Efficient Failure Pattern Identification of Predictive Algorithms

Given a (machine learning) classifier and a collection of unlabeled data, how can we efficiently identify misclassification patterns presented in this dataset? To address this problem, we propose a human-machine collaborative framework that consists of a team of human annotators and a sequential recommendation algorithm. The recommendation algorithm is conceptualized as a stochastic sampler that, in each round, queries the annotators a subset of samples for their true labels and obtains the feedback information on whether the samples are misclassified. The sampling mechanism needs to balance between discovering new patterns of misclassification (exploration) and confirming the potential patterns of classification (exploitation). We construct a determinantal point process, whose intensity balances the exploration-exploitation trade-off through the weighted update of the posterior at each round to form the generator of the stochastic sampler. The numerical results empirically demonstrate the competitive performance of our framework on multiple datasets at various signal-to-noise ratios.Comment: 19 pages, Accepted for UAI202

Wireless signal acquisition and processing techniques for horse lameness detection and evaluation

Title from PDF of title page; abstract from research PDF (University of Missouri--Columbia, viewed on June 26, 2014).The Equine Lameness Locator(R) (ELL) is a newly developed system that provides a robust and objective method to detect and evaluate equine lameness. To achieve objective lameness evaluation, the system analyzes a horse's head and pelvis vertical movement signals during trotting. Two uniaxial accelerometers are placed on the horse's torso, one each on the horse's head and pelvis to record vertical accelerations. Vertical position signals are obtained by numerical double integration of the acceleration signals. However, these integrated position signals contain very large moving averages and require advance methods of signal processing for correction. In this thesis, a combination of the Hilbert Huang transform and a conjugate-pair decomposition method is proposed and tested against the current ELL's signal processing method, a sliding-window curve-fitting method. Numerical simulations and experimental results show that the proposed new method involves more intense computation but does not provide better results for lameness evaluation of horses. Hence, the original sliding-window curve-fitting method is recommended for future use. Clinical and experimental observations reveal that a horse's head and pelvis also rotate during trotting. These rotations may cause inaccurate measurements of the true vertical accelerations. Hence, this work also numerically and experimentally examines the influences of rotations on the measured vertical accelerations. Numerical techniques, unique experimental devices and setups, and an algorithm for correcting accelerometer outputs to obtain true vertical accelerations have been developed and experimentally validated

Characterisation and modulation of the intracellular inflammatory signalling pathways activated during surgery with cardiopulmonary bypass

Surgery with cardiopulmonary bypass (CPB) is associated with post-operative complications due to systemic inflammation. However, the intracellular signalling pathways that promote inflammation in cardiac surgery with CPB are uncertain. The studies presented in this thesis were designed to illuminate these molecular mechanisms, thereby informing the development of novel anti-inflammatory strategies. This was addressed through a clinical trial to determine the effects of CPB on inflammatory signalling in leukocytes (Chapter 4). In this study, the induction of reactive oxygen species (ROS) and the activation of NF-κB and p38 MAP kinase within leukocytes was compared in patients exposed to miniaturised CPB (mCPB; an optimised form of CPB designed to attenuate systemic inflammatory activation) or conventional CPB (cCPB). Twenty-six patients undergoing surgical revascularisation for advanced coronary artery disease were randomised to undergo surgery with either cCPB or mCPB. Blood samples were collected pre-operatively and at various times after the initiation of CPB and analysed by intracellular staining and flow cytometry for intracellular markers of activation. p38 MAP kinase phosphorylation in granulocytes was enhanced in patients receiving cCPB compared to mCPB (p<0.05). Levels of ROS in lymphocytes were elevated in cCPB compared to mCPB (p<0.01) whereas ROS levels in granulocytes and monocytes were similar between groups. NF-κB phosphorylation in leukocyte sub-sets, leukocyte tissue migration as well as conventional markers of inflammation were comparable between the investigative groups. A porcine model was also established to study the signalling pathways that promote systemic inflammation in response to cardiac surgery with CPB under well-controlled experimental conditions. The influence of sulforaphane, an anti-inflammatory compound derived from green vegetables, on inflammation and injury in response to CPB was also studied. It was observed that pre-treatment of animals with sulforaphane reduced p38 MAP kinase (p<0.05) and NF-κB (p<0.05) phosphorylation in leukocytes exposed to CPB and protected porcine kidneys from exhibiting histological features of early injury. A small clinical study demonstrated biologically significant levels of sulforaphane could be determined in plasma, with lower levels of p38 MAP kinase (p<0.01) and attenuated ROS (p<0.01) in the early stages following consumption. In conclusion, systemic inflammatory responses following CPB were associated with activation of p38 MAP kinase and NF-κB pathways in circulating leukocytes in both porcine and clinical studies. Inflammatory responses to CPB can be reduced by miniaturisation of the CPB circuit and pharmacologically using sulforaphane.Open Acces

Cost-Adaptive Recourse Recommendation by Adaptive Preference Elicitation

Algorithmic recourse recommends a cost-efficient action to a subject to reverse an unfavorable machine learning classification decision. Most existing methods in the literature generate recourse under the assumption of complete knowledge about the cost function. In real-world practice, subjects could have distinct preferences, leading to incomplete information about the underlying cost function of the subject. This paper proposes a two-step approach integrating preference learning into the recourse generation problem. In the first step, we design a question-answering framework to refine the confidence set of the Mahalanobis matrix cost of the subject sequentially. Then, we generate recourse by utilizing two methods: gradient-based and graph-based cost-adaptive recourse that ensures validity while considering the whole confidence set of the cost matrix. The numerical evaluation demonstrates the benefits of our approach over state-of-the-art baselines in delivering cost-efficient recourse recommendations.Comment: 30 pages, 7 figure

Bellman Optimal Stepsize Straightening of Flow-Matching Models

Flow matching is a powerful framework for generating high-quality samples in various applications, especially image synthesis. However, the intensive computational demands of these models, especially during the finetuning process and sampling processes, pose significant challenges for low-resource scenarios. This paper introduces Bellman Optimal Stepsize Straightening (BOSS) technique for distilling flow-matching generative models: it aims specifically for a few-step efficient image sampling while adhering to a computational budget constraint. First, this technique involves a dynamic programming algorithm that optimizes the stepsizes of the pretrained network. Then, it refines the velocity network to match the optimal step sizes, aiming to straighten the generation paths. Extensive experimental evaluations across image generation tasks demonstrate the efficacy of BOSS in terms of both resource utilization and image quality. Our results reveal that BOSS achieves substantial gains in efficiency while maintaining competitive sample quality, effectively bridging the gap between low-resource constraints and the demanding requirements of flow-matching generative models. Our paper also fortifies the responsible development of artificial intelligence, offering a more sustainable generative model that reduces computational costs and environmental footprints. Our code can be found at https://github.com/nguyenngocbaocmt02/BOSS.Comment: 21 pages, 14 figure

New Technology, Human Capital and Growth in a Developing Country

In a developing country with three sectors: consumption goods, new tech- nology, and education, the productivity of the consumption goods depends on a new technology and skilled labor used to produce this new technology. There can be three stages of economic growth. In the …rst stage the country concentrates on the production of consumption goods; in the second the country must import both physical capital and new technology capital to produce consumption goods and new technology; in the third the country must import capital and invest in the training and education of high skilled labor.Optimal growth model; New technology capital;Human Capital; Developing country

New Technology, Human Capital and Growth for European Transitional Economies.

We consider a transitional country with three sectors in economy: con- sumption goods, new technology, and education. Productivity of the con- sumption goods sector depends on new technology and skilled labor used for production of the new technology. Then there might be three stages of economic growth. In the first stage the country concentrates on produc- tion of consumption goods; in the second stage the country imports both physical capital and new technology capital; in the last stage the country imports new technology capital and invests in training and education of high skilled labor in the same time.Optimal growth model, New technology capital, Human Capital, Developing country.

Channel and spatial attention mechanism for fashion image captioning

Image captioning aims to automatically generate one or more description sentences for a given input image. Most of the existing captioning methods use encoder-decoder model which mainly focus on recognizing and capturing the relationship between objects appearing in the input image. However, when generating captions for fashion images, it is important to not only describe the items and their relationships, but also mention attribute features of clothes (shape, texture, style, fabric, and more). In this study, one novel model is proposed for fashion image captioning task which can capture not only the items and their relationship, but also their attribute features. Two different attention mechanisms (spatial-attention and channel-wise attention) is incorporated to the traditional encoder-decoder model, which dynamically interprets the caption sentence in multi-layer feature map in addition to the depth dimension of the feature map. We evaluate our proposed architecture on Fashion-Gen using three different metrics (CIDEr, ROUGE-L, and BLEU-1), and achieve the scores of 89.7, 50.6 and 45.6, respectively. Based on experiments, our proposed method shows significant performance improvement for the task of fashion-image captioning, and outperforms other state-of-the-art image captioning methods