A push–relabel approximation algorithm for approximating the minimum-degree MST problem and its generalization to matroids

AbstractIn the minimum-degree minimum spanning tree (MDMST) problem, we are given a graph G, and the goal is to find a minimum spanning tree (MST) T, such that the maximum degree of T is as small as possible. This problem is NP-hard and generalizes the Hamiltonian path problem. We give an algorithm that outputs an MST of degree at most 2Δopt (G)+o(Δopt (G)), where Δopt (G) denotes the degree of the optimal tree. This result improves on a previous result of Fischer [T. Fischer, Optimizing the degree of minimum weight spanning trees. Technical Report 14853, Dept. of Computer Science, Cornell University, Ithaca, NY, 1993] that finds an MST of degree at most bΔopt (G)+logbn, for any b>1.The MDMST problem is a special case of the following problem: given a k-ary hypergraph G=(V,E) and weighted matroid M with E as its ground set, find a minimum-cost basis (MCB) T of M such that the degree of T in G is as small as possible. Our algorithm immediately generalizes to this problem, finding an MCB of degree at most k2Δopt (G,M)+O(kkΔopt (G,M)).We use the push–relabel framework developed by Goldberg [A. V. Goldberg, A new max-flow algorithm, Technical Report MIT/LCS/TM-291, Massachusetts Institute of Technology, 1985 (Technical Report)] for the maximum-flow problem. To our knowledge, this is the first use of the push–relabel technique in an approximation algorithm for an NP-hard problem.The MDMST problem is closely connected to the bounded-degree minimum spanning tree (BDMST) problem. Given a graph G and degree bound B on its nodes, the BDMST problem is to find a minimum cost spanning tree among the spanning trees with maximum degree B. Previous algorithms for this problem by Könemann and Ravi [J. Könemann, R. Ravi, A matter of degree: Improved approximation algorithms for degree-bounded minimum spanning trees, SIAM Journal on Computing 31(6) (2002) 1783–1793; J. Könemann, R. Ravi, Primal-dual meets local search: Approximating MST’s with nonuniform degree bounds, in: Proceedings of the Thirty-Fifth ACM Symposium on Theory of Computing, 2003, pp. 389–395] and by Chaudhuri et al. [K. Chaudhuri, S. Rao, S. Riesenfeld, K. Talwar, What would Edmonds do? Augmenting paths and witnesses for bounded degree MSTs, in: Proceedings of APPROX/RANDOM, 2005, pp. 26–39] incur a near-logarithmic additive error in the degree. We give the first BDMST algorithm that approximates both the degree and the cost to within a constant factor of the optimum. These results generalize to the case of nonuniform degree bounds

Discrete Audio Representation as an Alternative to Mel-Spectrograms for Speaker and Speech Recognition

Discrete audio representation, aka audio tokenization, has seen renewed interest driven by its potential to facilitate the application of text language modeling approaches in audio domain. To this end, various compression and representation-learning based tokenization schemes have been proposed. However, there is limited investigation into the performance of compression-based audio tokens compared to well-established mel-spectrogram features across various speaker and speech related tasks. In this paper, we evaluate compression based audio tokens on three tasks: Speaker Verification, Diarization and (Multi-lingual) Speech Recognition. Our findings indicate that (i) the models trained on audio tokens perform competitively, on average within $1\%$ of mel-spectrogram features for all the tasks considered, and do not surpass them yet. (ii) these models exhibit robustness for out-of-domain narrowband data, particularly in speaker tasks. (iii) audio tokens allow for compression to 20x compared to mel-spectrogram features with minimal loss of performance in speech and speaker related tasks, which is crucial for low bit-rate applications, and (iv) the examined Residual Vector Quantization (RVQ) based audio tokenizer exhibits a low-pass frequency response characteristic, offering a plausible explanation for the observed results, and providing insight for future tokenizer designs.Comment: Preprint. Submitted to ICASSP 202

VERNONIA CINEREA (NEICHITTI KEERAI) REGENERATES PROXIMAL TUBULES IN CISPLATININDUCED RENAL DAMAGE IN MICE

Objective: The aim of the study was to evaluate whether Vernonia cinerea (VC) regenerates the proximal renal tubular cells in cisplatin-induced necrosis in male Swiss albino mice.Methods: The crude aqueous extract (CAE) of VC was fractionated from non-polar to polar using different solvents. Mice were injected a single dose of cisplatin (15 mg/kg) on day 1, which took 5 days to cause maximal renal damage. From day 6, CAE and all fractions were orally administered (200, 300, and 400 mg/kg) for 5 continuous days. On day 11, blood was collected to estimate urea and creatinine. Kidney was collected for histology and grading was done.Results: Cisplatin induced proximal renal tubular damage (grade 5) in corticomedullary junction, characterized by necrosis, proximal tubular dilatation, inflammation and vasodilation. Aqueous fraction (AF) did not show any regeneration; whereas, 400 mg/kg dose of CAE and butanol fraction (BF) showed a significant reduction (p<0.001) in proximal tubular damage (Grade 3) and 50–75% regeneration of proximal tubular epithelial cells.Conclusion: This is the first study to demonstrate the regenerative potential of Neichitti kashayam (CAE of VC) and its BF in cisplatin-induced proximal tubular damage in kidney. Further study is warranted to find out the dose regimen for complete regeneration, lead compounds, and molecular mechanism

Why is the video analytics accuracy fluctuating, and what can we do about it?

It is a common practice to think of a video as a sequence of images (frames), and re-use deep neural network models that are trained only on images for similar analytics tasks on videos. In this paper, we show that this leap of faith that deep learning models that work well on images will also work well on videos is actually flawed. We show that even when a video camera is viewing a scene that is not changing in any human-perceptible way, and we control for external factors like video compression and environment (lighting), the accuracy of video analytics application fluctuates noticeably. These fluctuations occur because successive frames produced by the video camera may look similar visually, but these frames are perceived quite differently by the video analytics applications. We observed that the root cause for these fluctuations is the dynamic camera parameter changes that a video camera automatically makes in order to capture and produce a visually pleasing video. The camera inadvertently acts as an unintentional adversary because these slight changes in the image pixel values in consecutive frames, as we show, have a noticeably adverse impact on the accuracy of insights from video analytics tasks that re-use image-trained deep learning models. To address this inadvertent adversarial effect from the camera, we explore the use of transfer learning techniques to improve learning in video analytics tasks through the transfer of knowledge from learning on image analytics tasks. In particular, we show that our newly trained Yolov5 model reduces fluctuation in object detection across frames, which leads to better tracking of objects(40% fewer mistakes in tracking). Our paper also provides new directions and techniques to mitigate the camera's adversarial effect on deep learning models used for video analytics applications

Hydrogel–Electrospun Fiber Mat Composite Coatings for Neural Prostheses

Achieving stable, long-term performance of implanted neural prosthetic devices has been challenging because of implantation related neuron loss and a foreign body response that results in encapsulating glial scar formation. To improve neuron–prosthesis integration and form chronic, stable interfaces, we investigated the potential of neurotrophin-eluting hydrogel–electrospun fiber mat (EFM) composite coatings. In particular, poly(ethylene glycol)-poly(ε-caprolactone) (PEGPCL) hydrogel–poly(ε-caprolactone) EFM composites were applied as coatings for multielectrode arrays. Coatings were stable and persisted on electrode surfaces for over 1 month under an agarose gel tissue phantom and over 9 months in a PBS immersion bath. To demonstrate drug release, a neurotrophin, nerve growth factor (NGF), was loaded in the PEGPCL hydrogel layer, and coating cytotoxicity and sustained NGF release were evaluated using a PC12 cell culture model. Quantitative MTT assays showed that these coatings had no significant toxicity toward PC12 cells, and neurite extension at day 7 and 14 confirmed sustained release of NGF at biologically significant concentrations for at least 2 weeks. Our results demonstrate that hydrogel–EFM composite materials can be applied to neural prostheses to improve neuron–electrode proximity and enhance long-term device performance and function

Vertex Sparsifiers: New Results from Old Techniques

Given a capacitated graph $G = (V,E)$ and a set of terminals $K \subseteq V$, how should we produce a graph $H$ only on the terminals $K$ so that every (multicommodity) flow between the terminals in $G$ could be supported in $H$ with low congestion, and vice versa? (Such a graph $H$ is called a flow-sparsifier for $G$.) What if we want $H$ to be a "simple" graph? What if we allow $H$ to be a convex combination of simple graphs? Improving on results of Moitra [FOCS 2009] and Leighton and Moitra [STOC 2010], we give efficient algorithms for constructing: (a) a flow-sparsifier $H$ that maintains congestion up to a factor of $O(\log k/\log \log k)$, where $k = |K|$, (b) a convex combination of trees over the terminals $K$ that maintains congestion up to a factor of $O(\log k)$, and (c) for a planar graph $G$, a convex combination of planar graphs that maintains congestion up to a constant factor. This requires us to give a new algorithm for the 0-extension problem, the first one in which the preimages of each terminal are connected in $G$. Moreover, this result extends to minor-closed families of graphs. Our improved bounds immediately imply improved approximation guarantees for several terminal-based cut and ordering problems.Comment: An extended abstract appears in the 13th International Workshop on Approximation Algorithms for Combinatorial Optimization Problems (APPROX), 2010. Final version to appear in SIAM J. Computin

A distributed cancer care model with a technology-driven hub-and-spoke and further spoke hierarchy : findings from a pilot implementation programme in Kerala, India

Background: The technology enabled distributed model in Kerala is based on an innovative partnership model between Karkinos Healthcare and private health centers. The model is designed to address the barriers to cancer screening by generating demand and by bringing together the private health centers and service providers at various levels to create a network for continued care. This paper describes the implementation process and presents some preliminary findings. Methods: The model follows the hub-and-spoke and further spoke framework. In the pilot phases, from July 2021 to December 2021, five private health centers (partners) collaborated with Karkinos Healthcare across two districts in Kerala. Screening camps were organized across the districts at the community level where the target groups were administered a risk assessment questionnaire followed by screening tests at the spoke hospitals based on a defined clinical protocol. The screened positive patients were examined further for confirmatory diagnosis at the spoke centers. Patients requiring chemotherapy or minor surgeries were treated at the spokes. For radiation therapy and complex surgeries the patients were referred to the hubs. Results: A total of 2,459 individuals were screened for cancer at the spokes and 299 were screened positive. Capacity was built at the spokes for cancer surgery and chemotherapy. A total of 189 chemotherapy sessions and 17 surgeries were performed at the spokes for cancer patients. 70 patients were referred to the hub. Conclusion: Initial results demonstrate the ability of the technology Distributed Cancer Care Network (DCCN) system to successfully screen and detect cancer and to converge the actions of various private health facilities towards providing a continuum of cancer care. The lessons learnt from this study will be useful for replicating the process in other States

Gene Expression Profiling of Preovulatory Follicle in the Buffalo Cow: Effects of Increased IGF-I Concentration on Periovulatory Events

The preovulatory follicle in response to gonadotropin surge undergoes dramatic biochemical, and morphological changes orchestrated by expression changes in hundreds of genes. Employing well characterized bovine preovulatory follicle model, granulosa cells (GCs) and follicle wall were collected from the preovulatory follicle before, 1, 10 and 22 h post peak LH surge. Microarray analysis performed on GCs revealed that 450 and 111 genes were differentially expressed at 1 and 22 h post peak LH surge, respectively. For validation, qPCR and immunocytochemistry analyses were carried out for some of the differentially expressed genes. Expression analysis of many of these genes showed distinct expression patterns in GCs and the follicle wall. To study molecular functions and genetic networks, microarray data was analyzed using Ingenuity Pathway Analysis which revealed majority of the differentially expressed genes to cluster within processes like steroidogenesis, cell survival and cell differentiation. In the ovarian follicle, IGF-I is established to be an important regulator of the above mentioned molecular functions. Thus, further experiments were conducted to verify the effects of increased intrafollicular IGF-I levels on the expression of genes associated with the above mentioned processes. For this purpose, buffalo cows were administered with exogenous bGH to transiently increase circulating and intrafollicular concentrations of IGF-I. The results indicated that increased intrafollicular concentrations of IGF-I caused changes in expression of genes associated with steroidogenesis (StAR, SRF) and apoptosis (BCL-2, FKHR, PAWR). These results taken together suggest that onset of gonadotropin surge triggers activation of various biological pathways and that the effects of growth factors and peptides on gonadotropin actions could be examined during preovulatory follicle development

Triplet transport in thin films: fundamentals and applications

Triplet excitons are key players in multi-excitonic processes like singlet fission and triplet-triplet annihilation based photon upconversion, which may be useful in next-generation photovoltaic devices, photocatalysis and bioimaging. Here, we present an overview of experimental and theoretical work on triplet energy transfer, with a focus on triplet transport in thin films. We start with the theory describing Dexter-mediated triplet energy transfer and the fundamental parameters controlling this process. Then we summarize current experimental methods used to measure the triplet exciton diffusion length. Finally, the use of hierarchically ordered structures to improve the triplet diffusion length is presented, before concluding with an outlook on the remaining challenges