Miracle-dz/UniFAR-FTUs huggingface dataset:
Link Prediction and Unlink Prediction on Dynamic Networks

Link prediction on dynamic networks has been extensively studied and widely applied in various applications. However, existing methods only consider either the network structure or the temporal information, ignoring the potentialities of using both types of information together to comprehend the complex behaviors of dynamic networks. Moreover, temporal unlink prediction, which also plays an important role in the evolution of social networks, has not been paid much attention. Accurately predicting the links and unlinks on the future network greatly contributes to the network analysis that uncovers more latent relations between nodes. In this work, we assume that there are two kinds of relations between nodes, namely, long-term relations and short-term relations, and we propose an effective algorithm called LULS for temporal link prediction and unlink prediction based on such relations. Specifically, for each snapshot of a dynamic network, LULS first collects higher order structures as two topological matrices by applying short random walks. Then, LULS initializes and optimizes a global matrix and a sequence of temporary matrices for all the snapshots by using nonnegative matrix factorization (NMF) based on the topological matrices, where the global matrix denotes long-term relations and the temporary matrices represent short-term relations of snapshots. Finally, LULS calculates the similarity matrix of the future snapshot and predicts the links and unlinks for the future network. In addition, we further improve the prediction results by using graph regularization constraints to enhance the global matrix, resulting in that the global matrix contains a wealth of topological information and temporal information. The conducted experiments on real-world networks illustrate that LULS outperforms other baselines for both link prediction and unlink prediction tasks.
KOALA: A Kalman Optimization Algorithm with Loss Adaptivity

Optimization is often cast as a deterministic problem, where the solution is found through some iterative procedure such as gradient descent. However, when training neural networks the loss function changes over (iteration) time due to the randomized selection of a subset of the samples. This randomization turns the optimization problem into a stochastic one. We propose to consider the loss as a noisy observation with respect to some reference optimum. This interpretation of the loss allows us to adopt Kalman filtering as an optimizer, as its recursive formulation is designed to estimate unknown parameters from noisy measurements. Moreover, we show that the Kalman Filter dynamical model for the evolution of the unknown parameters can be used to capture the gradient dynamics of advanced methods such as Momentum and Adam. We call this stochastic optimization method KOALA, which is short for Kalman Optimization Algorithm with Loss Adaptivity. KOALA is an easy to implement, scalable, and efficient method to train neural networks. We provide convergence analysis and show experimentally that it yields parameter estimates that are on par with or better than existing state of the art optimization algorithms across several neural network architectures and machine learning tasks, such as computer vision and language modeling. The project page with the code and the supplementary materials is available at https://araachie.github. io/koala/.
Minimum Foundation Mass For Vibration Control

This note utilizes the analog models of forced vibration to express the mass of foundation block in terms of damping ratio and thereafter obtains the expression for the minimum foundation mass required to limit vertical machine vibration amplitude to a prescribed limit. The resulting formula, which accounts for internal damping, is constrained by the limitations of the original analog solutions. Moreover, formulas are derived for determining the damping ratio for conditions different from optimum that enable the evaluation of the nonoptimum foundation mass required to limit vibration. Since the ideal optimum solution may not be practical, an expression has been derived for the best nonoptimum foundation mass. The method presented in this note is believed to be easier to implement and to yield smaller foundation blocks than the existing traditional method, and thus will enable cheaper machine foundations to be constructed. It is believed that the proposed optimal solution may lead to the near elimination o.
Anycast Agility: Network Playbooks to Fight DDoS

IP anycast is used for services such as DNS and Content Delivery Networks (CDN) to provide the capacity to handle Distributed Denial-of-Service (DDoS) attacks. During a DDoS attack service operators redistribute traffic between anycast sites to take advantage of sites with unused or greater capacity. Depending on site traffic and attack size, operators may instead concentrate attackers in a few sites to preserve operation in others. Operators use these actions during attacks, but how to do so has not been described systematically or publicly. This paper describes several methods to use BGP to shift traffic when under DDoS, and shows that a response playbook can provide a menu of responses that are options during an attack. To choose an appropriate response from this playbook, we also describe a new method to estimate true attack size, even though the operator's view during the attack is incomplete. Finally, operator choices are constrained by distributed routing policies, and not all are helpful. We explore how specific anycast deployment can constrain options in this playbook, and are the first to measure how generally applicable they are across multiple anycast networks.
Synthetic transmit aperture imaging using orthogonal Golay coded excitation

The frame rate in medical ultrasound imaging may be increased significantly by reducing the number of transmits per image frame. Cooley et al. (1994) and Lockwood et al. (1995) have described synthetic transmit aperture (STA) systems where each frame is imaged using data obtained from a small number of point sources fired in succession. These systems have potential for very high frame rates, but they also suffer from low SNR. In this paper we present a computationally efficient method to increase the SNR of STA systems by using spatio-temporal encoding which increases SNR by 101og(ML) dB, where M is the number of active phase centers or transmits and L is the temporal code length. By using an orthogonal Golay set for the spatio-temporal encoding, the received data can be sorted by each transmit phase center and pulse-compressed for subsequent synthetic aperture beamforming. Computer simulations are used to demonstrate the method.
Real-time efficiency index of a steam power plant: A systems approach

Abstract The present system of recording efficiency parameters under process performance of a steam power plant (SPP) includes specific fuel consumption, auxiliary power consumption, and demineralized water for make-up used during past-specific period, without their integration into a single process performance or efficiency index. For developing integrated efficiency index in real time frame, the use of a process performance digraph (PPD) comprising of P i 's as nodes and p ij 's as interconnection among the nodes as obtained from the system structure graph (SSG) of SPP has been demonstrated. Each c ij of SSG corresponds to a process, such as air fuel mixing, combustion of mixtuer, generation and expansion of steam and its condensation back into water for recirculation in boiler, etc., taking place in the SPP and corresponds to a P i of PPD. The PPD is then analysed for determining the efficiency of the SPP using matrix method following systems approach. Variable permanent process performance matrix and its variable permanent function (VPF-p) have been derived from the PPD for the analysis. The real-time efficiency index (RTEI) has been introduced which is the ratio of the values of (VPF-p) in realtime (RT) situation (VPF-p)RT to its achievable design value (VPF-p). It reflects the RT rate of consumption of various inputs like demineralized water make-up, auxiliary power and fuel consumption, etc. It provides a more scientific method for ranking the performance of an SPP. The proposed model is capable of analysing in RT the performance of various systems of the SPP, taking into account simultaneously the failure of various equipments in these systems. A step-by-step procedure for calculation of RTEI has also been suggested and illustrated by giving an example.
Performance evaluation of turbo-codes

Parallel concatenated coding schemes employing convolutional codes as constituent codes linked by an interleaver have been proposed in the literature as "turbo codes". They yield very good performance in connection with simple suboptimum decoding algorithms. The authors derive an analytical upper bound to the average performance of such coding schemes, which is independent of the interleaver used and shows the influence of the interleaver length on the code performance. >
Cuk converter global control via fuzzy logic and scaling factors

A successful implementation of a low cost PI-fuzzy controller for a Cuk converter is presented in this paper. Properties of the proposed controller are: robustness around the operating point, good performance of transient responses in changing load conditions and/or input voltage, invariant dynamic performance in the face of varying output operating points. Simulations and experimental results have been obtained via a suitable variation of the scaling factors related to the input variables of the fuzzy controller.
International Journal of Information Management Data Insights

In this study, public opinion and emotions regarding diﬀerent stages of the Covid-19 pandemic from the outbreak of the disease to the distribution of vaccines were analyzed to predict the popularity of tweets. More than 1.25 million English tweets were collected, posted from January 20, 2020, to May 29, 2021. Five sets of content features, including topic analysis, topics plus TF-IDF vectorizer, bag of words (BOW) by TF-IDF vectorizer, document embedding, and document embedding plus TF-IDF vectorizer, were extracted and applied to supervised machine learning algorithms to generate a predictive model for the retweetability of posted tweets. The analysis showed that tweets with higher emotional intensity are more popular than tweets containing information on Covid-19 pandemic. This study can help to detect the public emotions during the pandemic and after vaccination and predict the retweetability of posted tweets in diﬀerent stages of Covid-19 pandemic.
Prevalence of adult sexual abuse in men with mental illness: Bayesian meta-analysis

Background Sexual abuse is a broad category of traumatic experiences that includes rape and any unwanted sexual contact with a body part or foreign object, whether penetrative, oral or otherwise. Although patients with mental illness have a higher risk of becoming victims of sexual abuse in adulthood, few studies investigate the proportion of male victims in this population. Their underrepresentation in research is a barrier to understanding the negative outcomes associated with sexual abuse in men. Aims We estimated the prevalence of recent (past year) and adulthood sexual abuse perpetrated by any perpetrator and separately by intimate partners in males diagnosed with a mental illness. Method To model the prevalences and heterogeneity arising from reports, we used Bayesian multilevel models. Prevalences were estimated for mixed-diagnosis, substance misuse, intellectual disability and post-traumatic stress disorder samples, and studies reporting specifically on intimate partner violence. This review was registered through PROSPERO (CRD42020169299) Results Estimated adult sexual abuse was 5.3% (95% Credibility Interval 1.6–12.8%) for past-year abuse and 14.1% (95% Credibility Interval 7.3–22.4%) for abuse in adulthood. There was considerable heterogeneity of prevalence between studies and diagnosis groups. Conclusions Our analyses show that the prevalence of sexual abuse of males diagnosed with a mental illness was much higher than for men in the general population. This has important implications regarding the proportion of undetected or untreated sexually abused men in clinical practice.
The Rohingya refugees: a conceptual framework of their psychosocial adversities, cultural idioms of distress and social suffering

Abstract Rohingya refugees, a group of religious and ethnic minorities, primarily reside in the South Asian nations. With decades of displacement, forced migration, limited freedom of movement, violence and oppression, they have been termed by the United Nations (UN) as the ‘most persecuted minority group’ in world history. Literature shows an increased prevalence of psychiatric disorders such as depression, anxiety, post-traumatic stress, insomnia, etc., in this population. However, beyond ‘medicalisation’, the psychosocial challenges of the Rohingyas need to be understood through the lens of ‘social suffering’, which results from a complex interplay of multiple social, political, environmental and geographical factors. Lack of essential living amenities, poverty, unemployment, overcrowding, compromised social identity, and persistent traumatic stressors lead to inequality, restricted healthcare access, human rights deprivation and social injustice in this group. Even though the United Nations High Commission for Refugees (UNHCR) has taken a renewed interest in Rohingya re-establishment with well-researched standards of care, there are several pragmatic challenges in their implementation and inclusion in policies. This paper reviews these multi-dimensional psychosocial challenges of the Rohingyas by synthesising various intersecting conceptual models including minority stress, health-stigma-discrimination framework, refugee ecological model and capability approach. Furthermore, it highlights multidisciplinary interventions to mitigate these adversities, improve their living situation and eventually foster healing via means which are culturally relevant and contextually appropriate. These interventions need to involve various stakeholders from a human rights and dignity based lens, including the voices of the Rohingyas and supported by more research in this area.
SunPower: A Path Toward Strategic Development

SunPower Corporation is an American organization working in the solar energy sector specialized in power generation and solar energy storage. This case highlights the strategic growth path of SunPower corporation, which originated as an outcome of effective solar cells production. This case also discussed how the energy sector considers solar energy the vital energy source, and solar energy organizations grow to capture the energy demand. The case also highlights the various consolidations of SunPower in terms of different mergers and acquisitions as essential strategies to succeed. The case also discusses the competitors’ strategy of the top five solar power generation and storage firms, which give a clear view of how different solar firms are building their space in the solar energy sector. With competitors’ discussion, this helps to understand the challenge faced by SunPower to beat the competition among the other solar firms. In the end, the case focused on the ongoing and future strategic choices of SunPower as SunPower Corporation.
The Science of Visual Data Communication: What Works

Effectively designed data visualizations allow viewers to use their powerful visual systems to understand patterns in data across science, education, health, and public policy. But ineffectively designed visualizations can cause confusion, misunderstanding, or even distrust—especially among viewers with low graphical literacy. We review research-backed guidelines for creating effective and intuitive visualizations oriented toward communicating data to students, coworkers, and the general public. We describe how the visual system can quickly extract broad statistics from a display, whereas poorly designed displays can lead to misperceptions and illusions. Extracting global statistics is fast, but comparing between subsets of values is slow. Effective graphics avoid taxing working memory, guide attention, and respect familiar conventions. Data visualizations can play a critical role in teaching and communication, provided that designers tailor those visualizations to their audience.
An Optimal Design Methodology of an Interleaved Boost Converter for Fuel Cell Applications

In this paper, an optimal selection methodology for the number of phases will be proposed for an interleaved boost converter (IBC). Also, the analysis of the input current ripple according to CCM and DCM is carried out. The proposed design methodology will be theoretically analyzed, and its validity verified by simulation as well as with experimental results. Moreover, a comparison of cost and efficiency based on a 600W laboratory prototype using the Ballard NEXA 1.2kW PEMFC system is demonstrated.
Potential of electric wheel motors as new chassis actuators for vehicle manoeuvring

Abstract The stability and yaw control systems in today's vehicles are increasingly appreciated for active safety. They use individual wheel braking to obtain an extra yaw moment. Adding individual traction, e.g. through electric wheel motors, would increase the available extra yaw moment. The present work investigates the manoeuvrability potential of such a concept. A passenger car employing electric wheel motors at the rear axle is compared with the same vehicle using only brake intervention. The potential, or ultimate limits, are investigated for two driving situations. The time delay in yaw speed change when entering a curve is estimated. A reduction of some 10-30 per cent is found, the highest gains being at low longitudinal speed. For pure lateral translation, the maximum initial lateral acceleration is estimated and a 40 per cent increase is predicted. The investigation is based on mathematical analysis employing a modified version of the common bicycle model. The feasibility of introducing these solutions in series-produced vehicles is discussed briefly. Cost aspects are not considered in any great detail, but it is emphasized that such aspects must be weighed not only against improved manoeuvrability but also against reduced environmental impact and improved longitudinal driveability, all of which are attainable with electric propulsion.
Radiography image analysis using cat swarm optimized deep belief networks

Abstract Radiography images are widely utilized in the health sector to recognize the patient health condition. The noise and irrelevant region information minimize the entire disease detection accuracy and computation complexity. Therefore, in this study, statistical Kolmogorov–Smirnov test has been integrated with wavelet transform to overcome the de-noising issues. Then the cat swarm-optimized deep belief network is applied to extract the features from the affected region. The optimized deep learning model reduces the feature training cost and time and improves the overall disease detection accuracy. The network learning process is enhanced according to the AdaDelta learning process, which replaces the learning parameter with a delta value. This process minimizes the error rate while recognizing the disease. The efficiency of the system evaluated using image retrieval in medical application dataset. This process helps to determine the various diseases such as breast, lung, and pediatric studies.
Creating a baseline for labour productivity of reinforced concrete building construction in Kuwait

Despite many technological advances, construction remains a labour intensive industry, and labour productivity remains the industry’s predominant determinant of performance. However, there is a lack of evidence-based labour productivity ‘baselines’ with which researchers or industry practitioners can compare the efficiency of the labour force, and subsequently establish sensible benchmarks. A rigorous approach to determining baselines for labour productivity of in situ reinforced concrete trades for major building elements under normal operating conditions in the State of Kuwait has therefore been developed. The data were collected from 208 in situ reinforced concrete building projects between 2006 and 2013. The intermittent observation data collection technique which requires the researcher to carry out weekly site visits to audit site staff daily records was used. Projects observed included residential, office and commercial buildings ranging from 1000 to 15 000 m2 in gross floor area, and US$300 000 to US$21 000 000 in construction cost, located across most major cities and suburbs within the State. For each dataset, the minimum, lower quartile, median, upper quartile and maximum labour productivity values are presented. Whilst the median and inter-quartile range represent the baseline or normal labour productivity range of the related activity, the lower and upper quartiles may be used as benchmarks to identify poor and exceptional performance. Although the metrics are specific to Kuwait, the principles of data collection, analysis and use are generic and could be applied to advantage in other countries.
A multi-organ biomechanical model to analyze prostate deformation due to large deformation of the rectum

Magnetic resonance imaging (MRI) with an endorectal receiver coil (ERC) provides superior visualization of the prostate gland and its surrounding anatomy at the expense of large anatomical deformation. The ability to correct for this deformation is critical to integrate the MR images into the CT-based treatment planning for radiotherapy. The ability to quantify and understand the physiological motion due to large changes in rectal filling can also improve the precision of image-guided procedures. The purpose of this study was to understand the biomechanical relationship between the prostate, rectum, and bladder using a finite element-based multi-organ deformable image registration method, 'Morfeus' developed at our institution. Patients diagnosed with prostate cancer were enrolled in the study. Gold seed markers were implanted in the prostate and MR scans performed with the ERC in place and its surrounding balloon inflated to varying volumes (0-100cc). The prostate, bladder, and rectum were then delineated, converted into finite element models, and assigned appropriate material properties. Morfeus was used to assign surface interfaces between the adjacent organs and deform the bladder and rectum from one position to another, obtaining the position of the prostate through finite element analysis. This approach achieves sub-voxel accuracy of image co-registration in the context of a large ERC deformation, while providing a biomechanical understanding of the multi-organ physiological relationship between the prostate, bladder, and rectum. The development of a deformable registration strategy is essential to integrate the superior information offered in MR images into the treatment planning process.
Influence of Variable Swirl Intake Manifolds for DI Diesel Engine on in-Cylinder Air Motion

This paper developed a new design of variable swirl intake manifolds for four-valve DI diesel engine by setting swirl control valves in the intake manifolds. The 3-D numerical model under the same experimental condition was established and particular flow information in intake manifolds and cylinder was achieved. Influence of variable swirl intake manifolds on intake flow characteristics and in-cylinder swirl characteristics were analyzed. The simulation results show that the swirl intensity in cylinder can be tuned by this device. The swirl intensity increases gradually with decrease flow coefficient and it can meet the requirements of swirl intensity for different operating conditions.