scholarly journals Fair Allocation of Indivisible Goods to Asymmetric Agents

2019 ◽  
Vol 64 ◽  
pp. 1-20 ◽  
Author(s):  
Alireza Farhadi ◽  
Mohammad Ghodsi ◽  
Mohammad Taghi Hajiaghayi ◽  
Sébastien Lahaie ◽  
David Pennock ◽  
...  
Keyword(s):  
Real World ◽  
Simple Algorithm ◽  
Fair Allocation ◽  
Indivisible Goods ◽  
Real World Data ◽  
The Subject ◽  
Approximation Guarantee ◽  
To Receive ◽  
Positive Results ◽  
Better Than

We study fair allocation of indivisible goods to agents with unequal entitlements. Fair allocation has been the subject of many studies in both divisible and indivisible settings. Our emphasis is on the case where the goods are indivisible and agents have unequal entitlements. This problem is a generalization of the work by Procaccia and Wang (2014) wherein the agents are assumed to be symmetric with respect to their entitlements. Although Procaccia and Wang show an almost fair (constant approximation) allocation exists in their setting, our main result is in sharp contrast to their observation. We show that, in some cases with n agents, no allocation can guarantee better than 1/n approximation of a fair allocation when the entitlements are not necessarily equal. Furthermore, we devise a simple algorithm that ensures a 1/n approximation guarantee. Our second result is for a restricted version of the problem where the valuation of every agent for each good is bounded by the total value he wishes to receive in a fair allocation. Although this assumption might seem without loss of generality, we show it enables us to find a 1/2 approximation fair allocation via a greedy algorithm. Finally, we run some experiments on real-world data and show that, in practice, a fair allocation is likely to exist. We also support our experiments by showing positive results for two stochastic variants of the problem, namely stochastic agents and stochastic items.

scholarly journals Distributed Pareto Optimization for Subset Selection

2018 ◽  
Author(s):  
Chao Qian ◽  
Guiying Li ◽  
Chao Feng ◽  
Ke Tang
Keyword(s):  
Real World ◽  
Large Scale ◽  
State Of The Art ◽  
Subset Selection ◽  
Data Sets ◽  
Mapreduce Framework ◽  
Real World Data ◽  
Real World Applications ◽  
Approximation Guarantee ◽  
Better Than

The subset selection problem that selects a few items from a ground set arises in many applications such as maximum coverage, influence maximization, sparse regression, etc. The recently proposed POSS algorithm is a powerful approximation solver for this problem. However, POSS requires centralized access to the full ground set, and thus is impractical for large-scale real-world applications, where the ground set is too large to be stored on one single machine. In this paper, we propose a distributed version of POSS (DPOSS) with a bounded approximation guarantee. DPOSS can be easily implemented in the MapReduce framework. Our extensive experiments using Spark, on various real-world data sets with size ranging from thousands to millions, show that DPOSS can achieve competitive performance compared with the centralized POSS, and is almost always better than the state-of-the-art distributed greedy algorithm RandGreeDi.

Fuzzy Logic

2012 ◽  
pp. 33-71 ◽  
Author(s):  
Siddhartha Bhattacharyya ◽  
Paramartha Dutta
Keyword(s):  
Fuzzy Logic ◽  
Real World ◽  
Fuzzy Inference ◽  
Real Life ◽  
Real World Data ◽  
World Data ◽  
Data Intensive ◽  
Logic Control ◽  
The Subject ◽  
Intelligent Management

The field of industrial informatics has emerged as one of the key disciplines for the purpose of intelligent management and dissemination of information in today’s world. With the advent of newer technical know-how, the subject of informative intelligence has assumed increasing importance in the industrial arena, thanks to the evolution of data intensive industry. Real world data exhibit varied amount of unquantifiable uncertainty in the information content. Conventional logic is often unable to explain the associated uncertainty and imprecision therein due to the principles of finiteness of observations and quantifying propositions employed. Fuzzy sets and fuzzy logic provide a logical framework for description of the varied amount of ambiguity, uncertainty and imprecision exhibited in real world data under consideration. The resultant fuzzy inference engine and the fuzzy logic control theory supplement the power of the framework in design of robust failsafe real life systems.

Model Data

2021 ◽  
pp. 95-188
Author(s):  
Kai R. Larsen ◽  
Daniel S. Becker
Keyword(s):  
Real World ◽  
Diabetes Patient ◽  
Model Data ◽  
Real World Data ◽  
Future Events ◽  
Patient’S Information ◽  
The Subject ◽  
Modeling Data ◽  
Using Data ◽  
World Environment

After preparing your dataset, the business problem should be quite familiar, along with the subject matter and the content of the dataset. This section is about modeling data, using data to train algorithms to create models that can be used to predict future events or understand past events. The section shows where data modeling fits in the overall machine learning pipeline. Traditionally, we store real-world data in one or more databases or files. This data is extracted, and features and a target (T) are created and submitted to the “Model Data” stage (the topic of this section). Following the completion of this stage, the model produced is examined (Section V) and placed into production. With the model in the production system, present data generated from the real-world environment is inputted into the system. In the example case of a diabetes patient, we enter a new patient’s information electronic health record into the system, and a database lookup retrieves additional data for feature creation.

scholarly journals Point-estimating observer models for latent cause detection

2021 ◽  
Vol 17 (10) ◽  
pp. e1009159
Author(s):  
Jennifer Laura Lee ◽  
Wei Ji Ma
Keyword(s):  
Real World ◽  
Spatial Cluster ◽  
Generative Model ◽  
Point Estimate ◽  
Real World Data ◽  
The World ◽  
Spatially Distributed ◽  
The Brain ◽  
Positive Effect ◽  
Better Than

The spatial distribution of visual items allows us to infer the presence of latent causes in the world. For instance, a spatial cluster of ants allows us to infer the presence of a common food source. However, optimal inference requires the integration of a computationally intractable number of world states in real world situations. For example, optimal inference about whether a common cause exists based on N spatially distributed visual items requires marginalizing over both the location of the latent cause and 2N possible affiliation patterns (where each item may be affiliated or non-affiliated with the latent cause). How might the brain approximate this inference? We show that subject behaviour deviates qualitatively from Bayes-optimal, in particular showing an unexpected positive effect of N (the number of visual items) on the false-alarm rate. We propose several “point-estimating” observer models that fit subject behaviour better than the Bayesian model. They each avoid a costly computational marginalization over at least one of the variables of the generative model by “committing” to a point estimate of at least one of the two generative model variables. These findings suggest that the brain may implement partially committal variants of Bayesian models when detecting latent causes based on complex real world data.

scholarly journals Point-estimating observer models for latent cause detection

2021 ◽  
Author(s):  
Jennifer L Lee ◽  
Wei Ji Ma
Keyword(s):  
Real World ◽  
Spatial Cluster ◽  
Generative Model ◽  
Point Estimate ◽  
Real World Data ◽  
The World ◽  
Spatially Distributed ◽  
The Brain ◽  
Positive Effect ◽  
Better Than

The spatial distribution of visual items allows us to infer the presence of latent causes in the world. For instance, a spatial cluster of ants allows us to infer the presence of a common food source. However, optimal inference requires the integration of a computationally intractable number of world states in real world situations. For example, optimal inference about whether a common cause exists based on N spatially distributed visual items requires marginalizing over both the location of the latent cause and 2N possible affiliation patterns (where each item may be affiliated or non-affiliated with the latent cause). How might the brain approximate this inference? We show that subject behaviour deviates qualitatively from Bayes-optimal, in particular showing an unexpected positive effect of N (the number of visual items) on the false-alarm rate. We propose several “point-estimating” observer models that fit subject behaviour better than the Bayesian model. They each avoid a costly computational marginalization over at least one of the variables of the generative model by “committing” to a point estimate of at least one of the two generative model variables. These findings suggest that the brain may implement partially committal variants of Bayesian models when detecting latent causes based on complex real world data.

scholarly journals A Classifier Graph Based Recurring Concept Detection and Prediction Approach

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Yange Sun ◽  
Zhihai Wang ◽  
Yang Bai ◽  
Honghua Dai ◽  
Saeid Nahavandi
Keyword(s):  
Real World ◽  
Data Streams ◽  
Concept Drift ◽  
Learning Performance ◽  
Concept Detection ◽  
Real World Data ◽  
Full Account ◽  
World Data ◽  
Prediction Approach ◽  
Better Than

It is common in real-world data streams that previously seen concepts will reappear, which suggests a unique kind of concept drift, known as recurring concepts. Unfortunately, most of existing algorithms do not take full account of this case. Motivated by this challenge, a novel paradigm was proposed for capturing and exploiting recurring concepts in data streams. It not only incorporates a distribution-based change detector for handling concept drift but also captures recurring concept by storing recurring concepts in a classifier graph. The possibility of detecting recurring drifts allows reusing previously learnt models and enhancing the overall learning performance. Extensive experiments on both synthetic and real-world data streams reveal that the approach performs significantly better than the state-of-the-art algorithms, especially when concepts reappear.

Compassionate use program with FDT-TPI (trifluridine-tipiracil) in pre-treated metastatic colorectal cancer patients: Spanish real world data.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e15019-e15019 ◽  
Author(s):  
Pilar Garcia-Alfonso ◽  
Ana Ruiz ◽  
Alfredo Carrato ◽  
Jose M Vieitez ◽  
Cristina Gravalos ◽  
...  
Keyword(s):  
Real World ◽  
Compassionate Use ◽  
Real World Data ◽  
Disease Treatment ◽  
World Data ◽  
Medical Needs ◽  
Grade 3 ◽  
Colorectal Cancer Patients ◽  
Ecog Ps ◽  
To Receive

e15019 Background: Trifluridine/tipiracil (FTD-TPI) is comprised of an antineoplastic nucleoside analog, trifluridine, and a thymidine phosphorylase inhibitor, tipiracil. Compassionate use programs (CUPs) provide a treatment option for patients with unmet medical needs and an early opportunity to obtain data on efficacy, safety and use in a real-world setting. Methods: Patients were registered and approved to receive 2 cycles of trifluridine/tipiracil treatment, which could be renewed as necessary, we analysed baseline characteristics, safety results and exposure to the treatment with trifluridine/tipiracil (FTD-TPI) in the Spanish CUP. Results: A total 636 were registered in Spain and 538 received treatment with trifluridine/tipiracil. Median age was 64 years, of which 25% were older than 70 years old and 60% were male, 67% of pts were ECOG PS 1. Oral trifluridine/tipiracil was initiated at 35 mg/m2 bid. Most pts had received 2, 3, or ≥4 lines of prior treatment for metastatic disease (27%, 28%, and 38%, respectively); and 4% unknown. 275 (47%) patients had KRAS mutated and 209 (36%) had KRAS wild type. 35% received adjuvant chemotherapy and 20% of the patients were treated with regorafenib in previous lines. The main reasons for not initiating treatment included cancellation of request due to worsening condition and progressive disease. Treatment was generally well tolerated. A total of 173 AEs were reported in the Spanish CUP, the majority were myelosuppressive AEs; febrile neutropenia (grade ≥3) was reported in 6 pts (1.3%), grade _ > 3 neutropenia was reported in 56 (33%), grade 4 neutropenia in 16 (9%). Grade 3 anemia was reported in 8 (15% of the total AEs reported).The majority of pts 306 (56%) were allocated 3-4 cycle of treatment, 95(17.3%) 5-6 cycles, and 30(5. 5%) between 7-8 cycles. Conclusions: Thisreal-world data analysis is consistent with those reported in phase 3 trials of trifluridine/tipiracil (FDT-TP)in pretreated mCRC. The efficacy analyses of this population is planned.

P6554Utility of accelerometer programmation in leadless pacemaker regardind location. CARDIOCHUS registry

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
J L Martinez Sande ◽  
J Garcia-Seara ◽  
L Gonzalez-Melchor ◽  
X A Fernandez-Lopez ◽  
M Rodriguez-Manero ◽  
...  
Keyword(s):  
Heart Rate ◽  
Right Ventricle ◽  
Real World ◽  
Exercise Test ◽  
P Value ◽  
Test Vector ◽  
Real World Data ◽  
Leadless Pacemaker ◽  
Positive Results ◽  
Center Study

Abstract Background Real-world data reinforce positive results in different implant locations of leadless pacemaker (LPM). LPM have special characteristics regarding accelerometer programmation. Purpose The purpose of the study was to describe our experience with LPM different implantation location and its programmation according the vector accelerometer. Methods We performed a prospective observational one center study including all patients with LPM implantation within 3 years (June 2015-December 2018). Location of deployment was classified as apicalseptal, midseptal or right ventricle outflow tract (RVOT). Vector programmation was performed from the second visit in patients with acceptable mobility and heart rate below 80 bpm, with the abbreviated protocol recommended by the brand. Clinical evaluation according to vector programmed was performed 3 to 6 months later. Results We include a total of 144 LPM, and exercise test was performed in 86 patients. There were 86 men (59.7%) with a mean age of 79.1±6.9 years-old (54 to 89). Location of deployment was distributed as follows: 32.4% in apicalseptal, 54.5% in midseptal and 13.1% in RVOT. Vector 1 was the more frequent programmation, specially in apicalseptal position. Correlation between location and vector of programmation could not be predicted (p=0.2381), but there was a non-significant tendency (p=0.08) between patients with LPM in RVOT location and Vector 3 programmation. Table 1 and Figure 1. Table 1. Micra Location and Activity Vector Apicalseptal Midseptal RVOT P Value Test Vector 39 (84.8%) 40 (58.5%) 7 (46.7%) NS Vector 1 22 (56.4%) 26 (65.0%) 2 (28.6%) NS Vector 2 8 (20.5%) 4 (10.0%) 1 (14.3%) NS Vector 3 9 (23.1%) 10 (25.0%) 4 (57.1%) 0.08 Figure 1 Conclusions In our series, Vector 1 was the predominant accelerometer programmation specially in apicalseptal LPM position and Vector 3 in RVOT position.

Path Reproduction Tests Using a Torus Treadmill

1999 ◽  
Vol 8 (6) ◽  
pp. 587-597 ◽  
Author(s):  
Hiroo Iwata ◽  
Yoko Yoshida
Keyword(s):  
Virtual Environments ◽  
Real World ◽  
Target Object ◽  
Virtual Space ◽  
Proprioceptive Feedback ◽  
The Real ◽  
The Subject ◽  
Locomotion Interface ◽  
Better Than ◽  
Reproduction Tests

This paper describes experiments regarding navigation performance using a new locomotion interface for walking through virtual space. Although traveling on foot is the most intuitive style of locomotion, proprioceptive feedback from walking is not provided in most applications of virtual environments. We developed an infinite surface driven by actuators for enabling a sense of walking. Torus-shaped surfaces are selected to realize the locomotion interface. The device employs twelve sets of treadmills, connected side by side and driven in perpendicular directions. The virtual infinite surface is generated by the motion of the treadmills. A walker can go in any direction while his/her position is fixed in the real world. The device is called a Torus Treadmill. Navigation performance was measured by path-reproduction tests. Subjects were immersed in a virtual grass-covered plain on which a cone-shaped target object was placed. The subjects first traveled to the target object. After they reached it, the target object disappeared and the rehomed subjects were asked to return to the place where the target object was placed. We also set two target objects, and the subject traveled along a bent path. We compared two locomotion modes: walking on the Torus Treadmill and moving purely by joystick operation. The results of the bent-path experiment showed that the accuracy of the path reproduction in the Torus Treadmill mode is better than that of joystick mode.

Abstract #946: Assessment of Acromegaly Patients with and Without Diabetes Treated with Lanreotide Depot: 2-Year Real World Data

2016 ◽  
Vol 22 ◽  
pp. 219
Author(s):  
Roberto Salvatori ◽  
Olga Gambetti ◽  
Whitney Woodmansee ◽  
David Cox ◽  
Beloo Mirakhur ◽  
...  
Keyword(s):  
Real World ◽  
Real World Data ◽  
World Data
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