Exploiting Sparse Representations for Robust Analysis of Noisy Complex Video Scenes

Abstract. Recent works have shown that, even with simple low level visual cues, complex behaviors can be extracted automatically from crowded scenes, e.g. those depicting public spaces recorded from video surveillance cameras. However, low level features as optical flow or fore-ground pixels are inherently noisy. In this paper we propose a novel unsupervised learning approach for the analysis of complex scenes which is specifically tailored to cope directly with features ’ noise and uncer-tainty. We formalize the task of extracting activity patterns as a matrix factorization problem, considering as reconstruction function the robust Earth Mover’s Distance. A constraint of sparsity on the computed basis matrix is imposed, filtering out noise and leading to the identification of the most relevant elementary activities in a typical high level behavior. We further derive an alternate optimization approach to solve the pro-posed problem efficiently and we show that it is reduced to a sequence of linear programs. Finally, we propose to use short trajectory snippets to account for object motion information, in alternative to the noisy optical flow vectors used in previous works. Experimental results demonstrate that our method yields similar or superior performance to state-of-the arts approaches.

HLA-J, a Non-Pseudogene as a New Prognostic Marker for Therapy Response and Survival in Breast Cancer

The human leukocyte antigen (HLA) genes are cell-surface proteins, essential for immune cell interaction. HLA-G is known for their high immunosuppressive effect and its potential as predictive marker in breast cancer. However, nothing is known about the HLA-J and its immunosuppressive, prognostic and predictive features, as it is assumed to be a pseudogene by in silico sequence interpretation. HLA-J, ESR1, ERBB2, KRT5 and KRT20 mRNA expression were analysed in 29 fresh frozen breast cancer biopsies and their corresponding resectates obtained from patients treated with neoadjuvant chemotherapy (NACT). mRNA was analysed with gene specific TaqMan-based Primer/Probe sets and normalized to Calmodulin 2. All breast cancer samples did express HLA-J and frequently increased HLA-J mRNA levels after NACT. HLA-J mRNA was significantly associated with overexpression of the ESR1 mRNA status (Spearman ρ 0,5679; p = 0.0090) and KRT5 mRNA (Spearman ρ 0,6121; p = 0.0041) in breast cancer core biopsies and dominated in luminal B subtype. Kaplan Meier analysis revealed that an increase of HLA-J mRNA expression after NACT had worse progression free survival (p = 0,0096), indicating a counterreaction of tumor tissues presumably to prevent elimination by enhanced immune infiltration induced by NACT. This counterreaction is associated with worse prognosis. To our knowledge this is the first study identifying HLA-J as a new predictive marker in breast cancer being involved in immune evasion mechanisms.Humane Leukozyten-Antigene (HLA) sind Proteine auf der Zelloberfläche, die essenziell für die Immunzellinteraktion sind. HLA-G ist für seine hohe immunosuppressive Wirkung sowie als potenzieller prädikativer Marker für Brustkrebs bekannt. Dagegen ist kaum etwas über HLA-J und seine immunosuppressiven, prognostischen und prädiktiven Eigenschaften bekannt, da es basierend auf In-silico-Sequenzanalysen als „Pseudogen“ interpretiert wurde. Die Expression von HLA-J, ESR1, ERBB2, KRT5 und KRT20 mRNA wurde in 29 frisch gefrorenen Brustkrebsbiopsien analysiert und mit den klinisch-pathologischen Daten von Patientinnen, welche mit neoadjuvanter Chemotherapie behandelt wurden, verglichen. Die mRNA-Expression wurde mit genspezifischen TaqMan-basierten Primer/Probe-Sets analysiert und auf Calmodulin 2 normalisiert. Alle Gewebeproben von Patientinnen mit Brustkrebs exprimierten HLA-J, und der HLA-J-mRNA-Spiegel war nach NACT oft erhöht. In den Brustkrebsstanzbiopsien war die HLA-J-mRNA-Expression signifikant mit der Überexpression von ESR1-mRNA (Spearmans ρ 0,5679; p = 0,0090) und KRT5-mRNA (Spearmans ρ 0,6121; p = 0,0041) assoziiert und dominierte im Luminal-B-Subtyp. Die Kaplan-Meier-Analyse zeigte, dass ein Anstieg der HLA-J-mRNA-Expression nach NACT mit einem schlechteren progressionsfreien Überleben einhergeht (p = 0,0096), womöglich als Gegenreaktion des Tumorgewebes, um eine Eliminierung durch tumorinfiltrierende Lymphozyten, welche durch eine NACT induziert wurden, zu verhindern. Diese Gegenreaktion ist mit einer schlechteren Prognose assoziiert. Soweit uns bekannt, handelt es sich hierbei um die erste Studie, die HLA-J als neuen prädiktiven Marker im Brustkrebs identifiziert hat und möglicherweise zur Immunevasion beiträgt

Does the history of food energy units suggest a solution to "Calorie confusion"?

The Calorie (kcal) of present U.S. food labels is similar to the original French definition of 1825. The original published source (now available on the internet) defined the Calorie as the quantity of heat needed to raise the temperature of 1 kg of water from 0 to 1°C. The Calorie originated in studies concerning fuel efficiency for the steam engine and had entered dictionaries by 1840. It was the only energy unit in English dictionaries available to W.O. Atwater in 1887 for his popular articles on food and tables of food composition. Therefore, the Calorie became the preferred unit of potential energy in nutrition science and dietetics, but was displaced when the joule, g-calorie and kcal were introduced. This article will explain the context in which Nicolas Clément-Desormes defined the original Calorie and the depth of his collaboration with Sadi Carnot. It will review the history of other energy units and show how the original Calorie was usurped during the period of international standardization. As a result, no form of the Calorie is recognized as an SI unit. It is untenable to continue to use the same word for different thermal units (g-calorie and kg-calorie) and to use different words for the same unit (Calorie and kcal). The only valid use of the Calorie is in common speech and public nutrition education. To avoid ongoing confusion, scientists should complete the transition to the joule and cease using kcal in any context

Conformance checking using activity and trace embeddings

Conformance checking describes process mining techniques used to compare an event log and a corresponding process model. In this paper, we propose an entirely new approach to conformance checking based on neural network-based embeddings. These embeddings are vector representations of every activity/task present in the model and log, obtained via act2vec, a Word2vec based model. Our novel conformance checking approach applies the Word Mover’s Distance to the activity embeddings of traces in order to measure fitness and precision. In addition, we investigate a more efficiently calculated lower bound of the former metric, i.e. the Iterative Constrained Transfers measure. An alternative method using trace2vec, a Doc2vec based model, to train and compare vector representations of the process instances themselves is also introduced. These methods are tested in different settings and compared to other conformance checking techniques, showing promising results

Long-term coding of personal and universal associations underlying the memory web in the human brain

Neurons in the medial temporal lobe (MTL), a critical area for declarative memory, have been shown to change their tuning in associative learning tasks. Yet, it is unclear how durable these neuronal representations are and if they outlast the execution of the task. To address this issue, we studied the responses of MTL neurons in neurosurgical patients to known concepts (people and places). Using association scores provided by the patients and a web-based metric, here we show that whenever MTL neurons respond to more than one concept, these concepts are typically related. Furthermore, the degree of association between concepts could be successfully predicted based on the neurons’ response patterns. These results provide evidence for a long-term involvement of MTL neurons in the representation of durable associations, a hallmark of human declarative memory

Modeling Checkpoint-Based Movement with the Earth Mover's Distance

Movement data comes in various forms, including trajectory data and checkpoint data. While trajectories give detailed information about the movement of individual entities, checkpoint data in its simplest form does not give identities, just counts at checkpoints. However, checkpoint data is of increasing interest since it is readily available due to privacy reasons and as a by-product of other data collection. In this paper we propose to use the Earth Mover’s Distance as a versatile tool to reconstruct individual movements or flow based on checkpoint counts at different times. We analyze the modeling possibilities and provide experiments that validate model predictions, based on coarse-grained aggregations of data about actual movements of couriers in London, UK. While we cannot expect to reconstruct precise individual movements from highly granular checkpoint data, the evaluation does show that the approach can generate meaningful estimates of object movements. B. Speckmann and K. Verbeek are supported by the Netherlands Organisation for Scientific Research (NWO) under project nos. 639.023.208 and 639.021.541, respectively. This paper arose from work initiated at Dagstuhl seminar 12512 “Representation, analysis and visualization of moving objects”, December 2012. The authors gratefully acknowledge Schloss Dagstuhl for their support