Miracle-dz/UniFAR-FTUs huggingface dataset:
Versatile, modular 3D microelectrode arrays for neuronal ensemble recordings: from design to fabrication, assembly, and functional validation in non-human primates

Objective. Application-specific designs of electrode arrays offer an improved effectiveness for providing access to targeted brain regions in neuroscientific research and brain machine interfaces. The simultaneous and stable recording of neuronal ensembles is the main goal in the design of advanced neural interfaces. Here, we describe the development and assembly of highly customizable 3D microelectrode arrays and demonstrate their recording performance in chronic applications in non-human primates. Approach. System assembly relies on a microfabricated stacking component that is combined with Michigan-style silicon-based electrode arrays interfacing highly flexible polyimide cables. Based on the novel stacking component, the lead time for implementing prototypes with altered electrode pitches is minimal. Once the fabrication and assembly accuracy of the stacked probes have been characterized, their recording performance is assessed during in vivo chronic experiments in awake rhesus macaques (Macaca mulatta) trained to execute reaching-grasping motor tasks. Main results. Using a single set of fabrication tools, we implemented three variants of the stacking component for electrode distances of 250, 300 and 350 µm in the stacking direction. We assembled neural probes with up to 96 channels and an electrode density of 98 electrodes mm−2. Furthermore, we demonstrate that the shank alignment is accurate to a few µm at an angular alignment better than 1°. Three 64-channel probes were chronically implanted in two monkeys providing single-unit activity on more than 60% of all channels and excellent recording stability. Histological tissue sections, obtained 52 d after implantation from one of the monkeys, showed minimal tissue damage, in accordance with the high quality and stability of the recorded neural activity. Significance. The versatility of our fabrication and assembly approach should significantly support the development of ideal interface geometries for a broad spectrum of applications. With the demonstrated performance, these probes are suitable for both semi-chronic and chronic applications.
Differential responses of antioxidants and dehydrin in two Switchgrass (Panicum virgatum L.) cultivars contrasting in drought tolerance

Drought stress is a major limiting factor for plant growth and production, including biofuel crops. This study was designed to investigate antioxidant metabolism and dehydrin responses to drought stress in two cultivars (drought tolerant Alamo and drought sensitive Dacotah) contrasting in drought tolerance of Switchgrass (Panicum virgatum). Alamo and Dacotah were subjected to control (25.7%–28.3% soil water content) or drought treatment (27.2%–4.7% soil water content) conditions for 24 d in growth chambers. Our results showed that drought treatment decreased leaf relative water content (RWC), increased leaf electrolyte leakage (EL) and leaf malondialdehyde (MDA) content in two Switchgrass cultivars. However, Alamo had higher leaf RWC, lower leaf EL and MDA compared to Dacotah at 24 d of drought treatment. Drought treatment also increased superoxide dismutase (SOD), catalase (CAT) and ascorbate peroxidase (APX) activities in Alamo and Dacotah, Alamo had higher SOD, CAT and APX activities and greater abundance of SOD and APX isozymes than Dacotah at 24 d of drought treatment. Alamo also had higher abundance of 55 KDa and 18 KDa dehydrin accumulation than Dacotah under 24 d of drought treatment. Relative genes expression level of PvCAT1, PvAPX2 and PvERD in Alamo were significantly higher than Dacotah at 24 d of drought treatment. Our results suggest that antioxidants and dehydrin played an important role in Switchgrass drought tolerance. Antioxidants and dehydrin abundance could be screening indicators to identify drought tolerance in Switchgrass populations.
Real Options and Idiosyncratic Skewness

We add to the explanatory theory of skewness showing how real options generate convexity of payoffs leading to skewness in returns. We document empirically that growth option variables are significant, positive and robust determinants of idiosyncratic skewness, over and above previously reported determinants. We further confirm that expected long-term idiosyncratic skewness deriving from growth options commands a negative return premium. These results suggest a behaviorally rational transmission mechanism from real options to stock returns through enhanced idiosyncratic skewness. Department of Financial Management and Control, ESADE Business School, Av. Pedralbes 60-62, 08034 Barcelona, Spain. Phone: +34 932 806 162 Ext. 2209. E-mail: luca.delviva@esade.edu Department of Finance, Aalto University School of Business, P.O. Box 21210, 00076 Helsinki, Finland. Phone: +358 403 538 434 . E-mail: eero.kasanen@aalto.fi Department of Accounting and Finance, University of Cyprus, P.O. Box 20537, CY-1678 Nicosia, Cyprus. Phone Number: +357 228 936 22. E-mail: lenos@ucy.ac. cy
Efficient Vehicle Detection System using OCR and REST API

– Number plate recognition has been one of the active and challenging research areas in the field of image processing and character recognition. It has numerous application which includes, law enforcement, fake vehicle registration, traffic control and electronic toll collection.In this paper an attempt is made to recognize characters in number plate using OCR through REST API.The vehicle plate image is obtained by the digital cameras and the image is processed in REST application program interface (API) to get the number plate information. In this context, the number plate area is recognised using Optical Character Recognition. The results show that the proposed system yields good recognition rates which are comparable to that neural network based number plate recognition.
Reviewing Design Movement Towards the Collective Computing Era: How will Future Design Activities Differ from Those in Current and Past Eras of Modern Computing?

This paper aims to envision design activities –design task, design process, and designer– for the collective computing era, an upcoming dominance of applications where many people interact with each other through many computing devices (Abowd, 2016). To achieve this aim, the typical design activities over the last two computing eras, personal and ubiquitous, are studied through a literature review. Based on the activities shift discovered there, new design activities for collective computing are envisioned. This study urges designers to change the way to work with user-data: from referring the small but in-depth ethnographical user-centered living-lab data (during the ubiquitous computing era), to create an understanding about both the individual user and the society or crowd as a whole (collective computing era). These different types of data provide designers with a rich understanding on the users in their contexts, both as individual and community levels. Also, the design process that authors envision is a process of co-exploration (merge of problem exploration and solution delivery), and co-evolution (the constant reflection back and forth with exchange of information) of design problem and solution spaces. Moreover, the designers in collective computing era have to take ethical and privacy issues into account. They have to create plans for dealing with the uncontrollability of the data generated and uncertainty about the social impact of the design that come with it. This research can trigger the designers to innovate and remodel their work at the forefront of new computing technologies.
Characterizing and Identifying Shills in Social Media

In addition to the lively discussion by genuine social media users, there is often an effort by duplicitous users to sway the conversation in a particular direction. Traditionally, these efforts are carried out by bots and crowdturfers who are programmed or recruited in mass to spread posts about a product, candidate or cause. In this work, we propose to study a new class of duplicitous user, “shills. ” Shills are professionals who carry out targeted replies to users with opposing views, often at the pleasure of a campaign. Shills are real users hired by an organization in order to promote an idea or agenda by targeted combating of any opposing opinions. We investigate whether we can effectively characterize shills and further verify if it is possible to automatically detect them. This work represents the ﬁrst effort in deﬁning social media shills, characterizing them, and suggesting a data mining framework to explore this new, challenging problem.
Role of Online Training Content in Enhancing Job Motivation

Online training content is a form of innovative training program where it may help to upgrade employees’ knowledge, skills and proactive behavior in times of Coronavirus Disease 2019 (COVID-19) and economic turbulent. Some of the challenges management face to adapt online training content included how to identify employees’ needs, define training priorities, fulfill employee expectations, provide latest training materials, plan relevant training topics and how to identify sophisticated electronic media to attract employee’s participation in daily operation. Recent published studies show that a well-designed online training will not be able to support its objective if online training content is not appropriately planned and implemented in organizations. Although the relationship has widely been examined, the role of online training content as an essential antecedent is not sufficiently discussed in the online training studies. Therefore, this research is done to evaluate the online training content and job motivation. Survey questionnaires were distributed to employees at regulatory federal government agencies in Malaysia. The hypothesis testing using SmartPLS showed that the ability of management to properly design online training objective and courses relevant to the job can lead to higher job motivation in organizations. This outcome can be used as significant recommendations by practitioners to understand different perspectives of online training content construct and formulate a proper online learning and teaching guidelines to maintain and enhance their organizations’ competitive advantage and performance in difficult times.
Online Appendix

In this appendix we use data from the National Diet and Nutrition Survey 20082011, which is an intake study of a representative sample of 3,073 UK adults and children. In Figure A.1 we document widespread excess consumption of added sugar. Panel (a) shows the cumulative distribution of calories from added sugar per day (separately for females and males) and panel (b) shows the cumulative distribution of the share of calories from added sugar. In both graphs we denote recommended medical levels with vertical lines. In the case of the level of calories from added sugar, the American Heart Association recommends no more than 100 calories per day from added sugar for females, and no more than 150 for males. In the case of the share of calories from added sugar, the World Health Organization recommends that ideally fewer than 5% of calories should be obtained from added sugar. The figure makes clear that the majority of individuals exceed these targets by a considerable amount.
Prospective study to identify the presence and prevalence of personality traits and other influences relevant to cosmetic surgery interest, patient selection and surgical outcome in a diverse aesthetic surgery practice

The key to a successful cosmetic surgery procedure lies in the selection of the patients. An anonymous voluntary participant study to evaluate whether not very well studied personality traits and secondary influences related to the decision to have cosmetic surgery could be negative predictors for cosmetic surgery patient selection and potentially surgical outcomes was performed. The mean Rosenberg SelfEsteem Scale score was 35.30 (SD = 4.22), indicating overall, the subjects had high self-esteem. Rosenberg score was 3 units lower for those that reported it would matter if no one thought the results looked good at least a little. There was a statistically significant difference in scores of Rosenberg based on age group. The mean Short Form-12 Mental Component Summary score was 51.93 (SD = 8.99), indicating that overall, the mental health of the subjects in this study may be slightly above the average. Short Form-12 scores were borderline significantly higher by almost two units for those that reported it would matter if no one thought the results looked good. The mean score on Body Image Questionnaire was 52.95 (SD = 5.48), indicating overall, subjects had a moderate level of body satisfaction. There was no statistically significant difference in scores of Body Image Questionnaire. The mean Dependent Personality Questionnaire score was 11.50 (SD = 2.88), indicating overall, subjects had mild to moderate level of dependent personality. Dependent Personality questionnaire yielded a significantly higher score for those that reported it would matter if no one thought the results looked good a little compared to not at all. Dependent Personality Questionnaire was borderline significantly lower for those reporting other race compared to white. The mean Beck’s Depression Inventory score was 4.90 (SD = 6.22), indicating overall, subjects had very mild depressive symptoms. When Beck’s was converted to a descriptive classification the majority of the subjects were considered to have minimal depression (86%) or normal ups and downs (84%). Beck’s was lower by over 5 units for women 31-45 compared to over 46.
A Comprehensive Review of Design Goals and Emerging Solutions for Adaptive Instructional Systems

This article is intended as a companion document to the more focused report provided by the author at the 2017 American Education Research Association (AERA) Conference as part of the Technology, Instruction, Cognition & Learning Special Interest Group’s Symposium on Intelligent Tutoring Systems (ITSs). Both the AERA talk and this article focus on adaptive instructional systems (AISs) which are comprised of learners, Intelligent Tutoring Systems (ITSs), and external (non-adaptive) instructional environments. AISs tailor instructional experiences for individual learners and teams of learners based on a model of their learning needs and preferences. An exemplar of an AIS is the Generalized Intelligent Framework for Tutoring (GIFT), an open source architecture for authoring, delivering, managing, and evaluating AIS technologies (tools and methods). This article reviews desired states for AISs in the context of enhancements to GIFT capabilities. This article covers a wide range of desired states for AISs and their affiliated design goals, challenges, and emerging solutions. While we consider the review presented in this paper comprehensive, we acknowledge that it is far from exhaustive. Our primary goal is to present the state of art, potential, and practice in ITS design in order to engage the education and training community in our quest to make AISs ubiquitous.
A Machine Learning Autism Classification Method based on Regression Analyses

: Autistic Spectrum Disorder (ASD) is a neurodevelopmental condition associated with significant healthcare costs; early diagnosis could substantially reduce these. The economic impact of autism reveals an urgent need for the development of easily implemented and effective screening methods. Therefore, time-efficient ASD screening is imperative to help health professionals and to inform individuals whether they should pursue formal clinical diagnosis. Presently, very limited autism datasets associated with screening are available and most of them are genetic in nature. We propose new machine learning framework related to autism screening of adults and adolescents that contain vital features and perform predictive analysis using Logistic Regression to reveal important information related to autism screening. We also perform an in-depth feature analysis on the two datasets using information gain (IG) and chi square testing (CHI) to determine the influential features that can be utilized in screening for autism. Results obtained reveal that machine learning technology was able to generate classification systems that have acceptable performance in terms of sensitivity, specificity and accuracy among others.
Cross Layer Design to Reduce the Latency for Effective Scheduling Technique for Mobile Ad hoc Networks

: Wireless Mesh Networking (WMN) technology provides a key revolutionary to the future backbone network access. WMN adopt a multihop based transmission to improve end to end data delivery. The 802.11s MAC (Medium Access Control) is designed using CSMA (Carrier Sense Medium Access) protocol which result in collision of slot due to failure in detecting hidden node in WMN. Various methodologies have been developed to optimize the MAC and hidden node and exposed node detection algorithm in recent time to utilize slot efficiently and reduce latency. Slot reutilization is an effective way in reducing latency but due to improper detection of hidden node of existing algorithm the latency is increased. To overcome this, our work propose an efficient device classification based MAC scheduler by adopting a cross layer design to reduce the latency. The experiment are conducted by varying network size and density and the outcome shows that the proposed approach perform better than existing CSMA/OCA in term of latency
Boosting Deep Neural Network Efficiency with Dual-Module Inference

Using deep neural networks (DNNs) in machine learning tasks is promising in delivering high-quality results but challenging to meet stringent latency requirements and energy constraints because of the memory-bound and the compute-bound execution pattern of DNNs. We propose a big-little dual-module inference to dynamically skip unnecessary memory accesses and computations to accelerate DNN inference. Leveraging the noise-resilient feature of nonlinear activation functions, we propose to use a lightweight little module that approximates the original DNN layer, termed as the big module, to compute activations of the insensitive region that are more noise-resilient. Hence, the expensive memory accesses and computations of the big module can be reduced as the results are only calculated in the sensitive region. For memory-bound models such as recurrent neural networks (RNNs), our method can reduce the overall memory accesses by 40% on average and achieve 1.54x to 1.75x speedup on a commodity CPU-based server platform with a negligible impact on model quality. In addition, our method can reduce the operations of the compute-bound models such as convolutional neural networks (CNNs) by 3.02x, with only a 0.5% accuracy drop.
G LOBAL -L OCAL N ETWORK FOR L EARNING D EPTH V IA I NTERACTION

How do babies learn to perceive the three-dimensional structure of the world? Motivated by the astonishing capabilities of natural intelligent agents and inspired by theories from psychology, this paper explores the idea that perception gets coupled to 3D properties of the world via interaction with the environment. Existing works for depth estimation require either massive amounts of annotated training data or some form of hard-coded geometrical constraint. This paper explores a new approach to learning depth perception requiring neither of those. Speciﬁcally, we make use of training data similar to what would be available to an agent interacting with the environment via haptic feedback: very sparse depth measurements, just a few pixels per image. To learn from such extremely sparse supervision, we design a specialized global-local network architecture that takes a pair of images and outputs a latent representation of the observer’s motion between the images and a dense depth map.
Spectral and 3D spatial granular synthesis in Csound

This work presents ongoing research based on the design of an environment for Spatial Synthesis of Sound using Csound through granular synthesis, spectral data based synthesis and 3D spatialisation. Spatial Synthesis of Sound may be conceived as a particular mode of sonic production in which the composer generates the sound together with its spatial features. Though this type of conception has long lived in the minds and work of most composers (specially in electroacoustic music), some strategies applied here were inspired by the work of Gary Kendall [13]. Kendall makes specifc mention to both Granular Synthesis and Spectral data based synthesis as examples of resources through which the composer may partition the sonic stream in both the time domain and the frequency domain, respectively. These procedures allow a detailed spatial treatment of each one of the obtained parts of a sound which, in turn, may lead to realistic or unusual spatial images. The aim is not to describe in detail granular synthesis, nor spectral data based synthesis or sound spatialisation techniques, but to describe the particular strategies in the design for the aforementioned purposes.
Importance of Digitech Adoption for Providing Efficient Healthcare Services during COVID-19

: In recent times, where covid-19 is spread across the world, physicians, as well as patient, needs to keep the social distancing. On the other side, during the lockdown period, where the patient avoids to go outside of their home due to the fear of n-cov infection digital technologies like mHealth serves the purpose to connect with the physician. Physicians are not only able to give the advice based on the symptoms of a disease but are also able to share the preventive care as well as able to answer the queries of patient and advise them. With the help of a digital platform, a physician can share their knowledge and train each other. The study qualitatively validates the use of digital technology during the pandemic situation and develops the scale for the future empirical study.
DETECTING CRISES, JUMPS, AND CHANGES IN REGIME

Automated model selection and impulse indicator saturation contribute two useful tools to a coherent empirical framework for detecting crises, jumps, and changes in regime. This paper proposes extensions to impulse indicator saturation that may provide more power to detect breaks in common economic situations. Empirical applications include seasonally unadjusted UK narrow money demand, U.S. equity prices, U.K. exchange rates, the U.S. Taylor rule, and forecasts of government debt.
Improving the Classification Accuracy using Recursive Feature Elimination with Cross-Validation

: In Machine Learning (ML) community, researchers are proposing complex models for real-life problems to achieve higher accuracy, which requires high computing and other resources. Fields like computer vision and NLP have given rise to deep learning with complex and high computational models setting the trend to apply them in almost all the fields While they help where we have an abundance of data and complex relationships, simpler models still can do wonders and on their day can challenge these behemoths. Here feature selection plays an important role and drastically improves the model accuracy. We have proposed the Recursive Feature Elimination with Cross-Validation (RFECV) approach for Type-II diabetes prediction to improve the classification accuracy. The major challenge with this approach is to deal with overfitting issue and improve the accuracy without unnecessary record deletion. We have applied other pre-processing methods and then have applied five different classical ML algorithms Logistic regression, Artificial Neural Networks, Naïve Bayes, Support Vector Machine, and Decision Tree (DT) to predict diabetes onset. LR provided the best accuracy (84%), and the rest of the models remains very close to each other.