CloudScan - A configuration-free invoice analysis system using recurrent neural networks

We present CloudScan; an invoice analysis system that requires zero configuration or upfront annotation. In contrast to previous work, CloudScan does not rely on templates of invoice layout, instead it learns a single global model of invoices that naturally generalizes to unseen invoice layouts. The model is trained using data automatically extracted from end-user provided feedback. This automatic training data extraction removes the requirement for users to annotate the data precisely. We describe a recurrent neural network model that can capture long range context and compare it to a baseline logistic regression model corresponding to the current CloudScan production system. We train and evaluate the system on 8 important fields using a dataset of 326,471 invoices. The recurrent neural network and baseline model achieve 0.891 and 0.887 average F1 scores respectively on seen invoice layouts. For the harder task of unseen invoice layouts, the recurrent neural network model outperforms the baseline with 0.840 average F1 compared to 0.788.Comment: Presented at ICDAR 201

Varying relationships between fish length and scale size under changing environmental conditions – Multidecadal perspective in Atlantic herring

Calcified structures are valuable indicators being used in fisheries research for the estimation of fish ages or back-calculations of fish lengths. Such back-calculations typically assume constant proportional growth of fish and calcified structures independent of internal or environmental factors. We analyzed extensive data (1935–2020) of scale measurements from Norwegian spring-spawning herring. We applied linear quantile regressions to investigate the fish length – scale size relationship and environmental influences on individuals growing at different rates. We demonstrated that the fish length – scale size relationship varied over time and between cohorts, individuals of the same year-class. Parts of this variation can be attributed to changing environmental conditions. We identified a negative effect of stock total biomass and a positive effect of temperature on fish length when conditioned on scale size. The effect of stock total biomass varied considerably but the effect of temperature was similar between fish characterized by different growth rates. Our results are essential for long-term studies highlighting potential biases associated with environmental effects and different growth rates of individuals. These biases should be accounted for in growth history reconstructions and applications of the calcified structures as ecological indicators.publishedVersio

Respiration rates of herring larvae at different salinities and effects of previous environmental history

Metabolic rates of early life history stages of marine fishes show considerable inter-individual differences and are highly influenced by extrinsic factors like temperature or food availability. Measuring oxygen uptake rates is a proxy for estimating metabolic rates. Still, the relationship between respiration rates and ambient or previous salinity conditions as well as parental and developmental acclimation to changes in salinity is largely unexplored. In the present study, we conducted experiments to investigate salinity effects on the routine metabolic rates (RMR) of euryhaline Atlantic herring (Clupea harengus) larvae at three levels of salinity: low (6 psu), intermediate (16 psu) and high (35 psu) reflecting ecological relevant conditions for its populations in the Atlantic and Baltic Sea. The larvae originated from different genetic backgrounds and salinity adaptations to account for cross-generation effects on metabolic rates. Closed respirometry carried out over 24 h on individual fish larvae generally confirmed near isometric respiration rates at all salinity regimes, with rates being 15.4% higher at 6 psu and 7.5% higher at 35 psu compared to 16 psu conditions. However, transgenerational acclimation to different salinity regimes of parents had no effect on the salinity specific metabolic rates of their offspring. Our study demonstrates the ability of herring to cope with a wide range of salinity conditions, irrespective of parental environmental history and genetic origin. This phenotypic plasticity is considered to be one of the main contributing factors to the success of herring as a widely distributed fish species in the North Atlantic and adjacent waters.acceptedVersio

Solving moment problems by dimensional extension

The first part of this paper is devoted to an analysis of moment problems in R^n with supports contained in a closed set defined by finitely many polynomial inequalities. The second part of the paper uses the representation results of positive functionals on certain spaces of rational functions developed in the first part, for decomposing a polynomial which is positive on such a semi-algebraic set into a canonical sum of squares of rational functions times explicit multipliers.Comment: 21 pages, published version, abstract added in migratio

Assessing the performance of statistical classifiers to discriminate fish stocks using Fourier analysis of otolith shape

The assignment of individual fish to its stock of origin is important for reliable stock assessment and fisheries management. Otolith shape is commonly used as the marker of distinct stocks in discrimination studies. Our literature review showed that the application and comparison of alternative statistical classifiers to discriminate fish stocks based on otolith shape is limited. Therefore, we compared the performance of two traditional and four machine learning classifiers based on Fourier analysis of otolith shape using selected stocks of Atlantic cod (Gadus morhua) in the southern Baltic and Atlantic herring (Clupea harengus) in the western Norwegian Sea, Skagerrak and the southern Baltic Sea. Our results showed that the stocks can be successfully discriminated based on their otolith shapes. We observed significant differences in the accuracy obtained by the tested classifiers. For both species, support vector machines (SVM) resulted in the highest classification accuracy. These findings suggest that modern machine learning algorithms, like SVM, can help to improve the accuracy of fish stock discrimination systems based on the otolith shape.Assessing the performance of statistical classifiers to discriminate fish stocks using Fourier analysis of otolith shapesubmittedVersio

Attend, Copy, Parse -- End-to-end information extraction from documents

Document information extraction tasks performed by humans create data consisting of a PDF or document image input, and extracted string outputs. This end-to-end data is naturally consumed and produced when performing the task because it is valuable in and of itself. It is naturally available, at no additional cost. Unfortunately, state-of-the-art word classification methods for information extraction cannot use this data, instead requiring word-level labels which are expensive to create and consequently not available for many real life tasks. In this paper we propose the Attend, Copy, Parse architecture, a deep neural network model that can be trained directly on end-to-end data, bypassing the need for word-level labels. We evaluate the proposed architecture on a large diverse set of invoices, and outperform a state-of-the-art production system based on word classification. We believe our proposed architecture can be used on many real life information extraction tasks where word classification cannot be used due to a lack of the required word-level labels

Efficient energy-constrained distribution of context in mobile systems

The recent improvements in smartphones nowadays offer a widespread application of sensor-based services. Each mobile phone is equipped with several sensors like a GPS module, a gyroscope, or a high-resolution camera. As a result of this sensor integration, a whole new way of usage is opened up for the end-user, like a location-based search or people-centric sensing. The main drawback related to a smartphone is an overall high energy consumption, combined with a limited energy capacity. Due to this fact, a continuous and fine grained sensing of the user's context is not possible, as it utilizes at least one acceleration sensor. Furthermore, the captured data is transmitted via a (mobile) communication infrastructure to post the context on the Internet. Both drain the battery very quickly. For that reason, an efficient energy-constrained distribution is required to minimize the update occurrence of a producer, while simultaneously maximizing the accuracy of a consumer. The primary issues to be addressed include a modeling of user behavior as well as a determination of optimal points in time for an update. Therefore, a probabilistic approach is used to forecast the user's context pattern. The prediction is based upon a Markov chain and enables the extraction of meaningful information. The proper times for an update are determined with the help of a constrained optimization problem. Different methods from mathematical optimization are applied like linear and nonlinear programming or a constrained Markov decision process, which obtain an update policy. For a better comparison of the weaknesses and strengths related to the developed methods, dynamic programming is used to achieve the optimal points in time for an update. The evaluation upon a real trace shows that an accuracy gain of more than 30% is achieved by sending the equal amount of messages

The dynamics of 0-group herring Clupea harengus and sprat Sprattus sprattus populations along the Norwegian Skagerrak coast

Coastal areas are important habitats for early life stages of many fish species. These habitats are used as nursery grounds and can provide a significant contribution to the recruitment of a fish population. In 1919, standardized sampling with a beach seine along the Norwegian Skagerrak coastline was established mainly to target 0-group fish. Here, we focus on Atlantic herring and European sprat to explore whether inter-annual variability in the abundance of these species is indicative of variability in recruitment. We investigated if the abundance of 0-group herring and sprat are affected by environmental factors. Further, the beach seine abundance indices were compared with recruitment estimates of neighboring stocks. There was a clear correlation between herring and sprat abundance in the beach seine samples. While sprat abundance was mainly affected by environmental factors such as temperature and current drift, herring abundance was positively affected by the recruitment of the neighboring stock of western Baltic spring spawners. One plausible explanation could be that sprat recruit to a more local component, while herring of the neighboring stock utilize the Skagerrak coastline as nursery grounds. This study demonstrates the importance of long time series and can provide new insight into the dynamics and structure of multiple fish species.publishedVersio

End-to-end information extraction without token-level supervision

Most state-of-the-art information extraction approaches rely on token-level labels to find the areas of interest in text. Unfortunately, these labels are time-consuming and costly to create, and consequently, not available for many real-life IE tasks. To make matters worse, token-level labels are usually not the desired output, but just an intermediary step. End-to-end (E2E) models, which take raw text as input and produce the desired output directly, need not depend on token-level labels. We propose an E2E model based on pointer networks, which can be trained directly on pairs of raw input and output text. We evaluate our model on the ATIS data set, MIT restaurant corpus and the MIT movie corpus and compare to neural baselines that do use token-level labels. We achieve competitive results, within a few percentage points of the baselines, showing the feasibility of E2E information extraction without the need for token-level labels. This opens up new possibilities, as for many tasks currently addressed by human extractors, raw input and output data are available, but not token-level labels

The Dynamics of 0-Group Herring Clupea harengus and Sprat Sprattus sprattus Populations Along the Norwegian Skagerrak Coast

Coastal areas are important habitats for early life stages of many fish species. These habitats are used as nursery grounds and can provide a significant contribution to the recruitment of a fish population. In 1919, standardized sampling with a beach seine along the Norwegian Skagerrak coastline was established mainly to target 0-group fish. Here, we focus on Atlantic herring and European sprat to explore whether inter-annual variability in the abundance of these species is indicative of variability in recruitment. We investigated if the abundance of 0-group herring and sprat are affected by environmental factors. Further, the beach seine abundance indices were compared with recruitment estimates of neighboring stocks. There was a clear correlation between herring and sprat abundance in the beach seine samples. While sprat abundance was mainly affected by environmental factors such as temperature and current drift, herring abundance was positively affected by the recruitment of the neighboring stock of western Baltic spring spawners. One plausible explanation could be that sprat recruit to a more local component, while herring of the neighboring stock utilize the Skagerrak coastline as nursery grounds. This study demonstrates the importance of long time series and can provide new insight into the dynamics and structure of multiple fish species