4,041 research outputs found

    Neuromodulatory effects on early visual signal processing

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    Understanding how the brain processes information and generates simple to complex behavior constitutes one of the core objectives in systems neuroscience. However, when studying different neural circuits, their dynamics and interactions researchers often assume fixed connectivity, overlooking a crucial factor - the effect of neuromodulators. Neuromodulators can modulate circuit activity depending on several aspects, such as different brain states or sensory contexts. Therefore, considering the modulatory effects of neuromodulators on the functionality of neural circuits is an indispensable step towards a more complete picture of the brain’s ability to process information. Generally, this issue affects all neural systems; hence this thesis tries to address this with an experimental and computational approach to resolve neuromodulatory effects on cell type-level in a well-define system, the mouse retina. In the first study, we established and applied a machine-learning-based classification algorithm to identify individual functional retinal ganglion cell types, which enabled detailed cell type-resolved analyses. We applied the classifier to newly acquired data of light-evoked retinal ganglion cell responses and successfully identified their functional types. Here, the cell type-resolved analysis revealed that a particular principle of efficient coding applies to all types in a similar way. In a second study, we focused on the issue of inter-experimental variability that can occur during the process of pooling datasets. As a result, further downstream analyses may be complicated by the subtle variations between the individual datasets. To tackle this, we proposed a theoretical framework based on an adversarial autoencoder with the objective to remove inter-experimental variability from the pooled dataset, while preserving the underlying biological signal of interest. In the last study of this thesis, we investigated the functional effects of the neuromodulator nitric oxide on the retinal output signal. To this end, we used our previously developed retinal ganglion cell type classifier to unravel type-specific effects and established a paired recording protocol to account for type-specific time-dependent effects. We found that certain retinal ganglion cell types showed adaptational type-specific changes and that nitric oxide had a distinct modulation of a particular group of retinal ganglion cells. In summary, I first present several experimental and computational methods that allow to study functional neuromodulatory effects on the retinal output signal in a cell type-resolved manner and, second, use these tools to demonstrate their feasibility to study the neuromodulator nitric oxide

    Suomen Teollisuussijoitus Oy:n (Tesi) arviointi

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    Arvioinnin kohteena on ollut työ- ja elinkeinoministeriön omistajaohjauksessa toimiva Suomen Teollisuussijoitus Oy:n (Tesi). Tesi on vuonna 1995 perustettu valtion omistama pääomasijoitusyhtiö, joka tekee vähemmistösijoituksia rahastoihin ja kohdeyrityksiin samoin ehdoin yksityisten sijoittajien kanssa. Arviointi tarkastelee Tesin toiminnan tehokkuutta, vaikuttavuutta ja yhteistyötä osana julkisen yritysrahoituksen kokonaisuutta pääomasijoitusmarkkinoilla vuosina 2015–2022. Pitkäjänteisen valtiollisen sijoittajan merkitys on korostunut epävarmoissa markkinatilanteissa ja markkinahäiriöissä. Viime vuosina erityisesti ilmastokriisi, koronapandemia ja Ukrainan sota ovat korostaneet Tesin roolia yhteiskunnallisten haasteiden ratkaisemisessa ja akuuttien markkinahäiriöiden tasaajana. Tesin toiminta muun muassa koronapandemian yhteydessä on laajasti koettu onnistuneena ja tärkeänä. Tesin rooli suomalaisessa pääomasijoitusmarkkinassa on merkittävä ja sen toiminta koetaan laadukkaana ja asiantuntevana. Kriittiset näkemykset liittyvät lähinnä Tesin koettuun portinvartijarooliin uusien rahastojen perustamisessa, sekä jossain määrin suoriin sijoituksiin. Tesillä on myös suuri merkitys tiedon tuottajana, sparraajana ja verkostojen rakentajana. Arviointi sisältää suosituksia sekä Tesin toiminnan kehittämiseksi että koko julkisen pääomasijoitustoiminnan vaikuttavuuden parantamiseksi

    Exploration of Driving Rehabilitation Best Practices to Sustain an Occupation Focused Driver Rehabilitation Program

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    In the United States driving dominates as the primary mode of transportation connecting people to resources including work, school, stores, and hospitals. Driving enables individuals to participate in the occupations they need or want to do, which is why it is a critical Instrumental Activity of Daily Living (IADL). Because driving falls within an occupational therapist’s scope of community mobility, many driving rehabilitation specialists have a background in occupational therapy. Driving rehabilitation is an expanding practice area, with emerging evidence supporting the use of an IADL based assessment for determining fitness to drive. Therefore, the main goal of this doctoral capstone experience was to evaluate the feasibility of incorporating an IADL based assessment at Origami Rehabilitation. To further support growth of the driving program, additional goals included streamlining the documentation process and developing a structured mentoring program. Delivered outcomes comprised of recommendations for the structure and format of driving evaluations, correlations between the selected IADL based assessment and on the road performance, updates to documentation processes and organization, and creation of a driving rehabilitation training and competency checklist. With the delivered outcomes, Origami Rehabilitation has the groundwork for sustaining an occupation focused and evidence based driver rehabilitation program

    Power, Poverty, and Knowledge – Reflecting on 50 Years of Learning with Robert Chambers

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    Robert Chambers is one of the most influential and prolific scholars to write about participation, poverty, and knowledge in development studies. His writing and thinking have revolutionised the discipline, inspiring both participatory processes and more inclusive practice. His work continues to inspire and provoke debate and discussion among development practitioners, activists, and academics from around the world. Here we present an Archive Collection of the IDS Bulletin in a celebration of Robert’s contribution to the journal over the last five decades. The eight articles included in this IDS Bulletin Archive Collection clearly show change – change in Robert’s evolving interests, change in the strategic focus of IDS as a research institute, change in the wider development studies field, as well as change in the world at large over the last 50 years. Robert’s earlier IDS Bulletin articles show a strong focus on local knowledge and rural development. Over time, this shifts to a concern with professional development management, and a focus on power and participatory methods. While each article stands alone, these themes re-occur and re-emerge. Bias or unfairness in the development sphere is a major concern which Robert highlights in his IDS Bulletin articles, whilst his advocacy for bottom-up, diverse, and process-led approaches to participation clearly emerges. As the editorial introduction explains and explores, the premise of this IDS Bulletin Archive Collection is to delve into Robert’s contribution to the journal, to resurface buried gems of development studies scholarship, and to reinvigorate debates about how we can do better – a question described by Robert as the eternal challenge of development

    Kenyon Collegian - October 12, 2023

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    https://digital.kenyon.edu/collegian/3610/thumbnail.jp

    Causal Sampling, Compressing, and Channel Coding of Streaming Data

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    With the emergence of the Internet of Things, communication systems, such as those employed in distributed control and tracking scenarios, are becoming increasingly dynamic, interactive, and delay-sensitive. The data in such real-time systems arrive at the encoder progressively in a streaming fashion. An intriguing question is: what codes can transmit streaming data with both high reliability and low latency? Classical non-causal (block) encoding schemes can transmit data reliably but under the assumption that the encoder knows the entire data block before the transmission. While this is a realistic assumption in delay-tolerant systems, it is ill-suited to real-time systems due to the delay introduced by collecting data into a block. This thesis studies causal encoding: the encoder transmits information based on the causally received data while the data is still streaming in and immediately incorporates the newly received data into a continuing transmission on the fly. This thesis investigates causal encoding of streaming data in three scenarios: causal sampling, causal lossy compressing, and causal joint source-channel coding (JSCC). In the causal sampling scenario, a sampler observes a continuous-time source process and causally decides when to transmit real-valued samples of it under a constraint on the average number of samples per second; an estimator uses the causally received samples to approximate the source process in real time. We propose a causal sampling policy that achieves the best tradeoff between the sampling frequency and the end-to-end real-time estimation distortion for a class of continuous Markov processes. In the causal lossy compressing scenario, the sampling frequency constraint in the causal sampling scenario is replaced by a rate constraint on the average number of bits per second. We propose a causal code that achieves the best causal distortion-rate tradeoff for the same class of processes. In the causal JSCC scenario, the noiseless channel and the continuous-time process in the previous scenarios are replaced by a discrete memoryless channel with feedback and a sequence of streaming symbols, respectively. We propose a causal joint sourcechannel code that achieves the maximum exponentially decaying rate of the error probability compatible with a given rate. Remarkably, the fundamental limits in the causal lossy compressing and the causal JSCC scenarios achieved by our causal codes are no worse than those achieved by the best non-causal codes. In addition to deriving the fundamental limits and presenting the causal codes that achieve the limits, we also show that our codes apply to control systems, are resilient to system deficiencies such as channel delay and noise, and have low complexities.</p

    Women studying and working in engineering : case studies from UCT and civil engineering in the Western Cape

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    In this thesis two case studies were used to investigate women's involvement in engineering in the Western Cape during the early to mid 1990s. The first study provided a statistical profile of women engineering students (WES) at UCT as background to the second, a qualitative, in-depth study that used life-story interviews to identify experiences and attitudes of professional women civil engineers (WE). In 1993, data on demographics, school and social background, reasons for doing engineering and diversity life of WES was collected in sections, by academic year, by groups of senior undergraduate Sociology research students for their individual final year research projects. The questionnaire they used was standardized and contained both closed and open-ended questions. The analysis in this thesis of the WES database combined the data from the different individual Sociology research projects, culminating in a 70% (59 of 83) sample of 2nd, 3"1, 4th and postgraduate year white, coloured and Indian women students registered at UCT in 1993. The results of this analysis showed that the majority of U CT students in the early 1990s were white, young, single women. By using data about parents education levels (50% of mothers and 75% of fathers had received some form of further or higher education) and parents occupations (two-fifths of the mothers and more than half of the fathers held qualified positions) they were fowid to be socially privileged. Individually, and as a group, their performance at school was outstanding, with the entire group achieving an A- or B- matric aggregate with many showing a clear preference for mathematics and science. Using a framework of categories refmed by Jawitz and Case (1998), three categories of reasons for doing engineering, namely "Socialisers" (including having an engineer in the family), "Contact with Engineering" (through open days organised by educational institutions or engineering organisations) and performance and ability in "School Subjects", were fowid to be particularly significant. Isolated incidents of sexist attitudes from male colleagues and lecturers did not detract from an overall positive attitude to studying engineering by the WES, as evidenced by nearly 90% willing to conditionally or wiconditionally support, over opposing, or not recommending, the decision by other women to do engineering. The transcription of audio-taped life-story interviews, conducted in 1998 and 1999, with Cape Town-based women civil engineers provided the qualitative data of the WE study. In the analysis, resistance to entry by women to, and the creation of an wiwelcome atmosphere for women on the building site, came strongly to the fore. Also, some women encowitered serious incidents of sexual harassment and gender discrimination on the building site and even in the office environment. The existence of women's engineering organizations were fowid to partly fill the need for women to network and support each other in the face of a hostile environment in traditional male-dominated industries. The conflicting demands of motherhood and career were fowid to exact a heavy physical and emotional toll on professional women. These women, however, negotiated this conflict in coming. up with Wlique, specialised and livable solutions

    Bag of Tricks for Training Data Extraction from Language Models

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    With the advance of language models, privacy protection is receiving more attention. Training data extraction is therefore of great importance, as it can serve as a potential tool to assess privacy leakage. However, due to the difficulty of this task, most of the existing methods are proof-of-concept and still not effective enough. In this paper, we investigate and benchmark tricks for improving training data extraction using a publicly available dataset. Because most existing extraction methods use a pipeline of generating-then-ranking, i.e., generating text candidates as potential training data and then ranking them based on specific criteria, our research focuses on the tricks for both text generation (e.g., sampling strategy) and text ranking (e.g., token-level criteria). The experimental results show that several previously overlooked tricks can be crucial to the success of training data extraction. Based on the GPT-Neo 1.3B evaluation results, our proposed tricks outperform the baseline by a large margin in most cases, providing a much stronger baseline for future research. The code is available at https://github.com/weichen-yu/LM-Extraction.Comment: ICML 202

    LIPIcs, Volume 261, ICALP 2023, Complete Volume

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    LIPIcs, Volume 261, ICALP 2023, Complete Volum
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