1,312 research outputs found

### Online Makespan Minimization with Parallel Schedules

In online makespan minimization a sequence of jobs $\sigma = J_1,..., J_n$
has to be scheduled on $m$ identical parallel machines so as to minimize the
maximum completion time of any job. We investigate the problem with an
essentially new model of resource augmentation. Here, an online algorithm is
allowed to build several schedules in parallel while processing $\sigma$. At
the end of the scheduling process the best schedule is selected. This model can
be viewed as providing an online algorithm with extra space, which is invested
to maintain multiple solutions. The setting is of particular interest in
parallel processing environments where each processor can maintain a single or
a small set of solutions.
We develop a (4/3+\eps)-competitive algorithm, for any 0<\eps\leq 1, that
uses a number of 1/\eps^{O(\log (1/\eps))} schedules. We also give a
(1+\eps)-competitive algorithm, for any 0<\eps\leq 1, that builds a
polynomial number of (m/\eps)^{O(\log (1/\eps) / \eps)} schedules. This value
depends on $m$ but is independent of the input $\sigma$. The performance
guarantees are nearly best possible. We show that any algorithm that achieves a
competitiveness smaller than 4/3 must construct $\Omega(m)$ schedules. Our
algorithms make use of novel guessing schemes that (1) predict the optimum
makespan of a job sequence $\sigma$ to within a factor of 1+\eps and (2)
guess the job processing times and their frequencies in $\sigma$. In (2) we
have to sparsify the universe of all guesses so as to reduce the number of
schedules to a constant.
The competitive ratios achieved using parallel schedules are considerably
smaller than those in the standard problem without resource augmentation

### Balanced Allocations: A Simple Proof for the Heavily Loaded Case

We provide a relatively simple proof that the expected gap between the
maximum load and the average load in the two choice process is bounded by
$(1+o(1))\log \log n$, irrespective of the number of balls thrown. The theorem
was first proven by Berenbrink et al. Their proof uses heavy machinery from
Markov-Chain theory and some of the calculations are done using computers. In
this manuscript we provide a significantly simpler proof that is not aided by
computers and is self contained. The simplification comes at a cost of weaker
bounds on the low order terms and a weaker tail bound for the probability of
deviating from the expectation

### Scheduling Packets with Values and Deadlines in Size-bounded Buffers

Motivated by providing quality-of-service differentiated services in the
Internet, we consider buffer management algorithms for network switches. We
study a multi-buffer model. A network switch consists of multiple size-bounded
buffers such that at any time, the number of packets residing in each
individual buffer cannot exceed its capacity. Packets arrive at the network
switch over time; they have values, deadlines, and designated buffers. In each
time step, at most one pending packet is allowed to be sent and this packet can
be from any buffer. The objective is to maximize the total value of the packets
sent by their respective deadlines. A 9.82-competitive online algorithm has
been provided for this model (Azar and Levy. SWAT 2006), but no offline
algorithms have been known yet. In this paper, We study the offline setting of
the multi-buffer model. Our contributions include a few optimal offline
algorithms for some variants of the model. Each variant has its unique and
interesting algorithmic feature. These offline algorithms help us understand
the model better in designing online algorithms.Comment: 7 page

### Improved algorithms for online load balancing

We consider an online load balancing problem and its extensions in the
framework of repeated games. On each round, the player chooses a distribution
(task allocation) over $K$ servers, and then the environment reveals the load
of each server, which determines the computation time of each server for
processing the task assigned. After all rounds, the cost of the player is
measured by some norm of the cumulative computation-time vector. The cost is
the makespan if the norm is $L_\infty$-norm. The goal is to minimize the
regret, i.e., minimizing the player's cost relative to the cost of the best
fixed distribution in hindsight. We propose algorithms for general norms and
prove their regret bounds. In particular, for $L_\infty$-norm, our regret bound
matches the best known bound and the proposed algorithm runs in polynomial time
per trial involving linear programming and second order programming, whereas no
polynomial time algorithm was previously known to achieve the bound.Comment: 16 pages; typos correcte

### Balanced Allocation on Graphs: A Random Walk Approach

In this paper we propose algorithms for allocating $n$ sequential balls into
$n$ bins that are interconnected as a $d$-regular $n$-vertex graph $G$, where
$d\ge3$ can be any integer.Let $l$ be a given positive integer. In each round
$t$, $1\le t\le n$, ball $t$ picks a node of $G$ uniformly at random and
performs a non-backtracking random walk of length $l$ from the chosen node.Then
it allocates itself on one of the visited nodes with minimum load (ties are
broken uniformly at random). Suppose that $G$ has a sufficiently large girth
and $d=\omega(\log n)$. Then we establish an upper bound for the maximum number
of balls at any bin after allocating $n$ balls by the algorithm, called {\it
maximum load}, in terms of $l$ with high probability. We also show that the
upper bound is at most an $O(\log\log n)$ factor above the lower bound that is
proved for the algorithm. In particular, we show that if we set $l=\lfloor(\log
n)^{\frac{1+\epsilon}{2}}\rfloor$, for every constant $\epsilon\in (0, 1)$, and
$G$ has girth at least $\omega(l)$, then the maximum load attained by the
algorithm is bounded by $O(1/\epsilon)$ with high probability.Finally, we
slightly modify the algorithm to have similar results for balanced allocation
on $d$-regular graph with $d\in[3, O(\log n)]$ and sufficiently large girth

### Resource augmentation in load balancing

We consider load-balancing in the following setting. The on-line algorithm is allowed to use $n$ machines, whereas the optimal off-line algorithm is limited to $m$ machines, for some fixed $m < n$. We show that while the greedy algorithm has a competitive ratio which decays linearly in the inverse of $n/m$, the best on-line algorithm has a ratio which decays exponentially in $n/m$. Specifically, we give an algorithm with competitive ratio of 1+2^{- frac{n{m (1- o (1)), and a lower bound of 1+ e^{ - frac{n{m (1+ o(1)) on the competitive ratio of any randomized algorithm. We also consider the preemptive case. We show an on-line algorithm with a competitive ratio of 1+ e^{ - frac{n{m (1+ o(1)). We show that the algorithm is optimal by proving a matching lower bound. We also consider the non-preemptive model with temporary tasks. We prove that for $n=m+1$, the greedy algorithm is optimal. (It is not optimal for permanent tasks.

### The Estimation of Oil Palm Carbon Stock in Sembilang Dangku Landscape, South Sumatra

Oil palm has the ability to sequester carbon dioxide stored as carbon stock. This study aimed to estimate carbon stock in some age classes, to determine the relationship between Normalized Difference Vegetation Index (NDVI) and carbon stock, and to estimate the distribution of oil palm carbon stock in Landscape Sembilang Dangku. Estimation of carbon stock were carried out at the non productive age plant phase namely <2 years, 2-3 years, and the productive plant age phase namely 4-10 years and> 10 years. The carbon stock estimation used allometric equations. Landsat 8 Operational Land Imager (OLI) /Thermal Infrared Sensor (TIRS) was analyzed to determine NDVI. Making a map of the classification of carbon stock distribution using Software QGIS Las Palmas 2.18.0. The results showed that the carbon stock in the age class <2 years was 9.50 ton C/ ha, the age class of 2-3 was 9.62 ton C/ha, the age of 4-10 was 28.23 ton C/ha and in the age class> 10 was 79.83 ton C/ha. The relation between NDVI with carbon stock had a strong correlation (r = 0.9972) with regression equation Y = 638.13x - 242.65. Carbon stock distribution was based on percentage of area as follows: <15 ton C/ha covering an area of 26.52%, 15-25 ton C/ha covering an area of 5.29%, 26-70 ton C ha covering an area of 35.41%, and > 70 ton C/ha 32.78%

### Upaya Keluarga Untuk Mencegah Penularan Dalam Perawatan Anggota Keluarga Dengan Tb Paru

Indonesia merupakan negara keempat dengan insiden kasus terbanyak untuk tuberkulosis (TB) paru didunia..Penelitian ini menggunakan desain kualitatif dengan pendekatan case study research, bertujuan untuk memberikan penjelasan tentang upaya keluarga untuk mencegah penularan dalam perawatan anggota keluarga dengan TB Paru. Dari hasil analisa data, didapatkan tiga tema dan tujuh subtema yaitu: (1) Modifikasi lingkungan dengan subtema modifikasi ventilasi yang memadai dan menjaga kebersihan. (2) Upaya memutus transmisi penyakit dengan subtema membuang dahak, pengunaan masker, dan menutup saat batuk. (3) Konsumsi obat dan kontrol rutin ke puskesmas dengan subtema pemantauan dari keluarga dalam minum obat (PMO), serta kontrol rutin ke Puskesmas.Berdasarkan hasil penelitian ini diharapkan Puskesmas dapat menambah dan memodifikasi program penanggulangan tuberkulosis (TB). Selain itu perlu dilakukan pengawasan secara berkala atau kunjungan rumah secara rutin untuk memantau pengobatan dan pencegahan penularan Tuberkulosis (TB) yang dilakukan keluarga di rumah

### Statistical mechanics of budget-constrained auctions

Finding the optimal assignment in budget-constrained auctions is a
combinatorial optimization problem with many important applications, a notable
example being the sale of advertisement space by search engines (in this
context the problem is often referred to as the off-line AdWords problem).
Based on the cavity method of statistical mechanics, we introduce a message
passing algorithm that is capable of solving efficiently random instances of
the problem extracted from a natural distribution, and we derive from its
properties the phase diagram of the problem. As the control parameter (average
value of the budgets) is varied, we find two phase transitions delimiting a
region in which long-range correlations arise.Comment: Minor revisio

### Global Ultrasound Elastography Using Convolutional Neural Network

Displacement estimation is very important in ultrasound elastography and
failing to estimate displacement correctly results in failure in generating
strain images. As conventional ultrasound elastography techniques suffer from
decorrelation noise, they are prone to fail in estimating displacement between
echo signals obtained during tissue distortions. This study proposes a novel
elastography technique which addresses the decorrelation in estimating
displacement field. We call our method GLUENet (GLobal Ultrasound Elastography
Network) which uses deep Convolutional Neural Network (CNN) to get a coarse
time-delay estimation between two ultrasound images. This displacement is later
used for formulating a nonlinear cost function which incorporates similarity of
RF data intensity and prior information of estimated displacement. By
optimizing this cost function, we calculate the finer displacement by
exploiting all the information of all the samples of RF data simultaneously.
The Contrast to Noise Ratio (CNR) and Signal to Noise Ratio (SNR) of the strain
images from our technique is very much close to that of strain images from
GLUE. While most elastography algorithms are sensitive to parameter tuning, our
robust algorithm is substantially less sensitive to parameter tuning.Comment: 4 pages, 4 figures; added acknowledgment section, submission type
late

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