Symmetrized models of last passage percolation and non-intersecting lattice paths

It has been shown that the last passage time in certain symmetrized models of directed percolation can be written in terms of averages over random matrices from the classical groups $U(l)$, $Sp(2l)$ and $O(l)$. We present a theory of such results based on non-intersecting lattice paths, and integration techniques familiar from the theory of random matrices. Detailed derivations of probabilities relating to two further symmetrizations are also given.Comment: 21 pages, 5 figure

Random walks and random fixed-point free involutions

A bijection is given between fixed point free involutions of $\{1,2,...,2N\}$ with maximum decreasing subsequence size $2p$ and two classes of vicious (non-intersecting) random walker configurations confined to the half line lattice points $l \ge 1$. In one class of walker configurations the maximum displacement of the right most walker is $p$. Because the scaled distribution of the maximum decreasing subsequence size is known to be in the soft edge GOE (random real symmetric matrices) universality class, the same holds true for the scaled distribution of the maximum displacement of the right most walker.Comment: 10 page

Sociodemographic and clinical characteristics of persons who experienced spontaneous hepatitis C viral clearance.

BackgroundIn the United States Hepatitis C virus (HCV) viral clearance is estimated to range between 20 and 30%. The objective of this study was to estimate the frequency of HCV clearance and identify correlates of viral clearance among patients newly identified as HCV antibody positive in a large urban health system in Los Angeles, California.MethodsWe identified patients between November 2015 and September 2017 as part of a newly implemented HCV screening and linkage-to-care program at University of California Los Angeles (UCLA) Health System. All patients were eligible for screening, though there were additional efforts to screen patients born between 1945 and 1965. We reviewed Medical records to categorize anti-HCV antibody positive patients as having spontaneously cleared HCV infection (HCV RNA not detected) or not (HCV RNA detected). We excluded those with a prior history of anti-HCV positivity or history of HCV treatment. We compared differences between those with and without detectable HCV RNA using chi-square test, Fisher's exact test, and t-test as appropriate. We assessed factors associated with HCV clearance using logistic regression analysis.ResultsAmong the 320 patients included in this study, 56% were male. Baby boomers (52-72 years of age) comprised the single largest age group (62%). We found spontaneous HCV clearance in 58% (n = 185). HCV viral clearance was slightly higher among women as compared to men (63% vs. 53%; p value = 0.07) and varied by race/ethnicity: clearance among Blacks/African Americans was 37% vs. 58% among whites (p value = 0.02). After adjusting for age, race/ethnicity, and sex we found that those diagnosed with chronic kidney disease had a tendency of decreased HCV viral clearance (adjusted OR = 0.34; 95% CI 0.14-1.03).ConclusionOf those patients newly identified as anti-HCV positive, 58% had cleared HCV virus, while the rest showed evidence of active infection. In addition, we found that clearance varied by race/ethnicity and clinical characteristics

Vicious Walkers and Hook Young Tableaux

We consider a generalization of the vicious walker model. Using a bijection map between the path configuration of the non-intersecting random walkers and the hook Young diagram, we compute the probability concerning the number of walker's movements. Applying the saddle point method, we reveal that the scaling limit gives the Tracy--Widom distribution, which is same with the limit distribution of the largest eigenvalues of the Gaussian unitary ensemble.Comment: 23 pages, 5 figure

Safety and efficacy of pralsetinib in RET fusion–positive non-small-cell lung cancer including as first-line therapy: update from the ARROW trial

RET inhibition; Pralsetinib; Targeted therapyInhibición de RET; Pralsetinib; Terapia dirigidaInhibició de RET; Pralsetinib; Teràpia dirigidaBackground RET fusions are present in 1%-2% of non-small-cell lung cancer (NSCLC). Pralsetinib, a highly potent, oral, central nervous system-penetrant, selective RET inhibitor, previously demonstrated clinical activity in patients with RET fusion–positive NSCLC in the phase I/II ARROW study, including among treatment-naive patients. We report an updated analysis from the ARROW study. Patients and methods ARROW is a multi-cohort, open-label, phase I/II study. Eligible patients were ≥18 years of age with locally advanced or metastatic solid tumours and an Eastern Cooperative Oncology Group performance status of 0-2 (later 0-1). Patients initiated pralsetinib at the recommended phase II dose of 400 mg once daily until disease progression, intolerance, consent withdrawal, or investigator’s decision. The co-primary endpoints (phase II) were overall response rate (ORR) by blinded independent central review and safety. Results Between 17 March 2017 and 6 November 2020 (data cut-off), 281 patients with RET fusion–positive NSCLC were enrolled. The ORR was 72% [54/75; 95% confidence interval (CI) 60% to 82%] for treatment-naive patients and 59% (80/136; 95% CI 50% to 67%) for patients with prior platinum-based chemotherapy (enrolment cut-off for efficacy analysis: 22 May 2020); median duration of response was not reached for treatment-naive patients and 22.3 months for prior platinum-based chemotherapy patients. Tumour shrinkage was observed in all treatment-naive patients and in 97% of patients with prior platinum-based chemotherapy; median progression-free survival was 13.0 and 16.5 months, respectively. In patients with measurable intracranial metastases, the intracranial response rate was 70% (7/10; 95% CI 35% to 93%); all had received prior systemic treatment. In treatment-naive patients with RET fusion–positive NSCLC who initiated pralsetinib by the data cut-off (n = 116), the most common grade 3-4 treatment-related adverse events (TRAEs) were neutropenia (18%), hypertension (10%), increased blood creatine phosphokinase (9%), and lymphopenia (9%). Overall, 7% (20/281) discontinued due to TRAEs. Conclusions Pralsetinib treatment produced robust efficacy and was generally well tolerated in treatment-naive patients with advanced RET fusion–positive NSCLC. Results from the confirmatory phase III AcceleRET Lung study (NCT04222972) of pralsetinib versus standard of care in the first-line setting are pending.This work was supported by Blueprint Medicines Corporation and F. Hoffmann-La Roche, Ltd, Switzerland (no grant number)

Saliency-directed prioritization of visual data in wireless surveillance networks

YesIn wireless visual sensor networks (WVSNs), streaming all imaging data is impractical due to resource constraints. Moreover, the sheer volume of surveillance videos inhibits the ability of analysts to extract actionable intelligence. In this work, an energy-efficient image prioritization framework is presented to cope with the fragility of traditional WVSNs. The proposed framework selects semantically relevant information before it is transmitted to a sink node. This is based on salient motion detection, which works on the principle of human cognitive processes. Each camera node estimates the background by a bootstrapping procedure, thus increasing the efficiency of salient motion detection. Based on the salient motion, each sensor node is classified as being high or low priority. This classification is dynamic, such that camera nodes toggle between high-priority and low-priority status depending on the coverage of the region of interest. High-priority camera nodes are allowed to access reliable radio channels to ensure the timely and reliable transmission of data. We compare the performance of this framework with other state-of-the-art methods for both single and multi-camera monitoring. The results demonstrate the usefulness of the proposed method in terms of salient event coverage and reduced computational and transmission costs, as well as in helping analysts find semantically relevant visual information.Supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2013R1A1A2012904)

Growth models, random matrices and Painleve transcendents

The Hammersley process relates to the statistical properties of the maximum length of all up/right paths connecting random points of a given density in the unit square from (0,0) to (1,1). This process can also be interpreted in terms of the height of the polynuclear growth model, or the length of the longest increasing subsequence in a random permutation. The cumulative distribution of the longest path length can be written in terms of an average over the unitary group. Versions of the Hammersley process in which the points are constrained to have certain symmetries of the square allow similar formulas. The derivation of these formulas is reviewed. Generalizing the original model to have point sources along two boundaries of the square, and appropriately scaling the parameters gives a model in the KPZ universality class. Following works of Baik and Rains, and Pr\"ahofer and Spohn, we review the calculation of the scaled cumulative distribution, in which a particular Painlev\'e II transcendent plays a prominent role.Comment: 27 pages, 5 figure

The 1+1-dimensional Kardar-Parisi-Zhang equation and its universality class

We explain the exact solution of the 1+1 dimensional Kardar-Parisi-Zhang equation with sharp wedge initial conditions. Thereby it is confirmed that the continuum model belongs to the KPZ universality class, not only as regards to scaling exponents but also as regards to the full probability distribution of the height in the long time limit.Comment: Proceedings StatPhys 2

An Efficient Anomaly Recognition Framework Using an Attention Residual LSTM in Surveillance Videos

Video anomaly recognition in smart cities is an important computer vision task that plays a vital role in smart surveillance and public safety but is challenging due to its diverse, complex, and infrequent occurrence in real-time surveillance environments. Various deep learning models use significant amounts of training data without generalization abilities and with huge time complexity. To overcome these problems, in the current work, we present an efficient light-weight convolutional neural network (CNN)-based anomaly recognition framework that is functional in a surveillance environment with reduced time complexity. We extract spatial CNN features from a series of video frames and feed them to the proposed residual attention-based long short-term memory (LSTM) network, which can precisely recognize anomalous activity in surveillance videos. The representative CNN features with the residual blocks concept in LSTM for sequence learning prove to be effective for anomaly detection and recognition, validating our model’s effective usage in smart cities video surveillance. Extensive experiments on the real-world benchmark UCF-Crime dataset validate the effectiveness of the proposed model within complex surveillance environments and demonstrate that our proposed model outperforms state-of-the-art models with a 1.77%, 0.76%, and 8.62% increase in accuracy on the UCF-Crime, UMN and Avenue datasets, respectively