Miracle-dz/UniFAR-FTUs huggingface dataset:
A concise model for innovation diﬀusion combining curvature-based opinion dynamics and zealotry

: A novel dimer automaton model for innovation diﬀusion based on a simpliﬁcation of the AB model and zealot model is proposed. The model assumes that two opposing opinions are competing to be the dominant opinion among individuals in a network. Zealots are stubborn individuals whose opinion is not susceptible to inﬂuence by others. The amount of zealots required for consensus is measured experimentally in a number of diﬀerent situations. It is found that when zealots are present, this density is signiﬁcantly lower than required in the control experiment. This threshold can further be reduced by placing zealots at critical nodes in the network, or by allowing zealots to diﬀuse randomly throughout the network. Thus, this model can aid in determining where resources can be eﬃciently spent in order to convert individuals to zealots and inﬂuence the network as a whole. Experiments also show that when both opinions have zealots, the outcome depends on the total number of zealots in addition to the ratio of zealots of opposite opinion, and often results in an “undecided” outcome.
22 nd International Conference on Automated Planning and Scheduling

This demo presents DUKC R (cid:13) Optimiser – a system for max-imising cargo throughput at a bulk export port by scheduling sailing times and drafts for a set of ships. An earlier prototype of the system underwent user testing in 2010 (Kelareva 2011), and a number of improvements resulting from user feedback have been incorporated in this updated version. DUKC R (cid:13) Optimiser is the ﬁrst system for automatically scheduling ship sailing times and drafts at a bulk export port which takes into account time-varying draft restrictions that take into account live environmental conditions. The system uses the Dynamic Under-Keel Clearance (DUKC R (cid:13) ) software developed by OMC International to calculate draft restrictions. These restrictions are then converted to a contraint programming model, and solved using the G12 ﬁnite domain solver, developed by NICTA. The software is able to ﬁnd optimal schedules for realistic problem sizes, and is able to produce schedules which allow ships to carry more cargo than would be permitted by traditional constant-draft or manual scheduling approaches.
Carver: Finding Important Parameters for Storage System Tuning

Storage systems usually have many parameters that affect their behavior. Tuning those parameters can provide signiﬁcant gains in performance. Alas, both manual and automatic tuning methods struggle due to the large number of parameters and exponential number of possible conﬁgu-rations. Since previous research has shown that some parameters have greater performance impact than others, focusing on a smaller number of more important parameters can speed up auto-tuning systems because they would have a smaller state space to explore. In this paper, we propose Carver, which uses (1) a variance-based metric to quantify storage parameters’ importance, (2) Latin Hypercube Sampling to sample huge parameter spaces; and (3) a greedy but efﬁcient parameter-selection algorithm that can identify important parameters. We evaluated Carver on datasets consisting of more than 500,000 experiments on 7 ﬁle systems, under 4 representative workloads. Carver successfully iden-tiﬁed important parameters for all ﬁle systems and showed that importance varies with different workloads. We demonstrated that Carver was able to identify a near-optimal set of important parameters in our datasets. We showed Carver’s efﬁciency by testing it with a small fraction of our dataset; it was able to identify the same set of important parameters with as little as 0.4% of the whole dataset.
University of Birmingham Modelling non-alcoholic fatty liver disease in human hepatocyte-like cells

• Users may freely distribute the URL that is used to identify this publication. • Users may download and/or print one copy of the publication from the University of Birmingham research portal for the purpose of private study or non-commercial research. • User may use extracts from the document in line with the concept of ‘fair dealing’ under the Copyright, Designs and Patents Act 1988 (? ) • Users may not further distribute the material nor use it for the purposes of commercial gain.
`∞-sums and the Banach space `∞/c0

This paper is concerned with the isomorphic structure of the Banach space `∞/c0 and how it depends on combinatorial tools whose existence is consistent with but not provable from the usual axioms of ZFC. Our main global result is that it is consistent that `∞/c0 does not have an orthogonal `∞-decomposition, that is, it is not of the form `∞(X) for any Banach space X. The main local result is that it is consistent that `∞(c0(c)) does not embed isomorphically into `∞/c0, where c is the cardinality of the continuum, while `∞ and c0(c) always do embed quite canonically. This should be compared with the results of Drewnowski and Roberts that under the assumption of the continuum hypothesis `∞/c0 is isomorphic to its `∞-sum and in particular it contains an isomorphic copy of all Banach spaces of the form `∞(X) for any subspace X of `∞/c0.
Scalable Pulsed Computational Module Using Integrate and Fire Structure and Margin Propagation Algorithm

—In this paper a novel circuit is proposed for pulse-based logarithmic computation using integrate-and-ﬁre (IF) structures. The smallest unit in the module is a network of three IF units that implements a margin propagation (MP) function using integration and threshold operations inherited in the response of an IF neuron. The three units are connected together through excitatory and inhibitory inputs to impose constraints on the network ﬁring-rate. The MP function is based on the log likelihood computation in which the multiplication of the inputs is translated into a simple addition. The advantage of using integrate-and-ﬁre margin propagation (IFMP) is to implement a complex non-linear and dynamic programming functions of spike based (pulse based) computation in a modular and scalable way. In addition to scalability, the objective of the proposed module is to map algorithms into low power circuits as an attempt to implement signal processing applications on silicon. The paper shows the mechanism of IFMP circuit, dynamic characteristics, the cascaded modularity, and ﬁnally a veriﬁcation of the algorithm in analog circuit using standard 0 . 5 µm CMOS technology.
CNN based Repeated Cropping for Photo Composition Enhancement

This paper proposes a novel method for aesthetic photo recomposition using a convolutional neural network (CNN). CNN has been showing remarkable performances in various tasks, such as object detection and recognition, and we exploit its usage for photo recomposition. In our framework, CNN is used to iteratively predict cropping directions for a given photo, generating an aesthetically enhanced photo in terms of composition. Experimental results and user study show that the proposed framework can automatically crop a photo to follow speciﬁc composition guidelines, such as the rule of thirds and the salient object size.
ASIC Implementation of Various Sorting Techniques for Image Processing Applications

: The direct implementation of parallel algorithms in hardware is possible with the help of current VLSI technology. The process of arranging the items systematically is known as Sorting. Different meanings of sorting are: ordering: items arrangement in a sequence ordered by using some criterion; categorizing: similar property items grouping. The latest VLSI model analyses the complexity of time. The novel model makes a distinction between “processing” circuits and “memory” circuits; the latter are less important since they are denser and consume less power. This paper addresses the design and analysis of various sorting algorithms, and its VLSI implementation based on a sorting network. The various sorting algorithms are Sinking sort, Merge sort and Library sort; all the three sorting algorithms are compared in terms of area, power and timing with a complete comparison table. Mainly these types of sorting algorithms are used in a real time system; signal processing, image and video processing applications. All the blocks were designed using Verilog HDL, simulated using ncvlog simulator, synthesized in cadence-RTL Compiler and finally implemented in ASIC Encounter using GPDK 45nm technology libraries.
Machine-to-Human Beeping: Unidirectional Communication in Industrial Environments

In this position paper, we want to present and discuss findings regarding unidirectional Machine-to-Human Communication in highly automated environments. These results are part of the research around the cooperative research project MMAssist II with the goal to research and implement modular, reusable assistance systems for employees in production companies. Most of the machines used in this context have limited ways of informing about their status: a unified beeping sound, used for different warnings or errors – and this seems to be highly annoying for operators and service engineers working with those machines. The second problem that arises is related to experienced control (i.e. workers aiming to keep in control of the processes) in this nearly automated environment. We want to tackle both topics by implementing a wearable Assistance Unit with the core functionality to provide machine data remotely to its user. Besides that, we want to add multimodal output options – from auditive signals to vibrational patterns – not only to reduce the levels of stress from to noise and beeping, but to address the problem of experienced control by offering more choices and a higher level of personalization and customizability to the user.
We agree with R3 and R4’s suggestion to expand our discussion of motivation and use-cases for determinantal point

1 processes (DPP) as a tool in machine learning (ML). In the camera ready version we will stress and clarify that DPP 2 sampling is already a well established tool in the field of ML. 3 DPP sampling has found applications in core ML problems such as stochastic optimization [20], data summarization 4 [3], Gaussian processes [2], recommender systems [11], and many more. Its effectiveness has been verified repeatedly 5 at top ML venues. Including only recent years (due to space), it has seen success at ICML’16 [14], ICML’17 [9, 12, 19], 6 ICML’18 [3] and ICML’19 [2, 7, 10, 18], UAI’17 [20], AAAI’17 [8] and AAAI’19 [21], NeurIPS’16 [13, 15, 17], 7 NeurIPS’17 [5, 16], NeurIPS’18 [1, 4, 6, 11], and even more successful applications of DPP sampling are likely to be 8 present in NeurIPS’19. 9
IMPORTANCE AND IMPEDIMENTS OF EDUCATION IN INDIA: PROPOSITION OF A MERIT-BASED AND SOCIAL-STRATIFICATION INSENSITIVE APPROACH

Social stratification is a severe impediment for education in India and the current approach with reservation quotas is not effective and even diminishes the quality of education. The complex system of social stratification in India poses a sensitive context for education and requires particular consideration. Social stratification in India exerts its effect through both economical as well as social-psychological pathways, so a possible remedy needs to consider both of these mediators and their possible interactions. To counteract the possible detrimental effects of the social stratification in India, we propose that the current reservation systems should be revised from being merely grounded on class or caste affiliation to being meritas well as income-based. Also, actions to foster social connections and the sense of belonging should be considered as a feasible and important measure to overcome psychological consequences of social-stratification in Indian education and to foster caste/classinsensitive education.
Machine Learning Based Ultra High Carbon Steel Image Segmentation

Mechanical and structural properties of ultra-high carbon steel are determined by their microstructures composed of constituents such as pearlite and spheroidites. Locating micro constituents and quantitatively measuring its presence is key for material researchers to study the physical properties of the carbon steel materials. This micrograph analysis is currently done manually and subjectively by material scientists, which is tedious and time-consuming. Here we propose to apply the image segmentation algorithm called U-Net to achieve automated labeling of steel microstructures on a subset of ultrahigh carbon steel image dataset containing pearlite and spheroidite as the primary micro constituents. Our work provides an automated way to micrograph segmentation using the deep learning algorithm. Our prediction model only needs annotating a few micrographic images manually, which are used to train the segmentation algorithm. The trained model will help the researchers to automatically annotate new micrograph images. In this work, 20 micrographs containing pearlite and spheroidite micro constituents are first manually annotated. Then this dataset is used to train the conventional U-Net segmentation model. The trained U-Net model successfully performed segmentation on new micrograph images containing pearlite and spheroidite with an accuracy of 87.39%. We also contribute the 20 annotated image dataset to public access. Our approach can be further extended to the rest of the UHCS dataset and it will help the material researchers to
Extra nodal lymphomas – An Experience in Tertiary Care Hospital

Introduction: Lymphomas are all cancers that affect the lymphocytes. There are many different types and subtypes of lymphoma. The two main categories are Hodgkin’s lymphoma, or HL, and Non-Hodgkin’s lymphoma, or NHL. Primary extranodal lymphomas (ENL) are much more common in NHL than in Hodgkin’s lymphoma. The most frequent site of primary extranodal lymphoma is in the gastrointestinal tract, and almost all of these are NHL. The next most frequent site after the GI tract is the skin. Extra nodal lymphomas can also involve very unusual sites like conjunctiva, lungs, urinary bladder, bones etc.Aim: Aim of the study is to see the response of ENL to regular chemotherapy and monoclonal antibody (Rituximab) and/ or local radiotherapy in some cases. Methods& Materials: Our study was a retrospective analysis of patient’s records (2006-2016) who attended our hospital and received chemotherapy with or withoutRituximab. The local treatment modalities like surgery and radiotherapy was offered to some of the sub-sites. Results: Patients with primary extranodal NHL received standard chemotherapy and Rituximab. The OS of primary extranodal patients compared to patients with extensive involvement had inferior OS (P<0.001). The DFS of primary extranodal NHL patients was superior to patients those with extensive involvement (P<0.001). Conclusion: Extra nodal lymphomas are seen in Stomach, Skin, Brain, Breast, Parotid gland , Eye , lungs, urinary bladder, bones etc. Definitely most of the ENLs require local as well as systemic therapy that’s where these cases differ from normal lymphoma cases.
Remote Sensing of Environment

Interactions between climate and ecosystem properties that control phenological responses to climate warming and drought are poorly understood. To determine contributions from these interactions, we used space-borne remotely sensed vegetation indices to monitor leaf development across climate gradients and ecoregions in the southeastern United States. We quantified how air temperature, drought severity, and canopy thermal stress contribute to changes in leaf flushing from mountainous to coastal plain regions by developing a hierarchical state-space Bayesian model. We synthesized daily field climate data with daily vegetation indices and canopy surface temperature during spring green-up season at 59 sites in the southeastern United States between 2001 and 2012. Our results demonstrated strong interaction effects between ecosystem properties and climate variables across ecoregions. We found spring green-up is faster in the mountains, while coastal forests express a larger sensitivity to inter-annual temperature anomalies. Despite our detection of a decreasing trend in sensitivity to warming with temperature in all regions, we identified an ecosystem interaction: Deciduous dominated forests are less sensitive to warming than are those with fewer deciduous trees, likely due to the continuous presence of leaves in evergreen species throughout the season. Mountainous forest green-up is more susceptible to intensifying drought and moisture deficit, while coastal areas are relatively resilient. We found that with increasing canopy thermal stress, defined as canopy-air temperature difference, leaf development slows following dry years, and accelerates following wet years.
An improved neuroendocrine–proportional–integral–derivative controller with sigmoid-based secretion rate for nonlinear multi-input–multi-output crane systems

This paper proposes an improved neuroendocrine–proportional–integral–derivative controller for nonlinear multi-input–multi-output crane systems using a sigmoid-based secretion rate of the hormone regulation. The main advantage of the sigmoid-based secretion rate neuroendocrine–proportional–integral–derivative is that the hormone secretion rate of neuroendocrine–proportional–integral–derivative can be varied according to the change of error. As a result, it can provide high accuracy control performance, especially in nonlinear multi-input–multi-output crane systems. In particular, the hormone secretion rate is designed to adapt with the changes of error using a sigmoid function, thus contributing to enhanced control accuracy. The parameters of the sigmoid-based secretion rate neuroendocrine–proportional–integral–derivative controller are tuned using the safe experimentation dynamics algorithm. The performance of the proposed sigmoid-based secretion rate neuroendocrine–proportional–integral–derivative controller-based safe experimentation dynamics algorithm is evaluated by tracking the error and the control input. In addition, the performances of proportional–integral–derivative and neuroendocrine–proportional–integral–derivative controllers are compared with the proposed sigmoid-based secretion rate neuroendocrine–proportional–integral–derivative performance. From the simulation work, it is discovered that the sigmoid-based secretion rate neuroendocrine–proportional–integral–derivative design provides better control performances in terms of the objective function, the total norm of error and the total norm of input compared to proportional–integral–derivative and neuroendocrine–proportional–integral–derivative controllers. In particular, it is shown the proposed sigmoid-based secretion rate neuroendocrine–proportional–integral–derivative controller contributes 5.12% of control accuracy improvement by changing the fixed hormone secretion rate into a variable hormone secretion rate based on the change of error.
Deformation mechanisms of idealised cermets under multi-axial loading

The response of idealised cermets comprising approximately 60% by volume steel spheres in a Sn/Pb solder matrix is investigated under a range of axisymmetric compressive stress states. Digital volume correlation (DVC) anal`ysis of X-ray micro-computed tomography scans (μ-CT), and the measured macroscopic stress-strain curves of the specimens revealed two deformation mechanisms. At low triaxialities the deformation is granular in nature, with dilation occurring within shear bands. Under higher imposed hydrostatic pressures, the deformation mechanism transitions to a more homogeneous incompressible mode. However, DVC analyses revealed that under all triaxialities there are regions with local dilatory and compaction responses, with the magnitude of dilation and the number of zones wherein dilation occurs decreasing with increasing triaxiality. Two numerical models are presented in order to clarify these mechanisms: (i) a periodic unit cell model comprising nearly rigid spherical particles in a porous metal matrix and (ii) a discrete element model comprising a large random aggregate of spheres connected by non-linear normal and tangential “springs”. The periodic unit cell model captured the measured stress-strain response with reasonable accuracy but under-predicted the observed dilation at the lower triaxialities, because the kinematic constraints imposed by the skeleton of rigid particles were not accurately accounted for in this model. By contrast, the discrete element model captured the kinematics and predicted both the overall levels of dilation and the simultaneous presence of both local compaction and dilatory regions with the specimens. However, the levels of dilation in this model are dependent on the assumed contact law between the spheres. Moreover, since the matrix is not explicitly included in the analysis, this model cannot be used to predict the stress-strain responses. These analyses have revealed that the complete constitutive response of cermets depends both on the kinematic constraints imposed by the particle aggregate skeleton, and the constraints imposed by the metal matrix filling the interstitial spaces in that skeleton.
Validation of the Western Ontario and McMaster Universities Arthritis Index Short Form (WOMAC-SF) and Its Relevance to Disability and Frailty

Purpose This study aimed to evaluate the validity of the Western Ontario and McMaster Universities Arthritis Index Short Form (WOMAC-SF) for the assessment of musculoskeletal disorders. We evaluated whether WOMAC-SF correlated with the World Health Organization Disability Assessment Schedule 12 (WHODAS-12) and Kaigo-Yobo questionnaires for assessing health-outcomes in Korea. Materials and Methods This cross-sectional study used data from the Namgaram-2 cohort. WOMAC, WOMAC-SF, WHODAS-12, and Kaigo-Yobo questionnaires were administered to patients with musculoskeletal disorders, including radiology-confirmed knee osteoarthritis (RKOA), sarcopenia, and osteoporosis. The relationships among WOMAC-SF, WHODAS-12, and Kaigo-Yobo scores were analyzed by stepwise multiple regression analysis. Results WOMAC-SF was associated with the WOMAC questionnaire. The results of confirmatory factor analysis for the hypothesized model with two latent factors, pain and function, provided satisfactory fit indices. WOMAC-SF pain and function were associated with RKOA. Kaigo-Yobo was associated with WOMAC-SF pain (B=0.140, p=0.001) and WOMAC-SF function (B=0.042, p=0.004). WHODAS-12 was associated with WOMAC-SF pain (B=0.679, p=0.003) and WOMAC-SF function (B=0.804, p<0.001). Conclusion WOMAC-SF was validated for the evaluation of low extremity musculoskeletal disorders and health-related quality of life in a community-based population. Furthermore, we confirmed that WOMAC-SF were reflective of disability and frailty, which affect health outcomes.
Survey on Reinforcement Learning Techniques

- There are many unsolved problems that computers could solve if the appropriate software existed. Many real time problems are currently unsolvable such as Flight control systems for aircraft, automated manufacturing systems, and sophisticated avionics systems, not because current computers are too slow or have too little memory, but simply because it is too difficult to determine what the program should do. If a computer could learn to solve the problems through trial and error, that would be of great practical value. Reinforcement Learning is an approach to machine intelligence that combines two disciplines: Dynamic Programming and supervised learning to successfully solve problems that neither discipline can address individually. Encouraged by this emerging technique, this document briefly reviews the basic study of reinforcement learning, its various techniques and applications of it in various fields.