Signed portrait of pianist Heida Hermanns née Goldschmidt Portraits Women

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Pianist Heida Hermanns née Goldschmidt seated at a piano; Berlin Portraits Women

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A flexible hierarchical approach for facial age estimation based on multiple features

Age estimation from facial images is increasingly receiving attention to solve age-based access control, age-adaptive targeted marketing, amongst other applications. Since even humans can be induced in error due to the complex biological processes involved, finding a robust method remains a research challenge today. In this paper, we propose a new framework for the integration of Active Appearance Models (AAM), Local Binary Patterns (LBP), Gabor wavelets (GW) and Local Phase Quantization (LPQ) in order to obtain a highly discriminative feature representation which is able to model shape, appearance, wrinkles and skin spots. In addition, this paper proposes a novel flexible hierarchical age estimation approach consisting of a multi-class Support Vector Machine (SVM) to classify a subject into an age group followed by a Support Vector Regression (SVR) to estimate a specific age. The errors that may happen in the classification step, caused by the hard boundaries between age classes, are compensated in the specific age estimation by a flexible overlapping of the age ranges. The performance of the proposed approach was evaluated on FG-NET Aging and MORPH Album 2 datasets and a mean absolute error (MAE) of 4.50 and 5.86 years was achieved respectively. The robustness of the proposed approach was also evaluated on a merge of both datasets and a MAE of 5.20 years was achieved. Furthermore, we have also compared the age estimation made by humans with the proposed approach and it has shown that the machine outperforms humans. The proposed approach is competitive with current state-of-the-art and it provides an additional robustness to blur, lighting and expression variance brought about by the local phase features.

Detecting rare events using Kullback-Leibler divergence: A weakly supervised approach

Video surveillance infrastructure has been widely installed in public places for security purposes. However, live video feeds are typically monitored by human staff, making the detection of important events as they occur difficult. As such, an expert system that can automatically detect events of interest in surveillance footage is highly desirable. Although a number of approaches have been proposed, they have significant limitations: supervised approaches, which can detect a specific event, ideally require a large number of samples with the event spatially and temporally localised; while unsupervised approaches, which do not require this demanding annotation, can only detect whether an event is abnormal and not specific event types. To overcome these problems, we formulate a weakly-supervised approach using Kullback-Leibler (KL) divergence to detect rare events. The proposed approach leverages the sparse nature of the target events to its advantage, and we show that this data imbalance guarantees the existence of a decision boundary to separate samples that contain the target event from those that do not. This trait, combined with the coarse annotation used by weakly supervised learning (that only indicates approximately when an event occurs), greatly reduces the annotation burden while retaining the ability to detect specific events. Furthermore, the proposed classifier requires only a decision threshold, simplifying its use compared to other weakly supervised approaches. We show that the proposed approach outperforms state-of-the-art methods on a popular real-world traffic surveillance dataset, while preserving real time performance.

Robust automatic face clustering in news video

Clustering identities in a video is a useful task to aid in video search, annotation and retrieval, and cast identification. However, reliably clustering faces across multiple videos is challenging task due to variations in the appearance of the faces, as videos are captured in an uncontrolled environment. A person's appearance may vary due to session variations including: lighting and background changes, occlusions, changes in expression and make up. In this paper we propose the novel Local Total Variability Modelling (Local TVM) approach to cluster faces across a news video corpus; and incorporate this into a novel two stage video clustering system. We first cluster faces within a single video using colour, spatial and temporal cues; after which we use face track modelling and hierarchical agglomerative clustering to cluster faces across the entire corpus. We compare different face recognition approaches within this framework. Experiments on a news video database show that the Local TVM technique is able effectively model the session variation observed in the data, resulting in improved clustering performance, with much greater computational efficiency than other methods.

Congregation Beth Israel Hartford Connecticut collection, 1920-1999

Congregation Beth Israel was founded in 1843 and is Connecticut's oldest synagogue. Originally established as an Orthodox congregation, the synagogue eventually converted to Reform and was one of the founding members of the Union of American Hebrew Congregations (Union for Reform Judaism) in 1877. This collection includes event flyers, programs for services, sermons, anniversary books with historical information, and bulletins. Box 1: General/Miscellaneous Materials 1927-1970. 100th Anniversary /Programs and Invitations 1943. 125th Anniversary/Program 1968. Publications/Bulletins 1920-1959. Box 2: Publications/ Bulletings 1950-1969. Box 3: Publications/ Bulletins 1960-1989. Box 4: Publications/ Bulletin 1989-1999.

Turing and the Trans-Pacific Partnership: Intellectual Property, Public Health, and Access to Essential Medicines

A recent controversy in the United States over drug pricing by Turing Pharmaceuticals AG has raised larger issues in respect of intellectual property, access to medicines, and the Trans-Pacific Partnership (TPP). In August 2015, Turing Pharmaceuticals AG – a private biopharmaceutical company with offices in New York, the United States, and Zug, Switzerland - acquired the exclusive marketing rights to Daraprim in the United States from Impax Laboratories Incorporated. Martin Shkreli, Turing’s Founder and Chief Executive Officer, maintained: “The acquisition of Daraprim and our toxoplasmosis research program are significant steps along Turing’s path of bringing novel medications to patients with serious disorders, some of whom often go undiagnosed and untreated.” He emphasised: “We intend to invest in the development of new drug candidates that we hope will yield an even better clinical profile, and also plan to launch an educational effort to help raise awareness and improve diagnosis for patients with toxoplasmosis.” In September 2015, there was much public controversy over the decision of Martin Shkreli to raise the price of a 62 year old drug, Daraprim, from $US13.50 to $US750 a pill. The drug is particularly useful in respect to the treatment and prevention of malaria, and in the treatment of infections in individuals with HIV/AIDS. Daraprim is listed on the World Health Organization’s (WHO) List of Essential Medicines. In the face of much criticism, Martin Shkreli has said that he will reduce the price of Daraprim. He observed: “We've agreed to lower the price on Daraprim to a point that is more affordable and is able to allow the company to make a profit, but a very small profit.” He maintained: “We think these changes will be welcomed.” However, he has been vague and ambiguous about the nature of the commitment. Notably, the lobby group, Pharmaceutical Research and Manufacturers of America (PhARMA), disassociated itself from the claims of Turing Pharmaceuticals. The group said: “PhRMA members have a long history of drug discovery and innovation that has led to increased longevity and improved lives for millions of patients.” The group noted: “Turing Pharmaceutical is not a member of PhRMA and we do not embrace either their recent actions or the conduct of their CEO.” The biotechnology peak body Biotechnology Industry Organization also sought to distance itself from Turing Pharmaceuticals. A hot topic: United States political debate about access to affordable medicines This controversy over Daraprim is unusual – given the age of drug concerned. Daraprim is not subject to patent protection. Nonetheless, there remains a monopoly in respect of the marketplace. Drug pricing is not an isolated problem. There have been many concerns about drug pricing – particularly in respect of essential medicines for HIV/AIDS, tuberculosis, and malaria. This recent controversy is part of a larger debate about access to affordable medicines. The dispute raises larger issues about healthcare, consumer rights, competition policy, and trade. The Daraprim controversy has provided impetus for law reform in the US. US Presidential Candidate Hillary Clinton commented: “Price gouging like this in this specialty drug market is outrageous.” In response to her comments, the Nasdaq Biotechnology Index fell sharply. Hillary Clinton has announced a prescription drug reform plan to protect consumers and promote innovation – while putting an end to profiteering. On her campaign site, she has emphasised that “affordable healthcare is a basic human right.” Her rival progressive candidate, Bernie Sanders, was also concerned about the price hike. He wrote a letter to Martin Shkreli, complaining about the price increase for the drug Daraprim. Sanders said: “The enormous, overnight price increase for Daraprim is just the latest in a long list of skyrocketing price increases for certain critical medications.” He has pushed for reforms to intellectual property to make medicines affordable. The TPP and intellectual property The Daraprim controversy and political debate raises further issues about the design of the TPP. The dispute highlights the dangers of extending the rights of pharmaceutical drug companies under intellectual property, investor-state dispute settlement, and drug administration. Recently, the civil society group Knowledge Ecology International published a leaked draft of the Intellectual Property Chapter of the TPP. Knowledge Ecology International Director, James Love, was concerned the text revealed that the US “continues to be the most aggressive supporter of expanded intellectual property rights for drug companies.” He was concerned that “the proposals contained in the TPP will harm consumers and in some cases block innovation.” James Love feared: “In countless ways, the Obama Administration has sought to expand and extend drug monopolies and raise drug prices.” He maintained: “The astonishing collection of proposals pandering to big drug companies make more difficult the task of ensuring access to drugs for the treatment of cancer and other diseases and conditions.” Love called for a different approach to intellectual property and trade: “Rather than focusing on more intellectual property rights for drug companies, and a death-inducing spiral of higher prices and access barriers, the trade agreement could seek new norms to expand the funding of medical research and development (R&D) as a public good, an area where the US has an admirable track record, such as the public funding of research at the National Institutes of Health (NIH) and other federal agencies.” In addition, there has been much concern about the Investment Chapter of the TPP. The investor-state dispute settlement regime would enable foreign investors to challenge government policy making, which affected their investments. In the context of healthcare, there is a worry that pharmaceutical drug companies will deploy their investor rights to challenge public health measures – such as, for instance, initiatives to curb drug pricing and profiteering. Such concerns are not merely theoretical. Eli Lilly has brought an investor action against the Canadian Government over the rejection of its drug patents under the investor-state dispute settlement regime of the North American Free Trade Agreement (NAFTA). The Health Annex to the TPP also raises worries that pharmaceutical drug companies will able to object to regulatory procedures in respect of healthcare. It is disappointing that the TPP – in the leaks that we have seen – has only limited recognition of the importance of access to essential medicines. There is a need to ensure that there are proper safeguards to provide access to essential medicines – particularly in respect of HIV/AIDs, malaria, and tuberculosis. Moreover, there must be protection against drug profiteering and price gouging in any trade agreement. There should be strong measures against the abuse of intellectual property rights. The dispute over Turing Pharmaceuticals AG and Daraprim is an important cautionary warning in respect of some of the dangers present in the secret negotiations in respect of the TPP. There is a need to preserve consumer rights, competition policy, and public health in trade negotiations over an agreement covering the Pacific Rim.

Activity recognition using binary tree SVM

This paper presents an effective classification method based on Support Vector Machines (SVM) in the context of activity recognition. Local features that capture both spatial and temporal information in activity videos have made significant progress recently. Efficient and effective features, feature representation and classification plays a crucial role in activity recognition. For classification, SVMs are popularly used because of their simplicity and efficiency; however the common multi-class SVM approaches applied suffer from limitations including having easily confused classes and been computationally inefficient. We propose using a binary tree SVM to address the shortcomings of multi-class SVMs in activity recognition. We proposed constructing a binary tree using Gaussian Mixture Models (GMM), where activities are repeatedly allocated to subnodes until every new created node contains only one activity. Then, for each internal node a separate SVM is learned to classify activities, which significantly reduces the training time and increases the speed of testing compared to popular the `one-against-the-rest' multi-class SVM classifier. Experiments carried out on the challenging and complex Hollywood dataset demonstrates comparable performance over the baseline bag-of-features method.

Class-specific sparse codes for representing activities

In this paper we investigate the effectiveness of class specific sparse codes in the context of discriminative action classification. The bag-of-words representation is widely used in activity recognition to encode features, and although it yields state-of-the art performance with several feature descriptors it still suffers from large quantization errors and reduces the overall performance. Recently proposed sparse representation methods have been shown to effectively represent features as a linear combination of an over complete dictionary by minimizing the reconstruction error. In contrast to most of the sparse representation methods which focus on Sparse-Reconstruction based Classification (SRC), this paper focuses on a discriminative classification using a SVM by constructing class-specific sparse codes for motion and appearance separately. Experimental results demonstrates that separate motion and appearance specific sparse coefficients provide the most effective and discriminative representation for each class compared to a single class-specific sparse coefficients.

Multiple instance dictionary learning for activity representation

This paper presents an effective feature representation method in the context of activity recognition. Efficient and effective feature representation plays a crucial role not only in activity recognition, but also in a wide range of applications such as motion analysis, tracking, 3D scene understanding etc. In the context of activity recognition, local features are increasingly popular for representing videos because of their simplicity and efficiency. While they achieve state-of-the-art performance with low computational requirements, their performance is still limited for real world applications due to a lack of contextual information and models not being tailored to specific activities. We propose a new activity representation framework to address the shortcomings of the popular, but simple bag-of-words approach. In our framework, first multiple instance SVM (mi-SVM) is used to identify positive features for each action category and the k-means algorithm is used to generate a codebook. Then locality-constrained linear coding is used to encode the features into the generated codebook, followed by spatio-temporal pyramid pooling to convey the spatio-temporal statistics. Finally, an SVM is used to classify the videos. Experiments carried out on two popular datasets with varying complexity demonstrate significant performance improvement over the base-line bag-of-feature method.

Passport of Judah Lyons

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Combat sports analytics: Boxing punch classification using overhead depth imagery

In competitive combat sporting environments like boxing, the statistics on a boxer's performance, including the amount and type of punches thrown, provide a valuable source of data and feedback which is routinely used for coaching and performance improvement purposes. This paper presents a robust framework for the automatic classification of a boxer's punches. Overhead depth imagery is employed to alleviate challenges associated with occlusions, and robust body-part tracking is developed for the noisy time-of-flight sensors. Punch recognition is addressed through both a multi-class SVM and Random Forest classifiers. A coarse-to-fine hierarchical SVM classifier is presented based on prior knowledge of boxing punches. This framework has been applied to shadow boxing image sequences taken at the Australian Institute of Sport with 8 elite boxers. Results demonstrate the effectiveness of the proposed approach, with the hierarchical SVM classifier yielding a 96% accuracy, signifying its suitability for analysing athletes punches in boxing bouts.

Report on statement made to mayor regarding congregation's property and revenue

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Valkoisen talon kappalaiset : Yhdysvaltain presidentti-instituution viralliset puheet retorisena kansalaisuskontona

Tutkimuksen aihepiirinä on viimeaikaisen (1997-2004) Yhdysvaltain presidentti-instituution uskonnollinen retoriikka William J. Clintonin ja George W. Bushin virkakausina. Tutkimuksen tehtävä on selvittää, miten presidentti-instituutio hyödyntää uskonnollista retoriikkaa legimitaationa virallisissa poliittisissa linjanvetopuheissaan, kuten virkaanastujais- ja liittovaltion tila -puheissaan. Tutkimustehtävä jakautuu seuraaviin alakysymyksiin: 1) Millaisilla kansalaisuskonnollisilla käsityksillä ja keinoilla legimitaatiota yritetään tuottaa? 2) Ovatko kansalaisuskonnon keinot viimeaikaisten presidenttien keskeisintä työvälineistöä identifioinnissa ja erottautumisissa? 3) Ovatko kansalaisuskonnolliset teemat naturalisoituneet, luonnollistuneet? Tiivistetysti on tarkoitus tutkia viimeaikaisten presidenttien virallisista puheiden kautta, kuinka uskonto ja valtio liittyvät toisiinsa. Perusolettamukseni on, että presidentit hyödyntävät keskeisesti kansalaisuskonnollista retoriikkaa vakuuttaakseen yleisönsä ja politiikkansa. Tutkimusaineisto koostuu koostuu kahden Yhdysvaltain presidentin William J. Clintonin (1993-2001) ja George W. Bushin (2001-) presidenttien virallisista puheista, jotka on pidetty vuosina 1997-2004. Viralliset puheet ovat virkaanastujais- (Inaugural Address) ja vuosittaiset liittovaltion tila -puheet (Address Before a Joint Session of the Congress on the State of the Union). Puheita on kymmenen ja niiden pituus on keskimäärin viisi sivua. Tutkielman teoreettinen viitekehys tukeutuu kanadalaisen uskontososiologin Marcela Cristin uudenlaiselle käsitykselle (2001) kansalaisuskonnosta poliittisena uskontona. Tämä hyödyntää kansalaisuskonnon durkheimilaista taustaa yhdistäen Jean-Jacques Rousseuaun religion civile-teesiä. Cristin mukaan kansalaisuskonto manifestoituu kahdella tavalla: sekä durkheimilaisena kulttuurisena kansalaismuotona (civil) ja rousseualaisena ideologiana (political). Nämä eivät hänen mukaansa ole toisilleen vastakohtia, vaan yhden jatkumon osia. Tutkielman metodologinen lähestymistapa on uusretorinen analyysi ja teoreettis-medologisena viitekehyksenä on sosiaalinen konstruktivismi. Analyysin perusolettamus on, että presidentti-instituution luomassa merkityksen rakennetaan sosiaalisesta todellisuutta. Instituution retoriikka suuntautuu tiettyyn sosiaaliseen yhteisöön yrittäen legimitoida mahdollisimman laajaa joukkoa politiikkansa taakse. Analyysin tuloksena huomattiin, että presidentti-instituutio hyödyntää aktiivisesti kansalaisuskonnollisia teemoja. Molempien presidentti-instituutioiden uskonnollisessa nationalismissa hyödynnetään kansalaisuskonnollisuuden sekä protestanttis-puritaanista että valistuksellista taustaa. Kyse on oikeastaan siitä,kuinka nämä puolet saavat painotuksensa. Selvää on, että molemmat presidentit hyödyntävät kaikkia kansalaisuskonnollisuuden kolmea ulottuvuutta. Molemmilta löytyy retorinen tukeutuminen transsendenssiin, moraalistinen yhteisöllisyyden vaatimus. Clintonin uskonnollinen retoriikkaa jakaantuu kahteen ulkoisesti ristiriitaiseen käsitteelliseen linssiin: lupauksellisuus ja perustajallisuus. Ne yhdistyvät hänen nimittämisissään millenniaaliseksi yhteisöksi. Bushin uskonnollinen retoriikka tukeutuu transsendenttiseen ulottuvuuteen. Hänen käsitteelisiä linssejä ovat aktiivisen Jumalan monet roolit ja ajan siunauksellisuus. (Ks. luku 8. johtopäätökset ja jatkotutkimus.) Avainsanat: Yhdysvallat, presidentti-instituutio, kansalaisuskonto, uskonnollinen retoriikka, William J. Clinton, George W.Bush