Miracle-dz/UniFAR-FTUs huggingface dataset:
Functional Sequential Treatment Allocation

Abstract Consider a setting in which a policy maker assigns subjects to treatments, observing each outcome before the next subject arrives. Initially, it is unknown which treatment is best, but the sequential nature of the problem permits learning about the effectiveness of the treatments. While the multi-armed-bandit literature has shed much light on the situation when the policy maker compares the effectiveness of the treatments through their mean, much less is known about other targets. This is restrictive, because a cautious decision maker may prefer to target a robust location measure such as a quantile or a trimmed mean. Furthermore, socio-economic decision making often requires targeting purpose specific characteristics of the outcome distribution, such as its inherent degree of inequality, welfare or poverty. In the present article, we introduce and study sequential learning algorithms when the distributional characteristic of interest is a general functional of the outcome distribution. Minimax expected regret optimality results are obtained within the subclass of explore-then-commit policies, and for the unrestricted class of all policies. Supplementary materials for this article are available online.
Shared Coded Picture Technique for Tile-Based Viewport-Adaptive Streaming of Omnidirectional Video

Tile-based viewport-adaptive streaming methods have been used in delivering omnidirectional video for virtual reality applications. In these methods, the 360° video is encoded in multiple quality versions by using the motion constrained tile set (MCTS) technique. A set of high-quality and low-quality tiles, corresponding to viewport and non-viewport areas, respectively, are selected and transmitted to the user. However, these methods require frequent intra random access points to ensure seamless viewport switching capability, very high decoding complexity, or a multi-layer coding scheme. The frequent intra random access points include very high bitrate in viewport switching points. The high decoding complexity and multi-layer decoder requirements are not aligned with the omnidirectional media format (OMAF) standard. Such requirements make these methods sub-optimal or impractical for streaming the omnidirectional video. This paper studies the current tile-based solutions for delivering the omnidirectional content. Moreover, the OMAF-compliant shared coded picture (SCP)-based scheme is proposed in this paper for streaming the omnidirectional video. The core concept of the SCP-based method is to manipulate the switching point pictures in a way that the frequent intra-coded pictures are no longer required for the viewport switching operations between different quality versions of the content. The experiments illustrated that the SCP-based method outperforms the MCTS-based method on average by 11% to 14% in terms of streaming bitrate reduction with only 4% extra decoding complexity.
Psychometric features of an assessment instrument with likert and dichotomous response formats.

OBJECTIVE To assess the psychometric properties of a Likert-formatted assessment instrument after altering the responses to a dichotomous format. DESIGN AND SAMPLE This methodological study used a 15-item instrument to obtain data from 183 participants who responded in both Likert and dichotomous formats. Response sets from each format were compared. MEASURES Each response set underwent factor analysis, Kuder-Richardson 20, Cronbach's α coefficient, item-total correlation, and parallel form equivalence tests. RESULTS Factor loads of the instrument varied between . 362 and . 754 when responses were Likert-formatted and between . 370 and . 713 when responses were dichotomous. The Cronbach's α coefficient with Likert-formatted responses was . 858; the Kuder-Richardson 20 coefficient of the dichotomous responses was . 827. Parallel form equivalences were significant at the level of r = . 753. CONCLUSIONS The instrument had valid results when either Likert or dichotomous responses were obtained.
Motion Analysis and Optimization Design of Back Support Mechanism Based on Pose Adjustment

In order to realize stable and safe operation of multi-mode bed back support mechanism, various angle adjustment mechanisms are studied and analyzed. An angle adjustment mechanism using a guide mechanism is proposed. The function of back support is achieved by using an electric push rod. A model of the back support mechanism was built. Based on the analysis of the geometric relationship and mechanical model of the back support mechanism, the optimal solution of the fixed position of the electric actuator was solved by using MATLAB. Dynamic simulation, performed by SOLIDWORKS Motion, determine the optimal stroke of the electric actuator. Finite element analysis, performed by SOLIDWORKS Simulation, verify the feasibility of the back support mechanism. The simulation results show that using the back support mechanism of the guide rod mechanism reduces the load of the load beam, improves the safety of the back support mechanism, and stabilizes the back support process.
ZeRO: Memory Optimization Towards Training A Trillion Parameter Models

Training large DL models with billions and potentially trillions of parameters is challenging. Existing solutions exhibit fundamental limitations to obtain both memory and scaling (computation/communication) efficiency together. Data parallelism does not help reduce memory footprint per device: a model with 1.5 billion parameters or more runs out of memory. Model parallelism hardly scales efficiently beyond multiple devices of a single node due to fine-grained computation and expensive communication. We develop a novel solution, Zero Redundancy Optimizer (ZeRO), to optimize memory, achieving both memory efficiency and scaling efficiency. Unlike basic data parallelism where memory states are replicated across data-parallel processes, ZeRO partitions model states instead, to scale the model size linearly with the number of devices. Furthermore, it retains scaling efficiency via computation and communication rescheduling and by reducing the model parallelism degree required to run large models. Our analysis on memory requirements and communication volume demonstrates: ZeRO has the potential to scale beyond 1 Trillion parameters using today's hardware (e.g., 1024 GPUs, 64 DGX-2 nodes). To meet near-term scaling goals and serve as a demonstration of ZeRO's capability, we implemented stage-1 optimizations of ZeRO (out of 3 stages in total described in the paper) and tested this ZeRO-OS version. ZeRO-OS reduces memory and boosts model size by 4x compared with the state-of-art, scaling up to 100B parameters. Moving forward, we will work on unlocking stage-2 optimizations, with up to 8x memory savings per device, and ultimately stage-3 optimizations, reducing memory linearly with respect to the number of devices and potentially scaling to models of arbitrary size. We are excited to transform very large models from impossible to train to feasible and efficient to train!
SUSY localization for Coulomb branch operators in omega-deformed 3d N=4 gauge theories

We perform SUSY localization for Coulomb branch operators of 3d $\mathcal{N}=4$ gauge theories in $\mathbb{R}^3$ with $\Omega$-deformation. For the dressed monopole operators whose expectation values do not involve non-perturbative corrections, our computations reproduce the results of the so-called abelianization procedure. For the expectation values of other operators and the correlation functions of multiple operators in $U(N)$ gauge theories, we compute the non-perturbative corrections due to monopole bubbling using matrix models obtained by brane construction. We relate the results of localization to algebraic structures discussed in the mathematical literature, and also point out a similar relation for line operators in 4d $\mathcal{N}=2$ gauge theories. For $U(N)$ (quiver) gauge theories in 3d we demonstrate a direct correspondence between wall-crossing and the ordering of operators.
4D MRI: Robust sorting of free breathing MRI slices for use in interventional settings

Purpose We aim to develop a robust 4D MRI method for large FOVs enabling the extraction of irregular respiratory motion that is readily usable with all MRI machines and thus applicable to support a wide range of interventional settings. Method We propose a 4D MRI reconstruction method to capture an arbitrary number of breathing states. It uses template updates in navigator slices and search regions for fast and robust vessel cross-section tracking. It captures FOVs of 255 mm x 320 mm x 228 mm at a spatial resolution of 1.82 mm x 1.82 mm x 4mm and temporal resolution of 200ms. A total of 37 4D MRIs of 13 healthy subjects were reconstructed to validate the method. A quantitative evaluation of the reconstruction rate and speed of both the new and baseline method was performed. Additionally, a study with ten radiologists was conducted to assess the subjective reconstruction quality of both methods. Results Our results indicate improved mean reconstruction rates compared to the baseline method (79.4% vs. 45.5%) and improved mean reconstruction times (24s vs. 73s) per subject. Interventional radiologists perceive the reconstruction quality of our method as higher compared to the baseline (262.5 points vs. 217.5 points, p = 0.02). Conclusions Template updates are an effective and efficient way to increase 4D MRI reconstruction rates and to achieve better reconstruction quality. Search regions reduce reconstruction time. These improvements increase the applicability of 4D MRI as a base for seamless support of interventional image guidance in percutaneous interventions.
The Complexity of Finding Stationary Points with Stochastic Gradient Descent

We study the iteration complexity of stochastic gradient descent (SGD) for minimizing the gradient norm of smooth, possibly nonconvex functions. We provide several results, implying that the classical $\mathcal{O}(\epsilon^{-4})$ upper bound (for making the average gradient norm less than $\epsilon$) cannot be improved upon, unless a combination of additional assumptions is made. Notably, this holds even if we limit ourselves to convex quadratic functions. We also show that for nonconvex functions, the feasibility of minimizing gradients with SGD is surprisingly sensitive to the choice of optimality criteria.
Industry-supported, and standardized modular platform : interconnecting fluidic circuit board and microfluidic building blocks

The research done is focussed on improving the gap between the number of academic proof of concept microfluidic devices and the number of commercial application. A start is made by analysing the factors that inhibit the commercialization of microfluidics. It turns out there are multiple: their multidisciplinary nature, the need for high volume production, the lack of focus due to their broad applicability. To overcome these inhibiting factors an industry-supported, and standardized modular platform is introduced and supported by the European MFManufacturing project. This platform consists out of two parts, the interconnecting fluidic circuit board and the microfluidic building blocks. This platform overcomes some of the inhibiting factors by using the building blocks. They are usable in multiple system, resulting in higher volumes. The building blocks make designing new systems easier. The design effort can be done in several stages, without the need to redo ever stage for a new system. The standardization of the platform is mainly concerned about interoperability, examples are fixed dimensions for the outside dimensions of the microfluidic building blocks and the locations of the interconnects. A library with building blocks and their characterization was also started, including a pressure sensor, valve and reservoir. Also different types of fluidic circuit board were demonstrated. To be able to effectively design new microfluidic systems, a new computer aided design tool was also developed in the MFManufacturing project. To check how well the new standardized platform performs two systems are designed according to it. An AC coulter counter and a cell culturing platform containing 192 individual culture chambers. The AC coulter counter system demonstrates the possibility of the platform to include electrical connection and mix multiple materials in a single system while maintaining a compact footprint of a credit card size. The cell culturing platform consists out of 3 microfluidic building blocks each containing 64 chamber. The fluidic circuit board has integrated valves to be able to individually activate or deactivate a building block. By using multiplexing in the building blocks and a chip select in the fluidic circuit board, over thousand valves can be controlled by only 16 external lines. Hereby greatly reducing the complexity of the setup. At the end of the MFManufacturing project a new Microfluidic Consortium was formed with members from existing project and new external member. In this new Microfluidic Consortium several workshop have been organized to continue standardization in the microfluidic field.
Fundamental limits of hot carrier injection from metal in nanoplasmonics

Abstract The evolution of non-equilibrium carriers excited in the process of decay of surface plasmon polaritons (SPPs) in metal is described for each step – from the generation of carriers to their extraction from the metal. The relative importance of various carrier-generating mechanisms is discussed. It is shown that both the generation of carriers and their decay are inherently quantum processes as, for realistic illumination conditions, no more than a single SPP per nanoparticle exists at a given time. As a result, the distribution of non-equilibrium carriers cannot be described by a single temperature. It is also shown that the originally excited carriers that have not undergone a single electron-electron scattering event are practically the only ones that contribute to the injection. The role of momentum conservation in carrier extraction is discussed, and it is shown that, if all the momentum conservation rules are relaxed, it is the density of states in the semiconductor/dielectric that determines the ultimate injection efficiency. A set of recommendations aimed at improving the efficiency of plasmonic-assisted photodetection and (to a lesser degree) photocatalysis is made in the end.
Character Feature Engineering for Japanese Word Segmentation

On word segmentation problems, machine learning architecture engineering often draws attention. The problem representation itself, however, has remained almost static as either word lattice ranking or character sequence tagging, for at least two decades. The latter of-ten shows stronger predictive power than the former for out-of-vocabulary (OOV) issue. When the issue escalating to rapid adaptation, which is a common scenario for industrial applications, active learning of partial annotations or re-training with additional lexical re-sources is usually applied, however, from a somewhat word-based perspective. Not only it is uneasy for end-users to comply with linguistically consistent word boundary decisions, but also the risk/cost of forking models permanently with estimated weights is seldom affordable. To overcome the obstacle, this work provides an alternative, which uses linguistic intuition about character compositions, such that a sophisticated feature set and its derived scheme can enable dynamic lexicon expansion with the model remaining intact. Experiment results suggest that the proposed solution, with or without external lexemes, performs competitively in terms of F1 score and OOV recall across various datasets.
Automating Data Monitoring: Detecting Structural Breaks in Time Series Data Using Bayesian Minimum Description Length.

In modern business modeling and analytics, data monitoring plays a critical role. Nowadays, sophisticated models often rely on hundreds or even thousands of input variables. Over time, structural changes such as abrupt level shifts or trend slope changes may occur among some of these variables, likely due to changes in economy or government policies. As a part of data monitoring, it is important to identify these changepoints, in terms of which variables exhibit such changes, and what time locations do the changepoints occur. Being alerted about the changepoints can help modelers decide if models need modification or rebuilds, while ignoring them may increase risks of model degrading. Simple process control rules often flag too many false alarms because regular seasonal fluctuations or steady upward or downward trends usually trigger alerts. To reduce potential false alarms, we create a novel statistical method based on the Bayesian Minimum Description Length (BMDL) framework to perform multiple change-point detection. Our method is capable of detecting all structural breaks occurred in the past, and automatically handling data with or without seasonality and/or autocorrelation. It is implemented with computation algorithms such as Markov chain Monte Carlo (MCMC), and can be applied to all variables in parallel. As an explainable anomaly detection tool, our changepoint detection method not only triggers alerts, but provides useful information about the structural breaks, such as the times of changepoints, and estimation of mean levels and linear slopes before and after the changepoints. This makes future business analysis and evaluation on the structural breaks easier.
The relation between use intensity of private and professional SNS, social comparison, self-esteem, and depressive tendencies in the light of self-regulation

ABSTRACT The present study's (N = 145) aim was to introduce a multidimensional behavioural report for assessing activity on the professional social network site (SNS) XING and to carry out a comparison with private SNS use (i.e. Facebook). Psychometric analyses revealed good internal consistency and construct validity of the new XING Activity Questionnaire. Results suggest that private and professional SNS use is positively correlated to people's ability-related social comparison orientation (SCO) and depressive tendencies on the one hand and negatively correlated to self-esteem on the other hand. In addition, both people's general SCO and opinion-related SCO were solely positively correlated to Facebook use. The findings are in line with the Social Online-Self-Regulation-Theory suggesting that people are using not only private but also professional SNSs for self-regulation.
An optimized stopping rule for statistical boosting algorithms

Boosting algorithms are a relatively new way to fit models on high-dimensional data sets, which can be interpreted in the same way as classically fitted models. The algorithm offers the possibility to select variables already during modeling as well as an automatic control of the effect estimates, only regulated by one main tuning parameter: the stop point of the algorithm. A pre-selection of the possible variables or other preparations are no longer needed. This thesis deals with the stop point of the algorithm and the optimization of the stopping rule by using the one standard error rule. Both classical and newly developed methods are applied on simulated data for two types of models: linear and logistic regression. Comparing the resulting models of both methods to choose the stop point, the newly developed method led to an improvement of prediction accuracy in both types of regression. Finally, the two stopping rules were applied on two high-dimensional genomic data, each for one type of regression. Comparing the logistic boosted models, the prediction accuracy improved again when using the new method. In the case of linear regression, however, no improvement could be achieved. Overall, both methods yield similar results, but in some cases, the new method produces better results.
MODEL FOR LAND COVER ESTIMATION USING UNSUPERVISED MACHINE LEARNING ON GOOGLE MAPS COLOR IMAGES

Abstract. Remote sensing data and satellite images are broadly used for land cover information. There are so many challenges to classify pixels on the basis of features and characteristics. Generally it is pixel classification that required the count of pixels for certain area of interest. In the proposed model, we are applying unsupervised machine learning to classify the content of the input images on the basis of pixels intensity. The study aims to compare classification accuracy of different landscape characteristics like water, forest, urban, agricultural areas, transport network and other classes adapted from CORINE (Coordination of information on the environment) nomenclature. To fulfil the aim of the model, accessing data from Google map using Google static API service which creates a map based on URL parameters sent through a standard HTTP (Hyper Text Transfer Protocol) request and returns the map as an image which can be display on any graphical user interface platform. The Google Static Maps API returns an image either in GIF, PNG or JPEG format in response to an HTTP request. To identify different land cover/use classes using k-means clustering. The model is dynamic in nature that describes the clustering as well formulate the area of the concerned class or clustered fields.
Assessing Lightweight Virtualization for Security-as-a-Service at the Network Edge

SUMMARY Accounting for the exponential increase of secu- rity threats, the development of new defense strategies for pervasive environments is acquiring an even growing importance. The expected avalanche of heterogeneous IoT devices which will popu-late our industrial factories and houses will increase the complex- ity of managing security requirements in a comprehensive way. To this aim, cloud-based security services are gaining notable im- petus to provide security mechanisms according to Security-as-a-Service (SECaaS) model. However, the deployment of security applications in remote cloud data-centers can introduce several drawbacks in terms of traﬃc overhead and latency increase. To cope with this, edge computing can provide remarkable advantages avoiding long routing detours. On the other hand, the re- duced capabilities of edge node introduce potential constraints in the overall management. This paper focuses on the provisioning of virtualized security services in resource-constrained edge nodes by leveraging lightweight virtualization technologies. Our anal- ysis aims at shedding light on the feasibility of container-based security solutions, thus providing useful guidelines towards the orchestration of security at the edge. Our experiments show that the overhead introduced by the containerization is very light.
A Comparative Analysis of Windowing Approaches in Dense Sensing Environments

Windowing is an established technique employed within dense sensing environments to extract relevant features from sensor data streams. Among the established approaches of Explicit, Time-based and Sensor-Event based windowing, Dynamic windowing approaches are beginning to emerge. These dynamic approaches claim to address the inherent shortcomings of the aforementioned established approaches by determining the appropriate window length for live sensor data streams in real-time, thereby offering the potential to optimize and increase the recognition of these sensor represented activities. Beyond these potential benefits, dynamic approaches can also support anomaly detection by actively uncovering new, unknown window patterns within a trained model. This paper presents findings from a study which utilizes data from a single source dataset, towards benchmarking and comparing more traditional windowing approaches against a dynamic windowing approach. The experiments conducted on a real-world smart home dataset suggest Time-based windowing is the best approach. Through evaluation of results, Dynamic windowing approaches may benefit from carefully annotated datasets.