cssi/SciSciGPT-SciSciNet huggingface dataset:
Project RISE: Recognizing Industrial Smoke Emissions

Industrial smoke emissions pose a significant concern to human health. Prior works have shown that using Computer Vision (CV) techniques to identify smoke as visual evidence can influence the attitude of regulators and empower citizens to pursue environmental justice. However, existing datasets are not of sufficient quality nor quantity to train the robust CV models needed to support air quality advocacy. We introduce RISE, the first large-scale video dataset for Recognizing Industrial Smoke Emissions. We adopted a citizen science approach to collaborate with local community members to annotate whether a video clip has smoke emissions. Our dataset contains 12,567 clips from 19 distinct views from cameras that monitored three industrial facilities. These daytime clips span 30 days over two years, including all four seasons. We ran experiments using deep neural networks to establish a strong performance baseline and reveal smoke recognition challenges. Our survey study discussed community feedback, and our data analysis displayed opportunities for integrating citizen scientists and crowd workers into the application of Artificial Intelligence for Social Impact.
Few-Shot Upsampling for Protest Size Detection

We propose a new task and dataset for a common problem in social science research: "upsampling" coarse document labels to fine-grained labels or spans. We pose the problem in a question answering format, with the answers providing the fine-grained labels. We provide a benchmark dataset and baselines on a socially impactful task: identifying the exact crowd size at protests and demonstrations in the United States given only order-of-magnitude information about protest attendance, a very small sample of fine-grained examples, and English-language news text. We evaluate several baseline models, including zero-shot results from rule-based and question-answering models, few-shot models fine-tuned on a small set of documents, and weakly supervised models using a larger set of coarsely-labeled documents. We find that our rule-based model initially outperforms a zero-shot pre-trained transformer language model but that further fine-tuning on a very small subset of 25 examples substantially improves out-of-sample performance. We also demonstrate a method for fine-tuning the transformer span on only the coarse labels that performs similarly to our rule-based approach. This work will contribute to social scientists' ability to generate data to understand the causes and successes of collective action.
Increasing paths in countable graphs

Abstract In this paper we study variations of an old result by Muller, Reiterman, and the last author stating that a countable graph has a subgraph with infinite degrees if and only if in any labeling of the vertices (or edges) of this graph by positive integers one can always find an infinite increasing path. We study corresponding questions for simple hypergraphs and paths of infinite as well as of arbitrary finite length. For example we show that the condition that a simple hypergraph contains a subhypergraph with infinite degrees is equivalent to the condition that any vertex labeling contains an infinite increasing loose path. We also find an equivalent condition for a graph to have a property that any vertex labeling with positive integers contains a path of arbitrary finite length.
New results and techniques in space radio astronomy.

The methods and results of early space radioastronomy experiments are reviewed, with emphasis on the RAE 1 spacecraft which was designed specifically and exclusively for radio astronomical studies. The RAE 1 carries two gravity-gradient-stabilized 229-m traveling-wave V-antennas, a 37-m dipole antenna, and a number of radiometer systems to provide measurements over the 0.2 to 9.2 MHz frequency range with a time resolution of 0.5 sec and an absolute accuracy of plus or minus 25%. Observations of solar bursts at frequencies down to 0.2 MHz provide new information on the density, plasma velocity, and dynamics of coronal streamers out to distances greater than 50 solar radii. New information on the distribution of the ionized component of the interstellar medium is being obtained from galactic continuum background maps at frequencies around 4 MHz. Cosmic noise background spectra measured down to 0.5 MHz produce new estimates on the interstellar flux of cosmic rays, on magnetic fields in the galactic halo, and on distant extragalactic radio sources.
Mitigating investment risk using modular technologies

Abstract We study logistical investment flexibility provided by modular processing technologies for mitigating risk. Specifically, we propose a multi-stage stochastic programming formulation that determines optimal capacity expansion plans that mitigate demand uncertainty. The formulation accounts for multi-product dependencies between small/large units and for trade-offs between expected profit and risk. The formulation uses a cumulative risk measure to avoid time-consistency issues of traditional, per-stage risk-minimization formulations, and we argue that this approach is more compatible with typical investment metrics such as the net present value. Case studies of different complexity are presented to illustrate the developments. Our studies reveal that the Pareto frontier of a flexible setting (allowing for deployment of small units) dominates the Pareto frontier of an inflexible setting (allowing only for deployment of large units). Notably, this dominance is prevalent despite benefits arising from economies of scale of large processing units.
Designing Human-Robot Coexistence Space

When the human-robot interactions become ubiquitous, the environment surrounding these interactions will have significant impact on the safety and comfort of the human and the effectiveness and efficiency of the robot. Although most robots are designed to work in the spaces created for humans, many environments, such as living rooms and offices, can be and should be redesigned to enhance and improve human-robot collaboration and interactions. This letter uses autonomous wheelchair as an example and investigates the computational design in the human-robot coexistence spaces. Given the room size and the objects $O$ in the room, the proposed framework computes the optimal layouts of $O$ that satisfy both human preferences and navigation constraints of the wheelchair. The key enabling technique is a motion planner that can efficiently evaluate hundreds of similar motion planning problems. Our implementation shows that the proposed framework can produce a design around three to five minutes on average comparing to 10 to 20 minutes without the proposed motion planner. Our results also show that the proposed method produces reasonable designs even for tight spaces and for users with different preferences.
Improving the Smoothed Complexity of FLIP for Max Cut Problems

Finding locally optimal solutions for MAX-CUT and MAX-k-CUT are well-known PLS-complete problems. An instinctive approach to finding such a locally optimum solution is the FLIP method. Even though FLIP requires exponential time in worst-case instances, it tends to terminate quickly in practical instances. To explain this discrepancy, the run-time of FLIP has been studied in the smoothed complexity framework. Etscheid and Roglin (ACM Transactions on Algorithms, 2017) showed that the smoothed complexity of FLIP for max-cut in arbitrary graphs is quasi-polynomial. Angel, Bubeck, Peres, and Wei (STOC, 2017) showed that the smoothed complexity of FLIP for max-cut in complete graphs is (OΦ5n15.1), where Φ is an upper bound on the random edge-weight density and Φ is the number of vertices in the input graph. While Angel, Bubeck, Peres, and Wei’s result showed the first polynomial smoothed complexity, they also conjectured that their run-time bound is far from optimal. In this work, we make substantial progress toward improving the run-time bound. We prove that the smoothed complexity of FLIP for max-cut in complete graphs is O(Φ n7.83). Our results are based on a carefully chosen matrix whose rank captures the run-time of the method along with improved rank bounds for this matrix and an improved union bound based on this matrix. In addition, our techniques provide a general framework for analyzing FLIP in the smoothed framework. We illustrate this general framework by showing that the smoothed complexity of FLIP for MAX-3-CUT in complete graphs is polynomial and for MAX-k-CUT in arbitrary graphs is quasi-polynomial. We believe that our techniques should also be of interest toward showing smoothed polynomial complexity of FLIP for MAX-k-CUT in complete graphs for larger constants k.
Learning Stochastic Decision Trees.

We give a quasipolynomial-time algorithm for learning stochastic decision trees that is optimally resilient to adversarial noise. Given an $\eta$-corrupted set of uniform random samples labeled by a size-$s$ stochastic decision tree, our algorithm runs in time $n^{O(\log(s/\varepsilon)/\varepsilon^2)}$ and returns a hypothesis with error within an additive $2\eta + \varepsilon$ of the Bayes optimal. An additive $2\eta$ is the information-theoretic minimum. Previously no non-trivial algorithm with a guarantee of $O(\eta) + \varepsilon$ was known, even for weaker noise models. Our algorithm is furthermore proper, returning a hypothesis that is itself a decision tree; previously no such algorithm was known even in the noiseless setting.
China – Xinjiang Uyghur Autonomous Region Regulation on De-Radicalization

In March 2017, Xinjiang, a territory in northwest China, enacted the Xinjiang Uyghur Autonomous Region Regulation on De-Radicalization (“2017 Regulation”), which designated fifteen types of statements and actions as “primary expressions of radicalization” and authorized punishment for nonconformity, including criminal penalties and forced participation in “individual and collective” education programs. Many of these designated statements and actions are not only common practices in Muslim communities but also mandated by traditional Islamic law. The 2017 Regulation, through restricting religious expression, has the effect of further stigmatizing the Islamic faith and dismantling the social infrastructure of Muslim communities in Xinjiang.
Quantitative resistance loci to southern rust mapped in a temperate maize diversity panel

Southern rust is a severe foliar disease of maize (Zea mays) resulting from infection with the obligate biotrophic fungus Puccinia polysora. This disease reduces photosynthetic productivity, which in turn reduces yields, with the greatest yield losses (up to 50%) associated with earlier onset infections. P. polysora urediniospores overwinter only in tropical and subtropical regions but cause outbreaks when environmental conditions favor initial infection. Increased temperatures and humidity during the growing season combined with an increased frequency of moderate winters are likely to increase the frequency of severe southern rust outbreaks in the US corn belt. In summer 2020, a severe outbreak of southern rust was observed in eastern Nebraska (NE), USA. We scored a replicated maize association panel planted in Lincoln, NE for disease severity and found that disease incidence and severity showed significant variation among maize genotypes. Genome-wide association studies identified four loci associated with significant quantitative variation in disease severity. These loci were associated with candidate genes with plausible links to quantitative disease resistance. A transcriptome-wide association study identified additional genes associated with disease severity. Together, these results indicate that substantial diversity in resistance to southern rust exists among current temperate-adapted maize germplasm, including several candidate loci that may explain the observed variation in resistance to southern rust.
Automatic Identification of Upper Extremity Rehabilitation Exercise Type and Dose Using Body-Worn Sensors and Machine Learning: A Pilot Study.

Background Prior studies suggest that participation in rehabilitation exercises improves motor function poststroke; however, studies on optimal exercise dose and timing have been limited by the technical challenge of quantifying exercise activities over multiple days. Objectives The objectives of this study were to assess the feasibility of using body-worn sensors to track rehabilitation exercises in the inpatient setting and investigate which recording parameters and data analysis strategies are sufficient for accurately identifying and counting exercise repetitions. Methods MC10 BioStampRC® sensors were used to measure accelerometer and gyroscope data from upper extremities of healthy controls (n = 13) and individuals with upper extremity weakness due to recent stroke (n = 13) while the subjects performed 3 preselected arm exercises. Sensor data were then labeled by exercise type and this labeled data set was used to train a machine learning classification algorithm for identifying exercise type. The machine learning algorithm and a peak-finding algorithm were used to count exercise repetitions in non-labeled data sets. Results We achieved a repetition counting accuracy of 95.6% overall, and 95.0% in patients with upper extremity weakness due to stroke when using both accelerometer and gyroscope data. Accuracy was decreased when using fewer sensors or using accelerometer data alone. Conclusions Our exploratory study suggests that body-worn sensor systems are technically feasible, well tolerated in subjects with recent stroke, and may ultimately be useful for developing a system to measure total exercise "dose" in poststroke patients during clinical rehabilitation or clinical trials.
Multiplicative Representations of Integers and Ramsey’s Theorem

Let \( \mathcal B= (B_1,\ldots , B_h)\) be an h-tuple of sets of positive integers. Let \(g_{ \mathcal B}(n)\) count the number of representations of n in the form \(n = b_1\cdots b_h\), where \(b_i \in B_i\) for all \(i \in \{1,\ldots , h\}\). It is proved that \(\liminf _{n\rightarrow \infty } g_{ \mathcal B}(n) \ge 2\) implies \(\limsup _{n\rightarrow \infty } g_{ \mathcal B}(n) = \infty \).
CFPNET: Channel-Wise Feature Pyramid For Real-Time Semantic Segmentation

Real-time semantic segmentation is playing a more important role in computer vision, due to the growing demand for mobile devices and autonomous driving. Therefore, it is very important to achieve a good trade-off among performance, model size and inference speed. In this paper, we propose a Channel-wise Feature Pyramid (CFP) module to balance those factors. Based on the CFP module, we built CFPNet for real-time semantic segmentation which applied a series of dilated convolution channels to extract effective features. Experiments on Cityscapes and CamVid datasets show that the proposed CFPNet achieves an effective combination of those factors. For the Cityscapes test dataset, CFPNet achieves 70.1% class-wise mIoU with only 0.55 million parameters and 2.5 MB memory. The inference speed can reach 30 FPS on a single RTX 2080Ti GPU with a 1024x2048-pixel image.
Top-Down Feedback Enables Flexible Coding Strategies in Olfactory Cortex

In chemical sensation, multiple models have been proposed to explain how odors are represented by patterns of neuronal activity in the olfactory cortex. One hypothesis is that the identity of combinations of active neurons within specific sniff-related time windows are critical for encoding information about odors. Another model is that patterns of neural activity evolve across time and it is this temporal structure that is essential for encoding odor information. Interestingly, we found that top-down feedback to the olfactory bulb dictates what information is transmitted to the olfactory cortex by switching between these two strategies. Using a detailed model of the early olfactory system, we demonstrate that feedback control of inhibitory granule cells in the main olfactory bulb influences the balance between excitatory and inhibitory synaptic currents in mitral cells, thereby restructuring the firing patterns of piriform cortical cells across time. This resulted in performance gains in both the accuracy and reaction time of odor discrimination tasks. These findings lead us to propose a new framework for early olfactory computation, one in which top-down feedback to the bulb flexibly controls the temporal structure of neural activity in olfactory cortex, allowing the early olfactory system to dynamically switch between two distinct models of coding
QFlow: A Learning Approach to High QoE Video Streaming at the Wireless Edge

The predominant use of wireless access networks is for media streaming applications, which are only gaining popularity as ever more devices become available for this purpose. However, current access networks treat all packets identically, and lack the agility to determine which clients are most in need of service at a given time. Software reconfigurability of networking devices has seen wide adoption, and this in turn implies that agile control policies can be now instantiated on access networks. The goal of this work is to design, develop and demonstrate QFlow, a learning approach to create a value chain from the application on one side, to algorithms operating over reconfigurable infrastructure on the other, so that applications are able to obtain necessary resources for optimal performance. Using YouTube video streaming as an example, we illustrate how QFlow is able to adaptively provide such resources and attain a high QoE for all clients at a wireless access point.
Supersymmetric field theories and the elliptic index theorem with complex coefficients

We present a cocycle model for elliptic cohomology with complex coefficients in which methods from 2–dimensional quantum field theory can be used to rigorously construct cocycles. For example, quantizing a theory of vector-bundle-valued fermions yields a cocycle representative of the elliptic Thom class. This constructs the complexified string orientation of elliptic cohomology, which determines a pushforward for families of rational string manifolds. A second pushforward is constructed from quantizing a supersymmetric σ–model. These two pushforwards agree, giving a precise physical interpretation for the elliptic index theorem with complex coefficients. This both refines and supplies further evidence for the long-conjectured relationship between elliptic cohomology and 2–dimensional quantum field theory. Analogous methods in supersymmetric mechanics recover path integral constructions of the Mathai–Quillen Thom form in complexified KO–theory and a cocycle representative of the Â–class for a family of oriented manifolds.
Farber’s conjecture for planar graphs

We prove that the ordered configuration spaces of planar graphs have the highest possible topological complexity generically, as predicted by a conjecture of Farber. Our argument establishes the same generic maximality for all higher topological complexities. We include some discussion of the non-planar case, demonstrating that the standard approach to the conjecture fails at a fundamental level.
Towards a Decomposition-Optimal Algorithm for Counting and Sampling Arbitrary Motifs in Sublinear Time

We consider the problem of sampling and approximately counting an arbitrary given motif H in a graph G, where access to G is given via queries: degree, neighbor, and pair, as well as uniform edge sample queries. Previous algorithms for these tasks were based on a decomposition of H into a collection of odd cycles and stars, denoted D^*(H) = {O_{k₁},...,O_{k_q}, S_{p₁},...,S_{p_𝓁}}. These algorithms were shown to be optimal for the case where H is a clique or an odd-length cycle, but no other lower bounds were known. We present a new algorithm for sampling arbitrary motifs which, up to poly(log n) factors, is always at least as good, and for most graphs G is strictly better. The main ingredient leading to this improvement is an improved uniform algorithm for sampling stars, which might be of independent interest, as it allows to sample vertices according to the p-th moment of the degree distribution. Finally, we prove that this algorithm is decomposition-optimal for decompositions that contain at least one odd cycle. These are the first lower bounds for motifs H with a nontrivial decomposition, i.e., motifs that have more than a single component in their decomposition.
The Labor Market Earnings of Veterans: Is Military Experience More or Less Valuable than Civilian Experience?

We assess the labor market experiences and earnings of military veterans, focusing on three major outcomes, among others, controlling for a wide array of demographic characteristics and industry and occupational fixed effects. First, we find that male and female veterans receive civilian earnings nearly equivalent to nonveteran men and women. This finding implies that military experience is valued in the labor market similarly to foregone civilian experience. Second, veterans are clustered in occupations with somewhat lower than average employment and real earnings growth, and in metropolitan areas with lower levels and growth of real GDP per capita. Third, veterans experience lower returns to formal educational investments (e.g., college) than do nonveterans. Veterans realize earnings gains from professional licenses, but their returns are lower than for nonveterans. These gains are concentrated among science, technology, engineering, and math (STEM) jobs, suggesting that veterans could help meet the growing demand for tech talent and artificial intelligence skills.
On the role of crystal defects on the lattice thermal conductivity of monolayer WSe2 (P63/mmc) thermoelectric materials by DFT calculation

Abstract As the energy problem becomes more prominent, research on thermoelectric (TE) materials has deepened over the past few decades. Low thermal conductivity enables thermoelectric materials better thermal conversion performance. In this study, based on the first principles and phonon Boltzmann transport equation, we studied the thermal conductivities of single-layer WSe2 under several defect conditions using density functional theory (DFT) as implemented in the Vienna Ab-initio Simulation Package (VASP). The lattice thermal conductivities of WSe2 under six kinds of defect states, i.e., PS, SS-c, DS-s, SW-c, SS-e, and DS-d, are 66.1, 41.2, 39.4, 8.8, 42.1, and 38.4 W/(m·K), respectively at 300 K. Defect structures can reduce thermal conductivity up to 86.7% (SW-c) compared with perfect structure. The influences of defect content, type, location factors on thermal properties have been discussed in this research. By introducing atom defects, we can reduce and regulate the thermal property of WSe2, which should provide an interesting idea for other thermoelectric materials to gain a lower thermal conductivity.
Evaluation of Saliva Self-Collection Devices for SARS-CoV-2 Diagnostics

There is an urgent need to expand testing for SARS-CoV-2 and other respiratory pathogens as the global community struggles to control the COVID-19 pandemic. Current diagnostic methods can be affected by supply chain bottlenecks and require the assistance of medical professionals, impeding the implementation of large-scale testing. Self-collection of saliva may solve these problems, as it can be completed without specialized training and uses generic materials. In this study, we observed thirty individuals who self-collected saliva using four different collection devices and analyzed their feedback. Two of these devices, a funnel and bulb pipette, were used to evaluate at-home saliva collection by 60 individuals. All devices enabled the safe, unsupervised self-collection of saliva. The quantity and quality of the samples received were acceptable for SARS-CoV-2 diagnostic testing, as determined by RNase P detection. Here, we demonstrate inexpensive, generic, buffer free collection devices suitable for unsupervised and home saliva self-collection. Funding Information: This work was funded by Tempus Labs, Inc (N.D.G and A.L.W), Yale Center for Clinical Investigation TL1 TR001864 (M.E.P.) and Fast Grant from Emergent Ventures at the Mercatus Center at George Mason University (N.D.G and A.L.W). Declaration of Interests: N.D.G. is a paid consultant for Tempus. The remaining authors declare no competing interests. Ethics Approval Statement: This study was conducted in accordance with an Institutional Review Board protocol reviewed and approved by the Yale University Human Research Protection Program (IRB Protocol ID: 2000028394).
Multi-Robot Distributed Semantic Mapping in Unfamiliar Environments through Online Matching of Learned Representations

We present a solution to multi-robot distributed semantic mapping of novel and unfamiliar environments. Most state-of-the-art semantic mapping systems are based on supervised learning algorithms that cannot classify novel observations online. While unsupervised learning algorithms can invent labels for novel observations, approaches to detect when multiple robots have independently developed their own labels for the same new class are prone to erroneous or inconsistent matches. These issues worsen as the number of robots in the system increases and prevent fusing the local maps produced by each robot into a consistent global map, which is crucial for cooperative planning and joint mission summarization. Our proposed solution overcomes these obstacles by having each robot learn an unsupervised semantic scene model online and use a multiway matching algorithm to identify consistent sets of matches between learned semantic labels belonging to different robots. Compared to the state of the art, the proposed solution produces 20-60% higher quality global maps that do not degrade even as many more local maps are fused.
Effectiveness for the Dual Ramsey Theorem

We analyze the dual Ramsey theorem for k partitions and l colors (DRTlk) in the context of reverse math, effective analysis, and strong reductions. Over RCA0, the dual Ramsey theorem stated for Baire colorings Baire-DRTlk is equivalent to the statement for clopen colorings ODRTlk and to a purely combinatorial theorem CDRTlk. When the theorem is stated for Borel colorings and k≥3, the resulting principles are essentially relativizations of CDRTlk. For each α, there is a computable Borel code for a Δα0-coloring such that any partition homogeneous for it computes ∅(α) or ∅(α−1) depending on whether α is infinite or finite. For k=2, we present partial results giving bounds on the effective content of the principle. A weaker version for Δn0-reduced colorings is equivalent to D2n over RCA0+IΣn−10 and in the sense of strong Weihrauch reductions.
Engineering a Non-Natural Photoenzyme for Improved Photon Efficiency*.

Photoenzymes are biological catalysts that use light to convert starting materials to products. These catalysts require photon absorption for each turnover, making quantum efficiency an important optimization parameter. Flavin-dependent 'ene'-reductases (EREDs) display latent photoenzymatic activity for synthetically valuable hydroalkylations; however, protein engineering has not been used to optimize this non-natural function. Here, we describe a protein engineering platform for the high throughput optimization of photoenzymes. A single round of engineering results in improved catalytic function toward the synthesis of 𝛾 , 𝛿 , 𝜀 -lactams, and acyclic amides. Mechanistic studies indicate that key mutations can alter the enzyme's excited state dynamics, enhance its photon efficiency, and ultimately increase catalyst performance. Transient absorption spectroscopy reveals that engineered variants display dramatically decreased radical lifetimes - indicating an evolution toward a concerted mechanism. Overall, this platform enables the development and optimization of photoenzymes for tailored applications in chemical synthesis.
A hierarchical behavior prediction framework at signalized intersections

Road user behavior prediction is one of the most critical components in trajectory planning for autonomous driving, especially in urban scenarios involving traffic signals. In this paper, a hierarchical framework is proposed to predict vehicle behaviors at a signalized intersection, using the traffic signal information of the intersection. The framework is composed of two phases: a discrete intention prediction phase and a continuous trajectory prediction phase. In the discrete intention prediction phase, a Bayesian network is adopted to predict the vehicle's high-level intention, after that, maximum entropy inverse reinforcement learning is utilized to learn the human driving model offline; during the online trajectory prediction phase, a driver characteristic is designed and updated to capture the different driving preferences between human drivers. We applied the proposed framework to one of the most challenging scenarios in autonomous driving: the yellow light running scenario. Numerical experiment results are presented in the later part of the paper which show the viability of the method. The accuracy of the Bayesian network for discrete intention prediction is 91.1%, and the prediction results are getting more and more accurate as the yellow time elapses. The average Euclidean distance error in continuous trajectory prediction is only 0.85 m in the yellow light running scenario.
Explanation and Use of Uncertainty Obtained by Bayesian Neural Network Classifiers for Breast Histopathology Images.

Despite the promise of Convolutional neural network (CNN) based classification models for histopathological images, it is infeasible to quantify its uncertainties. Moreover, CNNs may suffer from overfitting when the data is biased. We show that Bayesian–CNN can overcome these limitations by regularizing automatically and by quantifying the uncertainty. We have developed a novel technique to utilize the uncertainties provided by the Bayesian–CNN that significantly improves the performance on a large fraction of the test data (about 6% improvement in accuracy on 77% of test data). Further, we provide a novel explanation for the uncertainty by projecting the data into a low dimensional space through a nonlinear dimensionality reduction technique. This dimensionality reduction enables interpretation of the test data through visualization and reveals the structure of the data in a low dimensional feature space. We show that the Bayesian-CNN can perform much better than the state-of-the-art transfer learning CNN (TL-CNN) by reducing the false negative and false positive by 11% and 7.7% respectively for the present data set. It achieves this performance with only 1.86 million parameters as compared to 134.33 million for TL-CNN. Besides, we modify the Bayesian–CNN by introducing a stochastic adaptive activation function. The modified Bayesian–CNN performs slightly better than Bayesian–CNN on all performance metrics and significantly reduces the number of false negatives and false positives (3% reduction for both). We also show that these results are statistically significant by performing McNemar’s statistical significance test. This work shows the advantages of Bayesian-CNN against the state-of-the-art, explains and utilizes the uncertainties for histopathological images. It should find applications in various medical image classifications.
BFT Protocol Forensics

Byzantine fault-tolerant (BFT) protocols allow a group of replicas to come to consensus even when some of the replicas are Byzantine faulty. There exist multiple BFT protocols to securely tolerate an optimal number of faults t under different network settings. However, if the number of faults f exceeds t then security could be violated. In this paper we mathematically formalize the study of forensic support of BFT protocols: we aim to identify (with cryptographic integrity) as many of the malicious replicas as possible and in as distributed manner as possible. Our main result is that forensic support of BFT protocols depends heavily on minor implementation details that do not affect the protocol's security or complexity. Focusing on popular BFT protocols (PBFT, HotStuff, Algorand) we exactly characterize their forensic support, showing that there exist minor variants of each protocol for which the forensic supports vary widely. We show strong forensic support capability of LibraBFT, the consensus protocol of Diem cryptocurrency; our lightweight forensic module implemented on a Diem client is open-sourced and is under active consideration for deployment in Diem. Finally, we show that all secure BFT protocols designed for 2t+1 replicas communicating over a synchronous network forensic support is inherently nonexistent; this impossibility result holds for all BFT protocols and even if one has access to the states of all replicas (including Byzantine ones).
Measurement-Based Parameter Identification of DC-DC Converters with Adaptive Approximate Bayesian Computation

The recent advances in power plants and energy resources have extended the applications of DC-DC converters in the power systems (especially in the context of DC microgrids). Parameter identification can extract the parameters of the converters and generate accurate discrete simulation models. In this paper, we propose a measurement-based converter parameter calibration method by an adaptive Approximate Bayesian Computation with sequential Monte Carlo sampler (ABC SMC), which estimates the parameters related to passive and parasitic components. At first, we propose to find suitable prior distribution for the parameter which we do not know the prior information about them. With having prior distributions, we can use the ABC-SMC to find the exact values of the parameters of the converter. We chose the distance function carefully and based on the simulations we assigned the best method for the threshold sequencing. For improving the computationally of the algorithm, we propose an adaptive weight that helps the algorithm to find the optimal values with fewer simulations. The effectiveness of the proposed method is validated for a DC-DC buck converter. The results show that the proposed approach can accurately and efficiently estimate the posterior distributions of the buck parameters. The proposed algorithm can be applied to other parameter identifications and optimization applications such as rectifiers, filters, etc.
A novel transposable element based authentication protocol for Drosophila cell lines.

Drosophila cell lines are used by researchers to investigate various cell biological phenomena. It is crucial to exercise good cell culture practice. Poor handling can lead to both inter- and intraspecies cross-contamination. Prolonged culturing can lead to introduction of large- and small-scale genomic changes. These factors, therefore, make it imperative that methods to authenticate Drosophila cell lines are developed to ensure reproducibility. Mammalian cell line authentication is reliant on short tandem repeat (STR) profiling, however the relatively low STR mutation rate in D. melanogaster at the individual level is likely to preclude the value of this technique. In contrast, transposable elements (TE) are highly polymorphic among individual flies and abundant in Drosophila cell lines. Therefore, we investigated the utility of TE insertions as markers to discriminate Drosophila cell lines derived from the same or different donor genotypes, divergent sub-lines of the same cell line, and from other insect cell lines. We developed a PCR-based next-generation sequencing protocol to cluster cell lines based on the genome-wide distribution of a limited number of diagnostic TE families. We determined the distribution of five TE families in S2R+, S2-DRSC, S2-DGRC, Kc167, ML-DmBG3-c2, mbn2, CME W1 Cl.8+, and OSS Drosophila cell lines. Two independent downstream analyses of the NGS data yielded similar clustering of these cell lines. Double-blind testing of the protocol reliably identified various Drosophila cell lines. In addition, our data indicate minimal changes with respect to the genome-wide distribution of these five TE families when cells are passaged for at least 50 times. The protocol developed can accurately identify and distinguish the numerous Drosophila cell lines available to the research community, thereby aiding reproducible Drosophila cell culture research.
Limiting Spectrum of Randomized Hadamard Transform and Optimal Iterative Sketching Methods

We provide an exact analysis of the limiting spectrum of matrices randomly projected either with the subsampled randomized Hadamard transform, or truncated Haar matrices. We characterize this limiting distribution through its Stieltjes transform, a classical object in random matrix theory, and compute the first and second inverse moments. We leverage the limiting spectrum and asymptotic freeness of random matrices to obtain an exact analysis of iterative sketching methods for solving least squares problems. Our results also yield optimal step-sizes and convergence rates in terms of simple closed-form expressions. Moreover, we show that the convergence rate for Haar and randomized Hadamard matrices are identical, and uniformly improve upon Gaussian random projections. The developed techniques and formulas can be applied to a plethora of randomized algorithms that employ fast randomized Hadamard dimension reduction.
An Introduction to Modeling of Transport Processes: Applications to Biomedical Systems

Part I. Essential Steps: 1. Problem formulation 2. Software implementation: what to solve 3. Software implementation: how to solve (preprocessing) 4. Software implementation: visualizing and manipulating solution (postprocessing) 5. Validation, sensitivity analysis, optimization and debugging Part II. Case Studies: 6. Case studies Part III. Background Material: 7. Governing equations and boundary conditions 8. Source terms 9. Material properties and other input parameters 10. Solving the equations: numerical methods.
Novel Redox-Responsive Amphiphilic Copolymer Micelles for Drug Delivery: Synthesis and Characterization.

A novel redox-responsive amphiphilic polymer was synthesized with bioreductive trimethyl-locked quinone propionic acid for a potential triggered drug delivery application. The aim of this study was to synthesize and characterize the redox-responsive amphiphilic block copolymer micelles containing pendant bioreductive quinone propionic acid (QPA) switches. The redox-responsive hydrophobic block (polyQPA), synthesized from QPA-serinol and adipoyl chloride, was end-capped with methoxy poly(ethylene glycol) of molecular weight 750 (mPEG750) to achieve a redox-responsive amphiphilic block copolymer, polyQPA-mPEG750. PolyQPA-mPEG750 was able to self-assemble as micelles to show a critical micelle concentration (CMC) of 0.039% w/v (0.39 mg/ml, 0.107 mM) determined by a dye solubilization method using 1,6-diphenyl-1,3,5-hexatriene (DPH) in phosphate-buffered saline (PBS). The mean diameter of polymeric micelles was found to be 27.50 nm (PI = 0.064) by dynamic light scattering. Furthermore, redox-triggered destabilization of the polymeric micelles was confirmed by 1H-NMR spectroscopy and particle size measurements in a simulated redox state. PolyQPA-mPEG750 underwent triggered reduction to shed pendant redox-responsive QPA groups and its polymeric micelles were swollen to be dissembled in the presence of a reducing agent, thereby enabling the release of loaded model drug, paclitaxel. The redox-responsive polyQPA-mPEG750 polymer micelles would be useful as a drug delivery system allowing triggered drug release in an altered redox state such as tumor microenvironments with an altered redox potential and/or redox enzyme upregulation.
Abstract 5038: Role of the short isoform of the progesterone receptor in hormone-regulated invasiveness of breast cancer cells at pre- and post-menopausal levels of estrogen and progesterone

Proceedings: AACR 106th Annual Meeting 2015; April 18-22, 2015; Philadelphia, PA Introduction: High expression of the progesterone receptor (PR) isoform A, relative to PR isoform B, is clinically associated with progression to invasiveness in estrogen receptor (ER)-positive breast cancer and lower disease free survival. This is apparently at odds with in vitro studies implicating PR-B in mediating progestin-induced invasiveness. As the in vitro studies were conducted in the absence of estrogen (E2) signaling, we considered the possibility that modulation of E2 action may comprise a distinct aspect of the regulation of invasiveness by progestins. Methods: Standard ER+/PR+ and ER+/PR+/HER2+ breast cancer cell line models were studied, as well as recombinant cells expressing a single PR isoform. For the progestin hyper-sensitization studies, PR-A was over-expressed by lentiviral infection. Hormonal influence on invasiveness was studied using the Boyden chamber assay. Gene expression was examined by Illumina DNA microarray analysis and by Taq-Man real time RT-PCR assays. Results: E2 strongly suppressed invasiveness of ER+ breast cancer cells at concentrations below 0.01nM. At low (< 1 nM) concentrations, progesterone, R5020 and medroxyprogesterone acetate completely abrogated the inhibition of invasiveness by E2. It was only in a higher (5nM - 50 nM) concentration range that progestins induced invasiveness in the absence of E2. The ability of progestins to rescue invasiveness from E2 regulation was exclusively mediated by PR-A whereas PR-B mediated the E2-independent component of progestin-induced invasiveness. Overexpression of PR-A in PR-A+/PR-B+ cells lowered the progestin concentration needed to completely rescue invasiveness (to < 0.2 nM). Progesterone opposed E2 regulation of a distinctive set of genes, exclusively through PR-A. Conclusion: At relatively low concentrations, progesterone influences breast cancer cell invasiveness by rescuing it from E2 regulation via PR-A, whereas at higher concentrations the hormone also induces invasiveness independent of E2 signaling, through PR-B. The findings establish a direct functional link between PR-A and progression of luminal breast cancer in the context of the entire range of pre- and post-menopausal plasma and breast tissue estrogen and progesterone levels. Citation Format: Thomas B. McFall, Mugdha Patki, Rayna Rosati, Manohar Ratnam. Role of the short isoform of the progesterone receptor in hormone-regulated invasiveness of breast cancer cells at pre- and post-menopausal levels of estrogen and progesterone. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 5038. doi:10.1158/1538-7445.AM2015-5038
Solar energetic particles below 10 MeV

The information on solar acceleration and coronal propagation contained in low energy solar particle observations must be extracted from the effects of propagation in a dynamic interplanetary medium and the proximity of the earth's magnetosphere. The resulting separation reveals long-lived coronal injection and strong spatial ordering of coronal propagation.
Alternating Bi-Weekly Intravitreal Ranibizumab and Bevacizumab for Refractory Neovascular Age-Related Macular Degeneration with Pigment Epithelial Detachment.

AbstractObjective: To describe visual and anatomical outcomes following bi-weekly intravitreal ranibizumab/bevacizumab injections in eyes with refractory neovascular age-related macular degeneration (AMD) and pigment epithelial detachment (PED). Design: Retrospective, consecutive, interventional case series. Participants: Eighteen patients diagnosed with neovascular AMD that were refractory to anti-VEGF therapy and received alternating biweekly ranibizumab/bevacizumab injections were included. Methods: Patients with neovascular AMD and PED that were refractory to at least 11 monthly ranibizumab or bevacizumab injections were included in this study at a large, single retina practice. Following inclusion, patients received four bi-weekly alternating ranibizumab/bevacizumab intravitreal injections. After completing a course of four bi-weekly injections, patients were treated with variable regimens of intravitreal anti-vascular endothelial growth factor (VEGF) therapy. The primary outcomes of the study includ...
Chapter 5 Combining altimeter observations and oceanographie data for ocean circulation and climate studies

Abstract Calibrating Topography Experiment (TOPEX)JPoseidon (TJP) altimeter data to inverted echo sounder measurements is one of the methodologies developed to advance the study of the South Atlantic Ocean. The mass transport of the main boundary currents can be monitored using dynamic height time series obtained from altimeter-derived sea surface height anomalies. A 2-layer model yields upper-layer thickness and surface dynamic height of the ocean in areas of strong vertical stratification to identify and track anticyclonic rings, such as those shed from the Agulhas retroflection, which play an important role in the interocean exchange of heat and mass. Interannual variability of boundary currents, derived from analysis of the first five years of TJP data, is discussed. A combination of T/P data and in-situ observations provides significant contributions to the study of ocean dynamics for climate applications.
26 X 6.6 Radial-Belted Aircraft Tire Performance

Preliminary results from testing of 26 x 6.6 radial-belted and bias-ply aircraft tires at NASA Langley's Aircraft Landing Dynamics Facility (ALDF) are reviewed. The 26 x 6.6 tire size evaluation includes cornering performance tests throughout the aircraft ground operational speed range for both dry and wet runway surfaces. Static test results to define 26 x 6.6 tire vertical stiffness properties are also presented and discussed.
An Occupational Perspective on Leadership: Theoretical and Practical Dimensions, by Sandra Barker Dunbar

An Occupational Perspective on Leadership: Theoretical and Practical Dimensions is a concise review of leadership application and style regardless of profession. Written for the field of occupation...
Damaged Self I: Who Am I

A lot of people ask the question, who am I? You are the sum total of all of your life experiences plus two. Everything that you have experienced growing up makes you the person who you are plus genetics and nurturing. However, some of us just don’t realized how messed up we have become, until we find ourselves in repeated failed relationships. Since the 1960’s we have had a paradigm shift in the way we socialize and how families operated. Some of those social changes brought about a lot of trauma, self-destructive behavior, attachment problems, and problems with intimacy, jealousy, being over protective or controlling. These personality traits that we have developed have been absorbed into our subconscious from our environment and the skills that we used to cope with the problems.
Using state election results to predict the fortunes of national parties in federal polls in India

Using a multivariate regression analysis, this article examines linkages in major parties’ performance that contested legislative assembly and Parliamentary elections in India held within the subsequent 18 months over the period 1980–2009. The results are analysed according to a range of variables including party identity, incumbency and geographical region. The study’s finding that for a number of parties there is a strong and statistically significant linkage corroborates earlier studies and suggests that the allocation of resources by major parties to Legislative Assembly campaigns may be an effective strategy for maximising the party’s share of votes in subsequent Parliamentary elections.