1,101 research outputs found
Capacitated Dynamic Programming: Faster Knapsack and Graph Algorithms
No full textOne of the most fundamental problems in Computer Science is the Knapsack problem. Given a set of n items with different weights and values, it asks to pick the most valuable subset whose total weight is below a capacity threshold T. Despite its wide applicability in various areas in Computer Science, Operations Research, and Finance, the best known running time for the problem is O(T n). The main result of our work is an improved algorithm running in time O(TD), where D is the number of distinct weights. Previously, faster runtimes for Knapsack were only possible when both weights and values are bounded by M and V respectively, running in time O(nMV) [Pisinger, 1999]. In comparison, our algorithm implies a bound of O(n M^2) without any dependence on V, or O(n V^2) without any dependence on M. Additionally, for the unbounded Knapsack problem, we provide an algorithm running in time O(M^2) or O(V^2). Both our algorithms match recent conditional lower bounds shown for the Knapsack problem [Marek Cygan et al., 2017; Marvin Künnemann et al., 2017].
We also initiate a systematic study of general capacitated dynamic programming, of which Knapsack is a core problem. This problem asks to compute the maximum weight path of length k in an edge- or node-weighted directed acyclic graph. In a graph with m edges, these problems are solvable by dynamic programming in time O(k m), and we explore under which conditions the dependence on k can be eliminated. We identify large classes of graphs where this is possible and apply our results to obtain linear time algorithms for the problem of k-sparse Delta-separated sequences. The main technical innovation behind our results is identifying and exploiting concavity that appears in relaxations and subproblems of the tasks we consider
The novel mTOR inhibitor RAD001 (Everolimus) induces antiproliferative effects in human pancreatic neuroendocrine tumor cells
No full textBackground/Aim: Tumors exhibiting constitutively activated PI(3) K/Akt/mTOR signaling are hypersensitive to mTOR inhibitors such as RAD001 (everolimus) which is presently being investigated in clinical phase II trials in various tumor entities, including neuroendocrine tumors (NETs). However, no preclinical data about the effects of RAD001 on NET cells have been published. In this study, we aimed to evaluate the effects of RAD001 on BON cells, a human pancreatic NET cell line that exhibits constitutively activated PI(3) K/Akt/mTOR signaling. Methods: BON cells were treated with different concentrations of RAD001 to analyze its effect on cell growth using proliferation assays. Apoptosis was examined by Western blot analysis of caspase-3/PARP cleavage and by FACS analysis of DNA fragmentation. Results: RAD001 potently inhibited BON cell growth in a dose-dependent manner which was dependent on the serum concentration in the medium. RAD001-induced growth inhibition involved G0/G1-phase arrest as well as induction of apoptosis. Conclusion: In summary, our data demonstrate antiproliferative and apoptotic effects of RAD001 in NET cells in vitro supporting its clinical use in current phase II trials in NET patients. Copyright (c) 2007 S. Karger AG, Basel
Fast and Simple Modular Subset Sum
No full textWe revisit the Subset Sum problem over the finite cyclic group for some given integer . A series of recent works has providedasymptotically optimal algorithms for this problem under the Strong ExponentialTime Hypothesis. Koiliaris and Xu (SODA'17, TALG'19) gave a deterministicalgorithm running in time , which was later improved to randomized time by Axiotis et al. (SODA'19). In this work, wepresent two simple algorithms for the Modular Subset Sum problem running innear-linear time in , both efficiently implementing Bellman's iteration over. The first one is a randomized algorithm running in time, that is based solely on rolling hash and an elementarydata-structure for prefix sums; to illustrate its simplicity we provide a shortand efficient implementation of the algorithm in Python. Our second solution isa deterministic algorithm running in time , thatuses dynamic data structures for string manipulation. We further show that thetechniques developed in this work can also lead to simple algorithms for theAll Pairs Non-Decreasing Paths Problem (APNP) on undirected graphs, matchingthe asymptotically optimal running time of provided in therecent work of Duan et al. (ICALP'19).<br
Satellite Radio Interface and Radio Resource Management Strategy for the Delivery of Multicast/Broadcast Services via an Integrated Satellite-Terrestrial System
No full textHigh-spin structure of the spherical nucleus Y-90
No full textHigh-spin states in Y-90 were populated in the Se-82(B-11,3n) reaction at a beam energy of 37 MeV. gamma rays were detected with the spectrometer GASP. The level scheme of Y-90 was extended up to J(pi)=(18(+)) at 9.6 MeV. Mean lifetimes of four levels were determined using the Doppler-shift-attenuation method. The structure of Y-90 was interpreted in terms of the shell model. The calculations were performed in the model space pi(0f(5/2),1p(3/2),1p(1/2),0g(9/2)) nu(1p(1/2),0g(9/2),1d(5/2)) and in an extended space including the nu(0g(7/2)) orbital also. The calculations in the extended model space reveal a correspondence between states in Y-90 and Y-89. Moreover, a combination of the predicted states with J(pi)greater than or equal to14((+)) can be found that reproduces the large experimental B(M1) values of up to about 1 Weisskopf unit
Intel Stratix 10 FPGA design for track reconstruction for the ATLAS experiment at the HL-LHC
No full textThe fast reconstruction of charged particle tracks with high efficiency and track quality is an essential part of the online data selection for the ATLAS experiment at the High Luminosity LHC. Dedicated custom designed hardware boards and software simulations have been developed to assess the feasibility of a Hardware Tracking Trigger (HTT) system. The Pattern Recognition Mezzanine (PRM), as part of the HTT system, has been designed to recognize track candidates in silicon detectors with Associative Memory ASICs and to select and reconstruct tracks using linearized algorithms implemented in an Intel Stratix 10 MX FPGA. The highly parallelized FPGA design makes extensive use of the integrated High-Bandwidth-Memory. In this paper, the FPGA design for the PRM board is presented. Its functionalities have been verified in both simulations and hardware tests on an Intel Stratix 10 MX development kit
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