我们在运营研究和机器学习(ML)的Nexus中提出了一种方法,该方法利用了从ML提供的通用近似器,以加速混合智能线性两阶段随机程序的解决方案。我们旨在解决第二阶段高度要求的问题。我们的核心思想是通过用快速而准确的监督ML预测替换确切的第二阶段解决方案,从而在在线解决方案时间中大量减少,同时,在第一阶段解决方案准确性中略有降低。当随着时间的推移反复解决类似问题时,在与车队管理,路由和集装箱院子管理有关的运输计划中反复解决类似问题时,对ML的前期投资将是合理的。我们的数值结果集中在与整数和连续L形切口中的问题类别解决的问题类别。我们的广泛的经验分析基于从随机服务器位置(SSLP)和随机多主背包(SMKP)问题的标准化家族基础。所提出的方法可以在不到9%的时间内解决SSLP的最难实例,而在SMKP的情况下,同一图为20%。在大多数情况下,平均最佳差距少于0.1%。
translated by 谷歌翻译
货运运营商依靠战术规划,以以成本效益的方式设计他们的服务网络以满足需求。对于计算途径,确定性和循环服务网络设计(SND)配方用于解决大规模问题。中央投入是定期需求,即预期在规划地平线的每个时期中重复的需求。在实践中,通过时间序列预测模型预测需求,周期性需求是这些预测的平均值。然而,这只是许多可能的映射中的一个。在文献中忽略了选择该映射的问题。我们建议使用下游决策问题的结构来选择一个良好的映射。为此目的,我们介绍了一种多级数学编程制定,明确地将时间序列预测的时间序列联系起来对此感兴趣的SND问题。解决方案是定期要求估计,以最大限度地减少战术规划地平线的成本。我们报告了对加拿大国家铁路公司大规模申请的广泛实证研究。他们清楚地表明了定期需求估算问题的重要性。实际上,规划成本对不同的定期需求估计和不同于平均预测的估计产生了重要的变化,可能导致成本较低。此外,基于预测的定期需求估计相关的成本与使用实际需求的平均值获得的比较或甚至更好。
translated by 谷歌翻译
预订控制问题是收入管理领域中发生的顺序决策问题。更确切地说,货运预订控制重点是决定接受或拒绝预订的问题:鉴于有限的能力,接受预订请求或拒绝其保留能力,以预订可能更高收入的未来预订。该问题可以作为有限的摩尼斯随机动态程序提出,其中接受一组请求会在预订期结束时获得利润,取决于履行公认的预订的成本。对于许多货运申请,可以通过解决操作决策问题来获得满足请求的成本,该问题通常需要解决混合组织线性计划的解决方案。在部署强化学习算法时,通常会常规地解决此类操作问题,这可能太耗时了。大多数预订控制策略是通过解决特定问题的数学编程松弛来获得的,这些松弛通常是不宽松的,无法推广到新问题,并且在某些情况下提供了相当粗糙的近似值。在这项工作中,我们提出了一种两阶段的方法:我们首先训练一个监督的学习模型来预测操作问题的目标,然后我们将模型部署在加固学习算法中以计算控制政策。这种方法是一般的:每当可以预测Horizo​​n操作问题的目标函数时,都可以使用它,并且特别适合那些此类问题在计算上很难的情况。此外,它允许人们利用加强学习的最新进展,因为常规解决操作问题被单个预测所取代。我们的方法对文献中的两个预订控制问题进行了评估,即分销物流和航空公司货物管理。
translated by 谷歌翻译
Recent advances in open-domain question answering (ODQA) have demonstrated impressive accuracy on standard Wikipedia style benchmarks. However, it is less clear how robust these models are and how well they perform when applied to real-world applications in drastically different domains. While there has been some work investigating how well ODQA models perform when tested for out-of-domain (OOD) generalization, these studies have been conducted only under conservative shifts in data distribution and typically focus on a single component (ie. retrieval) rather than an end-to-end system. In response, we propose a more realistic and challenging domain shift evaluation setting and, through extensive experiments, study end-to-end model performance. We find that not only do models fail to generalize, but high retrieval scores often still yield poor answer prediction accuracy. We then categorize different types of shifts and propose techniques that, when presented with a new dataset, predict if intervention methods are likely to be successful. Finally, using insights from this analysis, we propose and evaluate several intervention methods which improve end-to-end answer F1 score by up to 24 points.
translated by 谷歌翻译
Differentiable Search Indices (DSIs) encode a corpus of documents in the parameters of a model and use the same model to map queries directly to relevant document identifiers. Despite the strong performance of DSI models, deploying them in situations where the corpus changes over time is computationally expensive because reindexing the corpus requires re-training the model. In this work, we introduce DSI++, a continual learning challenge for DSI to incrementally index new documents while being able to answer queries related to both previously and newly indexed documents. Across different model scales and document identifier representations, we show that continual indexing of new documents leads to considerable forgetting of previously indexed documents. We also hypothesize and verify that the model experiences forgetting events during training, leading to unstable learning. To mitigate these issues, we investigate two approaches. The first focuses on modifying the training dynamics. Flatter minima implicitly alleviate forgetting, so we optimize for flatter loss basins and show that the model stably memorizes more documents (+12\%). Next, we introduce a generative memory to sample pseudo-queries for documents and supplement them during continual indexing to prevent forgetting for the retrieval task. Extensive experiments on novel continual indexing benchmarks based on Natural Questions (NQ) and MS MARCO demonstrate that our proposed solution mitigates forgetting by a significant margin. Concretely, it improves the average Hits@10 by $+21.1\%$ over competitive baselines for NQ and requires $6$ times fewer model updates compared to re-training the DSI model for incrementally indexing five corpora in a sequence.
translated by 谷歌翻译
Recent work has reported that AI classifiers trained on audio recordings can accurately predict severe acute respiratory syndrome coronavirus 2 (SARSCoV2) infection status. Here, we undertake a large scale study of audio-based deep learning classifiers, as part of the UK governments pandemic response. We collect and analyse a dataset of audio recordings from 67,842 individuals with linked metadata, including reverse transcription polymerase chain reaction (PCR) test outcomes, of whom 23,514 tested positive for SARS CoV 2. Subjects were recruited via the UK governments National Health Service Test-and-Trace programme and the REal-time Assessment of Community Transmission (REACT) randomised surveillance survey. In an unadjusted analysis of our dataset AI classifiers predict SARS-CoV-2 infection status with high accuracy (Receiver Operating Characteristic Area Under the Curve (ROCAUC) 0.846 [0.838, 0.854]) consistent with the findings of previous studies. However, after matching on measured confounders, such as age, gender, and self reported symptoms, our classifiers performance is much weaker (ROC-AUC 0.619 [0.594, 0.644]). Upon quantifying the utility of audio based classifiers in practical settings, we find them to be outperformed by simple predictive scores based on user reported symptoms.
translated by 谷歌翻译
The UK COVID-19 Vocal Audio Dataset is designed for the training and evaluation of machine learning models that classify SARS-CoV-2 infection status or associated respiratory symptoms using vocal audio. The UK Health Security Agency recruited voluntary participants through the national Test and Trace programme and the REACT-1 survey in England from March 2021 to March 2022, during dominant transmission of the Alpha and Delta SARS-CoV-2 variants and some Omicron variant sublineages. Audio recordings of volitional coughs, exhalations, and speech were collected in the 'Speak up to help beat coronavirus' digital survey alongside demographic, self-reported symptom and respiratory condition data, and linked to SARS-CoV-2 test results. The UK COVID-19 Vocal Audio Dataset represents the largest collection of SARS-CoV-2 PCR-referenced audio recordings to date. PCR results were linked to 70,794 of 72,999 participants and 24,155 of 25,776 positive cases. Respiratory symptoms were reported by 45.62% of participants. This dataset has additional potential uses for bioacoustics research, with 11.30% participants reporting asthma, and 27.20% with linked influenza PCR test results.
translated by 谷歌翻译
The polynomial kernels are widely used in machine learning and they are one of the default choices to develop kernel-based classification and regression models. However, they are rarely used and considered in numerical analysis due to their lack of strict positive definiteness. In particular they do not enjoy the usual property of unisolvency for arbitrary point sets, which is one of the key properties used to build kernel-based interpolation methods. This paper is devoted to establish some initial results for the study of these kernels, and their related interpolation algorithms, in the context of approximation theory. We will first prove necessary and sufficient conditions on point sets which guarantee the existence and uniqueness of an interpolant. We will then study the Reproducing Kernel Hilbert Spaces (or native spaces) of these kernels and their norms, and provide inclusion relations between spaces corresponding to different kernel parameters. With these spaces at hand, it will be further possible to derive generic error estimates which apply to sufficiently smooth functions, thus escaping the native space. Finally, we will show how to employ an efficient stable algorithm to these kernels to obtain accurate interpolants, and we will test them in some numerical experiment. After this analysis several computational and theoretical aspects remain open, and we will outline possible further research directions in a concluding section. This work builds some bridges between kernel and polynomial interpolation, two topics to which the authors, to different extents, have been introduced under the supervision or through the work of Stefano De Marchi. For this reason, they wish to dedicate this work to him in the occasion of his 60th birthday.
translated by 谷歌翻译
Plastic shopping bags that get carried away from the side of roads and tangled on cotton plants can end up at cotton gins if not removed before the harvest. Such bags may not only cause problem in the ginning process but might also get embodied in cotton fibers reducing its quality and marketable value. Therefore, it is required to detect, locate, and remove the bags before cotton is harvested. Manually detecting and locating these bags in cotton fields is labor intensive, time-consuming and a costly process. To solve these challenges, we present application of four variants of YOLOv5 (YOLOv5s, YOLOv5m, YOLOv5l and YOLOv5x) for detecting plastic shopping bags using Unmanned Aircraft Systems (UAS)-acquired RGB (Red, Green, and Blue) images. We also show fixed effect model tests of color of plastic bags as well as YOLOv5-variant on average precision (AP), mean average precision (mAP@50) and accuracy. In addition, we also demonstrate the effect of height of plastic bags on the detection accuracy. It was found that color of bags had significant effect (p < 0.001) on accuracy across all the four variants while it did not show any significant effect on the AP with YOLOv5m (p = 0.10) and YOLOv5x (p = 0.35) at 95% confidence level. Similarly, YOLOv5-variant did not show any significant effect on the AP (p = 0.11) and accuracy (p = 0.73) of white bags, but it had significant effects on the AP (p = 0.03) and accuracy (p = 0.02) of brown bags including on the mAP@50 (p = 0.01) and inference speed (p < 0.0001). Additionally, height of plastic bags had significant effect (p < 0.0001) on overall detection accuracy. The findings reported in this paper can be useful in speeding up removal of plastic bags from cotton fields before harvest and thereby reducing the amount of contaminants that end up at cotton gins.
translated by 谷歌翻译
Informed consent has become increasingly salient for data privacy and its regulation. Entities from governments to for-profit companies have addressed concerns about data privacy with policies that enumerate the conditions for personal data storage and transfer. However, increased enumeration of and transparency in data privacy policies has not improved end-users' comprehension of how their data might be used: not only are privacy policies written in legal language that users may struggle to understand, but elements of these policies may compose in such a way that the consequences of the policy are not immediately apparent. We present a framework that uses Answer Set Programming (ASP) -- a type of logic programming -- to formalize privacy policies. Privacy policies thus become constraints on a narrative planning space, allowing end-users to forward-simulate possible consequences of the policy in terms of actors having roles and taking actions in a domain. We demonstrate through the example of the Health Insurance Portability and Accountability Act (HIPAA) how to use the system in various ways, including asking questions about possibilities and identifying which clauses of the law are broken by a given sequence of events.
translated by 谷歌翻译