Miracle-dz/UniFAR-FTUs huggingface dataset:
Ätstörningar hos unga med diabetes mellitus typ 1- En litteraturstudie

Utvecklandet av diabetes och atstorningar sammanfaller ofta kring pubertetsaldern hos bade unga kvinnor och man. Da prevalensen av bade diabetes typ 1 och atstorningar okar kan det vara intressant att studera ett eventuellt samband. Syftet med studien var att sammanstalla forskningsresultat kring atstorningar hos unga personer med diabetes mellitus typ 1. Studien genomfordes som en litteraturstudie baserad pa 10 vetenskapliga artiklar. Artiklarna analyserades med utgangspunkt fran analysmodell for kvantitativa artiklar samt delar av analysmodell for litteraturoversikt. Analysen resulterade i sju olika relevanta amnesomraden, varav tre stycken lyftes som huvudfynd i diskussionsdelen. Det forsta var att det ar vanligare med atstorningar och stort atbeteende hos unga med diabetes typ 1 jamfort med de utan. Nasta fynd som lyftes fram var att unga med diabetes typ 1 anvander sig av avsiktlig insulinbegransning for viktkontroll. Det sista centrala fyndet var att ungdomar som far diabetes i aldrarna 7-18 ar i storre utstrackning drabbas av atstorningar jamfort med de som far sin diabetes senare. Okad kunskap och forstaelse for dessa fynd kan bidra till att minska risken for att unga personer med diabetes typ 1 ska utveckla atstorningar.
SNF: serverless network functions

Our work addresses how a cloud provider can offer Network Functions (NF) as a Service, or NFaaS, using the emerging serverless computing paradigm. Serverless computing has the right NFaaS building blocks - usage-based billing, event-driven programming model and elastic scaling. But we identify two core limitations of existing serverless platforms that undermine support for NFaaS - coupling of the billing and work assignment granularities, and state sharing via an external store. Our framework, SNF, overcomes these limitations via two ideas. SNF allocates work at the granularity of flowlets observed in network traffic, whereas billing and programming occur at a finer level. SNF embellishes serverless platforms with ephemeral local state that lasts for the flowlet duration and supports high performance state operations. We demonstrate that our SNF prototype matches utilization closely with demand and reduces tail packet processing latency substantially compared to alternatives.
LibriVoxDeEn: A Corpus for German-to-English Speech Translation and German Speech Recognition

We present a corpus of sentence-aligned triples of German audio, German text, and English translation, based on German audio books. The speech translation data consist of 110 hours of audio material aligned to over 50k parallel sentences. An even larger dataset comprising 547 hours of German speech aligned to German text is available for speech recognition. The audio data is read speech and thus low in disfluencies. The quality of audio and sentence alignments has been checked by a manual evaluation, showing that speech alignment quality is in general very high. The sentence alignment quality is comparable to well-used parallel translation data and can be adjusted by cutoffs on the automatic alignment score. To our knowledge, this corpus is to date the largest resource for German speech recognition and for end-to-end German-to-English speech translation.
Using Bayes Linear Emulators to Analyse Networks of Simulators

The key dynamics of processes within physical systems are often represented in the form of computer models, or simulators. Often, the overarching system of interest, comprising of multiple processes, may be represented as a network of simulators, with some of the inputs to some simulators arising from the outputs to other simulators. Much of the computational statistics literature focusses on approximating computationally intensive simulators using statistical emulators. An important, yet underexplored question in this literature is: can emulating the individual simulators within a network be more powerful than emulating the composite simulator network as a single simulator? In this article we present two novel approaches for linking Bayes linear emulators of several component simulators together to obtain an approximation for the composite simulator, comparing these ideas to approximating the composite simulator using a single emulator. These techniques, termed the Bayes linear sampling approach and Uncertain Input Bayes linear emulator, will be demonstrated on a couple of illustrative simulated examples, as well as being applied to an important dispersion dose-response chain of simulators used for disease modelling.
Learning from My Partner's Actions: Roles in Decentralized Robot Teams

When teams of robots collaborate to complete a task, communication is often necessary. Like humans, robot teammates should implicitly communicate through their actions: but interpreting our partner's actions is typically difficult, since a given action may have many different underlying reasons. Here we propose an alternate approach: instead of not being able to infer whether an action is due to exploration, exploitation, or communication, we define separate roles for each agent. Because each role defines a distinct reason for acting (e.g., only exploit, only communicate), teammates now correctly interpret the meaning behind their partner's actions. Our results suggest that leveraging and alternating roles leads to performance comparable to teams that explicitly exchange messages. You can find more images and videos of our experimental setups at this http URL.
Deep Coregionalization for the Emulation of Spatial-Temporal Fields

Data-driven surrogate models are widely used for applications such as design optimization and uncertainty quantification, where repeated evaluations of an expensive simulator are required. For most partial differential equation (PDE) simulators, the outputs of interest are often spatial or spatial-temporal fields, leading to very high-dimensional outputs. Despite the success of existing data-driven surrogates for high-dimensional outputs, most methods require a significant number of samples to cover the response surface in order to achieve a reasonable degree of accuracy. This demand makes the idea of surrogate models less attractive considering the high computational cost to generate the data. To address this issue, we exploit the multi-fidelity nature of a PDE simulator and introduce deep coregionalization, a Bayesian non-parametric autoregressive framework for efficient emulation of spatial-temporal fields. To effectively extract the output correlations in the context of multi-fidelity data, we develop a novel dimension reduction technique, residual principal component analysis. Our model can simultaneously capture the rich output correlations and the fidelity correlations and make high-fidelity predictions with only a few expensive, high-fidelity simulation samples. We show the advantages of our model in three canonical PDE models and a fluid dynamics problem. The results show that the proposed method cannot only approximate a simulator with significantly less cost (at bout 10%-25%) but also further improve model accuracy.
Mixture-of-Experts Variational Autoencoder for clustering and generating from similarity-based representations on single cell data

Clustering high-dimensional data, such as images or biological measurements, is a long-standing problem and has been studied extensively. Recently, Deep Clustering has gained popularity due to its flexibility in fitting the specific peculiarities of complex data. Here we introduce the Mixture-of-Experts Similarity Variational Autoencoder (MoE-Sim-VAE), a novel generative clustering model. The model can learn multi-modal distributions of high-dimensional data and use these to generate realistic data with high efficacy and efficiency. MoE-Sim-VAE is based on a Variational Autoencoder (VAE), where the decoder consists of a Mixture-of-Experts (MoE) architecture. This specific architecture allows for various modes of the data to be automatically learned by means of the experts. Additionally, we encourage the lower dimensional latent representation of our model to follow a Gaussian mixture distribution and to accurately represent the similarities between the data points. We assess the performance of our model on the MNIST benchmark data set and challenging real-world tasks of clustering mouse organs from single-cell RNA-sequencing measurements and defining cell subpopulations from mass cytometry (CyTOF) measurements on hundreds of different datasets. MoE-Sim-VAE exhibits superior clustering performance on all these tasks in comparison to the baselines as well as competitor methods.
Abstract Transducers

Several abstract machines that operate on symbolic input alphabets have been proposed in the last decade, for example, symbolic automata or lattice automata. Applications of these types of automata include software security analysis and natural language processing. While these models provide means to describe words over infinite input alphabets, there is no considerable work on symbolic output (as present in transducers) alphabets, or even abstraction (widening) thereof. Furthermore, established approaches for transforming, for example, minimizing or reducing, finite-state machines that produce output on states or transitions are not applicable. A notion of equivalence of this type of machines is needed to make statements about whether or not transformations maintain the semantics. We present abstract transducers as a new form of finite-state transducers. Both their input alphabet and the output alphabet is composed of abstract words, where one abstract word represents a set of concrete words. The mapping between these representations is described by abstract word domains. By using words instead of single letters, abstract transducers provide the possibility of lookaheads to decide on state transitions to conduct. Since both the input symbol and the output symbol on each transition is an abstract entity, abstraction techniques can be applied naturally. We apply abstract transducers as the foundation for sharing task artifacts for reuse in context of program analysis and verification, and describe task artifacts as abstract words. A task artifact is any entity that contributes to an analysis task and its solution, for example, candidate invariants or source code to weave.
The role of friction in the yielding of adhesive non-Brownian suspensions

Yielding behavior is well known in attractive colloidal suspensions. Adhesive non-Brownian suspensions, in which the interparticle bonds are due to finite-size contacts, also show yielding behavior. We use a combination of steady-state, oscillatory and shear-reversal rheology to probe the physical origins of yielding in the latter class of materials, and find that yielding is not simply a matter of breaking adhesive bonds, but involves unjamming from a shear-jammed state in which the micro-structure has adapted to the direction of the applied load. Comparison with a recent constraint-based rheology model shows the importance of friction in determining the yield stress, suggesting novel ways to tune the flow of such suspensions.
HER2 and TP53 in human breast cancer : Studies of methods and prognostic value

Prognostic and predictive factors are needed for tailored therapy strategies, aimed at reducing breast cancer relapse and improving survival. The present arsenal of such factors is insufficient. The aims of this thesis were to study the TP53 gene, and the human epidermal growth factor receptor 2 (HER2), also known as c-erbB-2 and HER2/neu, in human breast cancer, especially in relation to outcome after tamoxifen therapy. In order to make the best out of the biological markers’ prognostic and predictive values, optimal methods for their determinations are required, and this is also addressed in the thesis. We found mutated TP53 to be associated with worse outcome after adjuvant chemotherapy (CMF), but to be of no statistically significant importance with regard to the outcome of adjuvant tamoxifen. However, in the total patient population, women with wild type TP53 tumours had better survival compared with women whose tumours were TP53 mutated. We used sequenced-based methodology for analysis of all TP53 exons, based on indications that this method is superior to immunohistochemistry IHC in prognostication of breast cancer relapse and survival. The monoclonal antibody trastuzumab, directed against HER2, is one of many examples of emerging targeting therapies, of which tamoxifen was the first. We describe the first patients in Sweden who received trastuzumab on a named patient basis, all with advanced breast cancer failing on conventional therapy. The previously demonstrated efficacy and acceptable toxicity in randomised studies were confirmed in our small population-based patient cohort. Endocrine therapy with tamoxifen, mediating both anti-oestrogen, and oestrogen-like effects, has been used for more than three decades. Subsequently, tamoxifen resistance is a problem for a large cohort of breast cancer patients. Preclinical as well as clinical studies have hypothesised HER2 to be involved in tamoxifen resistance through cross-talk signalling with the oestrogen receptor, via the mitogen activated protein kinase, extra cellular regulated kinases 1 and 2 (ERK1/2) pathway. We confirmed HER2 to be associated with worse outcome after tamoxifen therapy while, in contrast to other studies, we found that activated ERK1/2 immunohistochemical staining, prognosticate better outcome. The increasing number of patients eligible for trastuzumab therapy requires reproducible, costeffective, and high throughput assays for HER2 determinations. Immunohistochemistry and fluorescence in situ hybridisation are the most frequently used methods for evaluation of HER2 status. We demonstrate quantitative real-time PCR and an RNA expression-based methodology to generate high-quality HER2 assessments with equivalent, and in some aspects improved results, compared with immunohistochemistry and fluorescence in situ hybridisation/chromogenic in situ hybridisation.
Prevention of Traumatic Brain Injury with Liquid Shock Absorption.

The discovery that repetitive mild traumatic brain injury (mTBI) can result in chronic traumatic encephalopathy (CTE) in high risk contact sports has led to increased scrutiny of head protective gear. In this work, we asked if it was physically possible to prevent mTBI in American football with helmets alone. Here, we show that modern helmets of several types are unlikely to prevent mTBI from high speed collisions as might be seen in the NFL, but that introducing liquid as an energy absorbing medium can dramatically reduce the forces of impact across a spectrum of impact severities. We hypothesized that a helmet which transmits a nearly constant force during football impacts is sufficient to reduce biomechanical loading in the brain below the threshold of mTBI. To test this hypothesis, we first show that the optimal impact force transmitted to the head, in terms of brain strain, is in fact a constant force profile. Then, to generate a constant force with a helmet, we implement a computational model of a fluid-based shock absorber that adapts passively to any given impact speed. Computer simulation of head impacts with liquid shock absorption indicate that, at the highest impact speed, the average brain tissue strain is reduced by 27.6% $\pm$ 9.3 compared to existing helmet padding that is available on the market. These simulations are based on the NFL's helmet test protocol and predict that adding liquid shock absorbers could reduce the number of concussions by at least 75%. Taken together, these results suggest that the majority of mTBI in football could be prevented with more efficient helmet technology.
Convolutional Character Networks

Recent progress has been made on developing a unified framework for joint text detection and recognition in natural images, but existing joint models were mostly built on two-stage framework by involving ROI pooling, which can degrade the performance on recognition task. In this work, we propose convolutional character networks, referred as CharNet, which is an one-stage model that can process two tasks simultaneously in one pass. CharNet directly outputs bounding boxes of words and characters, with corresponding character labels. We utilize character as basic element, allowing us to overcome the main difficulty of existing approaches that attempted to optimize text detection jointly with a RNN-based recognition branch. In addition, we develop an iterative character detection approach able to transform the ability of character detection learned from synthetic data to real-world images. These technical improvements result in a simple, compact, yet powerful one-stage model that works reliably on multi-orientation and curved text. We evaluate CharNet on three standard benchmarks, where it consistently outperforms the state-of-the-art approaches [25, 24] by a large margin, e.g., with improvements of 65.33%->71.08% (with generic lexicon) on ICDAR 2015, and 54.0%->69.23% on Total-Text, on end-to-end text recognition. Code is available at: https://github.com/MalongTech/research-charnet.
Universal Text Representation from BERT: An Empirical Study

We present a systematic investigation of layer-wise BERT activations for general-purpose text representations to understand what linguistic information they capture and how transferable they are across different tasks. Sentence-level embeddings are evaluated against two state-of-the-art models on downstream and probing tasks from SentEval, while passage-level embeddings are evaluated on four question-answering (QA) datasets under a learning-to-rank problem setting. Embeddings from the pre-trained BERT model perform poorly in semantic similarity and sentence surface information probing tasks. Fine-tuning BERT on natural language inference data greatly improves the quality of the embeddings. Combining embeddings from different BERT layers can further boost performance. BERT embeddings outperform BM25 baseline significantly on factoid QA datasets at the passage level, but fail to perform better than BM25 on non-factoid datasets. For all QA datasets, there is a gap between embedding-based method and in-domain fine-tuned BERT (we report new state-of-the-art results on two datasets), which suggests deep interactions between question and answer pairs are critical for those hard tasks.
Exploiting locality and physical invariants to design effective Deep Reinforcement Learning control of the unstable falling liquid film.

Instabilities arise in a number of flow configurations. One such manifestation is the development of interfacial waves in multiphase flows, such as those observed in the falling liquid film problem. Controlling the development of such instabilities is a problem of both academic and industrial interest. However, this has proven challenging in most cases due to the strong nonlinearity and high dimensionality of the underlying equations. In the present work, we successfully apply Deep Reinforcement Learning (DRL) for the control of the one-dimensional (1D) depth-integrated falling liquid film. In addition, we introduce for the first time translational invariance in the architecture of the DRL agent, and we exploit locality of the control problem to define a dense reward function. This allows to both speed up learning considerably, and to easily control an arbitrary large number of jets and overcome the curse of dimensionality on the control output size that would take place using a naive approach. This illustrates the importance of the architecture of the agent for successful DRL control, and we believe this will be an important element in the effective application of DRL to large two-dimensional (2D) or three-dimensional (3D) systems featuring translational, axisymmetric or other invariants.
Modeling Spinal Muscular Atrophy in Mouse: A Disease of Splicing, Stability, and Timing

Proximal spinal muscular atrophy (SMA) is a progressive neurodegenerative disease associated with the loss of alpha motor neurons in the lumbar spinal cord. The loss of these motor neurons leads to the progressive atrophy of the associated proximal muscles, eventual respiratory distress, and death. SMA occurs in about 1 in 6,000 to 10,000 live births and is a leading genetic cause of infant mortality (Burnett et al. 2009). SMA is subdivided into several groupings based on disease onset, severity, and outcome. The most severe form of SMA is referred to as type 0, or embryonic SMA, wherein patients are born with severe muscle atrophy and have expected lifespans of less than 6 months. The most common type of SMA is type I, which is characterized by disease onset within 6 months and mortality by age 2. Children with SMA type I often fail to sit unaided or walk, have difficulty breathing, and often require respiratory assistance. SMA type II children experience disease onset between 6 to 18 months and will not gain the ability to walk. Two forms of SMA are characterized by disease onset later in life and have unaltered life expectancies: juvenile and adult onset SMA, or type III and IV, respectively. Juvenile onset SMA type III is distinguished by disease onset after the age of 2, but there is variability of onset that can extend to early teen years. Type III children are often able to walk, and the ability to remain ambulatory throughout life can further subdivide this juvenile onset SMA. The least severe form of SMA is adult onset SMA type IV. This form of SMA does not emerge until the adult years and often presents with difficulty in performing previously attainable activities, such as climbing stairs (Zerres et al. 1995; Zerres et al. 1997; Zerres et al. 1997). In SMA disease progression, the inability to achieve normal motor function is often the primary indication of disease. The level of motor function decline in SMA disease progression can be measured by the loss of functional motor units in SMA patients. In severe forms of SMA, there is a rapid loss of motor neurons innervating proximal muscles. This measure of reduced motor units can be observed by evaluating the Compound Motor Action Potential (CMAP) and Motor Unit Number Estimation (MUNE). The total number of innervated motor units is reduced in SMA disease as measured by these two tests and is correlated with age, severity of disease, and disease progression (Swoboda et al. 2005). The loss of motor neurons early in the onset of disease argues for the significance of motor neurons in disease, although additional components of the disease may provide for the
Chapter 8 Body Awareness in Prosthetic Hands

Cybernetics is a field developing at a fast pace aiming to improve the lives of innumerable people around the globe. In the not-so-distant future, artificial limbs will accurately emulate their biological counterparts. An amputee will have complete control over the movement of his or her artificial limb, allowing him or her to manipulate, reach, and even type on his or her computer. The sensors installed in the artificial hand will transmit relevant information from the environment to his or her body, allowing him or her to feel the smoothness of a cup, the warmth from a steaming coffee, or even the sharp pain of a needle. This bidirectional interaction with the artificial limb will allow the person to be aware of it (body awareness); in other