Miracle-dz/UniFAR-FTUs huggingface dataset:
PDS Analyst's Notebook for MSL and MER: Interface Update and Image Drawing Tools

Introduction: The PDS Analyst’s Notebooks (AN) [1] for Mars Science Laboratory (MSL) [2] and Mars Exploration Rover (MER) [3] provide integrated access to peer-reviewed, released data provided by the instrument teams, supported by documentation describing data format, content, and calibration and providing insight into why and how particular observations were made. Data, documentation, and support files are incorporated on a daily basis into the science team’s version of the Notebook. This approach assists with data validation and builds on strong collaboration between data producers and PDS archivists that begins soon after mission selection with creation of the project data management and archive plans. The public version of the MSL and MER AN (http://an.rsl.wustl.edu) is comprised of peerreviewed, released data and is updated coincident with PDS data releases as defined in mission archive plans. All data and documents have been ITAR cleared. Observation planning and targeting information is extracted from mission science plans, including instrument settings and observation parameters. Source commands are linked with resulting data products, albeit with limits due to the absence of round trip data tracking. In this paper, we focus on the updated user interface and Notebook organization as well as the addition of image drawing tools as a value-added component. Interface Update: The Analyst’s Notebook has always been available as a set of web-accessible pages divided into main sections: mission, sol, search, map, resources, user settings/cart, and help. Prior to the latest update, each section was a separate web page, and switching between sections resulted in loss of current selections. With the updated interface, the Notebook is transformed into a single-page web application (Fig. 1). While still accessed via a web browser, the Notebook operates as an application. Now the user may switch between sections without losing their selections. For example, a user might open a product detail page from Sol section. The product remains open when the user switches to traverse map or any other section. Shifting from a set of web pages to a web application is straightforward conceptually, but the implementation is more challenging. The Notebook application is a combination of client-side and server-side code using both off the shelf components and custom code. The Prograess Telerik DevCraft framework was seleted as the basis for layout and windowing tasks, saving two
Aplikasi Augmented Reality Dinamis Pengenalan Huruf Kanji (AR-Kanji) Berbasis Android

Huruf Kanji bagi pembelajar bahasa Jepang dirasa paling sulit untuk dipelajari karena Kanji memiliki karakteristik yang unik. Setiap karakteristik Kanji memiliki jumlah lebih dari satu, sehingga sulit untuk dihapal. Aplikasi Augmented Reality Kanji dibuat untuk mengatasi kesulitan pembelajar bahasa Jepang dalam mengenal atau mempelajari huruf Kanji. Augmented Reality merupakan teknologi penggabungan objek virtual dua dimensi atau tiga dimensi ke dalam lingkungan nyata secara real-time. Penelitian ini menggunakan software utama Unity 3D dan Web server Library Vuforia dengan Bahasa pemrograman C#. Aplikasi AR Kanji ini dibuat melalui beberapa tahapan proses yaitu memperoleh informasi kebutuhan sistem, perancangan dan dilanjutkan dengan tahapan implementasi. Hasil yang didapat yaitu image-target dari masing-masing huruf Kanji dan aplikasi AR Kanji berbasis Android. Pengujian aplikasi AR Kanji menunjukan, aplikasi dapat menampilkan setiap informasi beserta audio dari masing-masing huruf Kanji yang di-tracking dengan kondisi cahaya yang baik dan dapat digunakan sesuai fungsinya pada perangkat smartphone dengan spesifikasi yang berbeda. Tingkat kepuasan pengguna terhadap aplikasi AR Kanji menunjukkan angka 85%. Kata Kunci: Augmented Reality, Huruf Kanji, Unity 3D, Android
Optimization-Inspired Compact Deep Compressive Sensing

In order to improve CS performance of natural images, in this paper, we propose a novel framework to design an OPtimization-INspired Explicable deep Network, dubbed OPINE-Net, for adaptive sampling and recovery. Both orthogonal and binary constraints of sampling matrix are incorporated into OPINE-Net simultaneously. In particular, OPINE-Net is composed of three subnets: sampling subnet, initialization subnet and recovery subnet, and all the parameters in OPINE-Net (e.g. sampling matrix, nonlinear transforms, shrinkage threshold) are learned end-to-end, rather than hand-crafted. Moreover, considering the relationship among neighboring blocks, an enhanced version OPINE-Net$^+$ is developed, which allows image blocks to be sampled independently but reconstructed jointly to further enhance the performance. In addition, some interesting findings of learned sampling matrix are presented. Compared with existing state-of-the-art network-based CS methods, the proposed hardware-friendly OPINE-Nets not only achieve better performance but also require much fewer parameters and much less storage space, while maintaining a real-time running speed.
Power and Area Efficient Sub-threshold 6T SRAM with Horizontal Local Bit-Lines and Bit-Interleaving

SRAM in the typical microprocessor consumes a substantial amount of on-chip area and significantly contributes to static power dissipation. This study presents a new SRAM architecture with minimum area that utilizes a modified 6T SRAM cell for sub- and near-threshold operation in ultra-low power applications. This new architecture introduces horizontal bit-lines, mitigates half-select disturb, and supports bit-interleaving. The proposed design's stability was thoroughly tested in the presence of process, temperature, and voltage variations and compared to the standard 6T and traditional 8T cells. A 32kb SRAM block implementing the proposed architecture was designed, simulated, and compared to a traditional 8T SRAM cell block. The results show that the proposed design has lower power consumption than the 8T SRAM block, comparable read performance, and better write performance. This was all achieved while only having a 10\% increase in area per bit over the conventional 6T thin-cell layout.
Authentication by gesture recognition: a dynamic biometric application

With the expansion of digital information and the number of people potentially able to access it, there are increasing demands for efficient, secure systems which authenticate users effectively. At the end of the 1960s, IBM defined three authentication factors: knowledge factor, which relates to “something the entity knows”; ownership factor, relating to “something the entity has/possesses”; inherent factor, which can be summarised as “something the entity is or does”. Each of these factors possesses its own limitations: knowledge factors can be forgotten or discovered by a fraudster; ownership factors can be lost, stolen or counterfeited. These nuisances have led to increased use of biometric authentication as a means of increasing security. However, conventional biometrics are static, so if compromised cannot be changed by the user. This has led to interest in techniques that authenticate using changeable multi-factor authentication measures that are influenced by biometrics, rather than being completely reliant on them. Investigating the practicality and security of such techniques provided the motivation for this research. First we needed to identify these “easily changeable biometrics” and it led us to define a new biometric family, which we called the dynamic biometrics. We then studied the security characteristics of this new family. Out of the dynamic biometrics, we chose to focus on one of these elements, authentication based on gesture recognition. We conducted several experiments to assess the ability of this authentication technique to authenticate the genuine user and reject any impostors, either if these impostors do not know the gesture or do random movements. We continued by looking for a secure place to store the gesture recognition’s template and run the application. We evaluated the possibility of doing that on a personal limited device, such as a Smart Card. Then we designed a protocol to use a gesture recognition application which we analysed with respect to several threat vectors. Benoit Ducray 2017 2 PhD Thesis
Artificial Neural Networks in Swedish Speech Synthesis

Text-to-speech (TTS) systems have entered our daily lives in the form of smart assistants and many other applications. Contemporary research applies machine learning and artiﬁcial neural networks (ANNs) to synthesize speech. It has been shown that these systems outperform the older concatenative and parametric methods. In this paper, ANN-based methods for speech synthesis are explored and one of the methods is implemented for the Swedish language. The implemented method is dubbed “Tacotron” and is a ﬁrst step towards end-to-end ANN-based TTS which puts many different ANN-techniques to work. The resulting system is compared to a parametric TTS through a strength-of-preference test that is carried out with 20 Swedish speaking subjects. A statistically signiﬁcant preference for the ANN-based TTS is found. Test subjects indicate that the ANN-based TTS performs better than the parametric TTS when it comes to audio quality and naturalness but sometimes lacks in intelligibility.
Personalized Digital Body: Enhancing Body Ownership and Spatial Presence in Virtual Reality

person’s sense of acceptance of a virtual body as his or her own is generally called virtual body ownership (VBOI). Having such a mental model of one’s own body transferred to a virtual human surrogate is known to play a critical role in one’s sense of presence in a virtual environment. Our focus in this dissertation is on top-down processing based on visual perception in both the visuomotor and the visuotactile domains, using visually personalized body cues. The visual cues we study here range from ones that we refer to as direct and others that we classify as indirect. Direct cues are associated with body parts that play a central role in the task we are performing. Such parts typically dominate a person’s foveal view and will include one or both of their hands. Indirect body cues come from body parts that are normally seen in our peripheral view, e.g., legs and torso, and that are often observed through some mediation and are not directly associated with the current task. This dissertation studies how and to what degree direct and indirect cues affect a person’s sense of VBOI for which they are receiving direct and, sometimes, inaccurate cues, and to investigate the relationship between enhanced virtual body ownership and task performance. Our experiments support the importance of a personalized representation, even for indirect cues. Additionally, we studied gradual versus instantaneous transition between one’s own body and a virtual surrogate body, and between one’s real-world environment and a virtual environment. We demonstrate that gradual transition has a significant influence on virtual body ownership and presence. In a followon study, we increase fidelity by using a personalized hand. Here, we demonstrate that a personalized hand significantly improves dominant visual illusions, resulting in more accurate perception of virtual object sizes.
An Improved Approach For Multi-Robot Localization

Cooperative multi-robot localization techniques use sensor measurements to estimate poses (locations, orientations) of robots relative to a given map of the environment. Existing approaches update a robot's pose instantly whenever it detects another robot. However, such instant update may not be always necessary and effective, since both robots' pose estimates could be highly uncertain at the time of the detection. In this thesis, we develop a new information exchange mechanism to collaborative multi-robot localization. We also propose a new scheme to calculate how much information is contained in a robot's belief by using entropy. Instead of updating beliefs whenever detection occurs, our approach first compares the beliefs of the robots which are involved in the detection, and then decide whether the information exchange is necessary. Therefore, it avoids unnecessary information exchange whenever one robot perceives another robot. On the other hand, this approach does allow information exchange between detecting robots and such information exchange always contributes positively to the localization process, hence, improving the effectiveness and efficiency of multi-robot localization. The technique has been implemented and tested using two mobile robots as well as simulations. The results indicate significant improvements in localization speed and accuracy when compared to the single mobile robot localization.
Supervised Anomaly Detection using Clustering-based Normal Behaviour Modeling

In this paper we present a clustering based supervised anomaly detection technique. A set of training data consisting of normal data only are divided into clusters which are represented by their profiles to form the normality model. Any deviation from the normality model is treated as attack. Methods for clustering, training and detection are provided. Our technique produces good results for KDD CUP 1999 datasets. Performance measuring methods like Recall, Precision, and F1measure for good clustering are applied. Measurings of performance are evaluated with important algorithms of ANID
Ieee Transactions on Pattern Analysis and Machine Intelligence Learning to Generate Chairs, Tables and Cars with Convolutional Networks

—We train generative 'up-convolutional' neural networks which are able to generate images of objects given object style, viewpoint, and color. We train the networks on rendered 3D models of chairs, tables, and cars. Our experiments show that the networks do not merely learn all images by heart, but rather find a meaningful representation of 3D models allowing them to assess the similarity of different models, interpolate between given views to generate the missing ones, extrapolate views, and invent new objects not present in the training set by recombining training instances, or even two different object classes. Moreover, we show that such generative networks can be used to find correspondences between different objects from the dataset, outperforming existing approaches on this task.
Research on the Model Construction and Development of Computer Information Acquisition System

Nowadays, the number of crimes committed by computers is increasing, so computer forensics technology has become the focus of people’s research and attention. This paper designs the module structure of the computer information acquisition system, and focuses on the realization of the key technologies of the computer information acquisition system. It discusses the application of data mining on the web, and focuses on the analysis of web text mining technology. This paper proposes a method to acquire UNIX kernel information at the user level, and introduces a utility program to acquire index node table in UNIX system. At the same time, a prototype of a professional news information acquisition system is proposed. Taking the prototype of the system as the background, the application of information acquisition on the web is realized by using relevant technologies of web information search and web text mining.
Mitochondrial permeability transition following calcium overload. - Its role in neuronal cell death and potential as a pharmacological target.

There is currently no clinically available drug with neuroprotective properties to limit the evolving cell death following e.g. stroke or traumatic brain injury. The mitochondrial permeability transition (mPT) is a potential pathological mechanism causing cell death in the CNS. As the name implies, the mPT is defined by a sudden increase in permeability of the inner mitochondrial membrane, whose normal impermeable state is fundamental for the bioenergetic function of mitochondria. The objective of the present studies was to characterize the mPT phenomenon in isolated rodent brain mitochondria. Mitochondria serve an important role in cellular calcium homeostasis and buffer transient increases in calcium, but mitochondrial calcium overload is also the prime trigger for mPT. In the present studies, we found that brain mitochondria readily undergo changes attributable to mPT induction such as swelling, loss of membrane potential, uncoupling of oxidative phosphorylation and respiratory inhibition. The mitochondrial generation of reactive oxygen species (ROS) was also increased following mPT, and mitochondria became permeable to NAD(H). Cyclosporin A (CsA) binds to the mitochondrial protein cyclophilin D (CypD) and can thereby inhibit mPT, an effect that is unrelated to its immunosuppressive action, and mPT in brain mitochondria was found to be highly sensitive to CsA inhibition. CsA has demonstrated prominent neuroprotective properties in several different animal models of neurological disease, and recent experiments subjecting mice lacking CypD to ischemia support the conclusion that the effect of CsA at least in this model is mediated by mPT inhibition. A library of CsA analogs was evaluated for mPT-inhibiting and ROS-reducing properties, and two newly developed non-immunosuppressive CsA analogs were found to be potent inhibitors of mPT already at nanomolar concentrations. Mitochondria take up free calcium ions but retain them as inactive calcium phosphate complexes in order to prevent mPT. We find that increasing the conductance of potassium increases the pH of the matrix and this enhances the mitochondrial buffering of calcium, probably by increasing the complexation of calcium. Drugs that inhibit mPT or indirectly prevent mPT by enhancing the mitochondrial complexation of calcium and thereby their resistance to calcium overload may therefore prove to be successful strategies in limiting accidental cell death in the CNS.
Deep Learning Application to 5G Physical Layer for Channel Estimation and CSI Feedback Improvement

Starting from the last decades of the 20th century, Machine Learning has been widely applied in many engineering fields, such as communications, speech and image processing, computer vision and robotics, resulting particularly effective and useful in contexts where a rigorous mathematical model of the problem is too hard to be elaborated. Focusing on wireless communication systems, in recent years Machine Learning applications to the upper layers have been minutely explored for various purposes, like the deployment of cognitive radio and Self Organized Networks or the resource management, while its application to the physical layer has been somehow overlooked. The purpose of this thesis is to investigate the potential use of neural networks for the optimization of specific physical layer blocks in a communication system, taking into account the peculiar characteristics of the emerging radio technologies based on 5G standard (e.g. massive Multiple-Input Multiple-Output, beamforming, millimeter Waves) and all their related challenges. In particular, the analysis focuses on channel estimation and Channel State Information feedback reporting blocks, providing data and statistics that are representative of how Machine Learning algorithms introduction affects radio link performance, in terms of BLock Error Rate and throughput.
Investigation into the metabolic control of lipid accumulation in the marine diatom Phaeodactylum tricornutum

119 Background . 120 Materials and Methods . 123 Culture and Growth Conditions . 124 Physiological Parameter Analysis . 124 Fatty Acid Extraction and Identification via GC-MS . 126 vii TABLE OF CONTENTS – CONTINUED RNA Sampling, Extraction, and Sequencing . 127 Transcript Assembly . 128 Transcript Identification and Pathway Analysis . 129 Results and Discussion . 130 Growth Analysis Under Nitrate and Phosphate Depletion . 130 Fatty Acid Accumulation Under Nutrient Stress . 135 RNA Sequencing Analysis . 139 Urea Cycle is Coupled to Central Carbon Metabolism . 142 Carbon Concentrating Mechanisms . 144 Potential Metabolic Control Points for Lipid Accumulation . 148 Conclusions . 151 References . 152 5. OVERALL CONCLUSIONS . 157 Future Directions . 163 Specific Targeting of Predicted Metabolic Control Genes . 164 Pushing the Limit of Fatty Acid Biosynthesis . 165 References . 166 LITERATURE CITED . 168
Identification of yeast and yeast-like fungal species in the Upper Midwest using physiological and DNA sequence data

Morphological and biochemical testing of unknown yeast and yeast-like isolates is a useful and sometimes necessary step in identifying species. Morphological characters include such things as: colony color, presence or absence of hyphae, cell shape and size. Biochemical tests frequently include aerobic carbon and nitrogen utilization tests as well as carbon fermentation tests. The purpose of this research was to isolate and test yeast and yeast-like fungi isolated from various plant related environments in the Midwest with a barrage of phenotypic tests. Thirty-two yeast-like isolates were tested from 14 different environments. In general the isolates were capable of utilizing a wide variety of carbon compounds aerobically and were not capable of fermentation. A large portion of the isolates were capable of producing hyphae. A diversity of yeast-like fungi was found from the environments sampled. The isolates included representatives from both the basidiomycetes and the ascomycetes.
Reactive Programming with Events

The reactive programming model is largely different to what we’re used to as we don’t have a full control over the application’s control flow. As a result, reactive applications need to be structured differently and need to be written using different patterns. We build an easier way for developing reactive applications. Our work integrates declarative and imperative approach to reactive programming and uses the notion of event as the unifying concept. We discuss the problem of memory management in this scenario and develop a technique for collecting not only events that are not reachable, but also events that cannot trigger any action. Next, we present a language extension that makes it possible to wait for occurrences of events that match some defined pattern. The extension isn’t bound to the reactive programming model and can be used for concurrent and parallel programming scenarios as well. Finally, we develop a reactive programming library that builds on top of the ideas discussed earlier and we present semantics of the library to enable formal reasoning about reactive programs.
Packed malware variants detection using deep belief networks

Malware is one of the most serious network security threats. To detect unknown variants of malware, many researches have proposed various methods of malware detection based on machine learning in recent years. However, modern malware is often protected by software packers, obfuscation, and other technologies, which bring challenges to malware analysis and detection. In this paper, we propose a system call based malware detection technology. By comparing malware and benign software in a sandbox environment, a sensitive system call context is extracted based on information gain, which reduces obfuscation caused by a normal system call. By using the deep belief network, we train a malware detection model with sensitive system call context to improve the detection accuracy.