Miracle-dz/UniFAR-FTUs huggingface dataset:
Helix: Holistic Optimization for Accelerating Iterative Machine Learning

Machine learning workflow development is a process of trial-and-error: developers iterate on workflows by testing out small modifications until the desired accuracy is achieved. Unfortunately, existing machine learning systems focus narrowly on model training---a small fraction of the overall development time---and neglect to address iterative development. We propose H elix , a machine learning system that optimizes the execution across iterations ---intelligently caching and reusing, or recomputing intermediates as appropriate. H elix captures a wide variety of application needs within its Scala DSL, with succinct syntax defining unified processes for data preprocessing, model specification, and learning. We demonstrate that the reuse problem can be cast as a M ax -F low problem, while the caching problem is NP-H ard . We develop effective lightweight heuristics for the latter. Empirical evaluation shows that H elix is not only able to handle a wide variety of use cases in one unified workflow but also much faster, providing run time reductions of up to 19x over state-of-the-art systems, such as DeepDive or KeystoneML, on four real-world applications in natural language processing, computer vision, social and natural sciences.
Action Machine: Rethinking Action Recognition in Trimmed Videos

Existing methods in video action recognition mostly do not distinguish human body from the environment and easily overfit the scenes and objects. In this work, we present a conceptually simple, general and high-performance framework for action recognition in trimmed videos, aiming at person-centric modeling. The method, called Action Machine, takes as inputs the videos cropped by person bounding boxes. It extends the Inflated 3D ConvNet (I3D) by adding a branch for human pose estimation and a 2D CNN for pose-based action recognition, being fast to train and test. Action Machine can benefit from the multi-task training of action recognition and pose estimation, the fusion of predictions from RGB images and poses. On NTU RGB-D, Action Machine achieves the state-of-the-art performance with top-1 accuracies of 97.2% and 94.3% on cross-view and cross-subject respectively. Action Machine also achieves competitive performance on another three smaller action recognition datasets: Northwestern UCLA Multiview Action3D, MSR Daily Activity3D and UTD-MHAD. Code will be made available.
Stochastic Composite Mirror Descent: Optimal Bounds with High Probabilities

We study stochastic composite mirror descent, a class of scalable algorithms able to exploit the geometry and composite structure of a problem. We consider both convex and strongly convex objectives with non-smooth loss functions, for each of which we establish high-probability convergence rates optimal up to a logarithmic factor. We apply the derived computational error bounds to study the generalization performance of multi-pass stochastic gradient descent (SGD) in a non-parametric setting. Our high-probability generalization bounds enjoy a logarithmical dependency on the number of passes provided that the step size sequence is square-summable, which improves the existing bounds in expectation with a polynomial dependency and therefore gives a strong justification on the ability of multi-pass SGD to overcome overfitting. Our analysis removes boundedness assumptions on subgradients often imposed in the literature. Numerical results are reported to support our theoretical findings.
Simulation and inference algorithms for stochastic biochemical reaction networks: from basic concepts to state-of-the-art

Stochasticity is a key characteristic of intracellular processes such as gene regulation and chemical signalling. Therefore, characterizing stochastic effects in biochemical systems is essential to understand the complex dynamics of living things. Mathematical idealizations of biochemically reacting systems must be able to capture stochastic phenomena. While robust theory exists to describe such stochastic models, the computational challenges in exploring these models can be a significant burden in practice since realistic models are analytically intractable. Determining the expected behaviour and variability of a stochastic biochemical reaction network requires many probabilistic simulations of its evolution. Using a biochemical reaction network model to assist in the interpretation of time-course data from a biological experiment is an even greater challenge due to the intractability of the likelihood function for determining observation probabilities. These computational challenges have been subjects of active research for over four decades. In this review, we present an accessible discussion of the major historical developments and state-of-the-art computational techniques relevant to simulation and inference problems for stochastic biochemical reaction network models. Detailed algorithms for particularly important methods are described and complemented with Matlab® implementations. As a result, this review provides a practical and accessible introduction to computational methods for stochastic models within the life sciences community.
Selection of CEA and VEGFR 2 Binding Affibody ® Molecules Using Phage Display

Tumours often overexpress surface molecules which can be used as targets for affinity ligands in molecular imaging or targeted therapy. Affibody ® molecules are targeting proteins, that can be designed to bind specifically to almost any target. The aim of this study was to isolate Affibody ® molecules, through phage display selections, that bind to specific targets, carcinoembryonic antigen (CEA) and vascular endothelial growth factor receptor 2 (VEGFR2), respectively. The possibility to favour alternative epitopes, by blocking VEGFR2 with Affibody ® molecules derived from an earlier selection, was also investigated. A number of new candidate binders were identified and it was shown that it is possible to favour the selection of alternative Affibody ® molecules through blockage of the target protein.
The Projective Consciousness Model and Phenomenal Selfhood

We summarize our recently introduced Projective Consciousness Model (PCM) (Rudrauf et al., 2017) and relate it to outstanding conceptual issues in the theory of consciousness. The PCM combines a projective geometrical model of the perspectival phenomenological structure of the field of consciousness with a variational Free Energy minimization model of active inference, yielding an account of the cybernetic function of consciousness, viz., the modulation of the field’s cognitive and affective dynamics for the effective control of embodied agents. The geometrical and active inference components are linked via the concept of projective transformation, which is crucial to understanding how conscious organisms integrate perception, emotion, memory, reasoning, and perspectival imagination in order to control behavior, enhance resilience, and optimize preference satisfaction. The PCM makes substantive empirical predictions and fits well into a (neuro)computationalist framework. It also helps us to account for aspects of subjective character that are sometimes ignored or conflated: pre-reflective self-consciousness, the first-person point of view, the sense of minenness or ownership, and social self-consciousness. We argue that the PCM, though still in development, offers us the most complete theory to date of what Thomas Metzinger has called “phenomenal selfhood. ”
Characterizing the Global Crowd Workforce: A Cross-Country Comparison of Crowdworker Demographics

Since its emergence roughly a decade ago, micro-task crowdsourcing has been attracting a heterogeneous set of workers from all over the globe. This paper sets out to explore the characteristics of the international crowd workforce to date and offers a cross-national comparison of crowdworker populations from ten hand-selected countries. We provide an analysis and comparison of demographic characteristics and shed light on the significance of micro-task income for workers situated in different national contexts. With over 11,000 individual responses, this study is the first large-scale country-level analysis of the characteristics of workers on the platform Figure Eight (formerly CrowdFlower), one of the two platforms dominating the micro-task market. We find large differences between the characteristics of the crowd workforces of different countries, both regarding demography and regarding the importance of micro-task income for workers. Furthermore, we find that the composition of the workforce in the ten countries was largely stable across samples taken at different points in time.
Query Generation for Patent Retrieval with Keyword Extraction Based on Syntactic Features

This paper describes a new method to extract relevant keywords from patent claims, as part of the task of retrieving other patents with similar claims (search for prior art). The method combines a qualitative analysis of the writing style of the claims with NLP methods to parse text, in order to represent a legal text as a specialization arborescence of terms. In this setting, the set of extracted keywords are yielding better search results than keywords extracted with traditional method such as tf-idf. The performance is measured on the search results of a query made of the keywords.
Myosin Heavy Chain 10 (MYH10) Gene Silencing Reduces Cell Migration and Invasion in the Glioma Cell Lines U251, T98G, and SHG44 by Inhibiting the Wnt/β-Catenin Pathway

Background The myosin heavy chain 10 or MYH10 gene encodes non-muscle myosin II B (NM IIB), and is involved in tumor cell migration, invasion, extracellular matrix (ECM) production, and epithelial-mesenchymal transition (EMT). This study aimed to investigate the effects of the MYH10 gene on normal human glial cells and glioma cell lines in vitro, by gene silencing, and to determine the signaling pathways involved. Material/Methods The normal human glial cell line HEB, and the glioma cell lines, U251, T98G, and SHG44 were studied. Plasmid transfection silenced the MYH10 gene. The cell counting kit-8 (CCK-8) assay evaluated cell viability. Cell migration and invasion were evaluated using scratch and transwell assays. Western blot measured the protein expression levels, and quantitative real-time polymerase chain reaction (qRT-PCR) was used to detect the mRNA expression levels, for MYH10, metastasis-associated protein 1 (MTA-1), matrix metalloproteinase (MMP)-1, MMP-9, tissue inhibitor of metalloproteinases 2 (TIMP2), collagen 1, E-cadherin, vimentin, Wnt3a, β-catenin, and cyclin D1. Results The MYH10 gene was overexpressed in U251, T98G, and SHG44 cells. MYH10 expression was down-regulated following siMYH10 plasmid interference, which also inhibited glioma cell migration and invasion. MYH10 gene silencing resulted in reduced expression of MTA-1, MPP-2, MMP-9 and vimentin, and increased expression of TIMP-2, E-cadherin and collagen 1 at the protein and mRNA level, and inhibited the Wnt/β-catenin pathway. Conclusions In human glioma cell lines, silencing the MYH10 gene reduced cell migration and invasion, by inhibiting the Wnt/β-catenin pathway, which may regulate the ECM and inhibit EMT in human glioma.
A Study on Optimal Beam Patterns for Single User Massive MIMO Transmissions

This paper proposes optimal beam patterns of analog beamforming for SU (Single User) massive MIMO (Multi-Input Multi-Output) transmission systems. For hybrid beamforming in SU massive MIMO systems, there are several design parameters such as beam patterns, the number of beams (streams), the shape of array antennas, and so on. In conventional hybrid beamforming, rectangular patch array antennas implemented on a planar surface with linear phase shift beam patterns have been used widely. However, it remains unclear whether existing configurations are optimal or not. Therefore, we propose a method using OBPB (Optimal Beam Projection Beamforming) for designing configuration parameters of the hybrid beamforming. By using the method, the optimal beam patterns are derived first, and are projected on the assumed surface to calculate the achievable number of streams and the resulting channel capacity. The results indicate OBPB with a spherical surface yields at least 3.5 times higher channel capacity than conventional configurations.
25 ROUTE GENERATION FOR A SYNTHETIC CHARACTER ( BOT ) USING A PARTIAL OR INCOMPLETE KNOWLEDGE ROUTE GENERATION ALGORITHM IN UT 2004

This paper presents a new Route Generation Algorithm that accurately and realistically represents human route planning and navigation for Military Operations in Urban Terrain (MOUT). The accuracy of this algorithm in representing human behavior is measured using the Unreal TournamenFM 2004 (UT2004) Game Engine to provide the simulation environment in which the differences between the routes taken by the human player and those of a Synthetic Agent (BOT) executing the A-star algorithm and the new Route Generation Algorithm can be compared. The new Route Generation Algorithm computes the BOT route based on partial or incomplete knowledge received from the UT2004 game engine during game play. To allow BOT navigation to occur continuously throughout the game play with incomplete knowledge of the terrain, a spatial network model of the UT2004 MOUT terrain is captured and stored in an Oracle 11 9 Spatial Data Object (SOO). The SOO allows a partial data query to be executed to generate continuous route updates based on the terrain knowledge, and stored dynamic BOT, Player and environmental parameters returned by the query. The partial data query permits the dynamic adjustment of the planned routes by the Route Generation Algorithm based on the current state of the environment during a simulation. The dynamic nature of this algorithm more accurately allows the BOT to mimic the routes taken by the human executing under the same conditions thereby improving the realism of the BOT in a MOUT simulation environment.
A point-of-care diagnostic for differentiating Ebola from endemic febrile diseases

A multiplexed point-of-care diagnostic using surface-enhanced Raman scattering detects Ebola, Lassa, and malaria in blood and serum samples. Discerning febrile diseases Many infectious diseases present with common clinical symptoms, such as fever, which complicates diagnosis at the point of need. Sebba and colleagues developed an assay using surface-enhanced Raman scattering (SERS) nanotags to distinguish Ebola virus infections from Lassa and malaria. The no-wash triplex assay workflow adds a small volume of blood and buffer to dried SERS reagents and delivers a readout within 30 min. The assay detected parasite- and virus-specific antigens spiked into the blood, Ebola infections in nonhuman primates, and Ebola and malaria infections in human blood samples collected from endemic regions during field testing. Although further testing is required, this assay could be useful during febrile disease outbreaks. Hemorrhagic fever outbreaks such as Ebola are difficult to detect and control because of the lack of low-cost, easily deployable diagnostics and because initial clinical symptoms mimic other endemic diseases such as malaria. Current molecular diagnostic methods such as polymerase chain reaction require trained personnel and laboratory infrastructure, hindering diagnostics at the point of need. Although rapid tests such as lateral flow can be broadly deployed, they are typically not well-suited for differentiating among multiple diseases presenting with similar symptoms. Early detection and control of Ebola outbreaks require simple, easy-to-use assays that can detect and differentiate infection with Ebola virus from other more common febrile diseases. Here, we developed and tested an immunoassay technology that uses surface-enhanced Raman scattering (SERS) tags to simultaneously detect antigens from Ebola, Lassa, and malaria within a single blood sample. Results are provided in <30 min for individual or batched samples. Using 190 clinical samples collected from the 2014 West African Ebola outbreak, along with 163 malaria positives and 233 negative controls, we demonstrated Ebola detection with 90.0% sensitivity and 97.9% specificity and malaria detection with 100.0% sensitivity and 99.6% specificity. These results, along with corresponding live virus and nonhuman primate testing of an Ebola, Lassa, and malaria 3-plex assay, indicate the potential of the SERS technology as an important tool for outbreak detection and clinical triage in low-resource settings.
Freund's Complete Adjuvant (FCA) Could Reverse the Depressive-Like Symptoms Induced by Chronic Mild Stress (CMS) in Mice

Background: The objective of this work was a comparative study of two animal models in female mice: Chronic Mild Stress (CMS) and Chronic Mild Stress combined with chronic inflammation with complete Freund’s adjuvant (CMS+CFA). Methods: Mice were exposed to CMS for 40 days as food deprivation, water deprivation, forced swimming, flashing light, isolation, wet bedding and wet litter at 4°C. The stress was applied at times that change every day, in order to minimize its predictability. A subcutaneous injection of 0.03 mg/ml of CFA was administered to CMS groups the 18th day of experimentation. Recordings of sequences of behavior in elevated plus maze, open field test and water maze test was made. After decapitation, the brain, thymus, spleen, liver, kidneys, adrenals and ovaries were weighed and we calculated their relative weights. Results: Exposure of mice to CMS caused a depraessive-like behavior revealed by behavioral tests, accompanied by a decreased gain of body weight, locomotor activity and impaired spatial memory signaled by the Morris water maze test. The injection of CFA in mice submitted to a CMS has provided a partial repair of the memory in the Morris water maze, locomotor activity, gain of body weight, weight of adrenals. Conclusion: Freund’s complete adjuvant (FCA) could reverse the depressive-like symptoms induced by chronic mild stress (CMS) in mice.
Artificial microRNAs and their applications in plant molecular biology

Artificial microRNAs (amiRNAs) are modified endogenous microRNA precursors in which the miRNA:miRNA* duplex is replaced with sequences designed to silence any desired gene. amiRNAs are used as part of new genetic transformation techniques in eukaryotes and have proven to be effective and to excel over other RNA-mediated gene silencing methods in both specificity and stability. amiRNAs can be designed to silence single or multiple genes, it is also possible to construct dimeric amiRNA precursors to silence two non-related genes simultaneously. amiRNA expression is quantitative and allows using constitutive, inducible, or tissue-specific promoters. One main application of amiRNAs is gene functional validation and to this end they have been mostly used in model plants; however, their use can be extended to any species or variety. amiRNA-mediated antiviral defense is another important application with great potential for plant molecular biology and crop improvement, but it still needs to be optimized to prevent the escape of viruses from the silencing mechanism. Furthermore, amiRNAs have propelled research in related areas allowing the development of similar tools like artificial trans-acting small interference RNAs (tasiARNs) and artificial target mimicry. In this review, some applications and advantages of amiRNAs in plant molecular biology are analyzed.
A pupillary measure of forgetting: Memory interference is revealed by pupil size changes at retrieval

Pupil dilation is a reliable measure of cognitive load, and many studies have shown this effect in relation to various memory tasks. Interference from irrelevant memory traces encoded in the past lead to difficulty retrieving recent, sought after memory traces, and increased cognitive load. This investigation showed a significant decline in performance during buildup of interference and a significant improvement when interference was resolved. Significant differences in pupil size were observed when participants retrieved information, with increased pupil dilation during interference as compared to during resolved interference. The results reveal a relationship between inhibitory memory mechanisms and changes in pupil size, and show that pupillometry is a simple but sensitive physiological measure of memory interference. (Less)
Cold-start music recommendation using a hybrid representation.

Digital music systems are a new and exciting way to dis- cover, share, and listen to new music. Their success is so great, that digital downloads are now included alongside tra- ditional record sales in many o cial music charts [10]. In the past listeners would rely on magazine, radio, and friends reviews to decide on the music they listen to and purchase. In the internet age, this style of nding music is being su- perseded by music recommender systems. The shift from listening to hard copies of music, such as CDs, to online copies like MP3s, presents the interesting new challenge of how to recommend music to a listener. In such recommender systems, a user will typically provide a track that they like as a query, often implicitly as they listen to the track. The system must then provide a list of further tracks that the user will want to listen to. Many websites exist that provide such recommender systems, and many of the systems provide very good recommendations. However, there are still scenarios that these systems struggle to han- dle, and where recommendations can be unreliable. Online music systems allow users to tag any track with a free-text description. A recommender system can then determine the similarity between tracks based on these tags, and make recommendations. However, when a track is new to the system it will have no tags. This means that the track is never recommended, and in turn, the track is very unlikely to be tagged. Turnbull et. al [11] show that social tags tend to be very sparse, and that a huge popularity bias exists. This is further con rmed by data released by Last.fm [7] as part of the million song dataset [3]: from a vocabulary of over 500000 tags, each track, on average, has only 17 tags; 46% of tracks have no tags at all. This scenario is often referred to as the cold-start prob- lem; the results of which means large volumes of music are excluded from recommendations, even if they may be an excellent recommendation. The aim of our hybrid repre- sentation is to reduce the e ects of the cold-start problem, therefore increasing the recommendation quality of the over- all system.
Leucine pools in Escherichia coli biofilm discovered by Raman imaging

In structured communities of bacteria known as biofilms, a variety of biomolecules have been shown to play a unique role as signals and/or regulators in biofilm formation. Here, we report that high levels of the amino acid leucine (leucine pool) were detected, for the first time, within microcolonies in a 30-h-old Escherichia coli biofilm by Raman imaging. Localization of leucine revealed by multifrequency Raman images indicates leucine accumulation during the early stage of the E. coli biofilm formation, which may have resulted from physiological environment-specific metabolic adaptation. We demonstrate that our label-free Raman imaging method provides a useful platform for directly identifying still unknown natural products produced in biofilms as well as for visualizing heterogeneous distributions of biofilm constituents in situ. Copyright © 2011 John Wiley & Sons, Ltd.