Miracle-dz/UniFAR-FTUs huggingface dataset:
Four-Arm Collaboration: Two Dual-Arm Robots Work Together to Maneuver Tethered Tools

In this paper, we present a planner for a master dual-arm robot to manipulate tethered tools with an assistant dual-arm robot's help. The assistant robot provides assistance to the master robot by manipulating the tool cable and avoiding collisions. The provided assistance allows the master robot to perform tool placements on the robot workspace table to regrasp the tool, which would typically fail since the tool cable tension may change the tool positions. It also allows the master robot to perform tool handovers, which would normally cause entanglements or collisions with the cable and the environment without the assistance. Simulations and real-world experiments are performed to validate the proposed planner.
Role of Characteristics of External Mechanical Stress and Substrate on the Characteristics of Insulin Aggregates

The role of chemical nature of the surface and mechanical stress on the properties of insulin in solution kept in the container is explored. The mechanical stress can be applied in the form of shear force or shaking of content in vials. The process of shear can be continuous or intermittent periodic stoppage of shear. We have observed the secondary structures of insulin present over the surface and in the solution. In addition, we have observed the distribution of insulin size, which arises due to their aggregation in solution. The properties are found to depend on the processes of applying mechanical force on a solution. The conversions of α-helix to β-sheet for continuous shear, but to intermolecular β-sheet in presence of the interrupted shear are found. The later phenomenon leads to the formation of a bigger particle. The shaking of the content of vials leads to the formation of particles with the higher random coil. The combined effect of shaking and chemical nature of surface on the aggregates’ properties is also observed. The size distribution and secondary structures of aggregates of insulin in solution are strongly dependent on the chemical nature of the surface. These are explained through desorption of the adsorbed protein. The higher rate desorption of protein from lesser hydrophobic surfaces leads to the formation of bigger insulin aggregates.
Joint Spatio-Textual Reasoning for Answering Tourism Questions

Our goal is to answer real-world tourism questions that seek Points-of-Interest (POI) recommendations. Such questions express various kinds of spatial and non-spatial constraints, necessitating a combination of textual and spatial reasoning. In response, we develop the first joint spatio-textual reasoning model, which combines geo-spatial knowledge with information in textual corpora to answer questions. We first develop a modular spatial-reasoning network that uses geo-coordinates of location names mentioned in a question, and of candidate answer POIs, to reason over only spatial constraints. We then combine our spatial-reasoner with a textual reasoner in a joint model and present experiments on a real world POI recommendation task. We report substantial improvements over existing models without joint spatio-textual reasoning. To the best of our knowledge, we are the first to develop a joint QA model that combines reasoning over external geo-spatial knowledge along with textual reasoning.
Automating Graphic Design

Current graphic design processes entail using generalized styles and themes with human-made decisions of content and style interplay. The task to interplaying content and style representations has been attempted using convolutional neural networks [5]. CNNs are trained on images and used to learn input representations. The network ﬁnds representations based on details that it learned to focus on during training. In this paper, we explore the use of a parallel-branch CNN autoencoded architecture to learn input representations and blend an output image. The unsupervised blending of features is an interesting approach to this task, removing explicit bias towards either content or style. Our process seeks to automate the poster-making process of graphic design. Previous work attempting similar interplaying of content and style has not proven successful in recognizing textual bodies. Most of the previous work implementations are trained on objects and thus, text appears to be generalized as an object body, indistinguishable from other content. To successfully automate graphic design, the style must be applied to the textual image, all while preserving content details. Our network is able to recognize content as words, however, it is unable to distinguish between diﬀerent words. We believe the cause of this is the inability of using autoencoding to capture high-level abstract features.
A New Record of Genus Craterellus, Edible Basidiomycotous Fungus from Pakistan

INTRODUCTION The Cantharellaceae is an important family of ectomycorrhizal fungi, and popularly known as chanterelles. Several species (Cantharellus cibarius, Craterellus tubaeformis and Hydnum repandum) in the family are highly prized mushrooms. They have important nutrition value due to high level of proteins, lipids, minerals, vitamins, and neutraceuticals [1]. The chanterelle hymenium can be smooth, wrinkled, veined, or ridged, but never forms bladelike gills. Most chanterelles have spore bearing ridges that typically extend from the edge of the cap well down the tapered stipe. In a recent classification, the genera Craterellus along with Cantharellus, Hydnum, Clavulina, Multiclavula, Sistotrema and Membranomyces form one clade that is characterized by stichic basidia [2], variable septal pore morphology and ectomycorrhizal mode of nutrition [3].
Deep ReLU Neural Network Expression Rates for Data-to-QoI Maps in Bayesian PDE Inversion

For Bayesian inverse problems with input-to-response maps given by well-posed partial diﬀerential equations (PDEs) and subject to uncertain parametric or function space input, we establish (under rather weak conditions on the “forward”, input-to-response maps) the parametric holomorphy of the data-to-QoI map relating observation data δ to the Bayesian estimate for an unknown quantity of interest (QoI). We prove exponential expression rate bounds for this data-to-QoI map by deep neural networks with rectiﬁed linear unit (ReLU) activation function, which are uniform with respect to the data δ taking values in a compact subset of R K . Similar convergence rates are veriﬁed for polynomial and rational approximations of the data-to-QoI map.
Integrating the voluntary sector in personalised care: mixed methods study of the outcomes from wellbeing co-ordination for adults with complex needs

PurposeThis integrated care study seeks to highlight how voluntary sector “wellbeing co-ordinators” co-located in a horizontally and vertically integrated, multidisciplinary community hub within one locality of an Integrated Care Organisation contribute to complex, person-centred, co-ordinated care.Design/methodology/approachThis is a naturalistic, mixed method and mixed data study. It is complementing a before-and-after study with a sub-group analysis of people receiving input from the wider hub (including Wellbeing Co-ordination and Enhanced Intermediate Care), qualitative case studies, interviews, and observations co-produced with embedded researchers-in-residence.FindingsThe cross-case analysis uses trajectories and outcome patterns across six client groups to illustrate the bio-psycho-social complexity of each group across the life course, corresponding with the range of inputs offered by the hub.Research limitations/implicationsTo consider the effectiveness and mechanisms of complex system-wide interventions operating at horizontal and vertical interfaces and researching this applying co-produced, embedded, naturalistic and mixed methods approaches.Practical implicationsHow a bio-psycho-social approach by a wellbeing co-ordinator can contribute to improved person reported outcomes from a range of preventive, rehabilitation, palliative care and bereavement services in the community.Social implicationsTo combine knowledge about individuals held in the community to align the respective inputs, and expectations about outcomes while considering networked pathways based on functional status, above diagnostic pathways, and along a life-continuum.Originality/valueThe hub as a whole seems to (1) Enhance engagement through relationship, trust and activation, (2) Exchanging knowledge to co-create a shared bio-psycho-social understanding of each individual’s situation and goals, (3) Personalising care planning by utilising the range of available resources to ensure needs are met, and (4) Enhancing co-ordination and ongoing care through multi-disciplinary working between practitioners, across teams and sectors.
PARAMETER IDENTIFICATION OF A SPACE OBJECT IN THE PRE-CAPTURE PHASE

A method for identifying the full set of inertial parameters of a space object on orbit is presented, which is applicable at the pre-capture phase. The method’s objective is to reduce the risk during the capture phase, the most critical of a mission. Using data from visual and force sensors, the object’s center of mass, mass and moments of inertia are estimated. No information about accelerations, which contain substantial noise, is required. The proposed method is validated by a numerical simulation and followed by the experimental identification of a floating passive object, part of the NTUA Space Emulator System.
GUI-Guided Test Script Repair for Mobile Apps

Graphical User Interface (GUI) testing is widely used to test mobile apps. As mobile apps are frequently updated and need repeated testing, to reduce the test cost, their test cases are often coded as scripts to enable automated execution using test harnesses/tools. When those mobile apps evolve, many of the test scripts, however, may become broken due to changes made to the app GUIs. While it is desirable that the broken scripts get repaired, doing it manually can be preventively expensive if the number of tests need repairing is large. We propose in this paper a novel approach named Meter to repairing broken GUI test scripts automatically when mobile apps evolve. Meter leverages computer vision techniques to infer GUI changes between two versions of a mobile app and uses the inferred changes to guide the repair of GUI test scripts. Since Meter only relies on screenshots to repair GUI tests, it is applicable to apps targeting open or closed source mobile platforms. In experiments conducted on 22 Android apps and 6 iOS apps, repairs produced by Meter helped preserve 63.7 and 38.8 percent of all the test actions broken by the GUI changes, respectively.
Study of Stavudine Multiparticulate Floating Drug Delivery System Prepared by Emulsion Gelation Technique

Gastroretentive systems can remain in the gastric region for several hours and hence significantly prolong the gastric residence time of drugs. Prolonged gastric retention improves bioavailability, reduces drug waste, and improves solubility for drugs that are less soluble in a high pH environment. The present work describes the formulation and evaluation of gastroretentive system of an antiretroviral agent, Stavudine, based on the concept of altered density. Emulsion gelation technique was used to prepare the floating microcarriers using sodium alginate as the polymer. Microcarriers containing oil was prepared by gently mixing and homogenizing oil and water phase containing sodium alginate which was then extruded into calcium chloride solution. The prepared microcarriers were evaluated for drug entrapment efficiency, particle size and shape, micrometric properties, buoyancy and in-vitro drug release studies. The results of FTIR spectroscopy showed stable character of Stavudine. The mean particle size of microcarriers was in the range of 0.59-1.25mm. Microcarriers were spherical and free flowing. The drug entrapment efficiency was found to be 44.6-69.1%. The microcarriers remained buoyant for more than about 12h. The drug release study showed that Stavudine from the microcarriers was prolonged more than 10hrs. The results demonstrate that the amount of the oil entrapped in each microcarrier is play role in particle size entrapment efficiency and in vitro drug release.
Robust Digital Image Watermarking based on Hybrid DWT and GWO Optimization Technique

This paper proposes a novel optimization method for digital images in the Wavelet Transform (DWT) domain. Digital image watermarking has demonstrated its proficiency in ensuring illegal confirmation of data. The visibility factor of the watermark is the huge parameter that helps in enhancing the perceptual transparency and robustness of watermarking system. The trade-off between the transparency and robustness is considered as an optimization problem and is explained by applying GWO Optimization technique. The Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), and computational time are evaluated for an arrangement of images using the MATLAB R2014b software
Improving aerosol processes and radiative forcing in preparation for UKESM1

UKESM1 is developed on top of the coupled physical model, HadGEM3-GC3 (hereafter GC3). GC3 consists of the Unified Model (UM) atmosphere, JULES land surface scheme, NEMO ocean model and the CICE sea ice model. The UM atmosphere in GC3 is Global Atmosphere version 7 (GA7). Inclusion in GA7 of both a new cloud microphysics parameterization and the new GLOMAP aerosol scheme led to a concern the model might exhibit a strong negative historical aerosol radiative forcing (i.e. a strong aerosol-induced cooling due to increasing anthropogenic emission of aerosol and aerosol precursors over the past ~150 years) with potentially detrimental impacts on the overall historical simulation of both GC3 and UKESM1.
Risk in Time: The Intertwined Nature of Risk Taking and Time Discounting

Standard economic models view risk taking and time discounting as two independent dimensions of decision making. However, mounting experimental evidence demonstrates striking parallels in patterns of risk taking and time discounting behavior and systematic interaction effects, which suggests that there may be common underlying forces driving these interactions. Here we show that the inherent uncertainty associated with future prospects together with individuals’ proneness to probability weighting generates a unifying framework for explaining a large number of puzzling behavioral findings: delay-dependent risk tolerance, aversion to sequential resolution of uncertainty, preferences for the timing of the resolution of uncertainty, the differential discounting of risky and certain outcomes, hyperbolic discounting, subadditive discounting, and the order dependence of prospect valuation. Furthermore, all these phenomena can be accommodated by the same set of preference parameter values and plausible levels of inherent uncertainty.
Antidepressant Activity of the Methanol Stem-Bark Extract of Ficus Vallis Choudae (Moraceae) in Swiss Albino Mice

Although remarkable advances have been made in the detection and management of mood disorders over the last few decades, the increasing worldwide prevalence of depression continues to challenge researchers and clinicians alike. In spite of the availability of antidepressant drugs depression continue to be a major medical problem. Although there are many effective antidepressants available today, the current therapy is often inadequate with unsatisfactory results in about one third of all subjects treated. Various plants are being used in complementary and alternative medicines for management of mood disorders. The methanol stem-bark extract of Ficus vallis was investigated for antidepressant activity using the Forced Swim Test and Tail Suspension Test models. The locomotor activity was also evaluated using the Open Field Test so as to rule out possible psyco-stimulant activity. It was observed from our study that Ficus vallis methanol extract showed significant (p < 0.05) reduction in immobility at 200 and 400 mg/kg doses in the Tail Suspension Test. While in the Forced Swim Test there was a significant (p < 0.02) with the 400 mg/kg dose when compared with normal saline. The methanol stem-bark extract of Ficus vallis possesses antidepressant properties in mice with significant decrease in duration of immobility.
Hemophagocytic Lymphohistiocytosis: About 8 Cases

DOI: 10.21276/sjmps.2018. 4.3. 1 Abstract: Hemophagocytic lymphohistiocytosis (HLH) is a clinical-biological entity characterized by a hyperinflammatory state following the deregulation of the cytotoxic immune response, causing histiocytic proliferation with significant hemophagocytic activity in the bone marrow as well as the massive release of inflammatory cytokines. The diagnosis is established by the association of nonspecific clinical and biological signs. The aim of this study was to describe the clinical and laboratory presentation to determine the underlying pathologies and clarify the evolution and the prognosis of adult patients with HLH in our healthcare setting. A retrospective study of patients with HLH was conducted in the Military Teaching Hospital, Rabat, between January 2015 and January 2017. We were able to identify eight patients. Six were men, and the average age at diagnosis was 58 years. Infectious agents were responsible in 3 cases, malignant lymphoma in 3 others, one case of systemic lupus erythematosus, and in the last case etiology remains unknown. Clinical characteristics such as fever (100%), splenomegaly (62,5%), and lymphadenopathy (37,5%) were observed. Laboratory values presented with panytopenia (62, 5%), thrombopenia and anemia (100%), increased ferritin (100%), lactate dehydrogenase (87, 5%), triglycerides (62, 5%), and decreased fibrinogen (50%). Bone marrow showed hemophagocytis in all cases. The prognosis was poor. Five patients died. Absence of specific signs makes early diagnosis difficult. The prognosis varies according to the series but remains very unfavorable. Studies are needed to assess prevalence and develop a severity score and therapeutic recommendations to improve HLH prognosis.
Shot Detection in Screencasts of Web Browsing with Convolutional Neural Networks

Behavior analysis of users on a graphical interface, which can change its appearance by interactions or background scripts, is used to evaluate the interface and to be a help in its future designing process. The challenge is to turn data of interactions with an interface into comprehensible and useful information. In this work, we take a closer look into screen recordings of interactions with websites and how visual changes in the interfaces can be detected. Detecting visual change is a key part of the aggregation of interaction data of several users onto a common space for later analysis. For this, we observe how visual change gets detected in traditional videos and whether the same methods can ﬁnd a use for recordings of interaction with websites. We ﬁnd that, while artiﬁcial neural networks tasked with the detection of visual change get used with great success for videos recorded with cameras, there exists no such work for recordings of interactions of websites yet. Therefore, in this bachelor thesis, we use a convolutional neural network to detect visual changes in recordings of interactions of websites and compare our results with other state-of-the-art procedures. We ﬁnd out that in some cases we can reach comparable or even better results as state-of-the-art approaches but most of the time our approach does not offer a reasonable alternative.
A GAN framework for Instance Segmentation using the Mutex Watershed Algorithm

Modern deep learning approaches for image segmentation without semantics often utilize pixelwise loss functions for boundary prediction, especially when dense groundtruth is available. However, these approaches may not be sensitive to structural similarity (e.g. tiny holes), that can severely affect postprocessing. This work explores the possibility to augment the training with a GAN loss function and in conjunction with the Mutex Watershed graph clustering algorithm.We show that this method and additional auxiliary task losses improve the quality of the Adjusted Rand Score over the score reported in [1]. Additionally, we present an ablation study to show that when the images in the target domain are constrained to be discrete, adding an auxiliary task with smooth target images signiﬁcantly improves the training performance/stability. We also use the discriminator to train on unlabeled images which further improves our results.
Face Veriﬁcation using Convolutional Neural Networks

You will train your model on a dataset with a few thousand images of labelled ID’s (i.e., a set of images, each labeled by an ID that uniquely identifies the person). You will learn more about embeddings (in this case, embeddings for face information), several loss functions, and, of course, convolutional layers as effective shift-invariant feature extractors. You will also develop skills necessary for processing and training neural networks with big data, which is often the scale at which deep neural networks demonstrate excellent performance in practice.
BIOACTIVE POLY(BETA-AMINO ESTER) BIOMATERIALS FOR TREATMENT OF INFECTION AND OXIDATIVE STRESS

OF DISSERTATION BIOACTIVE POLY(β-AMINO ESTER) BIOMATERIALS FOR TREATMENT OF INFECTION AND OXIDATIVE STRESS Polymers have deep roots as drug delivery tools, and are widely used in clinical to private settings. Currently, however, numerous traditional therapies exist which may be improved through use of polymeric biomaterials. Through our work with infectious and oxidative stress disease prevention and treatment, we aimed to develop application driven, enhanced therapies utilizing new classes of polymers synthesized in-house. Applying biodegradable poly(β-amino ester) (PBAE) polymers, covalent-addition of bioactive substrates to these PBAEs avoided certain pitfalls of free-loaded and non-degradable drug delivery systems. Further, through variation of polymer ingredients and conditions, we were able to tune degradation rates, release profiles, cellular toxicity, and material morphology. Using these fundamentals of covalent drug-addition into biodegradable polymers, we addressed two problems that exist with the treatment of patients with high-risk woundsites, namely non-biodegradability that require second-surgeries, and free-loaded antibiotic systems where partially degraded materials fall below the minimum inhibitory concentration, allowing biofilm proliferation. Our in situ polymerizable, covalently-bound vancomycin hydrogel provided active antibiotic degradation products and drug release which closely followed the degradation rate over tunable periods. With applications of antioxidant delivery, we continued with this concept of covalent drug addition and modified a PBAE, utilizing a disulfide moiety to mimic redox processes which glutathione/glutathione disulfide performs. This material was found to not only be hydrolytically biodegradable, but tunable in reducibility through cleavage of the disulfide crosslinker, forming antioxidant groups of bound-thiols, similar to drugs currently used in radioprotective therapies. The differential cellular viability of degradation products containing disulfide or antioxidant thiol forms was profound, and the antioxidant form significantly aided cellular resistance to a superoxide attack, similar to that of a radiation injury. Pathophysiological oxidation in the form of radiation injury or oxidative stress based diseases are often region specific to the body and thus require specific targeting, and nanomaterials are widely researched to perform this. Utilizing a tertiary-amine basecatalyst, we were able to synthesize a high drug content (20-26 wt%) version of the disulfide PBAE previously unattainable. The reduced version of this material created a linear-chain polymer capable of single-emulsion nanoparticle formulation for use with intravenous antioxidant delivery applications instead of local.