NaCl and SSA is much lower, at ~75%, where strong heterogeneous freezing of 10% of the aerosol population occurs. Similar heterogeneous freezing behavior for NaCl and SSA aerosols, occurring near their predicted deliquescence RHw, points towards SSA freezing at the lowest temperatures being controlled by the crystalline salts. Finally, calculations of ice nucleation active surface site densities show that particle size does not dictate the efficiency of freezing for NaCl and SSA. These results indicate SSA as a potentially significant source of ice nucleating particles at cirrus temperatures, with the ability to contribute to cirrus-mediated climate impacts if sea spray emission and transport scenarios change in the future.
Predicting the mechanical properties of biopolymer gels using neural networks trained on discrete fiber network data

Abstract Biopolymer gels, such as those made out of fibrin or collagen, are widely used in tissue engineering applications and biomedical research. Moreover, fibrin naturally assembles into gels in vivo during wound healing and thrombus formation . The macroscale properties of fibrin and other biopolymer gels are dictated by the response of a microscale fiber network. Hence, accurate description of biopolymer gels can be achieved using representative volume elements (RVE) that explicitly model the discrete fiber networks of the microscale. These RVE models, however, cannot be efficiently used to model the macroscale due to the challenges and computational demands of multiscale coupling. Here, we propose the use of an artificial, fully connected neural network (FCNN) to efficiently capture the behavior of the RVE models. The FCNN was trained on 1100 fiber networks subjected to 121 biaxial deformations. The stress data from the RVE, together with the total energy on the fibers and the condition of incompressibility of the surrounding matrix , were used to determine the derivatives of an unknown strain energy function with respect to the deformation invariants . During training, the loss function was modified to ensure convexity of the strain energy function and symmetry of its Hessian. A general FCNN model was coded into a user material subroutine (UMAT) in the software Abaqus . The UMAT implementation takes in the structure and parameters of an arbitrary FCNN as material parameters from the input file. The inputs to the FCNN include the first two isochoric invariants of the deformation. The FCNN outputs the derivatives of the strain energy with respect to the isochoric invariants. In this work, the FCNN trained on the discrete fiber network data was used in finite element simulations of biopolymer gels using our UMAT. We anticipate that this work will enable further integration of machine learning tools with computational mechanics. It will also improve computational modeling of biological materials characterized by a multiscale structure.
Multi-channel Opus compression for far-field automatic speech recognition with a fixed bitrate budget

Automatic speech recognition (ASR) in the cloud allows the use of larger models and more powerful multi-channel signal processing front-ends compared to on-device processing. However, it also adds an inherent latency due to the transmission of the audio signal, especially when transmitting multiple channels of a microphone array. One way to reduce the network bandwidth requirements is client-side compression with a lossy codec such as Opus. However, this compression can have a detrimental effect especially on multi-channel ASR front-ends, due to the distortion and loss of spatial information introduced by the codec. In this publication, we propose an improved approach for the compression of microphone array signals based on Opus, using a modified joint channel coding approach and additionally introducing a multi-channel spatial decorrelating transform to reduce redundancy in the transmission. We illustrate the effect of the proposed approach on the spatial information retained in multi-channel signals after compression, and evaluate the performance on far-field ASR with a multi-channel beamforming front-end. We demonstrate that our approach can lead to a 37.5 % bitrate reduction or a 5.1 % relative word error rate reduction for a fixed bitrate budget in a seven channel setup.
Scaling laws for acoustic models

There is a recent trend in machine learning to increase model quality by growing models to sizes previously thought to be unreasonable. Recent work has shown that autoregressive generative models with cross-entropy objective functions exhibit smooth power-law relationships, or scaling laws, that predict model quality from model size, training set size, and the available compute budget. These scaling laws allow one to choose nearly optimal hyper-parameters given constraints on available training data, model parameter count, or training computation budget. In this paper, we demonstrate that acoustic models trained with an auto-predictive coding loss behave as if they are subject to similar scaling laws. We extend previous work to jointly predict loss due to model size, to training set size, and to the inherent "irreducible loss" of the task. We find that the scaling laws accurately match model performance over two orders of magnitude in both model size and training set size, and make predictions about the limits of model performance.
Memory Specific to Temporal Features of Sound Is Formed by Cue-Selective Enhancements in Temporal Coding Enabled by Inhibition of an Epigenetic Regulator

Recent investigation of memory-related functions in the auditory system have capitalized on the use of memory-modulating molecules to probe the relationship between memory and substrates of memory in auditory system coding. For example, epigenetic mechanisms, which regulate gene expression necessary for memory consolidation, are powerful modulators of learning-induced neuroplasticity and long-term memory (LTM) formation. Inhibition of the epigenetic regulator histone deacetylase 3 (HDAC3) promotes LTM, which is highly specific for spectral features of sound. The present work demonstrates for the first time that HDAC3 inhibition also enables memory for temporal features of sound. Adult male rats trained in an amplitude modulation (AM) rate discrimination task and treated with a selective inhibitor of HDAC3 formed memory that was highly specific to the AM rate paired with reward. Sound-specific memory revealed behaviorally was associated with a signal-specific enhancement in temporal coding in the auditory system; stronger phase locking that was specific to the rewarded AM rate was revealed in both the surface-recorded frequency following response and auditory cortical multiunit activity in rats treated with the HDAC3 inhibitor. Furthermore, HDAC3 inhibition increased trial-to-trial cortical response consistency (relative to naive and trained vehicle-treated rats), which generalized across different AM rates. Stronger signal-specific phase locking correlated with individual behavioral differences in memory specificity for the AM signal. These findings support that epigenetic mechanisms regulate activity-dependent processes that enhance discriminability of sensory cues encoded into LTM in both spectral and temporal domains, which may be important for remembering spectrotemporal features of sounds, for example, as in human voices and speech. SIGNIFICANCE STATEMENT Epigenetic mechanisms have recently been implicated in memory and information processing. Here, we use a pharmacological inhibitor of HDAC3 in a sensory model of learning to reveal the ability of HDAC3 to enable precise memory for amplitude-modulated sound cues. In so doing, we uncover neural substrates for memory9s specificity for temporal sound cues. Memory specificity was supported by auditory cortical changes in temporal coding, including greater response consistency and stronger phase locking. HDAC3 appears to regulate effects across domains that determine specific cue saliency for behavior. Thus, epigenetic players may gate how sensory information is stored in long-term memory and can be leveraged to reveal the neural substrates of sensory details stored in memory.
A PX-BAR protein Mvp1/SNX8 and a dynamin-like GTPase Vps1 drive endosomal recycling.

Membrane protein recycling systems are essential for maintenance of the endosome-lysosome system. In yeast, retromer and Snx4 coat complexes are recruited to the endosomal surface where they recognize cargos. They sort cargo and deform the membrane into recycling tubules that bud from the endosome and target to the Golgi. Here, we reveal that the SNX-BAR protein, Mvp1, mediates an endosomal recycling pathway which is mechanistically distinct from the retromer and Snx4 pathways. Mvp1 deforms the endosomal membrane and sorts cargos containing a specific sorting motif into a membrane tubule. Subsequently, Mvp1 recruits the dynamin-like GTPase Vps1 to catalyze membrane scission and release of the recycling tubule. Similarly, SNX8, the human homolog of Mvp1, which has been also implicated in Alzheimer's disease, mediates formation of an endosomal recycling tubule. Thus, we present evidence for a novel endosomal retrieval pathway that is conserved from yeast to humans.
Profiling sensory neuron microenvironment after peripheral and central axon injury reveals key pathways for neural repair

Sensory neurons with cell bodies in dorsal root ganglia (DRG) represent a useful model to study axon regeneration. Whereas regeneration and functional recovery occurs after peripheral nerve injury, spinal cord injury or dorsal root injury is not followed by regenerative outcomes. Regeneration of sensory axons in peripheral nerves is not entirely cell autonomous. Whether the DRG microenvironment influences the different regenerative capacities after injury to peripheral or central axons remains largely unknown. To answer this question, we performed a single-cell transcriptional profiling of mouse DRG in response to peripheral (sciatic nerve crush) and central axon injuries (dorsal root crush and spinal cord injury). Each cell type responded differently to the three types of injuries. All injuries increased the proportion of a cell type that shares features of both immune cells and glial cells. A distinct subset of satellite glial cells (SGC) appeared specifically in response to peripheral nerve injury. Activation of the PPARα signaling pathway in SGC, which promotes axon regeneration after peripheral nerve injury, failed to occur after central axon injuries. Treatment with the FDA-approved PPARα agonist fenofibrate increased axon regeneration after dorsal root injury. This study provides a map of the distinct DRG microenvironment responses to peripheral and central injuries at the single-cell level and highlights that manipulating non-neuronal cells could lead to avenues to promote functional recovery after CNS injuries or disease.
Understanding and Mitigating Annotation Bias in Facial Expression Recognition

The performance of a computer vision model depends on the size and quality of its training data. Recent studies have unveiled previously-unknown composition biases in common image datasets which then lead to skewed model outputs, and have proposed methods to mitigate these biases. However, most existing works assume that human-generated annotations can be considered gold-standard and unbiased. In this paper, we reveal that this assumption can be problematic, and that special care should be taken to prevent models from learning such annotation biases. We focus on facial expression recognition and compare the label biases between lab-controlled and in-the-wild datasets. We demonstrate that many expression datasets contain significant annotation biases between genders, especially when it comes to the happy and angry expressions, and that traditional methods cannot fully mitigate such biases in trained models. To remove expression annotation bias, we propose an AU-Calibrated Facial Expression Recognition (AUC-FER) framework that utilizes facial action units (AUs) and incorporates the triplet loss into the objective function. Experimental results suggest that the proposed method is more effective in removing expression annotation bias than existing techniques.
3D Gravity in a Box

The quantization of pure 3D gravity with Dirichlet boundary conditions on a finite boundary is of interest both as a model of quantum gravity in which one can compute quantities which are "more local" than S-matrices or asymptotic boundary correlators, and for its proposed holographic duality to $T \overline{T}$-deformed CFTs. In this work we apply covariant phase space methods to deduce the Poisson bracket algebra of boundary observables. The result is a one-parameter nonlinear deformation of the usual Virasoro algebra of asymptotically AdS$_3$ gravity. This algebra should be obeyed by the stress tensor in any $T\overline{T}$-deformed holographic CFT. We next initiate quantization of this system within the general framework of coadjoint orbits, obtaining - in perturbation theory - a deformed version of the Alekseev-Shatashvili symplectic form and its associated geometric action. The resulting energy spectrum is consistent with the expected spectrum of $T\overline{T}$-deformed theories, although we only carry out the explicit comparison to $\mathcal{O}(1/\sqrt{c})$ in the $1/c$ expansion.
Quantifying unlinkability in multi-hop wireless networks

Abstract Consider a multi-hop wireless network in which devices act as anonymizing routers. Even if devices anonymize their link transmissions, an adversary may still be able to infer key information by observing the traffic patterns in the network. In this work, we quantify how well an adversary can infer unlinkability, that is, the probability that different pairs of devices are communicating, from anonymized link transmissions. We first propose a metric to compute unlinkability using a Kalman-filter based adversary. Using this metric, we then evaluate how different network characteristics impact unlinkability. We assume that devices do not reorder packets to mix traffic and thereby increase unlinkability. Instead, we show that traffic mixing is still possible due to the use of multi-hop routing and broadcast transmissions, with the amount of mixing dependent on the network characteristics. In simulation, we find that (i) for unicast links, as network connectivity increases unlinkability decreases, while for broadcast links, as connectivity increases unlinkability increases, (ii) link dynamics tend to increase unlinkability with unicast links but decrease unlinkability with broadcast links, (iii) well-connected topologies, particularly with broadcast links, achieve the same level of unlinkability with fewer transmissions per packet delivered, (iv) a lattice topology has consistently good unlinkability in different scenarios, and (v) heterogeneous network traffic gives higher unlinkability and better anonymization efficiency than uniform traffic, even when the average rate of traffic is the same.
Provenance Attestation of Human Cells Using Physical Unclonable Functions

We introduce a novel methodology, namely CRISPR-Engineered Attestation of Mammalian Cells using Physical Unclonable Functions (CREAM-PUFs), which can serve as the cornerstone for formally verifying transactions in human cell line distribution networks. A PUF is a physical entity which provides a measurable output that can be used as a unique and irreproducible identifier for the artifact wherein it is embedded. Inspired by the success of silicon PUFs, we sought to exploit a combination of sequence-restricted barcodes and the inherent stochasticity of CRISPR-induced non-homologous end joining DNA error repair to create the first generation of genetic PUFs in three distinct human cells. We demonstrate that these CREAM-PUFs are robust (i.e., they repeatedly produce the same output), unique (i.e., they do not coincide with any other identically produced PUF), and unclonable (i.e., they are virtually impossible to replicate). Accordingly, CREAM-PUFs can serve as a foundational principle for establishing provenance attestation protocols.
Encoding and Decoding with Partitioned Complementary Sequences for Low-PAPR OFDM

In this study, we propose partitioned complementary sequences (CSs) where the gaps between the clusters encode information bits to achieve low peak-to-average-power ratio (PAPR) orthogonal frequency division multiplexing (OFDM) symbols. We show that the partitioning rule without losing the feature of being a CS coincides with the non-squashing partitions of a positive integer and leads to a symmetric separation of clusters. We analytically derive the number of partitioned CSs for given bandwidth and a minimum distance constraint and obtain the corresponding recursive methods for enumerating the values of separations. We show that partitioning can increase the spectral efficiency (SE) without changing the alphabet of the non-zero elements of the CS, i.e., standard CSs relying on Reed-Muller (RM) code. We also develop an encoder for partitioned CSs and a maximum-likelihood-based recursive decoder for additive white Gaussian noise (AWGN) and fading channels. Our results indicate that the partitioned CSs under a minimum distance constraint can perform similar to the standard CSs in terms of average block error rate (BLER) and provide a higher SE at the expense of a limited signal-to-noise ratio (SNR) loss.
Decentralized Collaborative Video Caching in 5G Small-Cell Base Station Cellular Networks

We consider the problem of video caching across a set of 5G small-cell base stations (SBS) connected to each other over a high-capacity short-delay back-haul link, and linked to a remote server over a long-delay connection. Even though the problem of minimizing the overall video delivery delay is NP-hard, the Collaborative Caching Algorithm (CCA) that we present can efficiently compute a solution close to the optimal, where the degree of sub-optimality depends on the worst case video-to-cache size ratio. The algorithm is naturally amenable to distributed implementation that requires no explicit coordination between the SBSs, and runs in O(N + K log K) time, where N is the number of SBSs (caches) and K the maximum number of videos. We extend CCA to an online setting where the video popularities are not known a priori but are estimated over time through a limited amount of periodic information sharing between the SBSs. We demonstrate that our algorithm closely approaches the optimal integral caching solution as the cache size increases. Moreover, via simulations carried out on real video access traces, we show that our algorithm effectively uses the SBS caches to reduce the video delivery delay and conserve the remote server’s bandwidth, and that it outperforms two other reference caching methods adapted to our system setting.
Log-concave polynomials, I: Entropy and a deterministic approximation algorithm for counting bases of matroids

We give a deterministic polynomial-time 2O(r)-approximation algorithm for the number of bases of a given matroid of rank r and the number of common bases of any two matroids of rank r. To the best of our knowledge, this is the first nontrivial deterministic approximation algorithm that works for arbitrary matroids. Based on a lower bound of Azar, Broder, and Frieze, this is almost the best possible result assuming oracle access to independent sets of the matroid. There are two main ingredients in our result. For the first, we build upon recent results of Adiprasito, Huh, Katz, and Wang on combinatorial Hodge theory to show that the basis generating polynomial of any matroid is a (completely) log-concave polynomial. Formally, we prove that the multivariate generating polynomial of the bases of any matroid is (and all of its directional derivatives along the positive orthant are) log-concave as functions over the positive orthant. For the second ingredient, we develop a general framework for approximate counting in discrete problems, based on convex optimization. The connection goes through subadditivity of the entropy. For matroids, we prove that an approximate superadditivity of the entropy holds by relying on the log-concavity of the basis generating polynomial.
Fluent: An AI Augmented Writing Tool for People who Stutter

Stuttering is a speech disorder which impacts the personal and professional lives of millions of people worldwide. To save themselves from stigma and discrimination, people who stutter (PWS) may adopt different strategies to conceal their stuttering. One of the common strategies is word substitution where an individual avoids saying a word they might stutter on and use an alternative instead. This process itself can cause stress and add more burden. In this work, we present Fluent, an AI augmented writing tool which assists PWS in writing scripts which they can speak more fluently. Fluent embodies a novel active learning based method of identifying words an individual might struggle pronouncing. Such words are highlighted in the interface. On hovering over any such word, Fluent presents a set of alternative words which have similar meaning but are easier to speak. The user is free to accept or ignore these suggestions. Based on such user interaction (feedback), Fluent continuously evolves its classifier to better suit the personalized needs of each user. We evaluated our tool by measuring its ability to identify difficult words for 10 simulated users. We found that our tool can identify difficult words with a mean accuracy of over 80% in under 20 interactions and it keeps improving with more feedback. Our tool can be beneficial for certain important life situations like giving a talk, presentation, etc. The source code for this tool has been made publicly accessible at github.com/bhavyaghai/Fluent.
Compound Figure Separation of Biomedical Images with Side Loss.

Unsupervised learning algorithms (e.g., self-supervised learning, auto-encoder, contrastive learning) allow deep learning models to learn effective image representations from large-scale unlabeled data. In medical image analysis, even unannotated data can be difficult to obtain for individual labs. Fortunately, national-level efforts have been made to provide efficient access to obtain biomedical image data from previous scientific publications. For instance, NIH has launched the Open-i\(^\circledR \) search engine that provides a large-scale image database with free access. However, the images in scientific publications consist of a considerable amount of compound figures with subplots. To extract and curate individual subplots, many different compound figure separation approaches have been developed, especially with the recent advances in deep learning. However, previous approaches typically required resource extensive bounding box annotation to train detection models. In this paper, we propose a simple compound figure separation (SimCFS) framework that uses weak classification annotations from individual images. Our technical contribution is three-fold: (1) we introduce a new side loss that is designed for compound figure separation; (2) we introduce an intra-class image augmentation method to simulate hard cases; (3) the proposed framework enables an efficient deployment to new classes of images, without requiring resource extensive bounding box annotations. From the results, the SimCFS achieved a new state-of-the-art performance on the ImageCLEF 2016 Compound Figure Separation Database. The source code of SimCFS is made publicly available at https://github.com/hrlblab/ImageSeperation.
Directional field-dependence of tunable magnetic domains in noncentrosymmetric ferromagnetic Weyl semimetal CeAlSi

Dynamics and textures of magnetic domain walls (DWs) may largely alter the electronic behaviors in a Weyl semimetal system via emergent gauge fields. However, very little is known about even the basic properties of these domain walls in Weyl materials. In this work, we imaged the spontaneous magnetization and magnetic susceptibility of a ferromagnetic (FM) Weyl semimetal CeAlSi using scanning SQUID microscopy. We observed the ferromagnetic DWs lined-up with the [100] direction (or other degenerate directions). We also discovered the coexistence of stable and metastable domain phases, which arise likely due to magnetoelastic and magnetostriction effects and are expected to be highly tunable with small strains. We applied an in-plane external field as the CeAlSi sample was cooled down to below the magnetic phase transition of 8.3K, showing that the pattern of FM domains is strongly correlated with both the amplitude and the orientation of the external field even for weak fields of a few Gausses. The area of stable domains increases with field and reaches maximum when the field is parallel to the main crystallographic axes of the CeAlSi crystal. Our results suggest that the manipulation of these heterogeneous phases can provide a practical way to study the interplay between magnetism and electronic properties in Weyl systems, and that these systems can even serve as a new platform for magnetic sensors.
Two electrons in harmonic confinement coupled to light in a cavity

The energy and wave function of a harmonically confined two-electron system coupled to light is calculated by separating the wave functions of the relative and center-of-mass (CM) motions. The relative motion wave function has a known quasianalytical solution. The light only couples to the CM variable and the coupled equation can be solved analytically. The approach works for any coupling strength. Examples of wave functions of light-matter hybrid states are presented.
Data Augmentation for Cross-Domain Named Entity Recognition

Current work in named entity recognition (NER) shows that data augmentation techniques can produce more robust models. However, most existing techniques focus on augmenting in-domain data in low-resource scenarios where annotated data is quite limited. In this work, we take this research direction to the opposite and study cross-domain data augmentation for the NER task. We investigate the possibility of leveraging data from high-resource domains by projecting it into the low-resource domains. Specifically, we propose a novel neural architecture to transform the data representation from a high-resource to a low-resource domain by learning the patterns (e.g. style, noise, abbreviations, etc.) in the text that differentiate them and a shared feature space where both domains are aligned. We experiment with diverse datasets and show that transforming the data to the low-resource domain representation achieves significant improvements over only using data from high-resource domains.
Remember What You Want to Forget: Algorithms for Machine Unlearning

We study the problem of unlearning datapoints from a learnt model. The learner first receives a dataset $S$ drawn i.i.d. from an unknown distribution, and outputs a model $\widehat{w}$ that performs well on unseen samples from the same distribution. However, at some point in the future, any training datapoint $z \in S$ can request to be unlearned, thus prompting the learner to modify its output model while still ensuring the same accuracy guarantees. We initiate a rigorous study of generalization in machine unlearning, where the goal is to perform well on previously unseen datapoints. Our focus is on both computational and storage complexity. For the setting of convex losses, we provide an unlearning algorithm that can unlearn up to $O(n/d^{1/4})$ samples, where $d$ is the problem dimension. In comparison, in general, differentially private learning (which implies unlearning) only guarantees deletion of $O(n/d^{1/2})$ samples. This demonstrates a novel separation between differential privacy and machine unlearning.
Scaling up Continuous-Time Markov Chains Helps Resolve Underspecification

Modeling the time evolution of discrete sets of items (e.g., genetic mutations) is a fundamental problem in many biomedical applications. We approach this problem through the lens of continuous-time Markov chains, and show that the resulting learning task is generally underspecified in the usual setting of cross-sectional data. We explore a perhaps surprising remedy: including a number of additional independent items can help determine time order, and hence resolve underspecification. This is in sharp contrast to the common practice of limiting the analysis to a small subset of relevant items, which is followed largely due to poor scaling of existing methods. To put our theoretical insight into practice, we develop an approximate likelihood maximization method for learning continuous-time Markov chains, which can scale to hundreds of items and is orders of magnitude faster than previous methods. We demonstrate the effectiveness of our approach on synthetic and real cancer data.
Data-driven yaw misalignment correction for utility-scale wind turbines

In recent years, wind turbine yaw misalignment that tends to degrade the turbine power production and impact the blade fatigue loads raises more attention along with the rapid development of large-scale wind turbines. The state-of-the-art correction methods require additional instruments such as LiDAR to provide the ground truths and are not suitable for long-term operation and large-scale implementation due to the high costs. In the present study, we propose a framework that enables the effective and efficient detection and correction of static and dynamic yaw errors by using only turbine SCADA data, suitable for a low-cost regular inspection for large-scale wind farms in onshore, coastal, and offshore sites. This framework includes a short-period data collection of the turbine operating under multiple static yaw errors, a data mining correction for the static yaw error, and ultra-short-term dynamic yaw error forecasts with machine learning algorithms. Three regression algorithms, i.e., linear, support vector machine, and random forest, and a hybrid model based on the average prediction of the three, have been tested for dynamic yaw error prediction and compared using the field measurement data from a 2.5 MW turbine. For the data collected in the present study, the hybrid method shows the best performance and can reduce total yaw error by up to 85% (on average of 71%) compared to the cases without static and dynamic yaw error corrections. In addition, we have tested the transferability of the proposed method in the application of detecting other static and dynamic yaw errors.
Fundamental research on surface modification of nano-size silica for underfill applications

In order to improve the rheological behavior of nanosilica composite no-flow underfill, a filler surface treatment using silane coupling agents was investigated, to reduce the filler-filler interaction and to achieve monodispersity of the nanosilica in the underfill resin. The experimental conditions of the surface treatment were investigated in a DOE in terms of the pre-treatment methods, coupling agent types, concentrations, and treatment durations. The particle dispersion after treatment was evaluated by a laser particle analyzer and TEM. A mono-dispersed nanosilica solution in the polar medium was achieved using optimal experimental conditions. The surface chemistry of the nanosilica was studied using FTIR. The wettability of the underfill resin and water on the silane treated glass slides was studied using a goniometer. Based on the investigations, the silane-treated nanosilica fillers were incorporated into an underfill resin to formulate a nanocomposite no-flow underfill. It was found that a proper filler treatment could significantly reduce the viscosity of the nanocomposite.
Efficiently approximating max-clique in a Hopfield-style network

The author approximates max-clique in a special case of the Hopfield network whose stable states are maximal cliques. Several energy-descent optimizing dynamics, both discrete and continuous, are presented. One of these emulates, as a special case, two well known greedy algorithms for approximating MAX-CLIQUE. Detailed empirical comparisons of random graphs are reported. Mean-field annealing, an efficient approximation to simulated annealing, is judged to be the most effective. >
PHACE without Face? Infantile Hemangiomas of the Upper Body Region with Minimal or Absent Facial Hemangiomas and Associated Structural Malformations

: Infantile hemangiomas can be associated with congenital anomalies such as PHACE syndrome with facial hemangiomas and genitourinary and spinal anomalies in the setting of lower body hemangiomas. We describe five infants in whom segmental hemangiomas involving the upper torso and extremities with absent or small facial hemangiomas were associated with structural anomalies similar to those reported with PHACE syndrome, including three with structural arterial anomalies of the subclavian arteries, three with aortic arch anomalies (right sided or narrowed arch), two with congenital heart disease (atrial septal defect and ventricular septal defect; tetralogy of Fallot), one with a retinal scar, and one with a sternal defect (scar). Two of five had small facial hemangiomas of the lower lip, but none had large segmental hemangiomas of the face. Three of five would have met diagnostic criteria for PHACE but lacked a facial hemangioma of 5 cm in diameter or greater. Patients with segmental arm and thorax hemangiomas may have associated structural abnormalities with overlapping features of PHACE, suggesting that a similar syndrome can occur in this clinical setting.
Copper and Selenium Deficiencies Do Not Enhance the Cardiotoxicity in Rats Due to Chronic Doxorubicin Treatment

This study tests the hypothesis that Cu and Se deficiencies enhance doxorubicin-induced cardiotoxicity and anemia. Male Sprague-Dawley rats (n = 48) were fed Cu and Se-adequate (+Cu+Se), Cu-deficient (-Cu), Se-deficient (-Se) or Cu and Se-deficient (-Cu-Se) diets for 5.5 wk. Doxorubicin (4 mg/kg body wt) or saline was administered once weekly for the last 4 wk of the study. Copper deficiency was confirmed by 79% lower liver Cu, 67% lower liver Cu,Zn superoxide dismutase (Cu,Zn SOD) activity and 76% lower erythrocyte Cu,Zn SOD activity. Selenium deficiency was confirmed by 90% lower liver glutathione peroxidase activity. Rats fed the -Cu diet had greater reductions in hematocrit than did those fed the +Cu diet after administration of doxorubicin. Doxorubicin, Cu deficiency and Se deficiency all produced electrocardiographic abnormalities and ultrastructural anatomical lesions. However, the dietary deficiencies did not enhance doxorubicin-induced cardiotoxicity. Doxorubicin, but not Cu or Se deficiency, raised lipid peroxidation 16% in liver (P < 0.01) and 18% in heart (not significant). These data suggest that the cardiomyopathies caused by doxorubicin and Cu and Se deficiencies have some similarities, but cardiac changes may be related to mechanisms other than lipid peroxidation.
Two-Dimensional Cross-Section Sensitivity and Uncertainty Analysis for Tritium Production Rate in Fusion-Oriented Integral Experiments

Several integral experiments on tritium breeding were jointly performed in connection with the U.S./JAERI Collaborative Program on Fusion Breeder Neutronics. Tritium production rates from /sup 6/Li (T/sub 6/) and /sup 7/Li(T/sub 7/) were measured at several locations in an Li/sub 2/O assembly embedded in the concrete wall of a 5- x 5- x 4.5-m room (reference experiment) in independent benchmark experiments with the Li/sub 2/O assembly located in a large room of negligible room-return neutrons. In the reference experiment, large discrepancies in T/sub 6/ were found at the front locations in the Li/sub 2/O assembly. At middle locations, the calculated-to-experimental (C/E) values for T/sub 6/ are -- 1.2 (U.S.) and -- l.1 (JAERI). The C/E values for T/sub 7/ are -- 1.18 (U.S.) and 1.05 (JAERI). To assess the contribution to the uncertainty is predicting T/sub 6/ and T/sub 7/ that results from the current uncertainties in the nuclear data base, an extensive two-dimensional cross-section sensitivity/uncertainty analysis was performed. For that purpose, the FORSS module, and the VIP and DOT 4.3 codes were used along with the PUFF-2 covariance code. Two systems were considered for the analysis: the benchmark system and the reference system. The models used simulate the geometricalmore » details and source conditions for the experiments.« less
A Hybrid Model for Reasoning about Composed Hardware Systems

To formally specify and reason about composed systems, a process algebra is developed that integrates an extended interpreter model. This approach utilizes the interpreter model for device decomposition, while also being able to reason about larger systems that require interdevice communication. By combining these approaches, convenient notations are available to specify and verify both device independent properties (e.g., instruction sets) and device interdependent properties (e.g., communication protocols).
The Educational Role of Philosophy (with a new commentary by Phillip Cam)

The history of the relationship between philosophy and education has been a long and troubled one. In part, this stemmed from the problematic nature of philosophy itself, but this difficulty was compounded by controversy as to the age at which training in philosophy should begin. Although Socrates seemed indifferent to whether he conversed philosophically with young or old, his pupil, Plato, was inclined to restrict philosophy to mature students, on the grounds that it made the younger ones unduly contentious. Since philosophers in those days had the reputation of being ‘friends of wisdom,’ and since being a friend of wisdom seemed to require extensive experience, it came to be taken for granted, generation after generation, that philosophy was not for the young. It has sometimes been made available, on a limited basis, at the secondary school level, but almost never to students in the lower grades. To the suggestion that this prevented children from having access to ideas, theories and abstract concepts, the stock response was that children were mired in the ‘concrete’ level of experience and had no interest in abstractions. To the report that very young children almost invariably greeted opportunities to discuss philosophy with joy and delight, the standard reply was that this proved that the children could not be doing philosophy, since the study of philosophy is a serious and difficult matter. The recent career of philosophy in elementary and secondary education has been a matter of overcoming precisely these objection and misconceptions. Unfortunately, a listing of the advantages to be derived by the young from the study of philosophy—its strengthening of reasoning and judgment, its fostering of concept-formation skills, its clarification of values and ideals—is likely to obscure the intrinsic satisfactions that children derive from their classroom communities of philosophical inquiry. But even here there are signs of change, and a new appreciation of the educational possibilities of philosophy is at last beginning to surface in the schools.
Molecular basis for the temperature-dependent insolubility of cryoglobulins--IV. Structural studies of the IgM monoclonal cryoglobulin McE.

A 20.1s02,w monoclonal IgM, κ immunoglobulin (McE) exhibiting reversible, temperaturedependent insolubility is described. The precipitation of the protein upon reduction in solution temperature displays sharp concentration dependence and is optimal between pH 6 and 8. The effect of temperature on the rate of cryoprecipitation suggests the involvement of either a low temperature-induced conformational change or nucleation event in cryoprecipitation. Structural studies have indicated that the masses (under denaturing conditions) of both heavy and light chains derived from the cryoimmunoglobulin and noncryoimmunoglobulin reference proteins are identical, and the protein possesses a characteristic J chain. The cryoimmunoglobulin is converted to (Fc)5μ§ and Fabμ fragments by limited digestion with trypsin at 56° C. The heavy chain of the McE protein is deficient in tyrosine and contains excess tryptophan relative to noncryoglobulin and other cryoglobulin proteins. Calculation of structural parameters from these amino acid compositions suggests that the heavy chains of cryoimmunoglobulins may contain significantly greater (P< 0.01) amounts of polypeptide chain turns than do noncryoglobulin references. With the exception of a Leu → Phe substitution at position 13, the McE light chain is homologous with other immunoglobulins of the VκII subgroup, while the heavy chain is of the less frequently encountered VHV subgroup. (Fc)5μ and Fabμ fragments, as well as reduction-derived monomeric subunits, do not cryoprecipitate. Removal of J chain does not affect cryoprecipitation of the reassembled molecule while partial enzymatic removal of neutral hexoses and glucosamine enhances cryoprecipitation. Cryoprecipitation of McE is interpreted as a general solubility effect as opposed to an antibody-antigen phenomenon.