cssi/SciSciGPT-SciSciNet huggingface dataset:
Measure Estimates, Harnack Inequalities and Ricci Lower Bound

On the Riemannian metric-measure space, we establish an Alexandrov–Bakelman–Pucci type estimate connecting the Bakry–E´ mery Ricci curvature lower bound, the modified Laplacian and the measure of certain special sets. We apply this estimate to prove the Harnack inequalities for the modified Laplacian operator (and fully non-linear operators, see the Appendix). These inequalities seem not available in the literature and our proof, based solely on the ABP estimate, does not use standard techniques.
Sparse bounds for spherical maximal functions

We consider the averages of a function f on ℝn over spheres of radius 0 < r < ∞ given by \({A_r}f(x) = \int_{{\mathbb{S}^{n - 1}}} {f(x - ry)d\sigma (y)} \), where σ is the normalized rotation invariant measure on 𝕊n−1. We prove a sharp range of sparse bounds for two maximal functions, the first the lacunary spherical maximal function, and the second the full maximal function. $${M_{1ac}}f = \mathop {\sup }\limits_{j \in \mathbb{Z}} {A_{{2^j}}}f,\;\;\;{M_{\text{full}}}f = \mathop {\sup }\limits_{r > 0} {A_r}f.$$
The Function-Inversion Problem: Barriers and Opportunities

The task of function inversion is central to cryptanalysis: breaking block ciphers, forging signatures, and cracking password hashes are all special cases of the function-inversion problem. In 1980, Hellman showed that it is possible to invert a random function \(f{:}\,[N] \rightarrow [N]\) in time \(T = \widetilde{O}(N^{2/3})\) given only \(S = \widetilde{O}(N^{2/3})\) bits of precomputed advice about f. Hellman’s algorithm is the basis for the popular “Rainbow Tables” technique (Oechslin 2003), which achieves the same asymptotic cost and is widely used in practical cryptanalysis.
A robust deep neural network for denoising task-based fMRI data: An application to working memory and episodic memory

In this study, a deep neural network (DNN) is proposed to reduce the noise in task-based fMRI data without explicitly modeling noise. The DNN artificial neural network consists of one temporal convolutional layer, one long short-term memory (LSTM) layer, one time-distributed fully-connected layer, and one unconventional selection layer in sequential order. The LSTM layer takes not only the current time point but also what was perceived in a previous time point as its input to characterize the temporal autocorrelation of fMRI data. The fully-connected layer weights the output of the LSTM layer, and the output denoised fMRI time series is selected by the selection layer. Assuming that task-related neural response is limited to gray matter, the model parameters in the DNN network are optimized by maximizing the correlation difference between gray matter voxels and white matter or ventricular cerebrospinal fluid voxels. Instead of targeting a particular noise source, the proposed neural network takes advantage of the task design matrix to better extract task-related signal in fMRI data. The DNN network, along with other traditional denoising techniques, has been applied on simulated data, working memory task fMRI data acquired from a cohort of healthy subjects and episodic memory task fMRI data acquired from a small set of healthy elderly subjects. Qualitative and quantitative measurements were used to evaluate the performance of different denoising techniques. In the simulation, DNN improves fMRI activation detection and also adapts to varying hemodynamic response functions across different brain regions. DNN efficiently reduces physiological noise and generates more homogeneous task-response correlation maps in real data.
The Crepant Transformation Conjecture implies the Monodromy Conjecture

In this note we prove that the crepant transformation conjecture for a crepant birational transformation of Lawrence toric DM stacks studied in \cite{CIJ} implies the monodromy conjecture for the associated wall crossing of the symplectic resolutions of hypertoric stacks, due to Braverman, Maulik and Okounkov.
Moment-Distribution Constants from Cardboard Analogs

An experimental method of determining moment-distribution and slope-deflection constants for beams with variable moments of inertia is proposed. Simple graphics, especially evolved for the purpose ...
Living Originalism in India? “Our Law” and Comparative Constitutional Law

Although Jack Balkin’s Living Originalism is about American constitutional practice, it raises two sets of interesting questions for scholars of comparative constitutional law. First, Balkin is largely silent on the role of comparative materials in living originalism. But the negative implication from his understanding of constitutions is that comparative materials are irrelevant to constitutional construction. Is it possible to reinterpret living originalism in a way that renders it comparatively engaged while still acknowledging the distinctiveness of the American constitutional identity? Indeed, can a comparatively inflected living originalism actually sharpen an awareness of national constitutional difference? Second, Balkin is self-consciously writing for an American audience about the nature of American constitutional practice. Does Balkin’s account of the phenomenology of constitutional argument have purchase outside the American constitutional tradition? If so, what can we learn by exploring the culture of constitutional argument in these foreign constitutional cultures?
My years with Julian Schwinger: From source theory through sonoluminescence

I recall my interactions with Julian Schwinger, first as a graduate student at Harvard, and then as a postdoc at UCLA, in the period 1968--81, and subsequently. Some aspects of his legacy to physics are discussed.
Liquid-like and rigid-body motions in molecular-dynamics simulations of a crystalline protein

To gain insight into crystalline protein dynamics, we performed molecular-dynamics (MD) simulations of a periodic 2 × 2 × 2 supercell of staphylococcal nuclease. We used the resulting MD trajectories to simulate X-ray diffraction and to study collective motions. The agreement of simulated X-ray diffraction with the data is comparable to previous MD simulation studies. We studied collective motions by analyzing statistically the covariance of alpha-carbon position displacements. The covariance decreases exponentially with the distance between atoms, which is consistent with a liquidlike motions (LLM) model, in which the protein behaves like a soft material. To gain finer insight into the collective motions, we examined the covariance behavior within a protein molecule (intraprotein) and between different protein molecules (interprotein). The interprotein atom pairs, which dominate the overall statistics, exhibit LLM behavior; however, the intraprotein pairs exhibit behavior that is consistent with a superposition of LLM and rigid-body motions (RBM). Our results indicate that LLM behavior of global dynamics is present in MD simulations of a protein crystal. They also show that RBM behavior is detectable in the simulations but that it is subsumed by the LLM behavior. Finally, the results provide clues about how correlated motions of atom pairs both within and across proteins might manifest in diffraction data. Overall, our findings increase our understanding of the connection between molecular motions and diffraction data and therefore advance efforts to extract information about functionally important motions from crystallography experiments.
Towards Stable and Efficient Training of Verifiably Robust Neural Networks

Training neural networks with verifiable robustness guarantees is challenging. Several existing approaches utilize linear relaxation based neural network output bounds under perturbation, but they can slow down training by a factor of hundreds depending on the underlying network architectures. Meanwhile, interval bound propagation (IBP) based training is efficient and significantly outperforms linear relaxation based methods on many tasks, yet it may suffer from stability issues since the bounds are much looser especially at the beginning of training. In this paper, we propose a new certified adversarial training method, CROWN-IBP, by combining the fast IBP bounds in a forward bounding pass and a tight linear relaxation based bound, CROWN, in a backward bounding pass. CROWN-IBP is computationally efficient and consistently outperforms IBP baselines on training verifiably robust neural networks. We conduct large scale experiments on MNIST and CIFAR datasets, and outperform all previous linear relaxation and bound propagation based certified defenses in L_inf robustness. Notably, we achieve 7.02% verified test error on MNIST at epsilon=0.3, and 66.94% on CIFAR-10 with epsilon=8/255.
Improved chemistry restraints for crystallographic refinement by integrating the Amber force field into Phenix.

The refinement of biomolecular crystallographic models relies on geometric restraints to help to address the paucity of experimental data typical in these experiments. Limitations in these restraints can degrade the quality of the resulting atomic models. Here, an integration of the full all-atom Amber molecular-dynamics force field into Phenix crystallographic refinement is presented, which enables more complete modeling of biomolecular chemistry. The advantages of the force field include a carefully derived set of torsion-angle potentials, an extensive and flexible set of atom types, Lennard–Jones treatment of nonbonded interactions and a full treatment of crystalline electrostatics. The new combined method was tested against conventional geometry restraints for over 22 000 protein structures. Structures refined with the new method show substantially improved model quality. On average, Ramachandran and rotamer scores are somewhat better, clashscores and MolProbity scores are significantly improved, and the modeling of electrostatics leads to structures that exhibit more, and more correct, hydrogen bonds than those refined using traditional geometry restraints. In general it is found that model improvements are greatest at lower resolutions, prompting plans to add the Amber target function to real-space refinement for use in electron cryo-microscopy. This work opens the door to the future development of more advanced applications such as Amber-based ensemble refinement, quantum-mechanical representation of active sites and improved geometric restraints for simulated annealing.
Classwide Extensions of Vocabulary Intervention Improve Learning of Academic Vocabulary by Preschoolers

Purpose Many preschoolers, especially those from low-income households, would benefit from instruction to enrich their vocabulary and language repertoires. Yet, explicit instruction of vocabulary and language skills generally occurs infrequently in early childhood education settings. This study investigated the additive effects of teacher-led, classwide review strategies to a previously studied small-group intervention on children's learning of academic vocabulary. Method Participants included 23 children with limited oral language skills at risk for reading difficulties enrolled in single-case experimental designs. Effects of the classroom strategies alone also were examined in 10 children with above-average language abilities from 2 classrooms. Results Visual analyses of the adapted alternating treatments designs showed consistent learning improvements when vocabulary instruction was extended into the classroom for 12 children, ceiling effects were evident for 3 participants regardless of condition, and inconsistent or minimal effects were demonstrated by 8 participants. Multilevel modeling used to evaluate the effects statistically revealed strong treatment effects. In addition, the 10 children with above-average language showed impressive learning of vocabulary words from books subject to teacher review strategies in comparison to words from books to which they were not exposed. Teachers varied in the extent to which they implemented review strategies in their classrooms. Nevertheless, their responses to social validity assessments were positive, supporting the feasibility of this intervention. Conclusions The addition of classwide review and practice opportunities is an effective means of enhancing the effects of an easy-to-implement small-group intervention that teaches challenging vocabulary words within prerecorded stories. This approach holds promise as a way to shrink the pervasive word gap that typically exists when children in high-poverty communities enter school.
Unsupervised clustering reveals spatially varying single neuronal firing patterns in the subthalamic nucleus of patients with Parkinson's disease.

Abstract Introduction Subthalamic nucleus (STN) is an effective target for deep brain stimulation (DBS) to reduce the motor symptoms of Parkinson's disease (PD). It is important to identify firing patterns within the structure for a better understanding of the electro-pathophysiology of the disease. Using recently established metrics, our study aims to autonomously identify the discharge patterns of individual cells and examine their spatial distribution within the STN. Methods We recorded single unit activity (SUA) from 12 awake PD patients undergoing a standard clinical DBS surgery. Three extracted features from raw SUA (local variation, bursting index and prominence of peak) were used with k-means clustering to achieve the aforementioned unsupervised grouping of firing patterns. Results 279 neurons were isolated and four distinct firing patterns were identified across patients: tonic (11%), irregular (55%), periodic (9%) and non-periodic bursts (25%). The mean firing rates for irregular discharges were significantly lower (p Conclusion Strengthening the application of unsupervised clustering for firing patterns of individual cells, this study shows a unique spatial affinity of tonic activity towards the ventral and bursting activity towards the dorsal region of STN in PD patients. This spatial preference, together with the correlation of clinical scores, can provide a clue towards understanding Parkinsonian symptom generation.
Sleep-related rhythmic movement disorder: A case report of head banging alopecia.

We present a case of alopecia associated with sleep-related rhythmic movement disorder (RMD) in an otherwise healthy 2-year-old boy. The alopecic patch he presented with on his scalp coincided with the location of repeated head banging in a video taken by the patient's mother. Alopecia secondary to RMD is an under recognized entity and should be included in the differential diagnosis of pediatric alopecia.
The Feudal Origins of the Western Legal Tradition

Harwick, Cameron and Root, Hilton L., The Feudal Origins of the Western Legal Tradition (March 16, 2020). 2020, Jahrbuch fur die Ordnung von Wirtschaft und Gesellschaft (Ordo), 70(1): 3-20. Available at SSRN: https://ssrn.com/abstract=3502955 --- Posted: 4 Jan 2020 Last revised: 2 Apr 2020
An Open-Source 7-Axis, Robotic Platform to Enable Dexterous Procedures within CT Scanners

This paper describes the design, manufacture, and performance of a highly dexterous, low-profile, 7 Degree-of-Freedom (DoF) robotic arm for CT-guided percutaneous needle biopsy. Direct CT guidance allows physicians to localize tumours quickly; however, needle insertion is still performed by hand. This system is mounted to a fully active motion stage superior to the patient’s head and teleoperated by a radiologist. Unlike other similar robots, this robot’s fully serial-link approach uses a unique combination of belt and cable drives for high-transparency and minimal-backlash, allowing for an expansive working area and numerous approach angles to targets, all while maintaining a small in-bore cross-section of less than 16cm2. Simulations verified the system’s expansive collision free work-space and ability to hit targets across the entire chest, as required for lung cancer biopsy. Targeting error was on average < 1mm on a teleoperated accuracy task, illustrating the system’s sufficient accuracy to perform biopsy procedures. While this system was developed for lung biopsies, it can be easily modified for other CT-guided needle based procedures. Additionally, this system is open-hardware.1
Optimization of Solidification in Die Casting using Numerical Simulations and Machine Learning

Abstract In this paper, we demonstrate the combination of machine learning and three dimensional numerical simulations for multi-objective optimization of low pressure die casting. The cooling of molten metal inside the mold is achieved typically by passing water through the cooling lines in the die. Depending on the cooling line location, coolant flow rate and die geometry, nonuniform temperatures are imposed on the molten metal at the mold wall. This boundary condition along with the initial molten metal temperature affect the product quality quantified in terms of micro-structure parameters and yield strength. A finite volume based numerical solver is used to determine the temperature-time history and correlate the inputs to outputs. The objective of this research is to develop and demonstrate a procedure to obtain the initial and wall temperatures so as to optimize the product quality. The non-dominated sorting genetic algorithm (NSGA-II) is used for multi-objective optimization in this work. The number of function evaluations required for NSGA-II can be of the order of millions and hence, the finite volume solver cannot be used directly for optimization. Therefore, a multilayer perceptron feed-forward neural network is first trained using the results from the numerical solution of the fluid flow and energy equations and is subsequently used as a surrogate model. As an assessment, simplified versions of the actual problem are designed to first verify results of the genetic algorithm. An innovative local sensitivity based approach is then used to rank the final Pareto optimal solutions and select a single best design.
Tobacco smoke exposure and socioeconomic factors are independent predictors of pulmonary decline in pediatric cystic fibrosis.

Abstract Background Pulmonary decline in CF is heterogeneous, with socio-environmental factors contributing to this variability. Few studies have attempted to disentangle the effects of tobacco smoke exposure and socioeconomic factors on lung function deterioration in pediatric CF. The current study evaluates their contributions longitudinally across the entire U.S. CF care network population. Methods Data from the CF Foundation Patient Registry were obtained on all individuals who at the end of 2016 were 6–18 years old. Lung function measures (ppFEV1) for each person were calculated at each attained age. Multivariable analyses used mixed modeling to assess the impact of smoke exposure and socioeconomic factors on initial lung function and change over time. Results The sample included 10,895 individuals contributing 65,581 person years. At age 6, ppFEV1 of smoke-exposed children was 4.7% lower than among unexposed. The deficit persisted through age 18. In adjusted mixed models, smoke exposure and socioeconomic factors had independent, additive associations with lung function. Median ppFEV1 declined 2.4% with smoke exposure, 4.9% with lower paternal education, 0.3% with public insurance, and increased 0.2% with each $10,000 annual household income. The effect of smoke exposure on ppFEV1 was larger in disadvantaged children compared to privileged counterparts (3.2% vs 1.2%). Conclusions Smoke exposure and socioeconomic factors are independent risk factors for decreased ppFEV1 in pediatric CF. Smoking cessation strategies should be emphasized at the time of CF diagnosis and reiterated during infancy and early childhood. Interventions may be prioritized in disadvantaged families, where the exposure has a disproportionately large effect.
Repurposing the Streptococcus mutans CRISPR-Cas9 System to Understand Essential Gene Function

A recent genome-wide screen identified ~300 essential or growth-supporting genes in the dental caries pathogen Streptococcus mutans. To be able to study these genes, we built a CRISPR interference tool around the Cas9 nuclease (Cas9Smu) encoded in the S. mutans UA159 genome. Using a xylose-inducible dead Cas9Smu with a constitutively active single-guide RNA (sgRNA), we observed titratable repression of GFP fluorescence that compared favorably to that of Streptococcus pyogenes dCas9 (Cas9Spy). We then investigated sgRNA specificity and proto-spacer adjacent motif (PAM) requirements. Interference by sgRNAs did not occur with double or triple base-pair mutations, or if single base-pair mutations were in the 3' end of the sgRNA. Bioinformatic analysis of >450 S. mutans genomes allied with in vivo assays revealed a similar PAM recognition sequence as Cas9Spy. Next, we created a comprehensive library of sgRNA plasmids that were directed at essential and growth-supporting genes. We discovered growth defects for 77% of the CRISPRi strains expressing sgRNAs. Phenotypes of CRISPRi strains, across several biological pathways, were assessed using fluorescence microscopy. A variety of cell structure anomalies were observed, including segregational instability of the chromosome, enlarged cells, and ovococci-to-rod shape transitions. CRISPRi was also employed to observe how silencing of cell wall glycopolysaccharide biosynthesis (rhamnose-glucose polysaccharide, RGP) affected both cell division and pathogenesis in a wax worm model. The CRISPRi tool and sgRNA library are valuable resources for characterizing essential genes in S. mutans, some of which could prove to be promising therapeutic targets.
Cathode depth sensing in CZT detectors

Measuring the depth of interaction in thick Cadmium-Zinc-Telluride (CZT) detectors allows improved imaging and spectroscopy for hard X-ray imaging above 100 keV. The Energetic X-ray Imaging Survey Telescope (EXIST) will employ relatively thick (5 - 10 mm) CZT detectors, which are required to perform the broad energy-band sky survey. Interaction depth information is needed to correct events to the detector "focal plane" for correct imaging and can be used to improve the energy resolution of the detector at high energies by allowing event-based corrections for incomplete charge collection. Background rejection is also improved by allowing low energy events from the rear and sides of the detector to be rejected. We present experimental results of intereaction depth sensing in a 5 mm thick pixellated Au-contact IMARAD CZT detector. The depth sensing was done by making simultaneous measurements of cathode and anode signals, where the interaction depth at a given energy is proportional to the ratio of cathode/anode signals. We demonstrate how a simple empirical formula describing the event distributions in the cathode/anode signal space can dramatically improve the energy resolution. We also estimate the energy and depth resolution of the detector as a function of the energy and the interaction depth. We also show a depth-sensing prototype system currently under development for EXIST in which cathode signals from 8, 16 or 32 crystals can be read-out by a small multi-channel ASIC board that is vertically edge-mounted on the cathode electrode along every second CZT crystal boundary. This allows CZT crystals to be tiled contiguously with minimum impact on throughput of incoming photons. The robust packaging is crucial in EXIST, which will employ very large area imaging CZT detector arrays.
A Joint Graph Based Coding Scheme for the Unsourced Random Access Gaussian Channel

This article introduces a novel communication paradigm for the unsourced, uncoordinated Gaussian multiple access problem. The major components of the envisioned framework are as follows. The encoded bits of every message are partitioned into two groups. The first portion is transmitted using a compressive sensing scheme, whereas the second set of bits is conveyed using a multi-user coding scheme. The compressive sensing portion is key in sidestepping some of the challenges posed by the unsourced aspect of the problem. The information afforded by the compressive sensing is employed to create a sparse random multi-access graph conducive to joint decoding. This construction leverages the lessons learned from traditional IDMA into creating low- complexity schemes for the unsourced setting and its inherent randomness. Under joint message- passing decoding, the proposed scheme offers superior performance compared to existing low- complexity alternatives. Findings are supported by numerical simulations.
On‐site surrogates for large‐scale calibration

Motivated by a computer model calibration problem from the oil and gas industry, involving the design of a honeycomb seal, we develop a new Bayesian methodology to cope with limitations in the canonical apparatus stemming from several factors. We propose a new strategy of on-site design and surrogate modeling for a computer simulator acting on a high-dimensional input space that, although relatively speedy, is prone to numerical instabilities, missing data, and nonstationary dynamics. Our aim is to strike a balance between data-faithful modeling and computational tractability in a calibration framework--tailoring the computer model to a limited field experiment. Situating our on-site surrogates within the canonical calibration apparatus requires updates to that framework. We describe a novel yet intuitive Bayesian setup that carefully decomposes otherwise prohibitively large matrices by exploiting the sparse blockwise structure. Empirical illustrations demonstrate that this approach performs well on toy data and our motivating honeycomb example.
High-performance quantization for spectral super-resolution

We show that the method of distributed noise-shaping beta-quantization offers superior performance for the problem of spectral super-resolution with quantization whenever there is redundancy in the number of measurements. More precisely, if the (integer) oversampling ratio λ is such that $\left\lfloor {M/\lambda } \right\rfloor - 1 \geq 4/\Delta $, where M denotes the number of Fourier measurements and ∆ is the minimum separation distance associated with the atomic measure to be resolved, then for any number K ≥ 2 of quantization levels available for the real and imaginary parts of the measurements, our quantization method guarantees reconstruction accuracy of order O(λ3/2 K−λ/2), up to constants which are independent of K and λ. In contrast, memoryless scalar quantization offers a guarantee of order O(K−1) only.
Expertise Rents from Insider Trading for Financial Experts on Audit Committees

We document the existence of expertise rents by finding that financial experts on audit committees obtain higher abnormal returns from insider purchases than do non‐financial experts on audit committees. We further investigate whether information processing skills work alone or jointly with an information advantage to generate expertise rents. While financial experts on audit committees outperform financial experts on other committees, financial experts on compensation, executive, nominating, and governance committees do not outperform non‐financial experts on these committees. These findings suggest that expertise rents are domain‐specific and can be obtained only when directors have both access to private information and information processing skills. In additional testing, we find that expertise rents for financial experts on audit committees are primarily driven by non‐accounting financial experts, whose finance or supervisory experience could make them better than accounting financial experts in understanding market conditions and assessing firm risk. Les auteurs documentent l'existence de rentes d'expertise en constatant que les membres de comites d'audit qui sont des experts financiers tirent des achats d'inities des rendements anormaux superieurs a ceux qu'obtiennent les membres des comites d'audit qui ne sont pas des experts financiers. Poussant plus loin leur analyse, ils se demandent si les competences dans le traitement de l'information ont sur la generation de rentes d'expertise une incidence propre, ou une incidence conjuguee a celle d'un avantage au chapitre de l'information. Bien que les experts financiers membres de comites d'audit affichent une performance superieure a celle des experts financiers appartenant a d'autres comites, la performance des experts financiers membres des comites de remuneration, de direction, de mises en candidature et de gouvernance n'est pas superieure a celle des membres de ces comites qui ne sont pas des experts financiers. Ces constatations semblent indiquer que les rentes d'expertise sont particulieres au domaine et ne peuvent etre obtenues que lorsque les dirigeants beneficient a la fois d'un acces a de l'information privilegiee et de competences dans le traitement de l'information. Les resultats de tests supplementaires effectues par les auteurs indiquent que les rentes d'expertise, dans le cas des experts financiers membres de comites d'audit, sont principalement le fait d'experts financiers ne possedant pas de competences en comptabilite, dont l'experience en finance ou en supervision pourrait expliquer leur superiorite sur les experts financiers dotes de competences en comptabilite dans la comprehension des conditions du marche et l'evaluation du risque auquel est exposee l'entreprise.
Footstep Planning for Autonomous Walking Over Rough Terrain

To increase the speed of operation and reduce operator burden, humanoid robots must be able to function autonomously, even in complex, cluttered environments. For this to be possible, they must be able to quickly and efficiently compute desired footsteps to reach a goal. In this work, we present a new A * footstep planner that utilizes a planar region representation of the environment enable footstep planning over rough terrain. To increase the number of available footholds, we present an approach to allow the use of partial footholds during the planning process. The footstep plan solutions are then post-processed to capture better solutions that lie between the lattice discretization of the footstep graph. We then demonstrate this planner over a variety of virtual and real world environments, including some that require partial footholds and rough terrain using the Atlas and Valkyrie humanoid robots.
Hybrid Beamformer Codebook Design and Ordering for Compressive mmWave Channel Estimation

In millimeter wave (mmWave) communication systems, beamforming with large antenna arrays is critical to overcome high path losses. Separating all-digital beamforming into analog and digital stages can provide the large reduction in power consumption needed for practical implementations with a tradeoff of a small loss in spectral efficiency. Developing algorithms with this favorable tradeoff is challenging due to the additional degrees of freedom in the analog stage and its accompanying hardware constraints. In hybrid beamforming systems, for example, channel estimation algorithms do not directly observe the channels, face a high channel count, and operate at low SNR before transmit-receive beam alignment. Since mmWave channels are sparse in time and beam domains, many compressed sensing (CS) channel estimation algorithms have been developed that randomly conFigure the analog beamformers, digital beamformers, and/or pilot symbols. In this paper, we propose to design deterministic beamformers and pilot symbols for open-loop channel estimation. We use CS approaches that rely on low coherence for their recovery guarantees, and hence seek to minimize the mutual coherence of the compressed sensing matrix. We also propose a precoder column ordering to design the pilot symbols. Simulation results show that our beamformer designs reduce channel estimation error over competing methods.
High-resolution Repli-Seq defines the temporal choreography of initiation, elongation and termination of replication in mammalian cells

DNA replication in mammalian cells occurs in a defined temporal order during S phase, known as the replication timing (RT) programme. Replication timing is developmentally regulated and correlated with chromatin conformation and local transcriptional potential. Here, we present RT profiles of unprecedented temporal resolution in two human embryonic stem cell lines, human colon carcinoma line HCT116, and mouse embryonic stem cells and their neural progenitor derivatives. Fine temporal windows revealed a remarkable degree of cell-to-cell conservation in RT, particularly at the very beginning and ends of S phase, and identified 5 temporal patterns of replication in all cell types, consistent with varying degrees of initiation efficiency. Zones of replication initiation (IZs) were detected throughout S phase and interacted in 3D space preferentially with other IZs of similar firing time. Temporal transition regions were resolved into segments of uni-directional replication punctuated at specific sites by small, inefficient IZs. Sites of convergent replication were divided into sites of termination or large constant timing regions consisting of many synchronous IZs in tandem. Developmental transitions in RT occured mainly by activating or inactivating individual IZs or occasionally by altering IZ firing time, demonstrating that IZs, rather than individual origins, are the units of developmental regulation. Finally, haplotype phasing revealed numerous regions of allele-specific and allele-independent asynchronous replication. Allele-independent asynchronous replication was correlated with the presence of previously mapped common fragile sites. Altogether, these data provide a detailed temporal choreography of DNA replication in mammalian cells.
Frobenius Coin-Exchange Generating Functions

We study variants of the Frobenius coin-exchange problem: Given n positive relatively prime parameters, what is the largest integer that cannot be represented as a nonnegative integral linear combi...
Hippo Signaling Pathway has a critical role in Zika Virus Replication and in the Pathogenesis of Neuroinflammation

Zika virus (ZIKV) is a reemerging human pathogen that causes congenital abnormalities, including microcephaly and eye disease. The cellular/molecular basis of ZIKV and host interactions inducing ocular and neuronal pathogenesis are unclear. Herein, we noted that the Hippo/Salvador-Warts-Hippo signaling pathway, which controls organ size through progenitor cell proliferation and differentiation, is dysregulated after ZIKV infection. In human fetal retinal pigment epithelial cells, there is an early induction of transcriptional coactivator, Yes-associated protein (YAP), which is later degraded with a corresponding activation of the TANK binding kinase 1/interferon regulatory factor 3 type I interferon pathway. YAP/transcriptional co-activator with a PDZ-binding domain (TAZ) silencing results in reduced ZIKV replication, indicating a direct role of Hippo pathway in regulating ZIKV infection. Using an in vivo Ifnar1−/− knockout mouse model, ZIKV infection was found to reduce YAP/TAZ protein levels while increasing phosphorylated YAP Ser127 in the retina and brain. Hippo pathway is activated in major cellular components of the blood-brain barrier, including endothelial cells and astrocytes. In addition, this result suggests AMP-activated protein kinase signaling pathway's role in regulating YAP/TAZ in ZIKV-infected cells. These data demonstrate that ZIKV infection might initiate a cross talk among AMP-activated protein kinase–Hippo–TBK1 pathways, which could regulate antiviral and energy stress responses during oculoneuronal inflammation.
Phonon-assisted diffusion in bcc phase of titanium and zirconium from first principles

Diffusion is the underlying mechanism for many complicated materials phenomena, and understanding it is basic to the discovery of novel materials with desired physical and mechanical properties. Certain groups of solid phases, such as the bcc phase of IIIB and IVB metals and their alloys, which are only stable when they reach high enough temperatures and experience anharmonic vibration entropic effects, exhibit ``anomalously fast diffusion.'' However, the underlying reason for the observed extraordinary fast diffusion is poorly understood and due to the existence of harmonic vibration instabilities in these phases the standard models fail to predict their diffusivity. Here, we indicate that the anharmonic phonon-phonon coupling effects can accurately describe the anomalously large macroscopic diffusion coefficients in the bcc phase of IVB metals and therefore yield understanding on the underlying mechanism for diffusion in these phases. We utilize temperature-dependent phonon analysis by combining ab initio molecular dynamics with lattice dynamics calculations to provide an approach to use the transition state theory beyond the harmonic approximation. We validate the diffusivity predictions for the bcc phase of titanium and zirconium with available experimental measurements, while we show that predictions based on harmonic transition state theory severely underestimate diffusivity in these phases.
Learning to Walk: Spike Based Reinforcement Learning for Hexapod Robot Central Pattern Generation

Learning to walk – i.e., learning locomotion under performance and energy constraints continues to be a challenge in legged robotics. Methods such as stochastic gradient, deep reinforcement learning (RL) have been explored for bipeds, quadrupeds and hexapods. These techniques are computationally intensive and often prohibitive for edge applications. These methods rely on complex sensors and pre-processing of data, which further increases energy and latency. Recent advances in spiking neural networks (SNNs) promise significant reduction in computing owing to the sparse firing of neuros and has been shown to integrate reinforcement learning mechanisms with biologically observed spike time dependent plasticity (STDP). However, training a legged robot to walk by learning the synchronization patterns of central pattern generators (CPG) in an SNN framework has not been shown. This can marry the efficiency of SNNs with synchronized locomotion of CPG based systems providing breakthrough end-to-end learning in mobile robotics. In this paper, we propose a reinforcement based stochastic weight update technique for training a spiking CPG. The whole system is implemented on a lightweight raspberry pi platform with integrated sensors, thus opening up exciting new possibilities.
Global Convergence of Policy Gradient Algorithms for Indefinite Least Squares Stationary Optimal Control

We consider policy gradient algorithms for the indefinite least squares stationary optimal control, e.g., linear-quadratic-regulator (LQR) with indefinite state and input penalization matrices. Such a setup has important applications in control design with conflicting objectives, such as linear quadratic dynamic games. We show the global convergence of gradient, natural gradient and quasi-Newton policies for this class of indefinite least squares problems.
The rotation of Uranus

The determination of the rotation rate of Uranus based on available observational evidence is discussed. Previous determinations of the rotation rate since the work of Lowell and Slipher (1912), which tended to converge on a period of 10.8 days until the mid 1970s, most likely due to mutual reinforcement between unreliable results, are reviewed. Recent independent determinations based on (1) the use of theoretical interior models together with observations of oblateness and the gravitational moment J2; (2) periodic brightness fluctuations; and (3) spectrographic measurements of Doppler shifts are then presented which yield a weighted mean value of 16.31 + or - 0.27 h for the rotational period of Uranus. It is noted that the detection of the motion of surface features across the disk by future high-resolution imaging and Voyager 2 observations may allow a direct determination of the rotational period of Uranus.
Downhill sledding at 40 AU: Mobilizing Pluto's chaotic mountain blocks

Abstract We present a force-balance analysis of the mobilization and emplacement of the large mountain block chains observed on the western rim of Pluto's Sputnik Planitia basin. These mountain blocks are likely disrupted pieces of Pluto's water ice crust that are grounded in the nitrogen ice that fills the basin. After fracturing from the basin rim, they could have slid downslope into the basin under their own weight on timescales as short as hundreds to thousands of years, collecting into the observed ranges where the basin slope shallows below a critical value. This movement would require a lubricating fluid, most likely nitrogen, to be present at the base of the blocks. Traction from flowing nitrogen glaciers could have initiated or aided transport for blocks that are small enough to be at least halfway immersed by the nitrogen. Lateral convective forces within the nitrogen ice sheet did not play a significant role in the mobilization of all but the smallest of the observed blocks that likely float buoyantly in the nitrogen ice. If the blocks did emplace by basal sliding, the requirement that liquid nitrogen be present to provide lubrication provides a key to Pluto's past climate history. This work adds to the body of evidence that erosional features and other surface modification could have been driven by the surface or near-surface flow of liquid nitrogen resulting from global climate change on Pluto.
Byar, David P†

Born in 1938 in Lockland, OH; died in 1991 in Washington D.C. Byar was a leading clinical trials methodologist and proponent of structured experiments for making treatment inferences. He played an important role in teaching and advocating the strengths of clinical trials through his positions at the National Cancer Institute. He also made substantial contributions to epidemiology and studies of disease prevention. Keywords: randomized clinical trials; cancer; methodology; treatment-covariate interactions
Fraternity membership and binge drinking

This paper examines the relationship that social fraternity and sorority membership has with binge drinking incidence and frequency among 18-24 year old full-time 4-year college students who participated in the 1995 National College Health Risk Behavior Survey. To net out unobserved heterogeneity, several measures of situational and total alcohol use are entered into the regressions as explanatory variables. Fraternity membership coefficients are substantially reduced in size, but remain large and highly significant, suggesting a causal effect on binge drinking. Otherwise, the estimates identify idiosyncratic selection into fraternities and binge drinking across students with similar overall drinking profiles. Particularly notable is that behavior by underage students appears to drive the relationship.
BICORN: An R package for integrative inference of de novo cis-regulatory modules

Genome-wide transcription factor (TF) binding signal analyses reveal co-localization of TF binding sites, based on which cis-regulatory modules (CRMs) can be inferred. CRMs play a key role in understanding the cooperation of multiple TFs under specific conditions. However, the functions of CRMs and their effects on nearby gene transcription are highly dynamic and context-specific and therefore are challenging to characterize. BICORN (Bayesian Inference of COoperative Regulatory Network) builds a hierarchical Bayesian model and infers context-specific CRMs based on TF-gene binding events and gene expression data for a particular cell type. BICORN automatically searches for a list of candidate CRMs based on the input TF bindings at regulatory regions associated with genes of interest. Applying Gibbs sampling, BICORN iteratively estimates model parameters of CRMs, TF activities, and corresponding regulation on gene transcription, which it models as a sparse network of functional CRMs regulating target genes. The BICORN package is implemented in R (version 3.4 or later) and is publicly available on the CRAN server at https://cran.r-project.org/web/packages/BICORN/index.html .
Automatic post-stroke lesion segmentation on MR images using 3D residual convolutional neural network

Abstract In this paper, we demonstrate the feasibility and performance of deep residual neural networks for volumetric segmentation of irreversibly damaged brain tissue lesions on T1-weighted MRI scans for chronic stroke patients. A total of 239 T1-weighted MRI scans of chronic ischemic stroke patients from a public dataset were retrospectively analyzed by 3D deep convolutional segmentation models with residual learning, using a novel zoom-in&out strategy. Dice similarity coefficient (DSC), average symmetric surface distance (ASSD), and Hausdorff distance (HD) of the identified lesions were measured by using manual tracing of lesions as the reference standard. Bootstrapping was employed for all metrics to estimate 95% confidence intervals. The models were assessed on a test set of 31 scans. The average DSC was 0.64 (0.51–0.76) with a median of 0.78. ASSD and HD were 3.6 mm (1.7–6.2 mm) and 20.4 mm (10.0–33.3 mm), respectively. The latest deep learning architecture and techniques were applied with 3D segmentation on MRI scans and demonstrated effectiveness for volumetric segmentation of chronic ischemic stroke lesions.
How the Degeneracy Helps for Triangle Counting in Graph Streams

We revisit the well-studied problem of triangle count estimation in graph streams. Given a graph represented as a stream of m edges, our aim is to compute a (1+-e)-approximation to the triangle count T, using a small space algorithm. For arbitrary order and a constant number of passes, the space complexity is known to be essentially Θ(min(m3/2 /T, m/√T)) (McGregor et al., PODS 2016, Bera et al., STACS 2017). We give a (constant pass, arbitrary order) streaming algorithm that can circumvent this lower bound for low degeneracy graphs. The degeneracy, K, is a nuanced measure of density, and the class of constant degeneracy graphs is immensely rich (containing planar graphs, minor-closed families, and preferential attachment graphs). We design a streaming algorithm with space complexity ~O(mK/T). For constant degeneracy graphs, this bound is ~O(m/T), which is significantly smaller than both m3/2 /T and m/√T. We complement our algorithmic result with a nearly matching lower bound of Ω(mK/T).
Hypergraph Coloring Games and Voter Models

AbstractWe analyze a network coloring game on hypergraphs that can also describe a voter model. Each node represents a voter and is colored according to its preferred candidate (or undecided). Each hyperedge is a subset of voters that can interact and influence one another. In each round of the game, one hyperedge is chosen randomly, and the voters in the hyperedge can change their colors according to some prescribed probability distribution. We analyze this interaction model based on random walks on the associated weighted directed state graph. Under certain “memoryless” restrictions, we can use semigroup spectral methods to explicitly determine the spectrum of the state graph, and the random walk on the state graph converges to its stationary distribution in O(mlog n) steps, where n is the number of voters and m the number of hyperedges. We can then estimate probabilities that events occur within an error bound of e by simulating the voting game for O(log (1/e)mlog n) rounds. We also consider a partiall...
Informativeness of mutual fund advertisements: Does advertising communicate fund quality to investors?

We uncover a positive relation between advertising expenditures and skill in the mutual fund industry. Motivated by economic signaling models, we find that funds advertising in magazines outperform their peers by 83 basis points in the subsequent year. We determine that the performance differential between advertising and non-advertising funds is largest when investment opportunities are high and persists for 36 months. Generally, the positive relation between advertising expenditures and future fund flows is not sensitive to the content of the advertisements and is strongest when investor attention is high.