cssi/SciSciGPT-SciSciNet huggingface dataset:
Autonomous Situational Awareness for UAS Swarms

This paper describes a technique for the autonomous mission planning of unmanned aerial system swarms. Given a swarm operating in a known area, a central command system generates measurements from the swarm. If those measurements indicate changes to the mission situation such as target movement, the swarm planning is updated to reflect the new situation and guidance updates are broadcast to the swarm. The primary algorithms featured in this work are A* pathfinding and the Generalized Labeled Multi-Bernoulli multi-target tracking method.
Unwinding the relative Tate diagonal

We show that a spectral sequence developed by Lipshitz and Treumann, for application to Heegaard Floer theory, converges to a localized form of topological Hochschild homology with coefficients. This allows us to show that the target of this spectral sequence can be identified with Hochschild homology when the topological Hochschild homology is torsion-free as a module over $\mathrm{THH}_*(\mathbb{F}_2)$, parallel to results of Mathew on degeneration of the Hodge-to-de Rham spectral sequence. To carry this out, we apply work of Nikolaus-Scholze to develop a general Tate diagonal for Hochschild-like diagrams of spectra that respect a decomposition into tensor products. This allows us to discuss the extent to which there can be a Tate diagonal for relative topological Hochschild homology.
Aβ receptors specifically recognize molecular features displayed by fibril ends and neurotoxic oligomers.

Several cell-surface receptors for neurotoxic forms of amyloid-β (Aβ) have been described, but their molecular interactions with Aβ assemblies and their relative contributions to mediating Alzheimer’s disease pathology have remained uncertain. Here, we used super-resolution microscopy to directly visualize Aβ-receptor interactions at the nanometer scale. We report that one documented Aβ receptor, PrPC, specifically inhibits the polymerization of Aβ fibrils by binding to the rapidly growing end of each fibril, thereby blocking polarized elongation at that end. PrPC binds neurotoxic oligomers and protofibrils in a similar fashion, suggesting that it may recognize a common, end-specific, structural motif on all of these assemblies. Finally, two other Aβ receptors, FcγRIIb and LilrB2, affect Aβ fibril growth in a manner similar to PrPC. Our results suggest that receptors may trap Aβ oligomers and protofibrils on the neuronal surface by binding to a common molecular determinant on these assemblies, thereby initiating a neurotoxic signal. PrPC, a receptor for Aβ oligomers, blocks polarized elongation of Aβ fibrils by binding to the rapidly growing end of each fibril. PrPC and other receptors may trap Aβ oligomers and protofibrils on the neuronal surface by binding to a common molecular determinant, initiating a neurotoxic signal.
Unpaired Image-to-Image Translation via Latent Energy Transport

Image-to-image translation aims to preserve source contents while translating to discriminative target styles between two visual domains. Most works apply adversarial learning in the ambient image space, which could be computationally expensive and challenging to train. In this paper, we propose to deploy an energy-based model (EBM) in the latent space of a pretrained autoencoder for this task. The pretrained autoencoder serves as both a latent code extractor and an image reconstruction worker. Our model, LETIT1, is based on the assumption that two domains share the same latent space, where latent representation is implicitly decomposed as a content code and a domain-specific style code. Instead of explicitly extracting the two codes and applying adaptive instance normalization to combine them, our latent EBM can implicitly learn to transport the source style code to the target style code while preserving the content code, an advantage over existing image translation methods. This simplified solution is also more efficient in the one-sided unpaired image translation setting. Qualitative and quantitative comparisons demonstrate superior translation quality and faithfulness for content preservation. Our model is the first to be applicable to 1024×1024-resolution unpaired image translation to the best of our knowledge. Code is available at https://github.com/YangNaruto/latent-energy-transport.
Analyzing the Travel and Charging Behavior of Electric Vehicles - A Data-driven Approach

The increasing market penetration of electric vehicles (EVs) may pose significant electricity demand on power systems. This electricity demand is affected by the inherent uncertainties of EVs' travel behavior that makes forecasting the daily charging demand (CD) very challenging. In this project, we use the National House Hold Survey (NHTS) data to form sequences of trips, and develop machine learning models to predict the parameters of the next trip of the drivers, including trip start time, end time, and distance. These parameters are later used to model the temporal charging behavior of EVs. The simulation results show that the proposed modeling can effectively estimate the daily CD pattern based on travel behavior of EVs, and simple machine learning techniques can forecast the travel parameters with acceptable accuracy.
RefSum: Refactoring Neural Summarization

Although some recent works show potential complementarity among different state-of-the-art systems, few works try to investigate this problem in text summarization. Researchers in other areas commonly refer to the techniques of reranking or stacking to approach this problem. In this work, we highlight several limitations of previous methods, which motivates us to present a new framework Refactor that provides a unified view of text summarization and summaries combination. Experimentally, we perform a comprehensive evaluation that involves twenty-two base systems, four datasets, and three different application scenarios. Besides new state-of-the-art results on CNN/DailyMail dataset (46.18 ROUGE-1), we also elaborate on how our proposed method addresses the limitations of the traditional methods and the effectiveness of the Refactor model sheds light on insight for performance improvement. Our system can be directly used by other researchers as an off-the-shelf tool to achieve further performance improvements. We open-source all the code and provide a convenient interface to use it: https://github.com/yixinL7/Refactoring-Summarization.
An High Resolution Near-Earth Objects Population Enabling Next-Generation Search Strategies

Over the past decade, the dedicated search for kilometer-size near-Earth objects (NEOs), potentially hazardous objects (PHOs), and potential Earth impactors has led to a boost in the rate of discoveries of these objects. The catalog of known NEOs is the fundamental ingredient used to develop a model for the NEOs population, either by assessing and correcting for the observational bias (Jedicke et al., 2002), or by evaluating the migration rates from the NEOs source regions (Bottke et al., 2002). The modeled NEOs population is a necessary tool used to track the progress in the search of large NEOs (Jedicke et al., 2003) and to try to predict the distribution of the ones still undiscovered, as well as to study the sky distribution of potential Earth impactors (Chesley & Spahr, 2004). We present a method to model the NEOs population in all six orbital elements, on a finely grained grid, allowing us the design and test of targeted and optimized search strategies. This method relies on the observational data routinely reported to the Minor Planet Center (MPC) by the Catalina Sky Survey (CSS) and by other active NEO surveys over the past decade, to determine on a nightly basis the efficiency in detecting moving objects as a function of observable quantities including apparent magnitude, rate of motion, airmass, and galactic latitude. The cumulative detection probability is then be computed for objects within a small range in orbital elements and absolute magnitude, and the comparison with the number of know NEOs within the same range allows us to model the population. When propagated to the present epoch and projected on the sky plane, this provides the distribution of the missing large NEOs, PHOs, and potential impactors.
A tale of two metrics:Polling and financial contributions as a measure of performance

Campaign analysis is an integral part of American democracy and has many complexities in its dynamics. Experts have long sought to understand these dynamics and evaluate campaign performance using a variety of techniques. We explore campaign financing and standing in the polls as two components of campaign performance in the context of the 2020 Democratic primaries. We show where these measures exhibit represent similar dynamics and where they differ. We focus on identifying change points in the trend for all candidates using joinpoint regression models. We find how these change points identify major events such as failure or success in a debate. Joinpoint regression reveals who the voters support when they stop supporting a specific candidate. This study demonstrates the value of joinpoint regression in political campaign analysis and it represents a crossover of this technique into the political domain building a foundation for continued exploration and use of this method.
Blind decision making: Reinforcement learning with delayed observations

Abstract Many applications, such as robotics, are increasingly utilizing Reinforcement Learning (RL). The current implementation of RL typically assumes that the state update from the previous actions happens instantaneously, and thus can be used for making future decisions. However, this may not always be true. When the state update is not available, the decision is taken partly in the blind since it cannot rely on the current state information. This paper proposes an approach, where the delay in the knowledge of the state can be used, and the decisions are made based on the available information which may not include the current state information. One approach could be to include the actions after the last-known state as a part of the state information, however, that leads to an increased state-space making the problem complex and slower in convergence. The proposed algorithm gives an alternate approach where the state space is not enlarged, as compared to the case when there is no delay in the state update. Evaluations on the control environments further illustrate the improved performance of the proposed algorithm.
High-Confidence Off-Policy (or Counterfactual) Variance Estimation

Many sequential decision-making systems leverage data collected using prior policies to propose a new policy. For critical applications, it is important that high-confidence guarantees on the new policy’s behavior are provided before deployment, to ensure that the policy will behave as desired. Prior works have studied high-confidence off-policy estimation of the expected return, however, high-confidence off-policy estimation of the variance of returns can be equally critical for high-risk applications. In this paper we tackle the previously open problem of estimating and bounding, with high confidence, the variance of returns from off-policy data.
Flap side-edge noise : Acoustic analysis of Sen's Model

The two-dimensional flap side-edge flow model developed by Sen (Sen, R., Local Dynamics and Acoustics in a Simple Model of Airfoil Lateral-Edge-Noise, AIAA Paper 96-1673, May 1996) is analyzed to reveal the noise-production potential of the proposed mechanism. The model assumes that a vortex will form at the equilibrium position off the side edge of the flap. The vortex is then perturbed away from the equilibrium position by incoming turbulence, causing it to oscillate and thus radiate sound. The noise field is calculated three-dimensionally by taking the flap to have a finite chord. Spectra and directivity of the far-field sound are presented. In addition, the effect of retarded time differences is evaluated. The parameters in the model are related to typical aircraft parameters and noise reduction possibilities are proposed.
Design, Mutate, Screen: High-throughput creation of genetic clocks with different period-amplitude characteristics

Directed evolution has become an invaluable tool in protein engineering and has also greatly influenced the construction of synthetic gene circuits. The ability to generate diversity at precise targets for directed evolution approaches has improved vastly, allowing researchers to create large, specific mutant libraries with relative ease. Screening approaches for large mutant libraries have similarly come a long way, especially when the desired behavior can easily be tested for with static, single time-point assays. For more complex gene circuits with dynamic phenotypes that change over time, directed evolution approaches to controlling and tuning circuit behavior have been hindered by the lack of sufficiently high-throughput screening methods to isolate variants with desired characteristics. Here we utilize directed mutagenesis and multiplexed microfluidics to develop a workflow for creating, screening and tuning dynamic gene circuits that operate at the population level. Specifically, we create a mutant library of an existing oscillator, the synchronized lysis circuit, and tune its dynamics while uncovering principles regarding its behavior. Lastly, we utilize this directed evolution workflow to construct a new synchronized genetic oscillator that exhibits robust dynamics over long time scales.
Compressive Neural Representations of Volumetric Scalar Fields

We present an approach for compressing volumetric scalar fields using implicit neural representations. Our approach represents a scalar field as a learned function, wherein a neural network maps a point in the domain to an output scalar value. By setting the number of weights of the neural network to be smaller than the input size, we achieve compressed representations of scalar fields, thus framing compression as a type of function approximation. Combined with carefully quantizing network weights, we show that this approach yields highly compact representations that outperform state-of-the-art volume compression approaches. The conceptual simplicity of our approach enables a number of benefits, such as support for time-varying scalar fields, optimizing to preserve spatial gradients, and random-access field evaluation. We study the impact of network design choices on compression performance, highlighting how simple network architectures are effective for a broad range of volumes.
Measuring Sex Trafficking: A National-Level Victimization Survey of an At-Risk Sample

The current study administered a self-report survey with behaviorally specific questions to a stratified sample of non-college educated women, aged 18 to 29, in the general population (N = 996). No...
Improving Compositional Generalization in Classification Tasks via Structure Annotations

Compositional generalization is the ability to generalize systematically to a new data distribution by combining known components. Although humans seem to have a great ability to generalize compositionally, state-of-the-art neural models struggle to do so. In this work, we study compositional generalization in classification tasks and present two main contributions. First, we study ways to convert a natural language sequence-to-sequence dataset to a classification dataset that also requires compositional generalization. Second, we show that providing structural hints (specifically, providing parse trees and entity links as attention masks for a Transformer model) helps compositional generalization.
NASA's Preparations for ESA's L3 Gravitational Wave Mission

Telescope Subsystem - Jeff Livas (GSFC): Demonstrate pathlength stability, straylight and manufacturability. Phase Measurement System - Bill Klipstein (JPL): Key measurement functions demonstrated. Incorporate full flight functionality. Laser Subsystem - Jordan Camp (GSFC): ECL master oscillator, phase noise of fiber power amplifier, demonstrate end-to-end performance in integrated system, lifetime. Micronewton Thrusters - John Ziemer (JPL): Propellant storage and distribution, system robustness, manufacturing yield, lifetime. Arm-locking Demonstration - Kirk McKenzie (JPL): Studying a demonstration of laser frequency stabilization with GRACE Follow-On. Torsion Pendulum - John Conklin (UF): Develop U.S. capability with GRS and torsion pendulum test bed. Multi-Axis Heterodyne Interferometry - Ira Thorpe (GSFC): Investigate test mass/optical bench interface. UV LEDs - John Conklin+ (UF): Flight qualify UV LEDs to replace mercury lamps in discharging system. Optical Bench - Guido Mueller (UF): Investigate alternate designs and fabrication processes to ease manufacturability. LISA researchers at JPL are leading the Laser Ranging Interferometer instrument on the GRACE Follow-On mission.
Science Communication Versus Science Education: The Graduate Student Scientist As A K-12 Classroom Resource

Science literacy is a major goal of science educational reform (NRC, 1996; AAAS, 1998; NCLB Act, 2001). Some believe that teaching science only requires pedagogical content knowledge (PCK) (Shulman, 1987). Others believe doing science requires knowledge of the methodologies of scientific inquiry (NRC, 1996). With these two mindsets, the challenge for science educators is to create models that bring the two together. The common ground between those who teach science and those who do science is science communication, an interactive process that galvanizes dialogue among scientists, teachers, and learners in a rich ambience of mutual respect and a common, inclusive language of discourse (Stocklmayer, 2001). The dialogue between science and non-science is reflected in the polarization that separates those who do science and those who teach science, especially as it plays out everyday in the science classroom. You may be thinking, why is this important? It is vital because, although not all science learners become scientists, all K-12 students are expected to acquire science literacy, especially with the implementation of the No Child Left Behind Act of 2001 (NCLB). Students are expected to acquire the ability to follow the discourse of science as well as connect the world of science to the context of their everyday life if they plan on moving to the next grade level, and in some states, to graduate from high school. This paper posits that science communication is highly effective in providing the missing link for K-12 students’ cognition in science and their attainment of science literacy. This paper will focus on the “Science For Our Schools” (SFOS) model implemented at California State University, Los Angeles (CSULA) as a project of the National Science Foundation’s GK-12 program, (NSF 2001) which has been a huge success in bridging the gap between those who “know” science and those who “teach” science. The SFOS model makes clear the distinctions that identify science, science communication, science education, and science literacy in the midst of science learning by bringing together graduate student scientists and science teachers to engage students in the two world’s dialogue in the midst of the school science classroom. The graduate student scientists and the science teachers worked as a team throughout the school year and became effective science communicators as they narrowed the gulf between the two worlds.
Evaluation of Aircraft Brake Materials

A test program was carried out to evaluate several new high-temperature friction materials for use in aircraft disk brakes. A specially built test apparatus utilizing a disk brake and wheel half from a small jet aircraft was used. The apparatus enabled control of brake pressure, velocity and braking time. Tests were run under both constant and variables velocity conditions and covered a kinetic energy range similar to that encountered in aircraft brake service. The materials evaluation showed that two newly developed friction materials show potential for use in aircraft disk brakes. One of the materials is a nickel-based sintered composite, while the other is a molybdenum-based material. Both materials show much lower wear rates than conventional copper-based materials and are better able to withstand the high temperatures encountered during braking. Additional materials improvement is necessary, however, since both materials show a significant negative slope of the friction-velocity curve at low velociti...
Advances in cryogenic engineering. Volume 27 - Proceedings of the Cryogenic Engineering Conference, San Diego, CA, August 11-14, 1981

Applications of superconductivity are considered, taking into account MHD and fusion, generators, transformers, transmission lines, magnets for physics, cryogenic techniques, electrtronics, and aspects of magnet stability. Advances related to heat transfer in He I are discussed along with subjects related to theat transfer in He II, refrigeration of superconducting systems, refrigeration and liquefaction, dilution and magnetic refrigerators, refrigerators for space applications, mass transfer and flow phenomena, and the properties of fluids. Developments related to cryogenic applications are also explored, giving attention to bulk storage and transfer of cryogenic fluids, liquefied natural gas operations, space science and technology, and cryopumping. Topics related to cryogenic instrumentation and controls include the production and use of high grade silicon diode temperature sensors, the choice of strain gages for use in a large superconducting alternator, microprocessor control of cryogenic pressure, and instrumentation, data acquisition and reduction for a large spaceborne helium dewar.
An Efficient SDN Architecture for Smart Home Security Accelerated by FPGA

With the rise of Internet of Things (IoT) devices, home network management and security are becoming complex. There is an urgent requirement to make smart home network management more efficient. This work proposes an SDN-based architecture to secure smart home networks through K-Nearest Neighbor (KNN) based device classifications and malicious traffic detection. The efficiency is enhanced by offloading the computation-intensive KNN model to a Field Programmable Gate Arrays (FPGA). Furthermore, we propose a custom KNN solution that exhibits the best performance on an FPGA compared with four alternative KNN instances (i.e., 78% faster than a parallel Bubble Sort-based implementation and 99% faster than three other sorting algorithms). Moreover, with 36,225 training samples, the proposed KNN solution classifies a test query with 95% accuracy in approximately 4 ms on an FPGA compared to 57 seconds on a CPU platform. This highlights the promise of FPGA-based platforms for edge computing applications in the smart home.
Fully Row/Column-Parallel In-memory Computing SRAM Macro employing Capacitor-based Mixed-signal Computation with 5-b Inputs

This paper presents an in-memory computing (IMC) macro in 28nm for fully row/column-parallel matrix-vector multiplication (MVM), exploiting precise capacitor-based analog computation to extend from binary input-vector elements to 5-b input-vector elements, for 16x increase in energy efficiency and 5x increase in throughput. The 1152(row)x256(col.) macro employs multi-level input drivers based on a digital-switch DAC implementation, which preserve compute accuracy well beyond the 8-b resolution of the output ADCs, and whose area is halved via a dynamic-range doubling (DRD) technique. The macro achieves the highest reported IMC energy efficiency of 5796 TOPS/W and compute density of 12 TOPS/mm2 (both normalized to 1-b ops). CIFAR-10 image classification is demonstrated with accuracy of 91%, equal to the level of ideal SW implementation.
Oncolytic Viruses for Malignant Glioma: On the Verge of Success?

Glioblastoma is one of the most difficult tumor types to treat with conventional therapy options like tumor debulking and chemo- and radiotherapy. Immunotherapeutic agents like oncolytic viruses, immune checkpoint inhibitors, and chimeric antigen receptor T cells have revolutionized cancer therapy, but their success in glioblastoma remains limited and further optimization of immunotherapies is needed. Several oncolytic viruses have demonstrated the ability to infect tumors and trigger anti-tumor immune responses in malignant glioma patients. Leading the pack, oncolytic herpesvirus, first in its class, awaits an approval for treating malignant glioma from MHLW, the federal authority of Japan. Nevertheless, some major hurdles like the blood–brain barrier, the immunosuppressive tumor microenvironment, and tumor heterogeneity can engender suboptimal efficacy in malignant glioma. In this review, we discuss the current status of malignant glioma therapies with a focus on oncolytic viruses in clinical trials. Furthermore, we discuss the obstacles faced by oncolytic viruses in malignant glioma patients and strategies that are being used to overcome these limitations to (1) optimize delivery of oncolytic viruses beyond the blood–brain barrier; (2) trigger inflammatory immune responses in and around tumors; and (3) use multimodal therapies in combination to tackle tumor heterogeneity, with an end goal of optimizing the therapeutic outcome of oncolytic virotherapy.
Originalism and the Unwritten Constitution

In his book, America’s Unwritten Constitution, Akhil Reed Amar contends that to properly engage the written Constitution, scholars and laymen alike must look to extratextual sources: among them America’s founding documents, institutional practices, and ethos, all of which constitute Amar’s “unwritten Constitution.” In this Article, I argue that contemporary originalist constitutional theory is consistent with reliance on extraconstitutional sources in certain circumstances. I establish a framework for revaluating the use of extratextual sources. That framework categorizes extratextual sources and explains their relevance to constitutional interpretation (the meaning of the text) and constitutional construction (elaboration of constitutional doctrine and decision of constitutional cases). I conclude by applying the framework to a question posed by Akhil Amar: Can vice presidents preside over their own trial upon impeachment? A negative answer to this question is consistent with an originalist constitutional theory that carefully cabins the use of extratextual sources in constitutional interpretation and construction.
Thioesterase induction by 2,3,7,8-tetrachlorodibenzo-p-dioxin results in a futile cycle that inhibits hepatic β-oxidation.

2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD), a persistent environmental contaminant, induces steatosis by increasing hepatic uptake of dietary and mobilized peripheral fats, inhibiting lipoprotein export, and repressing β-oxidation. In this study, the mechanism of β-oxidation inhibition was investigated by testing the hypothesis that TCDD dose-dependently repressed straight-chain fatty acid oxidation gene expression in mice following oral gavage every 4 days for 28 days. Untargeted metabolomic analysis revealed a dose-dependent decrease in hepatic acyl-CoA levels, while octenoyl-CoA and dicarboxylic acid levels increased. TCDD also dose-dependently repressed the hepatic gene expression associated with triacylglycerol and cholesterol ester hydrolysis, fatty acid binding proteins, fatty acid activation, and 3-ketoacyl-CoA thiolysis while inducing acyl-CoA hydrolysis. Moreover, octenoyl-CoA blocked the hydration of crotonyl-CoA suggesting short chain enoyl-CoA hydratase (ECHS1) activity was inhibited. Collectively, the integration of metabolomics and RNA-seq data suggested TCDD induced a futile cycle of fatty acid activation and acyl-CoA hydrolysis resulting in incomplete β-oxidation, and the accumulation octenoyl-CoA levels that inhibited the activity of short chain enoyl-CoA hydratase (ECHS1).
Characterization of Super-Stable Matchings

An instance of the super-stable matching problem with incomplete lists and ties is an undirected bipartite graph \(G = (A \cup B, E)\), with an adjacency list being a linearly ordered list of ties. Ties are subsets of vertices equally good for a given vertex. An edge \((x,y) \in E \backslash M\) is a blocking edge for a matching M if by getting matched to each other neither of the vertices x and y would become worse off. Thus, there is no disadvantage if the two vertices would like to match up. A matching M is super-stable if there is no blocking edge with respect to M. It has previously been shown that super-stable matchings form a distributive lattice [14, 23] and the number of super-stable matchings can be exponential in the number of vertices. We give two compact representations of size O(m) that can be used to construct all super-stable matchings, where m denotes the number of edges in the graph. The construction of the second representation takes O(mn) time, where n denotes the number of vertices in the graph, and gives an explicit rotation poset similar to the rotation poset in the classical stable marriage problem. We also give a polyhedral characterization of the set of all super-stable matchings and prove that the super-stable matching polytope is integral, thus solving an open problem stated in the book by Gusfield and Irving [4].
Thrombopoietin level predicts response to treatment with romiplostim in chemotherapy-induced thrombocytopenia

Chemotherapy-induced thrombocytopenia (CIT) is a common complication of cancer treatment. Evidence has emerged supporting use of romiplostim to treat CIT but predicting clinical response to romiplostim is not possible. To determine utility of endogenous thrombopoietin (TPO) as a biomarker of romiplostim response, we performed an observational cohort study of patients with CIT and known baseline TPO levels receiving romiplostim. For weekly on-romiplostim platelet (Plt) count assessment, clinical response was defined as Plt ≥ 75 × 109 /L and ≥ 30 × 109 /L above pretreatment baseline. Overall, moderate, and superior classes of treatment response were defined based on fraction of Plt assessments meeting clinical response criteria (> 0, ≥ 0.6, and ≥ 0.8, respectively). Sixty-three patients with CIT were included; median age was 62 years, 41.3% were female, and median (IQR) romiplostim treatment duration was 14 (4-38) weeks. Median (IQR) TPO was lower in patients achieving moderate response to romiplostim vs those who did not, 234 (135-1085) pg/mL vs 665 (244-1970) pg/mL (p = .034) and lower still in patients achieving superior response vs those who did not, 212 (91-690) pg/mL versus 559 (173-1851) pg/mL (p = .023). Negative correlations were found between TPO level and baseline Plt and TPO level and response fraction. A positive correlation was found between TPO level and lowest effective romiplostim dose. In receiver operating characteristic (ROC) analysis, optimally discriminant TPO level thresholds (as defined by Youden's Index) were ≤ 457 pg/mL for moderate response and ≤ 260 pg/mL for superior response. In conclusion, TPO levels predict response to romiplostim in CIT, with lower levels predicting improved probability and depth of response.
Wedge-Lifted Codes

We define wedge-lifted codes, a variant of lifted codes, and we study their locality properties. We show that (taking the trace of) wedge-lifted codes yields binary codes with the $t$ -disjoint repair property ( $t$ -DRGP). When $t= N^{1/2d}$ , where $N$ is the block length of the code and $d\geq 2$ is any integer, our codes give improved trade-offs between redundancy and locality among binary codes.
Revealing the effects of temporal orienting of attention on response conflict using continuous movements

Orienting attention in time enables us to prepare for forthcoming perception and action (e.g., estimating the duration of a yellow traffic light when driving). While temporal orienting can facilitate performance on simple tasks, its influence on complex tasks involving response conflict is unclear. Here, we adapted the flanker paradigm to a choice-reaching task where participants used a computer mouse to reach to the left or right side of the screen, as indicated by the central arrow presented with either the congruent or incongruent flankers. We assessed the effects of temporal orienting by manipulating goal-driven temporal expectation (using probabilistic variations in target timing) and stimulus-driven temporal priming (using sequential repetitions versus switches in target timing). We tested how temporal orienting influenced the dynamics of response conflict resolution. Recent choice-reaching studies have indicated that under response conflict, delayed movement initiation captures the response threshold adjustment process, whereas increased curvature toward the incorrect response captures the degree of coactivation of the response alternatives during the controlled response selection process. Both temporal expectation and priming reduced the initiation latency regardless of response conflict, suggesting that both lowered response thresholds independently of response conflict. Notably, temporal expectation, but not temporal priming, increased the curvature toward the incorrect response on incongruent trials. These results suggest that temporal orienting generally increases motor preparedness, but goal-driven temporal orienting particularly interferes with response conflict resolution, likely through its influence on response thresholds. Overall, our study highlights the interplay between temporal orienting and cognitive control in goal-directed action.
BPLight-CNN: A Photonics-based Backpropagation Accelerator for Deep Learning

Author(s): Dang, D; Chittamuru, SVR; Pasricha, S; Mahapatra, R; Sahoo, D | Abstract: Training deep learning networks involves continuous weight updates across the various layers of the deep network while using a backpropagation algorithm (BP). This results in expensive computation overheads during training. Consequently, most deep learning accelerators today employ pre-trained weights and focus only on improving the design of the inference phase. The recent trend is to build a complete deep learning accelerator by incorporating the training module. Such efforts require an ultra-fast chip architecture for executing the BP algorithm. In this article, we propose a novel photonics-based backpropagation accelerator for high performance deep learning training. We present the design for a convolutional neural network, BPLight-CNN, which incorporates the silicon photonics-based backpropagation accelerator. BPLight-CNN is a first-of-its-kind photonic and memristor-based CNN architecture for end-to-end training and prediction. We evaluate BPLight-CNN using a photonic CAD framework (IPKISS) on deep learning benchmark models including LeNet and VGG-Net. The proposed design achieves (i) at least 34x speedup, 34x improvement in computational efficiency, and 38.5x energy savings, during training; and (ii) 29x speedup, 31x improvement in computational efficiency, and 38.7x improvement in energy savings, during inference compared to the state-of-the-art designs. All these comparisons are done at a 16-bit resolution; and BPLight-CNN achieves these improvements at a cost of approximately 6% lower accuracy compared to the state-of-the-art.
Asking Clarifying Questions Based on Negative Feedback in Conversational Search

Users often need to look through multiple search result pages or reformulate queries when they have complex information-seeking needs. Conversational search systems make it possible to improve user satisfaction by asking questions to clarify users' search intents. This, however, can take significant effort to answer a series of questions starting with "what/why/how". To quickly identify user intent and reduce effort during interactions, we propose an intent clarification task based on yes/no questions where the system needs to ask the correct question about intents within the fewest conversation turns. In this task, it is essential to use negative feedback about the previous questions in the conversation history. To this end, we propose a Maximum-Marginal-Relevance (MMR) based BERT model (MMR-BERT) to leverage negative feedback based on the MMR principle for the next clarifying question selection. Experiments on the Qulac dataset show that MMR-BERT outperforms state-of-the-art baselines significantly on the intent identification task and the selected questions also achieve significantly better performance in the associated document retrieval tasks.
End-to-end spoken language understanding for generalized voice assistants

End-to-end (E2E) spoken language understanding (SLU) systems predict utterance semantics directly from speech using a single model. Previous work in this area has focused on targeted tasks in fixed domains, where the output semantic structure is assumed a priori and the input speech is of limited complexity. In this work we present our approach to developing an E2E model for generalized SLU in commercial voice assistants (VAs). We propose a fully differentiable, transformer-based, hierarchical system that can be pretrained at both the ASR and NLU levels. This is then fine-tuned on both transcription and semantic classification losses to handle a diverse set of intent and argument combinations. This leads to an SLU system that achieves significant improvements over baselines on a complex internal generalized VA dataset with a 43% improvement in accuracy, while still meeting the 99% accuracy benchmark on the popular Fluent Speech Commands dataset. We further evaluate our model on a hard test set, exclusively containing slot arguments unseen in training, and demonstrate a nearly 20% improvement, showing the efficacy of our approach in truly demanding VA scenarios.
Delta Baryon Photoproduction with Twisted Photons

A future gamma factory at CERN or accelerator-based gamma sources elsewhere can include the possibility of energetic twisted photons, which are photons with a structured wave front that can allow a pre-defined large angular momentum along the beam direction. Twisted photons are potentially a new tool in hadronic physics, and we consider here one possibility, namely the photoproduction of $\Delta$(1232) baryons using twisted photons. We show that particular polarization amplitudes isolate the smaller partial wave amplitudes and they are measurable without interference from the terms that are otherwise dominant.
Fate of Developmental Mechanisms of Myocardial Plasticity in the Postnatal Heart

Whether the heart's organ-founding, progenitor cell gene regulatory networks (CPC-GRNs) are sustained after birth and can be therapeutically evoked for regeneration in response to disease, remains elusive. Here, we report a spatiotemporally resolved analysis of CPC-GRN deployment dynamics, through the pan-CPC-GRN gene Isl1. We show that the Isl1-CPC-GRNs that are deployed during early cardiogenesis and generate the cardiomyocyte majority from mesoderm, undergo programmed silencing through proteasome- and PRC2-mediated Isl1 repression, selectively in the arterial pole. In contrast, we identify a neural crest (CNC)-specific Wnt/β-catenin/Isl1-CPC-GRN that is deployed through the venous pole during cardiac growth and partitioning, and contributes a minority of cardiomyocytes which, in turn, expand massively to build ~10% of the biventricular myocardium. These "dorsal CNCs" continue to sporadically generate cardiomyocytes throughout postnatal growth which, however, are non-proliferative, suggesting that partitioning-like, fetal proliferation signals could be therapeutically targeted to evoke clonal expansion capacity in postnatal CNC-cardiomyocytes for heart regeneration.
Developing High Performance Secure Multi-Party Computation Protocols in Healthcare: A Case Study of Patient Risk Stratification

We demonstrate that secure multi-party computation (MPC) using garbled circuits is viable technology for solving clinical use cases that require cross-institution data exchange and collaboration. We describe two MPC protocols, based on Yao's garbled circuits and tested using large and realistically synthesized datasets. Linking records using private set intersection (PSI), we compute two metrics often used in patient risk stratification: high utilizer identification (PSI-HU) and comorbidity index calculation (PSI-CI). Cuckoo hashing enables our protocols to achieve extremely fast run times, with answers to clinically meaningful questions produced in minutes instead of hours. Also, our protocols are provably secure against any computationally bounded adversary in a semi-honest setting, the de-facto mode for cross-institution data analytics. Finally, these protocols eliminate the need for an implicitly trusted third-party "honest broker" to mediate the information linkage and exchange.
Estimation and selection for high-order Markov chains with Bayesian mixture transition distribution models

We develop a mixture model and diagnostic for Bayesian estimation and selection in high-order, discrete-state Markov chains. Both extend the mixture transition distribution, which constructs a tran...
Extending the Frequency Limits of "Postage-Stamp PIV" to MHz Rates

The effective frequency limits of postage-stamp PIV, in which a pulse-burst laser and very small fields of view combine to achieve high repetition rates, have been extended by increasing the PIV acquisition rate to very nearly MHz rates (990 kHz) by using a faster camera. Charge leaked through the camera shift register at these framing rates but this was shown not to bias the measurements. The increased framing rate provided oversampled data and enabled use of multi-frame correlation algorithms for a lower noise floor, increasing the effective frequency response to 240 kHz where the interrogation window size begins to spatially filter the data. The velocity spectra suggest turbulence power-law scaling in the inertial subrange steeper than the theoretical -5/3 scaling, attributed to an absence of isotropy.
Infinite Nature: Perpetual View Generation of Natural Scenes From a Single Image

We introduce the problem of perpetual view generation -- long-range generation of novel views corresponding to an arbitrarily long camera trajectory given a single image. This is a challenging problem that goes far beyond the capabilities of current view synthesis methods, which work for a limited range of viewpoints and quickly degenerate when presented with a large camera motion. Methods designed for video generation also have limited ability to produce long video sequences and are often agnostic to scene geometry. We take a hybrid approach that integrates both geometry and image synthesis in an iterative render, refine, and repeat framework, allowing for long-range generation that cover large distances after hundreds of frames. Our approach can be trained from a set of monocular video sequences without any manual annotation. We propose a dataset of aerial footage of natural coastal scenes, and compare our method with recent view synthesis and conditional video generation baselines, showing that it can generate plausible scenes for much longer time horizons over large camera trajectories compared to existing methods. Please visit our project page at this https URL.
Tightening the Biological Constraints on Gradient-Based Predictive Coding

Predictive coding (PC) is a general theory of cortical function. The local, gradient-based learning rules found in one kind of PC model have been shown to closely approximate back-propagation under certain conditions [18, 26, 32]. This finding suggests that this PC model may be useful for understanding how the brain solves the credit assignment problem. The model may also be useful for developing local learning algorithms that are compatible with neuromorphic hardware. In this paper, we modify this PC model so that it better fits biological constraints, including the constraints that neurons can only have positive firing rates and the constraint that synapses only flow in one direction. We also compute the gradient-based weight and activity updates given the modified activity values. We show that, under certain conditions, these modified PC networks perform as well or nearly as well on MNIST data as the unmodified PC model and networks trained with back-propagation.
Industry Networks and the Geography of Firm Behavior

Using a network approach that circumvents well-known challenges in estimating peer effects, we show that interactions with a firm’s geographic neighbors play a significant causal role in corporate ...
Remote Learning of COVID-19 Kinetic Analysis in a Physical Chemistry Laboratory Class.

The COVID-19 pandemic has affected many in-person laboratory courses across the world. The viral spreading model is complicated but parameters, such as its reproduction number, Rt, can be estimated with the susceptible, infectious, or recovered model. COVID-19 data for many states and countries are widely available online. This provides an opportunity for the students to analyze its spreading kinetics remotely. Here, we reported a laboratory set up online during the third week of the spring semester of 2021 to minimize social contacts. Due to the wide interest in developing online physical chemistry and analytical laboratories during the pandemic, we would like to share this laboratory design. The method, technique, procedure, and grading are described in this report. The student participants were able to apply the kinetic techniques learned in physical chemistry to successfully analyze an ongoing real-world problem through a remote learning environment and prepare this report.
Electrochemical Modeling of GITT Measurements for Improved Solid-State Diffusion Coefficient Evaluation

Galvanostatic Intermittent Titration Technique (GITT) is widely used to evaluate solid-state diffusion coefficients in electrochemical systems. However, the existing analysis methods for GITT data require numerous assumptions, and the derived diffusion coefficients typically are not independently validated. To investigate the validity of the assumptions and derived diffusion coefficients, we employ a direct pulse fitting method for interpreting GITT data that involves numerically fitting an electrochemical pulse and subsequent relaxation to a one-dimensional, single-particle, electrochemical model coupled with non-ideal transport to directly evaluate diffusion coefficients that are independently verified through cycling predictions. Extracted from GITT measurements of the intercalation regime of FeS2 and used to predict the discharge behavior, our non-ideal diffusion coefficients prove to be two orders of magnitude more accurate than ideal diffusion coefficients extracted using conventional methods. We further extend our model to a polydisperse set of particles to show the validity of a single-particle approach when the modeled radius is proportional to the total volume-to-surface-area ratio of the system.
Organized Labor Effects on SG&A Cost Behavior†

This study examines the effect of organized labor on corporate SG&A cost behavior. Organized labor could reduce the stickiness of SG&A costs through reducing a firm’s SG&A spending, or increase cost stickiness through raising adjustment costs. Using multiple data sources, we find that a stronger power of organized labor is associated with a lower level of SG&A cost stickiness. Further analyses indicate that this relationship is consistent with labor unions’ influence on managers’ spending incentive that arises from unions’ rent seeking behavior. Our results suggest that organized labor is an important determinant of SG&A cost behavior.
Counting curves with local tangency constraints

We construct invariants for any closed semipositive symplectic manifold which count rational curves satisfying tangency constraints to a local divisor. More generally, we introduce invariants involving multibranched local tangency constraints. We give a formula describing how these invariants arise as point constraints are pushed together in dimension four, and we use this to recursively compute all of these invariants in terms of Gromov--Witten invariants of blowups. As a key tool, we study analogous invariants which count punctured curves with negative ends on a small skinny ellipsoid.