cssi/SciSciGPT-SciSciNet huggingface dataset:
An integer program and new lower bounds for computing the strong rainbow connection numbers of graphs

We present an integer programming model to compute the strong rainbow connection number $src(G)$ of any simple graph $G$. We introduce several enhancements to the proposed model, including a fast heuristic, a novel class of valid inequalities, and a variable elimination scheme. Moreover, we present a novel lower bound for $src(G)$ which may be of independent research interest. We evaluate our model with a traditional branch and cut approach as well as an alternative scheme based on iterative lower bound improvement, which we show to be highly effective in practice. To our knowledge, these are the first computational methods for the strong rainbow connection problem. We demonstrate the efficacy of our methods by computing the strong rainbow connection numbers of graphs with up to $167$ vertices.
Space-time wave packets violate the universal relationship between angular dispersion and pulse-front tilt.

Introducing angular dispersion into a pulsed field tilts the pulse front with respect to the phase front. There exists between the angular dispersion and pulse-front tilt a universal relationship that is device-independent, and also independent of the pulse shape and bandwidth. We show here that this relationship is violated by propagation-invariant space–time (ST) wave packets, which are pulsed beams endowed with precise spatiotemporal structure corresponding to a particular form of angular dispersion. We demonstrate theoretically and experimentally that ST wave packets represent, to the best of our knowledge, the first example in optics of non-differentiable angular dispersion, resulting in pulse-front tilt that depends on the square-root of the pulse bandwidth.
Effective diffusion equation in a random velocity field

The effects are studied of assumed random velocity fields on diffusion in a binary fluid. Random velocity fields can result, for example, from the high-frequency components of residual accelerations onboard spacecraft (often called g-jitter). An effective diffusion equation is derived for an average concentration which includes spatial and temporal correlations induced by the fluctuating velocity fields assumed to be Gaussianly distributed. The resulting equation becomes nonlocal, and if correlations between different components of the velocity field exist, it is also anisotropic. The simple limiting case of short correlation times is discussed and an effective diffusivity is obtained which reflects the enhanced mixing caused by the velocity fields. The results obtained in the limit of short correlation times are valid even if the probability distribution of the velocity field is not Gaussian.
Depolymerized lamins link nuclear envelope breakdown to mitotic transcriptional quiescence

The nuclear lamina, composed of polymers of nuclear lamins, maintains the structural integrity of animal nuclei and tethers heterochromatin to the nuclear periphery. Lamins depolymerize upon nuclear envelope breakdown at mitosis, however, no specific functions for depolymerized lamins have been described. We show that depolymerized Lamin A/C (dLMNA) links nuclear envelope breakdown to mitotic transcriptional quiescence in human fibroblasts. dLMNA was Ser22-phosphorylated and released to the nuclear interior during G2, immediately preceeding mitosis. dLMNA bound to active promoters and enhancers genome-wide in G2. dLMNA-bound genes were downregulated at G2/M. Following LMNA deletion, dLMNA-bound genes were de-repressed. RNA Polymerase II pausing at derepressed gene promoters and dLMNA-bound enhancers was also abrogated. In Hutchinson-Gilford progeria cells, dLMNA-bound genes were de-repressed, linking failed dLMNA-mediated transcriptional repression to progeria pathogenesis. We suggest that dLMNA targets active regulatory elements in G2 to promote RNA Polymerase II pausing, providing a novel molecular model for mitotic transcriptional quiescence.
The mathematics of tie knots

In 2000, Thomas Fink and Yong Mao studied neck ties and, with certain assumptions, found 85 different ways to tie a neck tie. They gave a formal language which describes how a tie is made, giving a sequence of moves for each neck tie. The ends of a neck tie can be joined together, which gives a physical model of a mathematical knot that we call a tie knot. In this paper we classify the knot type of each of Fink and Mao’s 85 tie knots. We describe how the unknot, left and right trefoil, twist knots and (2,p) torus knots can be recognized from their sequence of moves. We also view tie knots as a family within the set of all knots. Among other results, we prove that any tie knot is prime and alternating.
Identification and Physiological Significance of Temporal Nfκb Signaling Codewords Deployed by Macrophages to Classify Immune Threats

Inflammatory pathologies often involve dysregulated macrophage functions. In health, macrophages are immune sentinels that initiate inflammatory responses via the transcription factor NFkB. The temporal pattern of NFkB activity determines which genes are expressed, but how such temporal signaling code works in primary immune cells remains unexplored. For this study, we developed tools to enable high-throughput analysis of live, primary macrophages responding to host- and pathogen-derived stimuli. An information-theoretic workflow identified six dynamical features that constitute codewords that convey stimulus information to the nucleus. In particular, oscillatory trajectories are a hallmark of the responses to host cytokine TNF. Remarkably, examining macrophages derived from a systemic autoimmune disease model suggests that confusion of two NFkB signaling codewords, and thus miscoding of TNF as a pathogen-derived stimulus, may underlie sporadic inflammatory pathology. Overall, this study identifies six codewords of the temporal NFkB signaling code for classifying immune threats and demonstrates their biological significance.
Probing the spin structure of the fractional quantum Hall magnetoroton with polarized Raman scattering

Starting from the Luttinger model for the band structure of GaAs, we derive an effective theory that describes the coupling of the fractional quantum Hall (FQH) system with photons in resonant Raman scattering experiments. Our theory is applicable in the regime when the energy of the photons $\omega_0$ is close to the energy gap $E_G$, but $|\omega_0-E_G|$ is much larger than the energy scales of the quantum Hall problem. In the literature, it is often assumed that Raman scattering measures the dynamic structure factor $S(\omega,\mathbf{k})$ of the FQH. However, in this paper, we find that the light scattering spectrum measured in the experiments are proportional to the spectral densities of a pair of operators which we identified with the spin-2 components of the kinetic part of the stress tensor. In contrast with the dynamic structure factor, these spectral densities do not vanish in the long-wavelength limit $k\to0$. We show that Raman scattering with circularly polarized light can measure the spin of the magnetoroton excitation in the FQH system. We give an explicit expression for the kinetic stress tensor that works on any Landau level and which can be used for numerical calculations of the spectral densities that enter the Raman scattering amplitudes. We propose that Raman scattering provides a way to probe the bulk of the $\nu=5/2$ quantum Hall state to determine its nature.
Programming active cohesive granular matter with mechanically induced phase changes.

At the macroscale, controlling robotic swarms typically uses substantial memory, processing power, and coordination unavailable at the microscale, e.g., for colloidal robots, which could be useful for fighting disease, fabricating intelligent textiles, and designing nanocomputers. To develop principles that can leverage physical interactions and thus be used across scales, we take a two-pronged approach: a theoretical abstraction of self-organizing particle systems and an experimental robot system of active cohesive granular matter that intentionally lacks digital electronic computation and communication, using minimal (or no) sensing and control. As predicted by theory, as interparticle attraction increases, the collective transitions from dispersed to a compact phase. When aggregated, the collective can transport non-robot “impurities,” thus performing an emergent task driven by the physics underlying the transition. These results reveal a fruitful interplay between algorithm design and active matter robophysics that can result in principles for programming collectives without the need for complex algorithms or capabilities.
Elucidating the mechanisms of damage in foam core sandwich composites under impact loading and low temperatures

Recent interest in Arctic exploration has brought new challenges concerning the mechanical behavior of lightweight materials for offshore structures. Exposure to seawater and cold temperatures are ...
Graph Computing for Financial Crime and Fraud Detection: Trends, Challenges and Outlook

The rise of digital payments has caused consequential changes in the financial crime landscape. As a result, traditional fraud detection approaches such as rule-based systems have largely become in...
Moore-Penrose inverses of the signless Laplacian and edge-Laplacian of graphs

Abstract The signless Laplacian Q and signless edge-Laplacian S of a given graph may or may not be invertible. The Moore-Penrose inverses of Q and S are studied. In particular, using the incidence matrix, we find combinatorial formulas of the Moore-Penrose inverses of Q and S for trees. Also, we present combinatorial formulas of the inverses of Q and S for odd unicyclic graphs.
Neural Compression and Filtering for Edge-assisted Real-time Object Detection in Challenged Networks

The edge computing paradigm places compute-capable devices - edge servers - at the network edge to assist mobile devices in executing data analysis tasks. Intuitively, offloading compute-intense tasks to edge servers can reduce their execution time. However, poor conditions of the wireless channel connecting the mobile devices to the edge servers may degrade the overall capture-to-output delay achieved by edge offloading. Herein, we focus on edge computing supporting remote object detection by means of Deep Neural Networks (DNNs), and develop a framework to reduce the amount of data transmitted over the wireless link. The core idea we propose builds on recent approaches splitting DNNs into sections - namely head and tail models - executed by the mobile device and edge server, respectively. The wireless link, then, is used to transport the output of the last layer of the head model to the edge server, instead of the DNN input. Most prior work focuses on classification tasks and leaves the DNN structure unaltered. Herein, our focus is on DNNs for three different object detection tasks, which present a much more convoluted structure, and modify the architecture of the network to: (i) achieve in-network compression by introducing a bottleneck layer in the early layers on the head model, and (ii) prefilter pictures that do not contain objects of interest using a convolutional neural network. Results show that the proposed technique represents an effective intermediate option between local and edge computing in a parameter region where these extreme point solutions fail to provide satisfactory performance. The code and trained models are available at https://github.com/yoshitomo-matsubaralhnd-ghnd-object-detectors.
FIXME: Enhance Software Reliability with Hybrid Approaches in Cloud

With the promise of reliability in cloud, more enterprises are migrating to cloud. The process of continuous integration/deployment (CICD) in cloud connects developers who need to deliver value faster and more transparently with site reliability engineers (SREs) who need to manage applications reliably. SREs feed back development issues to developers, and developers commit fixes and trigger CICD to redeploy. The release cycle is more continuous than ever, thus the code to production is faster and more automated. To provide this higher level agility, the cloud platforms become more complex in the face of flexibility with deeper layers of virtualization. However, reliability does not come for free with all these complexities. Software engineers and SREs need to deal with wider information spectrum from virtualized layers. Therefore, providing correlated information with true positive evidences is critical to identify the root cause of issues quickly in order to reduce mean time to recover (MTTR), performance metrics for SREs. Similarity, knowledge, or statistics driven approaches have been effective, but with increasing data volume and types, an individual approach is limited to correlate semantic relations of different data sources. In this paper, we introduce FIXME to enhance software reliability with hybrid diagnosis approaches for enterprises. Our evaluation results show using hybrid diagnosis approach is about 17% better in precision. The results are helpful for both practitioners and researchers to develop hybrid diagnosis in the highly dynamic cloud environment.
Non-Financial Corporate Debt in Advanced Economies, 2010-17

This paper studies the evolution of non-financial corporate debt among publicly listed companies in major advanced economies between 2010 and 2017. Since 2010, firms have started to rely more on corporate bond markets and have used part of their debt to increase their holdings of cash. In our sample of some 5,000 firms, we find substantial differences across countries, industries, firms, and years in leverage and debt maturity, and we also identify time factors that are common drivers of capital structures. Within countries, loosening an index of financial conditions seems to be associated with lengthening debt maturity after controlling for firms' characteristics. Across firms and countries, leveraging and lengthening debt maturity have been greater where economic growth was stronger. Tighter financial conditions are positively associated with an increase in short-term debt financing. Quantile regressions suggest that there is substantial heterogeneity among firms on how they react to macro-financial conditions: large increases in long-term debt financing and large declines in short-term debt financing tend to be driven more by better macroeconomic performance, while large increases in short-term debt financing are more strongly impacted by tighter financial conditions. Since the paper uses data up to 2017, it does not reflect developments that occurred during the coronavirus pandemic. Nonetheless, sensitivity analysis shows that a significant amount of corporate debt, representing more than 5 percent of GDP, could be at risk in some countries, with an adverse spillover to the financial system if financial conditions tighten or economic growth slows down. This suggests that vulnerabilities should be closely monitored and policy action taken if warranted.
Fast period searches using the Lomb–Scargle algorithm on Graphics Processing Units for large datasets and real-time applications

Abstract Computing the periods of variable objects is well-known to be computationally expensive. Modern astronomical catalogs contain a significant number of observed objects. Therefore, even if the period ranges for particular classes of objects are well-constrained due to expected physical properties, periods must be derived for a tremendous number of objects. In this paper, we propose a GPU-accelerated Lomb–Scargle period finding algorithm that computes periods for single objects or for batches of objects as is necessary in many data processing pipelines. We demonstrate the performance of several optimizations, including comparing the use of shared and global memory GPU kernels and using multiple CUDA streams to copy periodogram data from the GPU to the host. Also, we quantify the difference between 32-bit and 64-bit floating point precision on two classes of GPUs, and show that the performance degradation of using 64-bit over 32-bit is greater on the CPU than a GPU designed for scientific computing. We find that the GPU algorithm achieves superior performance over the baseline parallel CPU implementation, achieving a speedup of up to 174.53 × . The Vera C. Rubin Observatory will carry out the Legacy Survey of Space and Time (LSST). We perform an analysis that shows we can derive the rotation periods of batches of Solar System objects at LSST scale in near real-time, which will be employed in a future LSST event broker. All source codes have been made publicly available.
Modeling Human Mental States with an Entity-based Narrative Graph

Understanding narrative text requires capturing characters’ motivations, goals, and mental states. This paper proposes an Entity-based Narrative Graph (ENG) to model the internal- states of characters in a story. We explicitly model entities, their interactions and the context in which they appear, and learn rich representations for them. We experiment with different task-adaptive pre-training objectives, in-domain training, and symbolic inference to capture dependencies between different decisions in the output space. We evaluate our model on two narrative understanding tasks: predicting character mental states, and desire fulfillment, and conduct a qualitative analysis.
Spectroscopy of quasibound states formed by molecular collisions in the presence of a laser

A theory is reported which describes spectroscopic transitions between quasibound states formed during molecular collisions in a laser field. A preliminary calculation suggests that the shapes of absorption lines are sensitive to the shapes of the potentials; hence a method for determining the latter. The calculation also shows that respectable cross-sections for scattering can be obtained at low laser powers.
On the Slightly Perturbed De Gregorio Model on $$S^1$$

It is conjectured that the generalization of the Constantin–Lax–Majda model (gCLM) $$\omega _t + a u\omega _x = u_x \omega $$ , due to Okamoto, Sakajo and Wunsch, can develop a finite time singularity from smooth initial data for $$a < 1$$ . For the endpoint case where a is close to and less than 1, we prove finite time asymptotically self-similar blowup of gCLM on a circle from a class of smooth initial data. For the gCLM on a circle with the same initial data, if the strength of advection a is slightly larger than 1, we prove that the solution exists globally with $$|| \omega (t)||_{H^1}$$ decaying in a rate of $$O(t^{-1})$$ for large time. The transition threshold between two different behaviors is $$a=1$$ , which corresponds to the De Gregorio model.
CARPe Posterum: A Convolutional Approach for Real-Time Pedestrian Path Prediction

Pedestrian path prediction is an essential topic in computer vision and video understanding. Having insight into the movement of pedestrians is crucial for ensuring safe operation in a variety of applications including autonomous vehicles, social robots, and environmental monitoring. Current works in this area utilize complex generative or recurrent methods to capture many possible futures. However, despite the inherent real-time nature of predicting future paths, little work has been done to explore accurate and computationally efficient approaches for this task. To this end, we propose a convolutional approach for real-time pedestrian path prediction, CARPe. It utilizes a variation of Graph Isomorphism Networks in combination with an agile convolutional neural network design to form a fast and accurate path prediction approach. Notable results in both inference speed and prediction accuracy are achieved, improving FPS considerably in comparison to current state-of-the-art methods while delivering competitive accuracy on well-known path prediction datasets.
Aberrant Epithelial Differentiation Contributes to Pathogenesis in a Murine Model of Congenital Tufting Enteropathy.

Background & Aims Congenital tufting enteropathy (CTE) is an intractable diarrheal disease of infancy caused by mutations of epithelial cell adhesion molecule (EpCAM). The cellular and molecular basis of CTE pathology has been elusive. We hypothesized that the loss of EpCAM in CTE results in altered lineage differentiation and defects in absorptive enterocytes thereby contributing to CTE pathogenesis. Methods Intestine and colon from mice expressing a CTE-associated mutant form of EpCAM (mutant mice) were evaluated for specific markers by quantitative real-time polymerase chain reaction, Western blotting, and immunostaining. Body weight, blood glucose, and intestinal enzyme activity were also investigated. Enteroids derived from mutant mice were used to assess whether the decreased census of major secretory cells could be rescued. Results Mutant mice exhibited alterations in brush-border ultrastructure, function, disaccharidase activity, and glucose absorption, potentially contributing to nutrient malabsorption and impaired weight gain. Altered cell differentiation in mutant mice led to decreased enteroendocrine cells and increased numbers of nonsecretory cells, though the hypertrophied absorptive enterocytes lacked key features, causing brush border malfunction. Further, treatment with the Notch signaling inhibitor, DAPT, increased the numbers of major secretory cell types in mutant enteroids (graphical abstract 1). Conclusions Alterations in intestinal epithelial cell differentiation in mutant mice favor an increase in absorptive cells at the expense of major secretory cells. Although the proportion of absorptive enterocytes is increased, they lack key functional properties. We conclude that these effects underlie pathogenic features of CTE such as malabsorption and diarrhea, and ultimately the failure to thrive seen in patients.
Central moment analysis for cost accumulators in probabilistic programs

For probabilistic programs, it is usually not possible to automatically derive exact information about their properties, such as the distribution of states at a given program point. Instead, one can attempt to derive approximations, such as upper bounds on tail probabilities. Such bounds can be obtained via concentration inequalities, which rely on the moments of a distribution, such as the expectation (the first raw moment) or the variance (the second central moment). Tail bounds obtained using central moments are often tighter than the ones obtained using raw moments, but automatically analyzing central moments is more challenging. This paper presents an analysis for probabilistic programs that automatically derives symbolic upper and lower bounds on variances, as well as higher central moments, of cost accumulators. To overcome the challenges of higher-moment analysis, it generalizes analyses for expectations with an algebraic abstraction that simultaneously analyzes different moments, utilizing relations between them. A key innovation is the notion of moment-polymorphic recursion, and a practical derivation system that handles recursive functions. The analysis has been implemented using a template-based technique that reduces the inference of polynomial bounds to linear programming. Experiments with our prototype central-moment analyzer show that, despite the analyzer’s upper/lower bounds on various quantities, it obtains tighter tail bounds than an existing system that uses only raw moments, such as expectations.
Adversarial Training and Provable Robustness: A Tale of Two Objectives

We propose a principled framework that combines adversarial training and provable robustness verification for training certifiably robust neural networks. We formulate the training problem as a joint optimization problem with both empirical and provable robustness objectives and develop a novel gradient-descent technique that can eliminate bias in stochastic multi-gradients. We perform both theoretical analysis on the convergence of the proposed technique and experimental comparison with state-of-the-arts. Results on MNIST and CIFAR-10 show that our method can consistently match or outperform prior approaches for provable l∞ robustness. Notably, we achieve 6.60% verified test error on MNIST at e = 0.3, and 66.57% on CIFAR-10 with e = 8/255.
EECBS: A Bounded-Suboptimal Search for Multi-Agent Path Finding.

Multi-Agent Path Finding (MAPF), i.e., finding collision-free paths for multiple robots, is important for many applications where small runtimes are necessary, including the kind of automated warehouses operated by Amazon. CBS is a leading two-level search algorithm for solving MAPF optimally. ECBS is a bounded-suboptimal variant of CBS that uses focal search to speed up CBS by sacrificing optimality and instead guaranteeing that the costs of its solutions are within a given factor of optimal. In this paper, we study how to decrease its runtime even further using inadmissible heuristics. Motivated by Explicit Estimation Search (EES), we propose Explicit Estimation CBS (EECBS), a new bounded-suboptimal variant of CBS, that uses online learning to obtain inadmissible estimates of the cost of the solution of each high-level node and uses EES to choose which high-level node to expand next. We also investigate recent improvements of CBS and adapt them to EECBS. We find that EECBS with the improvements runs significantly faster than the state-of-the-art bounded-suboptimal MAPF algorithms ECBS, BCP-7, and eMDD-SAT on a variety of MAPF instances. We hope that the scalability of EECBS enables additional applications for bounded-suboptimal MAPF algorithms.
Calculation of helicopter airfoil characteristics for high tip-speed applications

In this paper, we have applied a new aerodynamic tool to the study of helicopter airfoil characteristics. We have shown that the computed airloads reproduce completely the experimental behavior of representative airfoils across the transonic regime. In addition, the computational details of the flow fields, the surface pressure distributions, and the viscous-layer characteristics enable us to trace the evolution of the physical changes that occur as m infinity or Re increases. Descriptions of the complicated development of shock waves, shock-induced separation supplement the information that has been obtained heretofore in wind tunnels. In validating our calculations and assessing the accuracy of the results, including extensive grid-refinement studies and comparisons with data from numerous wind tunnels, we have defined the capabilities and limitations of the code ARC2D more precisely. This important aspect of the investigations can complement wind-tunnel tests, by providing flow-field details that are difficult to measure and by extending the range of low parameters beyond the capabilities of existing wind tunnels. The code has now progressed from a purely research stage to almost a production stage, where it can be run by specialists in the helicopter industry.
Machine unlearning via Algorithmic stability

We study the problem of machine unlearning and identify a notion of algorithmic stability, Total Variation (TV) stability, which we argue, is suitable for the goal of exact unlearning. For convex risk minimization problems, we design TV-stable algorithms based on noisy Stochastic Gradient Descent (SGD). Our key contribution is the design of corresponding efficient unlearning algorithms, which are based on constructing a near-maximal coupling of Markov chains for the noisy SGD procedure. To understand the trade-offs between accuracy and unlearning efficiency, we give upper and lower bounds on excess empirical and populations risk of TV stable algorithms for convex risk minimization. Our techniques generalize to arbitrary non-convex functions, and our algorithms are differentially private as well.
On the Spectra of Separable 2D Almost Mathieu Operators

We consider separable 2D discrete Schrodinger operators generated by 1D almost Mathieu operators. For fixed Diophantine frequencies, we prove that for sufficiently small couplings the spectrum must be an interval. This complements a result by J. Bourgain establishing that for fixed couplings the spectrum has gaps for some (positive measure) Diophantine frequencies. Our result generalizes to separable multidimensional discrete Schrodinger operators generated by 1D quasiperiodic operators whose potential is analytic and whose frequency is Diophantine. The proof is based on the study of the thickness of the spectrum of the almost Mathieu operator and utilizes the Newhouse Gap Lemma on sums of Cantor sets.
Investigating socio-spatial differences between solo ridehailing and pooled rides in diverse communities

Abstract Transformative mobility services present both considerable opportunities and challenges for urban mobility systems. Increasing attention is being paid to ridehailing platforms and connections between demand and continuous innovation in service features; one of these features is dynamic ride-pooling. To disentangle how ridehailing impacts existing transportation networks and its ability to support economic vitality and community livability it is essential to consider the distribution of demand across diverse communities. In this paper we expand the literature on ridehailing demand by exploring community variation and spatial dependence in ridehailing use. Specifically, we investigate the diffusion and role of solo requests versus ride-pooling to shed light on how different mobility services, with different environmental and accessibility implications, are used by diverse communities. This paper employs a Social Disadvantage Index, Transit Access Analysis, and a Spatial Durbin Model to investigate the influence of both local and spatial spillover effects on the demand for shared and solo ridehailing. The analysis of 127 million ridehailing rides, of which 15% are pooled, confirms the presence of spatial effects. Results indicate that density and vibrancy variables have analogue effects, both direct and indirect, on demand for solo vs pooled rides. Instead, our analysis reveals significant contrasting effects for socio-economic disadvantage, which is positively correlated with ride-pooling and negatively with solo rides. Additionally, we find that higher rail transit access is associated with higher demand for both solo and pooled ridehailing along with substantial spatial spillovers. We discuss implications for policy, operations and research related to the novel insight on how pooled ridesourcing relate to geography, living conditions, and transit interactions.
Actions of diagonal endomorphisms on conformally invariant measures on the 2-torus

Let $$\nu $$ be a probability measure that is ergodic under the endomorphism $$(\times p, \times p)$$ of the torus $${\mathbb {T}}^2$$ , such that $$\dim \pi \mu < \dim \mu $$ for some non-principal projection $$\pi $$ . We show that, if both $$m\ne n$$ are independent of p, the $$(\times m, \times n)$$ orbits of $$\nu $$ typical points will equidistribute towards the Lebesgue measure. If $$m>p$$ then typically the $$(\times m, \times p)$$ orbits will equidistribute towards the product of the Lebesgue measure with the marginal of $$\mu $$ on the y-axis. We also prove results in the same spirit for certain self similar measures. These are higher dimensional analogues of results due (among others) to Host, Lindenstrauss, and Hochman-Shmerkin.
Transcriptome features of striated muscle aging and predictability of protein level changes

We performed total RNA sequencing and multi-omics analysis comparing skeletal muscle and cardiac muscle in young adult (4 months) vs. early aging (20 months) mice to examine the molecular mechanisms of striated muscle aging. We observed that aging cardiac and skeletal muscles both invoke transcriptomic changes in innate immune system and mitochondria pathways but diverge in extracellular matrix processes. On an individual gene level, we identified 611 age-associated signatures in skeletal and cardiac muscles, including a number of myokine and cardiokine encoding genes. Because RNA and protein levels correlate only partially, we reason that differentially expressed transcripts that accurately reflect their protein counterparts will be more valuable proxies for proteomic changes and by extension physiological states. We applied a computational data analysis workflow to estimate which transcriptomic changes are more likely relevant to protein-level regulation using large proteogenomics data sets. We estimate about 48% of the aging-associated transcripts predict protein levels well (r ≥ 0.5). In parallel, a comparison of the identified aging-regulated genes with public human transcriptomics data showed that only 35-45% of the identified genes show an age-dependent expression in corresponding human tissues. Thus, integrating both RNA-protein correlation and human conservation across data sources, we nominate 134 prioritized aging striated muscle signatures that are predicted to correlate strongly with protein levels and that show age-dependent expression in humans. The results here reveal new details into how aging reshapes gene expression in striated muscles at the transcript and protein levels.
Visible light photonic integrated Brillouin laser.

Narrow linewidth visible light lasers are critical for atomic, molecular and optical (AMO) physics including atomic clocks, quantum computing, atomic and molecular spectroscopy, and sensing. Stimulated Brillouin scattering (SBS) is a promising approach to realize highly coherent on-chip visible light laser emission. Here we report demonstration of a visible light photonic integrated Brillouin laser, with emission at 674 nm, a 14.7 mW optical threshold, corresponding to a threshold density of 4.92 mW μm-2, and a 269 Hz linewidth. Significant advances in visible light silicon nitride/silica all-waveguide resonators are achieved to overcome barriers to SBS in the visible, including 1 dB/meter waveguide losses, 55.4 million quality factor (Q), and measurement of the 25.110 GHz Stokes frequency shift and 290 MHz gain bandwidth. This advancement in integrated ultra-narrow linewidth visible wavelength SBS lasers opens the door to compact quantum and atomic systems and implementation of increasingly complex AMO based physics and experiments.
Frame Spectral Pairs and Exponential Bases

Given a domain $$\varOmega \subset {\mathbb {R}}^d$$ with positive and finite Lebesgue measure and a discrete set $$\varLambda \subset {\mathbb {R}}^d$$ , we say that $$(\varOmega , \varLambda )$$ is a frame spectral pair if the set of exponential functions $${\mathcal {E}}(\varLambda ):=\{e^{2\pi i \lambda \cdot x}: \lambda \in \varLambda \}$$ is a frame for $$L^2(\varOmega )$$ . Special cases of frames include Riesz bases and orthogonal bases. In the finite setting $${\mathbb {Z}}_N^d$$ , $$d, N\ge 1$$ , a frame spectral pair can be similarly defined. In this paper we show how to construct and obtain new classes of frame spectral pairs in $${\mathbb {R}}^d$$ by “adding” a frame spectral pair in $${\mathbb {R}}^{d}$$ to a frame spectral pair in $${\mathbb {Z}}_N^d$$ . Our construction unifies the well-known examples of exponential frames for the union of cubes with equal volumes. We also remark on the link between the spectral property of a domain and sampling theory.
Islands and Page curves in 4d from Type IIB

Variants of the black hole information paradox are studied in Type IIB string theory setups that realize four-dimensional gravity coupled to a bath. The setups are string theory versions of doubly-holographic Karch/Randall brane worlds, with black holes coupled to non-gravitating and gravitating baths. The 10d versions are based on fully backreacted solutions for configurations of D3, D5 and NS5 branes, and admit dual descriptions as $$ \mathcal{N} $$ = 4 SYM on a half space and 3d $$ {T}_{\rho}^{\sigma } $$ [SU(N)] SCFTs. Island contributions to the entanglement entropy of black hole radiation systems are identified through Ryu/Takayanagi surfaces and lead to Page curves. Analogs of the critical angles found in the Karch/Randall models are identified in 10d, as critical parameters in the brane configurations and dual field theories.
Spectral analysis of weighted Laplacians arising in data clustering

Abstract Graph Laplacians computed from weighted adjacency matrices are widely used to identify geometric structure in data, and clusters in particular; their spectral properties play a central role in a number of unsupervised and semi-supervised learning algorithms. When suitably scaled, graph Laplacians approach limiting continuum operators in the large data limit. Studying these limiting operators, therefore, sheds light on learning algorithms. This paper is devoted to the study of a parameterized family of divergence form elliptic operators that arise as the large data limit of graph Laplacians. The link between a three-parameter family of graph Laplacians and a three-parameter family of differential operators is explained. The spectral properties of these differential operators are analyzed in the situation where the data comprises of two nearly separated clusters, in a sense which is made precise. In particular, we investigate how the spectral gap depends on the three parameters entering the graph Laplacian, and on a parameter measuring the size of the perturbation from the perfectly clustered case. Numerical results are presented which exemplify the analysis and which extend it in the following ways: the computations study situations in which there are two nearly separated clusters, but which violate the assumptions used in our theory; situations in which more than two clusters are present, also going beyond our theory; and situations which demonstrate the relevance of our studies of differential operators for the understanding of finite data problems via the graph Laplacian. The findings provide insight into parameter choices made in learning algorithms which are based on weighted adjacency matrices; they also provide the basis for analysis of the consistency of various unsupervised and semi-supervised learning algorithms, in the large data limit.
Distortion-Based Outer-Bounds for Channels with Rate-Limited Feedback

We present a new technique to obtain outer-bounds on the capacity region of networks with ultra low-rate feedback. We establish a connection between the achievable rates in the forward channel and the minimum distortion that can be attained over the feedback channel.
Instantaneous SED coding over a DMC

In this paper, we propose a novel code for transmitting a sequence of $n$ message bits in real time over a discrete-memoryless channel (DMC) with noiseless feedback, where the message bits stream into the encoder one by one at random time instants. Similar to existing posterior matching schemes with block encoding, the encoder in our work takes advantage of the channel feedback to form channel inputs that contain the information the decoder does not yet have, and that are distributed close to the capacity-achieving input distribution, but dissimilar to the existing posterior matching schemes, the encoder performs instantaneous encoding - it immediately weaves the new message bits into a continuing transmission. A posterior matching scheme by Naghshvar et al. partitions the source messages into groups so that the group posteriors have a small-enough difference (SED) to the capacity-achieving distribution, and transmits the group index that contains the actual message. Our code adopts the SED rule to apply to the evolving message alphabet that contains all the possible variable-length strings that the source could have emitted up to that time. Our instantaneous SED code achieves better delay-reliability tradeoffs than existing feedback codes over 2-input DMCs: we establish this dominance both by simulations and via an analysis comparing the performance of the instantaneous SED code to Burnashev's reliability function. Due to the message alphabet that grows exponentially with time $t$ , the complexity of the instantaneous SED code is double-exponential in $t$ . To overcome this complexity barrier to practical implementation, we design a low-complexity code for binary symmetric channels that we name the instantaneous type set SED code. It groups the message strings into sets we call type sets and tracks their prior and posterior probabilities jointly, resulting in the reduction of complexity from double-exponential to $O(t^{4})$ . Simulation results show that the gap in performance between the instantaneous SED code and the instantaneous type-set SED code is negligible.
Carbon dioxide capture with concentrated, aqueous piperazine

Abstract Concentrated, aqueous piperazine (PZ) has been investigated as a novel amine solvent for carbon dioxide (CO2) absorption. The CO2 absorption rate of aqueous PZ is more than double that of 7 m MEA and the amine volatility at 40 °C ranges from 11 to 21 ppm. Thermal degradation is negligible in concentrated, aqueous PZ up to a temperature of 150 °C, a significant advantage over MEA systems. Oxidation of concentrated, aqueous PZ is appreciable in the presence of copper (4 mM), but negligible in the presence of chromium (0.6 mM), nickel (0.25 mM), iron (0.25 mM), and vanadium (0.1 mM). Initial system modeling suggests that 8 m PZ will use 10–20% less energy than 7 m MEA. The fast mass transfer and low degradation rates suggest that concentrated, aqueous PZ has the potential to be a preferred solvent for CO2 capture.
Partition Eisenstein series and semi-modular forms

We identify a class of “semi-modular” forms invariant on special subgroups of $$GL_2({\mathbb {Z}})$$ , which includes classical modular forms together with complementary classes of functions that are also nice in a specific sense. We define an Eisenstein-like series summed over integer partitions, and use it to construct families of semi-modular forms.
Proficiency Constrained Multi-Agent Reinforcement Learning for Environment-Adaptive Multi UAV-UGV Teaming

A mixed aerial and ground robot team, which includes both unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs), is widely used for disaster rescue, social security, precision agriculture, and military missions. However, team capability and corresponding configuration vary since robots have different motion speeds, perceiving ranges, reaching areas, and resilient capabilities to the dynamic environment. Due to heterogeneous robots inside a team and the resilient capabilities of robots, it is challenging to perform a task with an optimal balance between reasonable task allocations and maximum utilization of robot capability. To address this challenge for effective mixed ground and aerial teaming, this paper developed a novel teaming method, proficiency aware multi-agent deep reinforcement learning (Mix-RL), to guide ground and aerial cooperation by considering the best alignments between robot capabilities, task requirements, and environment conditions. Mix-RL largely exploits robot capabilities while being aware of the adaption of robot capabilities to task requirements and environment conditions. Mix-RL's effectiveness in guiding mixed teaming was validated with the task “social security for criminal vehicle tracking”.
Intrinsic synaptic plasticity of ferroelectric field effect transistors for online learning

Nanoelectronic devices emulating neuro-synaptic functionalities through their intrinsic physics at low operating energies are imperative toward the realization of brain-like neuromorphic computers. In this work, we leverage the non-linear voltage dependent partial polarization switching of a ferroelectric field effect transistor to mimic plasticity characteristics of biological synapses. We provide experimental measurements of the synaptic characteristics for a 28 nm high-k metal gate technology based device and develop an experimentally calibrated device model for large-scale system performance prediction. Decoupled read-write paths, ultra-low programming energies, and the possibility of arranging such devices in a cross-point architecture demonstrate the synaptic efficacy of the device. Our hardware-algorithm co-design analysis reveals that the intrinsic plasticity of the ferroelectric devices has potential to enable unsupervised local learning in edge devices with limited training data.
Study Group Learning: Improving Retinal Vessel Segmentation Trained with Noisy Labels

Retinal vessel segmentation from retinal images is an essential task for developing the computer-aided diagnosis system for retinal diseases. Efforts have been made on high-performance deep learning-based approaches to segment the retinal images in an end-to-end manner. However, the acquisition of retinal vessel images and segmentation labels requires onerous work from professional clinicians, which results in smaller training dataset with incomplete labels. As known, data-driven methods suffer from data insufficiency, and the models will easily over-fit the small-scale training data. Such a situation becomes more severe when the training vessel labels are incomplete or incorrect. In this paper, we propose a Study Group Learning (SGL) scheme to improve the robustness of the model trained on noisy labels. Besides, a learned enhancement map provides better visualization than conventional methods as an auxiliary tool for clinicians. Experiments demonstrate that the proposed method further improves the vessel segmentation performance in DRIVE and CHASE\(\_\)DB1 datasets, especially when the training labels are noisy. Our code is available at https://github.com/SHI-Labs/SGL-Retinal-Vessel-Segmentation.
Short-term elevations in glucocorticoids do not alter telomere lengths: A systematic review and meta-analysis of non-primate vertebrate studies.

Author(s): Zane, Lauren; Ensminger, David C; Vazquez-Medina, Jose Pablo | Abstract: BackgroundThe neuroendocrine stress response allows vertebrates to cope with stressors via the activation of the Hypothalamic-Pituitary-Adrenal (HPA) axis, which ultimately results in the secretion of glucocorticoids (GCs). Glucocorticoids have pleiotropic effects on behavior and physiology, and might influence telomere length dynamics. During a stress event, GCs mobilize energy towards survival mechanisms rather than to telomere maintenance. Additionally, reactive oxygen species produced in response to increased GC levels can damage telomeres, also leading to telomere shortening. In our systematic review and meta-analysis, we tested whether GC levels impact telomere length and if this relationship differs among time frame, life history stage, or stressor type. We hypothesized that elevated GC levels are linked to a decrease in telomere length.MethodsWe conducted a literature search for studies investigating the relationship between telomere length and GCs in non-human vertebrates using four search engines: Web of Science, Google Scholar, Pubmed and Scopus, last searched on September 27th, 2020. This review identified 31 studies examining the relationship between GCs and telomere length. We pooled the data using Fisher's Z for 15 of these studies. All quantitative studies underwent a risk of bias assessment. This systematic review study was registered in the Open Science Framework Registry (https://osf.io/rqve6).ResultsThe pooled effect size from fifteen studies and 1066 study organisms shows no relationship between GCs and telomere length (Fisher's Z = 0.1042, 95% CI = 0.0235; 0.1836). Our meta-analysis synthesizes results from 15 different taxa from the mammalian, avian, amphibian groups. While these results support some previous findings, other studies have found a direct relationship between GCs and telomere dynamics, suggesting underlying mechanisms or concepts that were not taken into account in our analysis. The risk of bias assessment revealed an overall low risk of bias with occasional instances of bias from missing outcome data or bias in the reported result.ConclusionWe highlight the need for more targeted experiments to understand how conditions, such as experimental timeframes, stressor(s), and stressor magnitudes can drive a relationship between the neuroendocrine stress response and telomere length.