On randomness of random number generators

Many computer simulations use random number generators and since most computer languages have a built-in generator it is very easy just to use that one. However these random number generators can be very non-random

Improving disk efficiency in video servers by random redundant storage

Random redundant storage strategies have proven to be an interesting solution for the problem of storing data in a video server. Several papers describe how a good load bal ance is obtained by using the freedom of choice for the data blocks that are stored more than once. We improve on these results by exploiting the multi-zone character of hard disks. In our model of the load balancing problem we incorporate the actual transfer times of the blocks, depending on the zones in which the blocks are stored. We give an MILP model of the load balancing problem which we use to de rive a number of good load balancing algorithms. We show that, by using these algorithms, the amount of data that is read from the fast zones is substantially larger than with conventional strategies, such that the disks are used more efficiently

QoS control strategies for high-quality video processing

Video processing in software is often characterized by highly fluctuating, content-dependent processing times, and a limited tolerance for deadline misses. We present an approach that allows close-to-average-case resource allocation to a single video processing task, based on asynchronous, scalable processing, and QoS adaptation. The QoS adaptation balances different QoS parameters that can be tuned by user-perception experiments: picture quality, deadline misses, and quality changes. We model the balancing problem as a discrete stochastic decision problem, and propose two closely related solution strategies, for which the processing-time statistics are determined offline and at run time, respectively. We enhance both strategies with a compensation for structural (non-stochastic) load fluctuations. Finally, we validate our approach by means of simulation experiments, and conclude that both enhanced strategies perform close to the theoretical optimum

Methods to optimally trade bandwidth against buffer size for a VBR stream

To reduce the peak bit-rate for transmitting a variable-bit-rate stream, one can prefetch and buffer data at the receiving side. Previous work shows how to minimize the required buffer size given the available bandwidth [Feng, 19971 and how to minimize the required bandwidth given the available buffer size [Salehi et al., 1998]. Instead of taking either bandwidth or buffer size fixed, we as sume both to be decision variables with given cost coefficients. We explain our method [Den Boef et al., 2003a] and the method by Chang et al. [1998] and compare them. These methods find the optimal values by starting with a minimum value for either the bandwidth [Chang et al., 19981 or the buffer size [Den Boef et al., 2003a] and then increasing this value, while at the same time decreasing the value of the buffer size or the bandwidth, respectively. We conclude that our method has slightly better run times than the method by Chang et al. and uses about half the amount of memory

Scheduling TV recordings for a recommender-based DVR

\u3cp\u3eIn a recommender-based digital video recorder, TV programs are considered for automatic recording on a hard disk. The choice of which programs to record depends on (i) the scores assigned to the programs by the recommender, (ii) the times and channels at which the programs are broadcast, and (iii) the number of tuners available for recording. For a given set of programs that are broadcast in a given time interval, and a given number m of tuners, we consider the problem of determining a subset S'⊆ of programs with a maximum sum of scores that can be recorded with the m tuners. We show that this problem can be formulated as a min-cost flow problem and can be solved to optimality in O (mn\u3csup\u3e2\u3c/sup\u3e)time. In addition, we indicate how the min-cost flow approach can be adapted to take into account practical considerations such as uncertainties in the actual broadcast times of programs and programs that are broadcast multiple times in the given time interval. We present experimental results that suggest that, for realistic settings, near-optimal subsets can be determined on low-cost hardware.\u3c/p\u3

Artificial intelligence in clinical health care applications:viewpoint

\u3cp\u3eThe idea of artificial intelligence (AI) has a long history. It turned out, however, that reaching intelligence at human levels is more complicated than originally anticipated. Currently, we are experiencing a renewed interest in AI, fueled by an enormous increase in computing power and an even larger increase in data, in combination with improved AI technologies like deep learning. Healthcare is considered the next domain to be revolutionized by artificial intelligence. While AI approaches are excellently suited to develop certain algorithms, for biomedical applications there are specific challenges. We propose six recommendations-the 6Rs-to improve AI projects in the biomedical space, especially clinical health care, and to facilitate communication between AI scientists and medical doctors: (1) Relevant and well-defined clinical question first; (2) Right data (ie, representative and of good quality); (3) Ratio between number of patients and their variables should fit the AI method; (4) Relationship between data and ground truth should be as direct and causal as possible; (5) Regulatory ready; enabling validation; and (6) Right AI method.\u3c/p\u3

Efficient timing constraint derivation for optimally retiming high speed processing units

Retiming, including pipelining, is applied to make the processing units (PUs) run at a required throughput rate with a minimum number of registers. In the first step, a timing analysis of a PU is performed which results in inequality constraints on the operations' retimings. The constraints, together with a cost function expressing the number of registers in a retimed PU, form an instance of an integer linear programming problem, which is solved to optimality in the second step. In this paper, we concentrate on the constraint derivation task. We present two new constraint derivation algorithms, one of which is more memory efficient and the other more run-time efficient. We show that the run-time efficient algorithm makes it possible to minimize the area of a huge standard cell network, possibly representing a complete IC, within acceptable run-time limits

Multidimensional periodic scheduling model and complexity

We discuss the multidimensional periodic scheduling problem, which originates from the design of high-throughput real-time digital signal processing systems. We introduce the concept of multidimensional periodic operations in order to cope with problems originating from loop hierarchies and explicit timing requirements. We present a model of the multidimensional periodic scheduling problem and show that this problem and two related sub-problems are NP-hard. Furthermore, we identify several special cases induced by practical situations. Some of these special cases are proven to be well-solvable. Finally, we present a sketch of a solution approach