Miracle-dz/UniFAR-FTUs huggingface dataset:
A Decomposition Algorithm for Arborescence Inventory Systems

This paper develops an algorithm for solving an arborescence-structured production and inventory system. Arborescence structures model multi-echelon production systems in which each facility requires input from a unique immediate predecessor. Assuming known demands, and no backlogging, the objective is to schedule production over a finite planning horizon to minimize production and holding costs. The algorithm is applicable to systems in which, at all facilities with followers in the system, the production costs consist of a set-up charge plus linear costs and holding costs are linear. General costs are permitted at the lowest-echelon facilities (those without followers) with special computational efficiency resulting when these costs are concave. The algorithm exploits the known results on the structure of optimal policies in arborescence systems to decompose the problem into single-stage problems at each lowest-echelon facility. This decomposition is achieved by enumerating implicitly the feasible produ.
Effects of Larval Density and Habitat Drying on Developmental Success of Ochlerotatus albifasciatus (Diptera: Culicidae) in Urban Rain Pools: Evidence from Field and Experimental Studies

ABSTRACT Ochlerotatus albifasciatus (Macquart) (Diptera: Culicidae) larvae develop synchronously after rainfall events in ephemeral or temporary pools, where they occasionally attain very high abundance. The aims of the current study were to analyze the response of life history parameters such as daily larval mortality, time to pupation, and adult size of Oc. albifasciatus to increasing larval density under controlled conditions, and to analyze the relationships of daily larval mortality with density and environmental variables (drying rate, temperature, and season) in urban rain pools in Buenos Aires, Argentina. An exponential increase in mortality was observed at high larval densities under controlled conditions. Development times and adult size (wing length) differed between males and females, and were also affected by density. Development times extended for 0.36 d for each order of magnitude of increase in larval density, and wing length decreased 0.0021 mm per additional larva in 600 cm2. Larval density in the field varied from <1 larva per square meter to nearly 1100 larvae per square meter. Daily larval mortality values in the field were variable (0.02–0.91), positively related to the drying rate, and exhibited seasonal differences. No significant relation with larval density or temperature was found in the field. It remains to be established whether the density-independent mortality observed in this study is a generalized pattern of Oc. albifasciatus populations in Buenos Aires Province or a pattern restricted only to urban habitats.
Learning to Learn and Compositionality with Deep Recurrent Neural Networks: Learning to Learn and Compositionality

Deep neural network representations play an important role in computer vision, speech, computational linguistics, robotics, reinforcement learning and many other data-rich domains. In this talk I will show that learning-to-learn and compositionality are key ingredients for dealing with knowledge transfer so as to solve a wide range of tasks, for dealing with small-data regimes, and for continual learning. I will demonstrate this with several examples from my research team: learning to learn by gradient descent by gradient descent, neural programmers and interpreters, and learning communication.
Technical Note - Comments on the Distribution of Inventory Position in a Continuous-Review (s, S) Inventory System

This paper comments on the limiting distribution of the inventory position in a continuous-review (s, S) inventory system with arbitrary customer interarrival-time distribution. The limiting distribution is derived for the general case in which the demand quantity is random. We show that the limiting distribution is uniform on (s + 1, …, S) if and only if units are demanded one at a time. However, with a slight modification of the procurement policy, the uniform distribution is recaptured even with random demand quantities.
Genetic Basis of the Epilepsies

receptor multiplicity proposed. Perhaps the most novel and most interesting of the chapters contained in this volume are the small mainly philosophical works included throughout the volume which deal with future approaches and strategies to research in the central nervous system. Although short, the views expressed by Costa, Carlsson, Bunney and Bloom give considerable insight into the direction that research might take and the problems it will face in future. In conclusion, a patchy book not dealing with any subject in particular, but which will no doubt find its place on many library shelves.
The Sharing Problem

Many important problems are concerned with the equitable distribution of resources. A new approach to resource distribution problems is presented as a network flow problem with a maximum objective function. The value of the smallest linear tradeoff function at the terminal points in a capacitated network is maximized. We develop a polynomially bounded algorithm and give computational experience. We illustrate the importance and usefulness of the sharing-problem model by considering the equitable distribution of coal during a prolonged coal strike.
Understanding GPU Power

Modern graphics processing units (GPUs) have complex architectures that admit exceptional performance and energy efficiency for high-throughput applications. Although GPUs consume large amounts of power, their use for high-throughput applications facilitate state-of-the-art energy efficiency and performance. Consequently, continued development relies on understanding their power consumption. This work is a survey of GPU power modeling and profiling methods with increased detail on noteworthy efforts. As direct measurement of GPU power is necessary for model evaluation and parameter initiation, internal and external power sensors are discussed. Hardware counters, which are low-level tallies of hardware events, share strong correlation to power use and performance. Statistical correlation between power and performance counters has yielded worthwhile GPU power models, yet the complexity inherent to GPU architectures presents new hurdles for power modeling. Developments and challenges of counter-based GPU power modeling are discussed. Often building on the counter-based models, research efforts for GPU power simulation, which make power predictions from input code and hardware knowledge, provide opportunities for optimization in programming or architectural design. Noteworthy strides in power simulations for GPUs are included along with their performance or functional simulator counterparts when appropriate. Last, possible directions for future research are discussed.
Alteration in serum levels of selenium and glutathione peroxidase in acute myocardial infarction

Atherosclerosis is a vascular disease that occurs primarily in the aorta, coronary arteries and cerebral arteries with thickening of arteries and loss of elasticity. Despite decreased antioxidant activity documented among patients with atherosclerosis, the relationship between atherosclerosis and levels of antioxidants is still unknown. The present study was designed to evaluate serum levels of selenium and glutathione peroxidase (GPx) in patients with acute myocardial infarction with respect to healthy controls. A total of 20 patients (mean age 53.9 ± 11.1 years; 65% were males) in the myocardial infarction with ST-segment elevation group were compared with 29 subjects (mean age 46.9 ± 13.6 years; 55.2% were females) in the control group in terms of serum selenium and GPx levels. Patients with chronic diseases such as diabetes mellitus, hypertension, chronic renal disease, chronic liver disease, malignancy, prior cardiac events were not included. As result, there was a significant reduction in serum selenium levels of patient with myocardial infarction when compared to control subjects. However patient and control groups were similar in terms of serum GPx levels. In conclusion, since serum levels of Se are directly proportional to GPx activity but inversely proportional to the age, addition of Se to the diet in elderly patients seems to have a benefit in increasing antioxidant protection.
Healing the Wounds of Domestic Abuse

Feminist therapy is a powerful means of helping abused women because of its focus on systemic sources of oppression and hegemony. These forms of oppression are intimately linked to a woman's cultural and socioeconomic background and affect the way she experiences domestic abuse. Although researchers have shown that feminist therapy can be an effective approach for counseling abused women, it is criticized here for failing to acknowledge the diversity among immigrant and racially visible women who have been abused. The author will examine both the effectiveness of feminist therapy and its limitations when counseling immigrant and racially visible women who have been abused. The author will also describe the reality of domestic violence among these women and how their needs differ from women in the dominant culture. Strategies for a more inclusive form of feminist therapy will also be offered as alternatives to existing modes of practice and assessment.
Nutritional assessment of serum and hepatic vitamin A levels in patients with cirrhosis

BACKGROUND Serum vitamin A (retinol) levels may not correlate with hepatic vitamin A stores in patients with cirrhosis; thus, supplementation of vitamin A based on serum levels may have a detrimental effect. Our aim was to determine whether serum levels correlate with hepatic stores in cirrhotic patients. METHODS A prospective study of patients with cirrhosis undergoing orthotopic liver transplantation was completed. Serum and hepatic levels of vitamin A were measured by high-performance liquid chromatography. Statistical analysis was performed using rank sum tests and Spearman rank correlation coefficients. RESULTS Fifty cirrhotic patients (33 men and 17 women, mean age 53 years) were compared with a control group (25 men and 25 women, mean age 47 years) of liver donors. Median serum levels of retinol were 259 microg/L in controls and 166 microg/L in cirrhotic patients (p < .001). Median hepatic levels of retinol were 25 microg/g in controls and 27.5 +/- g/g in cirrhotic patients (p not significant). Total hepatic vitamin A levels (retinol plus retinyl esters) were 471 microg/g in controls and 244 microg/g in cirrhotic patients (p = .028). Serum retinol did not correlate with total hepatic vitamin A stores in cirrhotic patients (rs = .10, p = .332). CONCLUSIONS Serum retinol and total hepatic vitamin A stores are lower in cirrhotic patients than in controls. However, because levels of serum retinol do not correlate with hepatic vitamin A levels, the decision to prescribe vitamin A replacement for patients with cirrhosis should not be made solely on the basis of serum retinol levels.
Efficient Mobile Implementation of A CNN-based Object Recognition System

Because of the recent progress on deep learning studies, Convolutional Neural Network (CNN) based method have outperformed conventional object recognition methods with a large margin. However, it requires much more memory and computational costs compared to the conventional methods. Therefore, it is not easy to implement a CNN-based object recognition system on a mobile device where memory and computational power are limited. In this paper, we examine CNN architectures which are suitable for mobile implementation, and propose multi-scale network-in-networks (NIN) in which users can adjust the trade-off between recognition time and accuracy. We implemented multi-threaded mobile applications on both iOS and Android employing either NEON SIMD instructions or the BLAS library for fast computation of convolutional layers, and compared them in terms of recognition time on mobile devices. As results, it has been revealed that BLAS is better for iOS, while NEON is better for Android, and that reducing the size of an input image by resizing is very effective for speedup of CNN-based recognition.
Spectral and Cepstral Audio Noise Reduction Techniques in Speech Emotion Recognition

Signal noise reduction can improve the performance of machine learning systems dealing with time signals such as audio. Real-life applicability of these recognition technologies requires the system to uphold its performance level in variable, challenging conditions such as noisy environments. In this contribution, we investigate audio signal denoising methods in cepstral and log-spectral domains and compare them with common implementations of standard techniques. The different approaches are first compared generally using averaged acoustic distance metrics. They are then applied to automatic recognition of spontaneous and natural emotions under simulated smartphone-recorded noisy conditions. Emotion recognition is implemented as support vector regression for continuous-valued prediction of arousal and valence on a realistic multimodal database. In the experiments, the proposed methods are found to generally outperform standard noise reduction algorithms.
Fast address lookups using controlled prefix expansion

Internet (IP) address lookup is a major bottleneck in high-performance routers. IP address lookup is challenging because it requires a longest matching prefix lookup. It is compounded by increasing routing table sizes, increased traffic, higher-speed links, and the migration to 128-bit IPv6 addresses. We describe how IP lookups and updates can be made faster using a set of of transformation techniques. Our main technique, controlled prefix expansion, transforms a set of prefixes into an equivalent set with fewer prefix lengths. In addition, we use optimization techniques based on dynamic programming, and local transformations of data structures to improve cache behavior. When applied to trie search, our techniques provide a range of algorithms (Expanded Tries) whose performance can be tuned. For example, using a processor with 1MB of L2 cache, search of the MaeEast database containing 38000 prefixes can be done in 3 L2 cache accesses. On a 300MHz Pentium II which takes 4 cycles for accessing the first word of the L2 cacheline, this algorithm has a worst-case search time of 180 nsec. , a worst-case insert/delete time of 2.5 msec. , and an average insert/delete time of 4 usec. Expanded tries provide faster search and faster insert/delete times than earlier lookup algirthms. When applied to Binary Search on Levels, our techniques improve worst-case search times by nearly a factor of 2 (using twice as much storage) for the MaeEast database. Our approach to algorithm design is based on measurements using the VTune tool on a Pentium to obtain dynamic clock cycle counts. Our techniques also apply to similar address lookup problems in other network protocols.
Technical Note - Mean Drifts and the Non-Ergodicity of Markov Chains

THE STABILITY of many important systems is determined by the ergodicity of a related Markov chain. Since the steady-state distribution of a Markov chain is, however, often difficult to calculate, it is important to have simple criteria for the ergodicity or non-ergodicity of a chain. Let (Xn) (n = 0, 1, ***) denote an irreducible aperiodic Markov chain with state space IO, 1, * *. Let P = (pij) (i, j = 0, 1, *. ) be its transition matrix and let pn = (p('7)) denote the nth power of P. It is well known that lim,. p(') = irj exists and is independent of i. Furthermore, either rj = 0 (j > 0), or rj > 0 (j > 0) and is the steady state distribution of the chain. The steady state probability vector also satisfies 7rP = 7r, that is
A Dichotomous Search for a Geometric Random Variable

We are given a two-state system that starts in state 0, ends in state 1, and makes a single transition from state 0 to state 1 during N periods. If the system is in state 0, it moves to state 1 in the next period with a known positive time-independent probability. Once it reaches state 1, it remains there. By observing the state of the system at some intermediate period, we can learn whether this transition occurred earlier or not. An optimal search strategy minimizes the expected number of observations needed to locate the exact transition time. In this paper we show how to compute efficiently and how to approximate the optimal strategy. Applications to the problem arise in the areas of quality control and maintenance of communication and supply lines.
Approximating a Point Process by a Renewal Process, II: Superposition Arrival Processes to Queues

We develop an approximation for a queue having an arrival process that is the superposition of independent renewal processes, i.e., ∑GI1/G/1. This model is useful, for example, in analyzing networks of queues where the arrival process to an individual queue is the superposition of departure processes from other queues. If component arrival processes are approximated by renewal processes, the ∑GI1/G/1 model applies. The approximation proposed is a hybrid that combines two basic methods described by Whitt. All these methods approximate the complex superposition process by a renewal process and yield a GI/G/1 queue that can be solved analytically or approximately. In the hybrid method, the moments of the intervals in the approximating renewal process are a convex combination of the moments determined by the basic methods. The weight in the convex combination is identified using the asymptotic properties of the basic methods together with simulation. When compared to simulation estimates, the error in hybrid .
Promoting Evidence-Based Health Policy, Programming, and Practice for Seniors: Lessons from a National Knowledge Transfer Project*

ABSTRACT In response to Canada's pressing need for effective evidence-based policy, services, and practices specific to seniors, national leaders representing all concerned stakeholders designed and implemented a National Consensus Process to promote spread, exchange, choice, and uptake of research evidence on social and health issues associated with an aging population. This article presents the innovative methods and evaluation of this three-year project, illuminating for all constituencies the challenges and opportunities associated with promoting seniors' independence through collaborative knowledge transfer efforts. A total of 198 organizations and 65 individuals were surveyed at baseline, throughout the intervention, immediately post-intervention, and one year post-intervention. Knowledge from 783 studies was spread to 63,387 people, 90 per cent of whom reported knowledge exchange. Over 50 per cent of stakeholders reported using the research evidence, although processes for facilitating knowledge choice did not achieve consensus. Significant knowledge uptake occurred in two of the four research theme areas. RÉSUMÉ En réponse au besoin pressant de politiques, de services et de pratiques documentés, efficaces et adaptés aux aînés, les chefs de file nationaux représentant les diverses parties intéressées ont conçu et mis en place un Consensus national pour promouvoir la propagation, l'échange, le choix et l'adoption de recherches sur des questions sociales et de santé inhérentes à une population vieillissante. Ce document présente les méthodes innovatrices et un examen de ce projet de trois ans, faisant ressortir dans tous les cas les défis et les occasions associés à la promotion de l'autonomie des aînés par le biais d'échange de connaissances entre divers groupes. Pendant la durée du programme, immédiatement après et un an plus tard, on a enquêté auprès de 198 organismes et de 65 personnes. Les connaissances tirées de 783 études ont été communiquées à 63 387 personnes, parmi lesquelles 90 % ont déclaré avoir échangé ces connaissances. Plus de 50 % des parties intéressées ont signalé avoir utilisé les résultats des recherches bien que les méthodes employées pour orienter les choix n'aient pas donné lieu à un consensus. Une application importante de connaissances s'est produite à partir de deux des quatre thèmes de recherche.
Towards multimodal deep learning for activity recognition on mobile devices

Current smartphones and smartwatches come equipped with a variety of sensors, from light sensor and inertial sensors to radio interfaces, enabling applications running on these devices to make sense of their surrounding environment. Rather than using sensors independently, combining their sensing capabilities facilitates more interesting and complex applications to emerge (e.g., user activity recognition). But differences between sensors ranging from sampling rate to data generation model (event triggered or continuous sampling) make integration of sensor streams challenging. Here we investigate the opportunity to use deep learning to perform this integration of sensor data from multiple sensors. The intuition is that neural networks can identify nonintuitive features largely from cross-sensor correlations which can result in a more accurate estimation. Initial results with a variant of a Restricted Boltzmann Machine (RBM), show better performance with this new approach compared to classic solutions.
Context-aware real-time population estimation for metropolis

Achieving accurate, real-time, and spatially fine-grained population estimation for a metropolitan city is extremely valuable for a variety of applications. Previous solutions look at data generated by human activities, such as night time lights and phone calls, for population estimation. However, these mechanisms cannot achieve both real-time and fine-grained population estimation because the data sampling rate is low and spatial granularity chosen is improper. We address these two problems by leveraging a key insight --- people frequently use data plan on cellphones and leave mobility signatures on cellular networks. Therefore, we are able to exploit these cellular signatures for real-time population estimation. Extracting population information from cellular data records is not easy because the number of users recorded by a cellular tower is not equal to the population covered by the tower, and mobile users' behavior is spatially and temporally different, where static estimating model does not work. We exploit context-aware city segmentation and dynamic population estimation model to address these challenges. We show that the population estimation error is reduced by 22.5% on a cellular dataset that includes 1 million users.