cssi/SciSciGPT-SciSciNet huggingface dataset:
FastDepth: Fast Monocular Depth Estimation on Embedded Systems

Depth sensing is a critical function for robotic tasks such as localization, mapping and obstacle detection. There has been a significant and growing interest in depth estimation from a single RGB image, due to the relatively low cost and size of monocular cameras. However, state-of-the-art single-view depth estimation algorithms are based on fairly complex deep neural networks that are too slow for real-time inference on an embedded platform, for instance, mounted on a micro aerial vehicle. In this paper, we address the problem of fast depth estimation on embedded systems. We propose an efficient and lightweight encoder-decoder network architecture and apply network pruning to further reduce computational complexity and latency. In particular, we focus on the design of a low-latency decoder. Our methodology demonstrates that it is possible to achieve similar accuracy as prior work on depth estimation, but at inference speeds that are an order of magnitude faster. Our proposed network, FastDepth, runs at 178 fps on an NVIDIA Jetson TX2 GPU and at 27 fps when using only the TX2 CPU, with active power consumption under 10 W. FastDepth achieves close to state-of-the-art accuracy on the NYU Depth v2 dataset. To the best of the authors’ knowledge, this paper demonstrates real-time monocular depth estimation using a deep neural network with the lowest latency and highest throughput on an embedded platform that can be carried by a micro aerial vehicle.
Dexterous Manipulation with Deep Reinforcement Learning: Efficient, General, and Low-Cost

Dexterous multi-fingered robotic hands can perform a wide range of manipulation skills, making them an appealing component for general-purpose robotic manipulators. However, such hands pose a major challenge for autonomous control, due to the high dimensionality of their configuration space and complex intermittent contact interactions. In this work, we propose deep reinforcement learning (deep RL) as a scalable solution for learning complex, contact rich behaviors with multi-fingered hands. Deep RL provides an end-to-end approach to directly map sensor readings to actions, without the need for task specific models or policy classes. We show that contact-rich manipulation behavior with multi-fingered hands can be learned by directly training with model-free deep RL algorithms in the real world, with minimal additional assumption and without the aid of simulation. We learn to perform a variety of tasks on two different low-cost hardware platforms entirely from scratch, and further study how the learning can be accelerated by using a small number of human demonstrations. Our experiments demonstrate that complex multi-fingered manipulation skills can be learned in the real world in about 4-7 hours for most tasks, and that demonstrations can decrease this to 2-3 hours, indicating that direct deep RL training in the real world is a viable and practical alternative to simulation and model-based control. https:// sites.google.com/view/deeprl-handmanipulation
Multi-scale structural analysis of proteins by deep semantic segmentation.

Motivation Recent advances in computational methods have facilitated large-scale sampling of protein structures, leading to breakthroughs in protein structural prediction and enabling de novo protein design. Establishing methods to identify candidate structures that can lead to native folds or designable structures remains a challenge, since few existing metrics capture high-level structural features such as architectures, folds and conformity to conserved structural motifs. Convolutional Neural Networks (CNNs) have been successfully used in semantic segmentation-a subfield of image classification in which a class label is predicted for every pixel. Here, we apply semantic segmentation to protein structures as a novel strategy for fold identification and structure quality assessment. Results We train a CNN that assigns each residue in a multi-domain protein to one of 38 architecture classes designated by the CATH database. Our model achieves a high per-residue accuracy of 90.8% on the test set (95.0% average per-class accuracy; 87.8% average per-structure accuracy). We demonstrate that individual class probabilities can be used as a metric that indicates the degree to which a randomly generated structure assumes a specific fold, as well as a metric that highlights non-conformative regions of a protein belonging to a known class. These capabilities yield a powerful tool for guiding structural sampling for both structural prediction and design. Availability and implementation The trained classifier network, parser network, and entropy calculation scripts are available for download at https://git.io/fp6bd, with detailed usage instructions provided at the download page. A step-by-step tutorial for setup is provided at https://goo.gl/e8GB2S. All Rosetta commands, RosettaRemodel blueprints, and predictions for all datasets used in the study are available in the Supplementary Information. Supplementary information Supplementary data are available at Bioinformatics online.
An Improved Lower Bound for Sparse Reconstruction from Subsampled Hadamard Matrices

We give a short argument that yields a new lower bound on the number of subsampled rows from a bounded, orthonormal matrix necessary to form a matrix with the restricted isometry property. We show that a matrix formed by uniformly subsampling rows of an N × N Hadamard matrix contains a K-sparse vector in the kernel, unless the number of subsampled rows is Ω(K log K log (N/K)) --- our lower bound applies whenever min(K, N/K) > log^C N. Containing a sparse vector in the kernel precludes not only the restricted isometry property, but more generally the application of those matrices for uniform sparse recovery.
On Increasing Self-Confidence in Non-Bayesian Social Learning over Time-Varying Directed Graphs

We study the convergence of the log-linear non-Bayesian social learning update rule, for a group of agents that collectively seek to identify a parameter that best describes a joint sequence of observations. Contrary to recent literature, we focus on the case where agents assign decaying weights to its neighbors, and the network is not connected at every time instant but over some finite time intervals. We provide a necessary and sufficient condition for the rate at which agents decrease the weights and still guarantees social learning.
Labor Market Trends and the Changing Value of Time

Abstract During the past two decades, households experienced increases in their average wages and expenditures alongside with divergent trends in their wages, expenditures, and time allocation. We develop a model with incomplete asset markets and household heterogeneity in market and home technologies and preferences to account for these labor market trends and assess their welfare consequences. Using micro data on expenditures and time use, we identify the sources of heterogeneity across households, document how these sources have changed over time, and perform counterfactual analyses. Given the observed increase in leisure expenditures relative to leisure time and the complementarity of these inputs in leisure technology, we infer a significant increase in the average productivity of time spent on leisure. The increasing productivity of leisure time generates significant welfare gains for the average household and moderates negative welfare effects from the rising dispersion of expenditures and time allocation across households.
Probabilistic Circuits for Autonomous Learning: A Simulation Study

Modern machine learning is based on powerful algorithms running on digital computing platforms and there is great interest in accelerating the learning process and making it more energy efficient. In this paper we present a fully autonomous probabilistic circuit for fast and efficient learning that makes no use of digital computing. Specifically we use SPICE simulations to demonstrate a clockless autonomous circuit where the required synaptic weights are read out in the form of analog voltages. This allows us to demonstrate a circuit that can be built with existing technology to emulate the Boltzmann machine learning algorithm based on gradient optimization of the maximum likelihood function. Such autonomous circuits could be particularly of interest as standalone learning devices in the context of mobile and edge computing.
Genetic Architecture of Chilling Tolerance in Sorghum Dissected with a Nested Association Mapping Population.

Dissecting the genetic architecture of stress tolerance in crops is critical to understand and improve adaptation. In temperate climates, early planting of chilling-tolerant varieties could provide longer growing seasons and drought escape, but chilling tolerance ( 75,000 data points from ∼16,000 plots) in multi-environment field trials in Kansas under natural chilling stress (sown 30–45 days early) and normal growing conditions. Joint linkage mapping with early-planted field phenotypes revealed an oligogenic architecture, with 5–10 chilling tolerance loci explaining 20–41% of variation. Surprisingly, several of the major chilling tolerance loci co-localize precisely with the classical grain tannin (Tan1 and Tan2) and dwarfing genes (Dw1 and Dw3) that were under strong directional selection in the US during the 20th century. These findings suggest that chilling sensitivity was inadvertently selected due to coinheritance with desired nontannin and dwarfing alleles. The characterization of genetic architecture with NAM reveals why past chilling tolerance breeding was stymied and provides a path for genomics-enabled breeding of chilling tolerance.
The Fate of Deleterious Variants in a Barley Genomic Prediction Population

Targeted identification and purging of deleterious genetic variants has been proposed as a novel approach to animal and plant breeding. This strategy is motivated, in part, by the observation that demographic events and strong selection associated with cultivated species pose a "cost of domestication." This includes an increase in the proportion of genetic variants that are likely to reduce fitness. Recent advances in DNA resequencing and sequence constraint-based approaches to predict the functional impact of a mutation permit the identification of putatively deleterious SNPs (dSNPs) on a genome-wide scale. Using exome capture resequencing of 21 barley lines, we identified 3855 dSNPs among 497,754 total SNPs. We generated whole-genome resequencing data of Hordeum murinum ssp. glaucum as a phylogenetic outgroup to polarize SNPs as ancestral vs. derived. We also observed a higher proportion of dSNPs per synonymous SNPs (sSNPs) in low-recombination regions of the genome. Using 5215 progeny from a genomic prediction experiment, we examined the fate of dSNPs over three breeding cycles. Adjusting for initial frequency, derived alleles at dSNPs reduced in frequency or were lost more often than other classes of SNPs. The highest-yielding lines in the experiment, as chosen by standard genomic prediction approaches, carried fewer homozygous dSNPs than randomly sampled lines from the same progeny cycle. In the final cycle of the experiment, progeny selected by genomic prediction had a mean of 5.6% fewer homozygous dSNPs relative to randomly chosen progeny from the same cycle.
Lidar-Monocular Visual Odometry with Genetic Algorithm for Parameter Optimization.

Lidar-Monocular Visual Odometry (LIMO), an odometry estimation algorithm, combines camera and LIght Detection And Ranging sensor (LIDAR) for visual localization by tracking features from camera images and LIDAR measurements. LIMO then estimates the motion using Bundle Adjustment based on robust key frames. LIMO uses semantic labelling and weights of the vegetation landmarks for rejecting outliers. A drawback of LIMO as well as many other odometry estimation algorithms is that it has many parameters that need to be manually adjusted according to dynamic changes in the environment in order to decrease translational errors. In this paper, we present and argue the use of Genetic Algorithm (GA) to optimize parameters with reference to LIMO and maximize LIMO’s localization and motion estimation performance. We evaluate our approach on the well-known KITTI odometry dataset and show that the GA helps LIMO to reduce translation error in different datasets.
Elastomeric Microlattice Impact Attenuators

Summary Impact-attenuating materials are designed to absorb impact energy through the collapse of pores within the material below a threshold force (or acceleration), thereby mitigating damage or injury. Recent advances in additive manufacturing techniques have enabled the fabrication of cellular materials with architected lattice topology. Here it is demonstrated that, via design of cellular architecture, the dynamic stress-strain response of elastomeric lattices can be tailored to achieve impact-attenuation performance exceeding state-of-the-art foams for both single- and multi-hit scenarios. The additional degrees of freedom in the design of the cellular architecture of lattice-based impact attenuators are then leveraged to optimize their performance for a typical helmet impact scenario wherein the contact area increases during deformation. An improvement over state-of-the-art vinyl-nitrile foam helmet pads is achieved during a standard helmet test, leading to lower head acceleration. This breakthrough could pave the way to helmets with improved injury protection.
Integration of segmental and tonal information in speech perception: a cross-linguistic study

For speakers of a tone language, a close functional association exists between segmental structure and F0 contour (i.e., tone) in speech because both dimensions are needed to identify words. Using the speeded classification paradigm, which does not require lexical access, we examined the hypothesis that segmental and tonal dimensions are perceptually more strongly integrated for speakers of a tone language (Mandarin Chinese) than for speakers of a nontone language (English). In four classification tasks, requiring attention to one dimension (either segmental or tonal) of CV syllables while ignoring the other, both subject groups showed strong perceptual integrality (i.e., interference from orthogonal variation in the unattended dimension). The Chinese subjects showed significantly more integrality than the English subjects in only one of the four tasks. After correcting for the fact that subjects took longer to respond to tonal distinctions than to segmental distinctions, we interpreted the results as suggesting that Chinese and English listeners both show an underlying processing asymmetry between consonants and tones in CV syllables, whereas only Chinese listeners show such an asymmetry between vowels and tones (vowels being more integral with tones than vice versa). This may be a reflection of specific language experience.
Global Fossil-fuel Subsidy Reform and Paris Agreement

Abstract The recent downward trend in the international energy prices, combined with the rapid transition in the national energy mix and widespread efforts towards national energy market liberalization have challenged the mitigation potential of the global fossil-fuel subsidy reform. In this paper, we focus on 25 countries with large fossil-fuel consumption subsidies and estimate the impacts of fossil-fuel subsidy reform on achieving country-specific emissions reduction targets defined by the Nationally Determined Contributions (NDCs) under different oil price projections. Our results suggest that the elimination of the fossil-fuel consumption subsidies could be an effective mechanism for meeting NDC targets for an important subset of countries, even if only leading to global emissions reduction of between 1.8 and 3.2% in 2030 depending on the oil price scenario. Out of 17 countries with binding unconditional NDC targets, nine overreach their 2030 commitments under high oil price scenario and six under low global oil prices. Furthermore, eleven countries reach at least 50% of their NDC reduction targets following subsidies elimination under low global oil price path.
Controller Synthesis for Multi-Agent Systems With Intermittent Communication. A Metric Temporal Logic Approach

This paper develops a controller synthesis approach for a multi-agent system (MAS) with intermittent communication. We adopt a leader-follower scheme, where a mobile leader with absolute position sensors switches among a set of followers without absolute position sensors to provide each follower with intermittent state information. We model the MAS as a switched system. The followers are to asymptotically reach a predetermined consensus state. To guarantee the stability of the switched system and the consensus of the followers, we derive maximum and minimal dwell-time conditions to constrain the intervals between consecutive time instants at which the leader should provide state information to the same follower. Furthermore, the leader needs to satisfy practical constraints such as charging its battery and staying in specific regions of interest. Both the maximum and minimum dwell-time conditions and these practical constraints can be expressed by metric temporal logic (MTL) specifications. We iteratively compute the optimal control inputs such that the leader satisfies the MTL specifications, while guaranteeing stability and consensus of the followers. We implement the proposed method on a case study with three mobile robots as the followers and one quadrotor as the leader.
Deep learning of turbulent scalar mixing

Based on recent developments in physics-informed deep learning and deep hidden physics models, we put forth a framework for discovering turbulence models from scattered and potentially noisy spatio-temporal measurements of the probability density function.
Orbital debris characterization with impact flash signatures

Orbital debris is recognized as a serious and growing threat to man's utilization and exploration of space. While some information is available on the material composition of orbital debris, most measurements of orbital debris size and velocity distributions do not distinguish material type. The analysis and understanding of impact flash signatures can lead to an in-situ detector system with the ability to determine size and impact velocity distributions for orbital debris segregated by material type. This detector concept is based on an understanding of bow material shock propenies govern the flash signature arising from the impact of a piece of orbital debris (impactor) against a wimess plate (target). Analytical results are presented that identify the most promising wimess plate materials with respect to producing impact flash signatures that characterize the orbiral debris material.
Combining retrosynthesis and mixed-integer optimization for minimizing the chemical inventory needed to realize a WHO essential medicines list

The access to essential medicines remains a problem in many low-income countries for logistic and expiration limits, among other factors. Enabling flexible replenishment and easier supply chain management by on demand manufacturing from stored starting materials provides a solution to this challenge. Recent developments in computer-aided chemical synthesis planning have benefited from machine learning in different aspects. In this manuscript, we use those techniques to perform a combined analysis of a WHO essential medicines list to identify synthetic routes that minimize chemical inventory that would be required to synthesize the all the active pharmaceutical ingredients. We use a synthesis planning tool to perform retrosynthetic analyses for 99 targets and solve a mixed-integer programming problem to select a combination of pathways that uses the minimal number of chemicals. This work demonstrates the technical feasibility of reducing storage of active pharmaceutical ingredients to a minimal inventory of starting materials.
Unsupervised Learning of Depth and Ego-Motion From Cylindrical Panoramic Video

We introduce a convolutional neural network model for unsupervised learning of depth and ego-motion from cylindrical panoramic video. Panoramic depth estimation is an important technology for applications such as virtual reality, 3d modeling, and autonomous robotic navigation. In contrast to previous approaches for applying convolutional neural networks to panoramic imagery, we use the cylindrical panoramic projection which allows for the use of the traditional CNN layers such as convolutional filters and max pooling without modification. Our evaluation of synthetic and real data shows that unsupervised learning of depth and ego-motion on cylindrical panoramic images can produce high-quality depth maps and that an increased field-of-view improves ego-motion estimation accuracy. We also introduce Headcam, a novel dataset of panoramic video collected from a helmet-mounted camera while biking in an urban setting.
Inner Layer Capacitance of Organic Electrolytes from Constant Voltage Molecular Dynamics

The response of electrochemical interfaces to applied voltages dictates both the chemical and physical properties of electrochemical systems. The capacitance of an interface intrinsically depends o...
Secret-Shared Shuffle

Generating additive secret shares of a shuffled dataset - such that neither party knows the order in which it is permuted - is a fundamental building block in many protocols, such as secure collaborative filtering, oblivious sorting, and secure function evaluation on set intersection. Traditional approaches to this problem either involve expensive public-key based crypto or using symmetric crypto on permutation networks. While public-key-based solutions are bandwidth efficient, they are computation-heavy. On the other hand, constructions based on permutation networks are communication-bound, especially when the dataset contains large elements, for e.g., feature vectors in an ML context.
The multi-region index of a knot

Using region crossing changes, we define a new invariant called the multi-region index of a knot. We prove that the multi-region index of a knot is bounded from above by twice the crossing number o...
Anomalous protein kinetics on low-fouling surfaces

In this work, protein–surface interactions were probed in terms of adsorption and desorption of a model protein, bovine serum albumin, on a low-fouling surface with single-molecule localization microscopy. Single-molecule experiments enable precise determination of both adsorption and desorption rates. Strikingly the experimental data show anomalous desorption kinetics, evident as a surface dwell time that exhibits a power-law distribution, i.e. a heavy-tailed rather than the expected exponential distribution. As a direct consequence of this heavy-tailed distribution, the average desorption rate depends upon the time scale of the experiment and the protein surface concentration does not reach equilibrium. Further analysis reveals that the observed anomalous desorption emerges due to the reversible formation of a small fraction of soluble protein multimers (small oligomers), such that each one desorbs from the surface with a different rate. The overall kinetics can be described by a series of elementary reactions, yielding simple scaling relations that predict experimental observations. This work reveals a mechanistic origin for anomalous desorption kinetics that can be employed to interpret observations where low-protein fouling surfaces eventually foul when in long-term contact with protein solutions. The work also provides new insights that can be used to define design principles for non-fouling surfaces and to predict their performance.
Correction of Missing-Wedge Artifacts in Filamentous Tomograms by Template-Based Constrained Deconvolution

Cryo-electron tomography maps often exhibit considerable noise and anisotropic resolution, due to the low-dose requirements and the missing wedge in Fourier space. These spurious features are visually unappealing and, more importantly, prevent an automated segmentation of geometric shapes, requiring a subjective and labor-intensive manual tracing. We developed a novel computational strategy for objectively denoising and correcting missing-wedge artifacts in homogeneous specimen areas of tomograms, where it is assumed that a template repeats itself across the volume under consideration, as happens in the case of filaments. In our deconvolution approach, we use a template and a map of corresponding template locations, allowing us to compensate for the information lost in the missing wedge. We applied the method to tomograms of actin-filament bundles of inner-ear stereocilia, which are critical for the senses of hearing and balance. In addition, we demonstrate that our method can be used for cell membrane detection.
A trichotomy for rectangles inscribed in Jordan loops

We prove a general structural theorem about rectangles inscribed in Jordan loops. One corollary is that all but at most 4 points of any Jordan loop are vertices of inscribed rectangles. Another corollary is that a Jordan loop has an inscribed rectangle of every aspect ratio provided it has 3 points which are not vertices of inscribed rectangles.
Testing the Agreement of Trees with Internal Labels.

The input to the agreement problem is a collection \(\mathcal {P}= \{\mathcal {T}_1, \mathcal {T}_2, \dots , \mathcal {T}_k\}\) of phylogenetic trees, called input trees, over partially overlapping sets of taxa. The question is whether there exists a tree \(\mathcal {T}\), called an agreement tree, whose taxon set is the union of the taxon sets of the input trees, such that for each \(i \in \{1, 2, \dots , k\}\), the restriction of \(\mathcal {T}\) to the taxon set of \(\mathcal {T}_i\) is isomorphic to \(\mathcal {T}_i\). We give a \(\mathcal {O}(n k (\sum _{i \in [k]} d_i + \log ^2(nk)))\) algorithm for a generalization of the agreement problem in which the input trees may have internal labels, where n is the total number of distinct taxa in \(\mathcal {P}\), k is the number of trees in \(\mathcal {P}\), and \(d_i\) is the maximum number of children of a node in \(\mathcal {T}_i\).
A Variational Approach to Local Asymptotic and Exponential Stabilization of Nonlinear Systems

Local asymptotic stabilizability is a topic of great theoretical interest and practical importance. Broadly, if a system $\dot {x} = f(x,u)$ is locally asymptotically stabilizable, we are guaranteed a feedback controller u(x) that forces convergence to an equilibrium for trajectories initialized sufficiently close to it. A necessary condition was given by Brockett: such controllers exist only when f is open at the equilibrium. Recently, Gupta, Jafari, Kipka, and Mordukhovich considered a modification to this condition, replacing Brockett’s topological openness by the linear openness property of modern variational analysis. In this paper, we show that, under the linear openness assumption, there exists a local diffeomorphism rendering the system exponentially stabilizable by means of continuous stationary feedback laws. Introducing a transversality property and relating it to the above diffeomorphism, we prove that linear openness and transversality on a punctured neighborhood of an equilibrium is sufficient for local exponential stabilizability of systems with a rank-deficient linearization. The main result goes beyond the usual Kalman and Hautus criteria for the existence of exponential stabilizing feedback laws, since it allows us to handle systems for which exponential stabilization is achieved through higher order terms. However, it is implemented so far under a rather restrictive row-rank conditions. This suggests a twofold approach to the use of these properties: a pointwise version is enough to ensure stability via the linearization, while a local version is enough to overcome deficiencies in the linearization.
Live-Cell Imaging and Quantification of PolyQ Aggregates by Stimulated Raman Scattering of Selective Deuterium Labeling.

Polyglutamine (polyQ) diseases are a group of neurodegenerative disorders, involving the deposition of aggregation-prone proteins with long polyQ expansions. However, the cytotoxic roles of these a...
Gromov-Hausdorff limit of Wasserstein spaces on point clouds

We consider a point cloud $$X_n := \{ {\mathbf {x}}_1, \ldots , {\mathbf {x}}_n \}$$ uniformly distributed on the flat torus $${\mathbb {T}}^d : = \mathbb {R}^d / \mathbb {Z}^d $$, and construct a geometric graph on the cloud by connecting points that are within distance $$\varepsilon $$ of each other. We let $${\mathcal {P}}(X_n)$$ be the space of probability measures on $$X_n$$ and endow it with a discrete Wasserstein distance $$W_n$$ as introduced independently in Chow et al. (Arch Ration Mech Anal 203:969–1008, 2012), Maas (J Funct Anal 261:2250–2292, 2011) and Mielke (Nonlinearity 24:1329–1346, 2011) for general finite Markov chains. We show that as long as $$\varepsilon = \varepsilon _n$$ decays towards zero slower than an explicit rate depending on the level of uniformity of $$X_n$$, then the space $$({\mathcal {P}}(X_n), W_n)$$ converges in the Gromov–Hausdorff sense towards the space of probability measures on $${\mathbb {T}}^d$$ endowed with the Wasserstein distance. The analysis presented in this paper is a first step in the study of stability of evolution equations defined over random point clouds as the number of points grows to infinity.
Metric dimension and pattern avoidance in graphs

Abstract In this paper, we prove a number of results about pattern avoidance in graphs with bounded metric dimension or edge metric dimension. We show that the maximum possible number of edges in a graph of diameter D and edge metric dimension k is at most ( ⌊ 2 D 3 ⌋ + 1 ) k + k ∑ i = 1 ⌈ D 3 ⌉ ( 2 i ) k − 1 , sharpening the bound of k 2 + k D k − 1 + D k from Zubrilina (2018). We also show that the maximum value of n for which some graph of metric dimension ≤ k contains the complete graph K n as a subgraph is n = 2 k . We prove that the maximum value of n for which some graph of metric dimension ≤ k contains the complete bipartite graph K n , n as a subgraph is 2 Θ ( k ) . Furthermore, we show that the maximum value of n for which some graph of edge metric dimension ≤ k contains K 1 , n as a subgraph is n = 2 k . We also show that the maximum value of n for which some graph of metric dimension ≤ k contains K 1 , n as a subgraph is 3 k − O ( k ) . In addition, we prove that the d -dimensional grids ∏ i = 1 d P r i have edge metric dimension at most d . This generalizes two results of Kelenc et al. (2016), that non-path grids have edge metric dimension 2 and that d -dimensional hypercubes have edge metric dimension at most d . We also provide a characterization of n -vertex graphs with edge metric dimension n − 2 , answering a question of Zubrilina. As a result of this characterization, we prove that any connected n -vertex graph G such that edim ( G ) = n − 2 has diameter at most 5. More generally, we prove that any connected n -vertex graph with edge metric dimension n − k has diameter at most 3 k − 1 .
Spitzer’s perspective of polycyclic aromatic hydrocarbons in galaxies

Polycyclic aromatic hydrocarbon (PAH) molecules are abundant and widespread throughout the Universe, as revealed by their distinctive set of emission bands at 3.3, 6.2, 7.7, 8.6, 11.3 and 12.7 μm, which are characteristic of their vibrational modes. They are ubiquitously seen in a wide variety of astrophysical regions, ranging from planet-forming disks around young stars to the interstellar medium of the Milky Way and other galaxies out to high redshifts at z ≳ 4. PAHs profoundly influence the thermal budget and chemistry of the interstellar medium by dominating the photoelectric heating of the gas and controlling the ionization balance. Here I review the current state of knowledge of the astrophysics of PAHs, focusing on their observational characteristics obtained from the Spitzer Space Telescope and their diagnostic power for probing the local physical and chemical conditions and processes. Special attention is paid to the spectral properties of PAHs and their variations revealed by the Infrared Spectrograph onboard Spitzer across a much broader range of extragalactic environments (for example, distant galaxies, early-type galaxies, galactic halos, active galactic nuclei and low-metallicity galaxies) than was previously possible with the Infrared Space Observatory or any other telescope facilities. Also highlighted is the relation between the PAH abundance and the galaxy metallicity established for the first time by Spitzer. Spitzer revealed the power of astrophysical polycyclic aromatic hydrocarbon molecules to probe the local physical and chemical conditions and processes, for example, establishing the relation between their abundance and galaxy metallicity for the first time.
Flaming as part of aseptic technique increases CO2 (g) and decreases pH in freshwater culture media

Abstract Aseptic technique has historically served as a fundamental practice in microbiology, helping maintain culture purity and integrity. This technique has been widely encouraged and employed for use with cultures of heterotrophic bacteria as well as freshwater and marine algae. Yet, recent observations have suggested these approaches may bring their own influences. We observed variations in growth amongst replicate experimental cyanobacterial cultures upon flaming of the culture tube opening during sample transfer and collection. Investigation revealed the pH of culture media had decreased from the initial pH established during media preparation. Flaming of sterile culture media alone confirmed a significant decrease, by as much as 1.7 pH units, and correlated with increased flaming events over time. We hypothesized that the causative factor was the introduction of carbon dioxide into the media. To test this hypothesis, qualitative and quantitative analyses were used to identify the primary driver of pH decline. We further assessed the direct effects of flaming and subsequent pH changes on Microcystis aeruginosa cultures, showing flame-driven pH changes and/or the introduction of carbon dioxide influenced experimental results. Our observations provide a cautionary tale of how classic and well-accepted approaches may not always provide the results promised, suggesting new approaches may be necessary in research areas assessing pH or carbon related-effects on microbial communities.
An Unexpected Role of H During SiC Corrosion in Water

During aqueous corrosion, atoms in the solid react chemically with oxygen, leading either to the formation of an oxide film or to the dissolution of the host material. Commonly, the first step in c...
Targeting and exploitation of tumor-associated neutrophils to enhance immunotherapy and drug delivery for cancer treatment

Neutrophils, the most abundant leukocytes in human blood, are essential fighter immune cells against microbial infection. Based on the finding that neutrophils can either restrict or promote cancer progression, tumor-associated neutrophils (TAN) are classified into anti-tumor N1 and pro-tumor N2 subsets. One of the major mechanisms underlying the tumor-promoting function of N2-TANs is suppression of adaptive immune cells, in particular, cytotoxic T lymphocytes. Currently, no established methodologies are available that can unequivocally distinguish immunosuppressive TANs and granulocytic/polymorphonuclear myeloid-derived suppressor cells (G/PMN-MDSC). In view of the critical role of PMN-MDSCs in immune evasion and resistance to cancer immunotherapy, as established from data obtained with diverse cancer models, therapeutic strategies targeting these cells have been actively developed to enhance the efficacy of immunotherapy. Here, we have reviewed the available literature on strategies targeting PMN-MDSCs and summarized the findings into four categories: (1) depletion of existing PMN-MDSCs, (2) blockade of the development of PMN-MDSCs, (3) blockade of PMN-MDSC recruitment, (4) inhibition of immunosuppressive function. Owing to their high mobility to inflamed organs and ability to trespass the blood-brain barrier, neutrophils are outstanding candidate carriers in nanoparticle-based therapies. Another attractive application of neutrophils in cancer therapy is the use of neutrophil membrane-derived nanovesicles as a surrogate of extracellular vesicles for more efficient and scalable drug delivery. In the second part of the review, we have highlighted recent advances in the field of neutrophil-based cancer drug delivery. Overall, we believe that neutrophil-based therapeutics are a rapidly growing area of cancer therapy with significant potential benefits.
Almost minimizers of the one-phase free boundary problem

We consider almost minimizers to the one-phase energy functional and we prove their optimal Lipschitz regularity and partial regularity of their free boundary. These results were recently obtained ...
The Desert Grassland, Past and Present

Originally an area of open grassland or grassland with scattered shrubs (Fig. 1), much of this area today supports a mixture of shrubs and grasses with shrubs dominant. In Arizona, mesquite (Prosopis juliflora (Swartz) DC.), burroweed (Aplopappus tenuisectus (Greene) Blake), and snakeweed (Gutierrezia spp.), together with acacias (Acacia spp.) and a number of cacti are the principal shrubs. In eastern Arizona, across southern New Mexico and into Texas, mesquite, creosote bush (Larrea tridentata (DC.) Coville), Spanish bayonet (Yucca spp.), tarbush (Flourensia cernua DC.), cacti, and a variety of other thorny shrubs overtop the grasses. The increase in shrubs has been ascribed to several causes. These have included overgrazing, dissemination of the seeds by grazing animals, and fire control. There seems to be little doubt that more than one factor has been responsible in most instances. The essential cessation of grassland fires, however, and those factors related to this change, appear to have had more to do with the increase in brush than any others. Over rather extensive areas the grasses originally dominant have largely disappeared. In others, there is a fairly good understory of many of the same grasses that were common when the country was first occupied by white men. Although there is a wide variety of grasses within the desert grassland, only a few are sufficiently abundant to be classed as association dominants. Shantz and Zon (1924) subdivides the desert grassland on a basis of dominant grasses into four communities: black grama (Bouteloua eriopoda), crowfoot (Rothrock) grama (B. rothrockii), curly mesquite (Hilaria belangeri) and tobosa grass (H. mutica). It would seem that a fifth, blue grama (Bouteloua gracilis), should have been listed also. Although there have been extenisive changes in the floristic composition of these communities in the 28 years that have elapsed since this classification was made, a similar breakdown would be valid if made today. In general, the black grama association is best developed in New Mexico and Texas, the Rothrock grama and curly mesquite in Arizona, the tobosa grass and the blue grama throughout the Southwest. In Arizona, the Rothrock grama community seems to have changed more down through the years than any of the others. Most of the areas at one time dominated by Rothrock grama have been
Chapter 2 – Five-Power-Law Creep

In pure metals and Class M alloys (similar creep behavior similar to pure metals), there is an early, established, largely phenomenological relationship between the steady-state strain rate, ɛ˙ss (or creep rate), and stress, σss, for steady-state five-power-law (PL) creep: equation(3) ɛ˙ss=A0exp[−Qc/kT](σss/E)n where A0 is a constant, k is Boltzmann's constant, and E is Young's modulus (although, as will be discussed subsequently, the shear modulus, G, can also be used). This is consistent with Norton's Law [34]. The activation energy for creep, Qc, has been found to often be about that of lattice self-diffusion, Qsd. The exponent n is constant and is about 5 (4–7) for pure metals, ceramics, and many alloys over a relatively wide range of temperatures and strain rates (hence “five-power-law” behavior) until the temperature decreases below roughly 0.5–0.6 Tm, where power-law breakdown (PLB) occurs, and n increases and Qc generally decreases. Steady-state creep is often emphasized over primary or tertiary creep due to the relatively large fraction of creep life within this regime. The importance of steady-state creep is evidenced by the empirical Monkman-Grant relationship [35]: equation(4) ɛ˙ssm″tf=kMG where tf is the time to rupture and m″m″ and kMG are constants.
Trade Diversion and Production Sharing

Abstract This paper examines the repercussions of cross-border production sharing for the welfare effects of preferential trade liberalization. In a general-equilibrium context, a free trade agreement (FTA), which incorporates production sharing, raises the likelihood of welfare improvement. Thus, two members of a free trade area, who each have comparative disadvantage in the production of a final product relative to a non-member, may nevertheless enjoy net trade creation if they jointly possess comparative advantage in key components of that product. At a minimum, cross-border production sharing reduces the trade-diverting elements of an FTA. It follows, that rules of origin, viewed as constraints on cross-border fragmentation, augment the negative, trade-diverting elements of free trade areas.
ACHIEVING EFFICIENCY AND EQUITY IN IRRIGATION MANAGEMENT: AN OPTIMIZATION MODEL OF THE EL ANGEL WATERSHED, CARCHI, ECUADOR

The objective of this paper is to address the problems of inefficiency and inequity in water allocation in the El Angel watershed, located in Ecuador's Sierra region. Water is captured in a high-altitude region of the watershed and distributed downstream to producers in four elevation-defined zones via a system of canals. Upstream and downstream producers face radically different conditions with respect to climate and terrain. A mathematical programming model was created to study the consequences of addressing chronic water scarcity problems in the watershed by shifting water resources between the four zones. The model captures the nature of water use by humans, crops and dual purpose cattle. Its objective function maximizes producer welfare as measured by aggregate gross margin, subject to limited supplies of land, labor and water. Five water allocation scenarios are evaluated with respect to efficiency in land and water use and equity in income distribution. Results reveal that although water is the primary constrained resource downstream, in the upstream zones, land is far more scarce. The current distribution of water rights does not consider these differences and therefore is neither efficient nor equitable. Improvements in efficiency (resource use) and equity (income distribution) are associated with (1) a shift of water to the lower zone, and (2) the use of lower levels of irrigation intensity upstream. Furthermore, the scenarios that result in the most efficient use of resources also bring the greatest degree of equity in income distribution, indicating that these may be complementary, not conflicting, goals.
13 – DC–DC Converters

Publisher Summary Modern electronic systems require high quality, small, lightweight, reliable, and efficient power supplies. Linear power regulators, whose principle of operation is based on a voltage or current divider, are inefficient. The dc–dc converters can be divided into hard-switching pulse width modulated (PWM) converters and resonant and soft-switching converters. This chapter deals with the former type of dc–dc converters. The step-down dc–dc converter is commonly known as a buck converter. The state of the converter in which the inductor current is never zero for any period of time is called the continuous conduction mode (CCM). The CCM is preferred for high efficiency and good utilization of semiconductor switches and passive components. The DCM may be used in applications with special control requirements, since the dynamic order of the converter is reduced. It is uncommon to mix these two operating modes because of different control algorithms. An economical mean of achieving such isolation is to employ a transformer version of a dc–dc converter. High-frequency transformers are of a small size and weight and provide high efficiency.
Improving College Performance and Retention the Easy Way: Unpacking the ACT Exam

Improving college performance and retention can be difficult. We propose a simple and low-cost change in the way colleges use the ACT exam in their admission decisions that can greatly increase their ability to identify students at a high risk of underperforming and dropping out. Specifically, we show that only two of the four subtests of the ACT, English and Mathematics, can effectively predict outcomes in college. This result is robust across various samples, specifications and outcome measures. We demonstrate that by eliminating the noise associated with the two nonpredictive subtests, student-college matches can be significantly improved. (JEL I23)
Challenges for Rechargeable Li Batteries

The challenges for further development of Li rechargeable batteries for electric vehicles are reviewed. Most important is safety, which requires development of a nonflammable electrolyte with either a larger window between its lowest unoccupied molecular orbital (LUMO) and highest occupied molecular orbital (HOMO) or a constituent (or additive) that can develop rapidly a solid/ electrolyte-interface (SEI) layer to prevent plating of Li on a carbon anode during a fast charge of the battery. A high Li-ion conductivity (σ Li > 10 ―4 S/cm) in the electrolyte and across the electrode/ electrolyte interface is needed for a power battery. Important also is an increase in the density of the stored energy, which is the product of the voltage and capacity of reversible Li insertion/extraction into/from the electrodes. It will be difficult to design a better anode than carbon, but carbon requires formation of an SEI layer, which involves an irreversible capacity loss. The design of a cathode composed of environmentally benign, low-cost materials that has its electrochemical potential μ C well-matched to the HOMO of the electrolyte and allows access to two Li atoms per transition-metal cation would increase the energy density, but it is a daunting challenge. Two redox couples can be accessed where the cation redox couples are "pinned" at the top of the O 2p bands, but to take advantage of this possibility, it must be realized in a framework structure that can accept more than one Li atom per transition-metal cation. Moreover, such a situation represents an intrinsic voltage limit of the cathode, and matching this limit to the HOMO of the electrolyte requires the ability to tune the intrinsic voltage limit. Finally, the chemical compatibility in the battery must allow a long service life.
On character formulas for simple and tilting modules

Abstract We show that the characters of tilting modules can be used, in a concrete and explicit way, to obtain the simple characters of a connected reductive algebraic group G over an algebraically closed field k of characteristic p, for all p. Thus, once an algorithm for computing the characters of the indecomposable tilting G-modules has been found, an algorithm for the simple characters has been also. An immediate implication is that work by Achar, Makisumi, Riche, and Williamson can be used to obtain the characters for simple G-modules when p > h , the Coxeter number of G.