nils-herrmann/clean_scirepeval huggingface dataset:
Advanced image fusion to overlay coronary sinus anatomy with real-time fluoroscopy to facilitate left ventricular lead implantation in CRT.

Failure rate for left ventricular (LV) lead implantation in cardiac resynchronization therapy (CRT) is up to 12%. The use of segmentation tools, advanced image registration software, and high-fidelity images from computerized tomography (CT) and cardiac magnetic resonance (CMR) of the coronary sinus (CS) can guide LV lead implantation. We evaluated the feasibility of advanced image registration onto live fluoroscopic images to allow successful LV lead placement.



Twelve patients (11 male, 59 ± 16.8 years) undergoing CRT had three-dimensional (3D) whole-heart imaging (six CT, six CMR). Eight patients had at least one previously failed LV lead implant. Using segmentation software, anatomical models of the cardiac chambers, CS, and its branches were overlaid onto the live fluoroscopy using a prototype version of the Philips EP Navigator software to guide lead implantation.



We achieved high-fidelity segmentations of cardiac chambers, coronary vein anatomy, and accurate registration between the 3D anatomical models and the live fluoroscopy in all 12 patients confirmed by balloon occlusion angiography. The CS was cannulated successfully in every patient and in 11, an LV lead was implanted successfully. (One patient had no acceptable lead values due to extensive myocardial scar).



Using overlaid 3D segmentations of the CS and cardiac chambers, it is feasible to guide CRT implantation in real time by fusing advanced imaging and fluoroscopy. This enabled successful CRT in a group of patients with previously failed implants. This technology has the potential to facilitate CRT and improve implant success.
Cognitive Radio Ad-hoc Networks: A Routing Perspective

Acknowledgments First and foremost, I would like to thank my supervisor Prof. Luigi Paura, for his trust and unwavering support. I am really grateful to him for his kind assistance towards the difficulties that I have faced in my personal life as well as his infinite passion in research work, which inspired me to work hard. I would like to express my gratitude to Dr. Marcello Caleffi and Dr. Angela Sara Cacciapuoti for their invaluable guidance and great support throughout my PhD program. I am also grateful to my colleagues, Roberto Savoia and Domenico Izzo as well as my friends in other departments, Naveed Ahmad, Tayyab Naqash and Adnan Daud Khan. A special thanks to my family members for their open-mindedness and endless support. Finally, thanks to my wife Nadia Refat for her love and understanding .
Model compression via distillation and quantization

Deep neural networks (DNNs) continue to make significant advances, solving tasks from image classification to translation or reinforcement learning. One aspect of the field receiving considerable attention is efficiently executing deep models in resource-constrained environments, such as mobile or embedded devices. This paper focuses on this problem, and proposes two new compression methods, which jointly leverage weight quantization and distillation of larger networks, called “teachers,” into compressed “student” networks. The first method we propose is called quantized distillation and leverages distillation during the training process, by incorporating distillation loss, expressed with respect to the teacher network, into the training of a smaller student network whose weights are quantized to a limited set of levels. The second method, differentiable quantization, optimizes the location of quantization points through stochastic gradient descent, to better fit the behavior of the teacher model. We validate both methods through experiments on convolutional and recurrent architectures. We show that quantized shallow students can reach similar accuracy levels to state-of-the-art full-precision teacher models, while providing up to order of magnitude compression, and inference speedup that is almost linear in the depth reduction. In sum, our results enable DNNs for resource-constrained environments to leverage architecture and accuracy advances developed on more powerful devices.
Automatic Car Parking and Controlling System Using Programmable Logic Controller ( PLC )

Well-organized vehicle parking can assist drivers gratifying by protecting car energy as well as time consuming. In this paper, the automation process of an automatic car parking system is designed using a fully functional ladder logic based LOGO!12/24 RC, which is a small programmable logic controller (PLC). Infrared sensor (IR) electronic sensors were installed at the entrance and departure gates to sense the car those are waiting for either entry or exit. After that it gives the input signals to PLC to count the number of vehicles entering and leaving the park respectively. The developed system automatically can monitor and restrict the vehicles inside the parking space. The number of cars available in the park will be the difference of the number of vehicles entering and the number of vehicles leaving. When a car approaches to the entry gate, PLC will decide whether any space is available or not. If no space is available, the PLC will then send signal to entry gate to keep the gate closed and turn on the indication “Car Park Full”. If there is space in the park, the entry gate will open to allow the car to enter the park. Similarly, at the time of exit, the PLC will send signal to the exit gate to open and allow the car to leave the park after paid the parking payment. All these activities make the car parking system completely automatic. The development of this system is cost effective and useful to make solutions to car parking space problems in city areas.  Automate car parking, Sensor, PLC, Hardware, Software
High Power Laser Diode Driver Based on Power Converter Technology

This paper describes the design of a high speed semiconductor laser diode driver designed for driving 500 mW to 1.5 W diodes at full optical power modulation up to frequencies of 10 MHz. The duty cycle of the modulation may be varied. A switching power-converter based current source allows a higher power delivery efficiency to the diode than in previous designs, allowing for a more modest power supply and dissipation requirements. A dynamic ripple cancellation circuit reduces the power converter output current ripple to less than 1% of fullscale current. The circuit is capable of delivering up to 2.5 A to a laser load, with a 10–90% switching risetime from laser threshold to full on of less than 20 ns.
A new method on designing and simulating CNTFET_based ternary gates and arithmetic circuits

This paper presents a new design of three-valued logic gates on the basis of carbon nanotube transistors. Next, the proposed circuit is compared with the existing models of circuits. The simulation results, using HSPICE indicate that the proposed model outperform the existing models in terms of power and delay. Moreover, using load-transistor results in 48.23% reductions in terms of power-delay-product (PDP) compared with the best model ever observed. Furthermore, the implementation of ternary arithmetic circuits such as half adder and multiplier using load-transistor has been investigated. An added common term between Sum and Carry output in half-adder (HA) and Carry and Product output in lower multiplier (MUL) has been used in arithmetic circuits. Considering the fact that a better proposal with higher performance has been utilized for the implementation of three-valued gates, our model results in the reduction of 9.95% and 23.87% PDP in HA and MUL respectively compared with the best models observed.
3 DCityDB-a 3 D geodatabase solution for the management , analysis , and visualization of semantic 3 D city models based on CityGML

Over the last decade, more and more cities and even countries worldwide are creating semantic 3D city models of their physical environment based on the international CityGML standard issued by the Open Geospatial Consortium (OGC). CityGML is an open data model and XML-based data exchange format describing the most relevant urban and landscape objects along with their spatial and non-spatial attributes, relations, and their complex hierarchical structures in five levels of detail. 3D city models, which are structured according to CityGML, are often used for various complex GIS simulation and analysis tasks, which go far beyond pure 3D visualization. Due to the large size and complexity of the sometimes country-wide 3D geospatial data, the GIS software vendors and service providers face many challenges when building 3D spatial data infrastructures for realizing the efficient storage, analysis, management, interaction, and visualization of the 3D city models based on the CityGML standard. Hence, there has been strong demand for an open and comprehensive software solution that can provide full support of the aforementioned functionalities. The ‘3D City Database’ (3DCityDB) is a free 3D geo-database solution for CityGMLbased 3D city models. 3DCityDB has been developed as an Open Source and platform-independent software suite to facilitate the development and deployment of 3D city model applications. The 3DCityDB software package consists of a database schema for spatially enhanced relational database management systems (ORACLE Spatial or PostgreSQL/PostGIS) with a set of database procedures and software tools allowing to import, manage, analyze, visualize, and export virtual 3D city models according to the CityGML standard. Within this paper, the software suite is illustrated and explained in detail with respect to the related technical implementations and the underlying conceptual software design. Moreover, the utilization of 3DCityDB in different projects and practical application fields are also presented in this paper.
Oriented Bounding Box Computation Using Particle Swarm Optimization

The problem of finding the optimal oriented bounding box (OBB) for a given set of points in R, yet simple to state, is computationally challenging. Existing state-of-the-art methods dealing with this problem are either exact but slow, or fast but very approximative and unreliable. We propose a method based on Particle Swarm Optimization (PSO) to approximate solutions both effectively and accurately. The original PSO algorithm is modified so as to search for optimal solutions over the rotation group SO(3). Particles are defined as 3D rotation matrices and operations are expressed over SO(3) using matrix products, exponentials and logarithms. The symmetry of the problem is also exploited. Numerical experiments show that the proposed algorithm outperforms existing methods, often by far.
New Branch Prediction Vulnerabilities in OpenSSL and Necessary Software Countermeasures

Software based side-channel attacks allow an unprivileged spy process to extract secret information from a victim (cryptosystem) process by exploiting some indirect leakage of “side-channel” information. It has been realized that some components of modern computer microarchitectures leak certain side-channel information and can create unforeseen security risks. An example of such MicroArchitectural Side-Channel Analysis is the Cache Attack — a group of attacks that exploit information leaks from cache latencies [4, 7, 13, 15, 17]. Public awareness of Cache Attack vulnerabilities lead software writers of OpenSSL (version 0.9.8a and subsequent versions) to incorporate countermeasures for preventing these attacks. In this paper, we present a new and yet unforeseen side channel attack that is enabled by the recently published Simple Branch Prediction Analysis (SBPA) which is another type of MicroArchitectural Analysis, cf. . We show that modular inversion — a critical primitive in public key cryptography — is a natural target of SBPA attacks because it typically uses the Binary Extended Euclidean algorithm whose nature is an input-centric sequence of conditional branches. Our results show that SBPA can be used to extract secret parameters during the execution of the Binary Extended Euclidean algorithm. This poses a new potential risk to crypto-applications such as OpenSSL, which already employs Cache Attack countermeasures. Thus, it is necessary to develop new software mitigation techniques for BPA and incorporate them with cache analysis countermeasures in security applications. To mitigate this new risk in full generality, we apply a security-aware algorithm design methodology and propose some changes to the CRT-RSA algorithm flow. These changes either avoid some of the steps that require modular inversion, or remove the critical information leak from this procedure. In addition, we also show by example that, independently of the required changes in the algorithms, careful software analysis is also required in order to assure that the software implementation does not inadvertently introduce branches that may expose the application to SBPA attacks. These offer several simple ways for modifying OpenSSL in order to mitigate Branch Prediction Attacks.
Series Short-Circuit Failures in Three-Level ANPC Converters

Three-level converters like the Active-Neutral-Point-Clamped type can suffer from series short-circuit failures. These cases cannot exist for the standard two-level converter except when a series connection of modules is used to increase the voltage rating. Even then, the course of the short circuits differs in some aspects due to the different structures of the converters. A series short-circuit scenario consists of two IGBTs and / or diodes, a freewheeling diode and the shorted module. It leads to different variants of failure types and semiconductor stresses in comparison to a two-level converter. The behavior of the faults is derived from equivalent circuits and proven by measurements on a three-level converter equipped with state-of-the-art 3.3 kV-IGBT modules including diodes. Some of the series short circuits are yet described for the Neutral-Point-Clamped converter, but not in total for the active one. The presented investigations within this paper examine the origin, the possible trends of voltage and current as well as possible secondary failure cases.
Infrapatellar saphenous neuralgia - diagnosis and treatment.

Persistent anterior knee pain, especially after surgery, can be very frustrating for the patient and the clinician. Injury to the infrapatellar branch of the saphenous nerve (IPS) is not uncommon after knee surgeries and trauma, yet the diagnosis and treatment of IPS neuralgia is not usually taught in pain training programs. In this case report, we describe the anatomy of the saphenous nerve and specifically the infrapatellar saphenous nerve branch; we also discuss the types of surgical trauma, the clinical presentation, the diagnostic modalities, the diagnostic injection technique, and the treatment options. As early as 1945, surgeons were cautioned regarding the potential surgical trauma to the IPS. Although many authors dismissed the nerve damage as unavoidable, the IPS is now recognized as a potential cause of persistent anterior and anteriomedial knee pain. Even more concerning, damage to peripheral nerves such as the IPS has been identified as the cause and potential perpetuating factor for conditions such as complex regional pain syndromes (CRPS). Because the clinical presentation may be vague, it has often been misdiagnosed and underdiagnosed. There is a documented vasomotor instability, but, unfortunately, sympathetic blocks will not address the underlying pathology, and therefore patients often will not respond to this modality, although the correct diagnosis can lead to rapid and gratifying resolution of the pathology. An entity unknown to the clinician is never diagnosed, and so it is important to familiarize pain physicians with IPS neuropathy so that they may be able to offer assistance when this painful condition arises.
The effect of music background on the emotional appraisal of film sequences

In this study the effects of musical background on the emotional appraisal of film sequences was investigated. Four pairs of polar emotions defined in Plutchik’s model were used as basic emotional qualities: joy-sadness, anticipation-surprise, fear-anger, and trustdisgust. In the preliminary study eight film sequences and eight music themes were selected as the best representatives of all eight Plutchik’s emotions. In the main experiment the participant judged the emotional qualities of film-music combinations on eight seven-point scales. Half of the combinations were congruent (e.g. joyful film joyful music), and half were incongruent (e.g. joyful film sad music). Results have shown that visual information (film) had greater effects on the emotion appraisal than auditory information (music). The modulation effects of music background depend on emotional qualities. In some incongruent combinations (joysadness) the modulations in the expected directions were obtained (e.g. joyful music reduces the sadness of a sad film), in some cases (anger-fear) no modulation effects were obtained, and in some cases (trust-disgust, anticipation-surprise) the modulation effects were in an unexpected direction (e.g. trustful music increased the appraisal of disgust of a disgusting film). These results suggest that the appraisals of conjoint effects of emotions depend on the medium (film masks the music) and emotional quality (three types of modulation effects).
Issues in User Authentication 1

CHI 2007  Security user studies: methodol gies and best practices, April 2007.  In this brief paper we outline our recent work on u sability studies of user authentication systems. In particul ar, we have conducted studies on password managers and graphical password systems, using in-lab experiment s, interviews, and broader field studies. We then disc us some of the important issues arising, including mental m odels of security, the role of persuasion, and the nature of identify theft.
Intentional modulation of emotional responding to unpleasant pictures: an ERP study.

Intentionally altering responses to unpleasant stimuli affects physiological and hemodynamic activity associated with emotional and cognitive processing. In the present experiment, we measured the late-positive potential (LPP) of the visually evoked event-related brain potential to examine the effects of intentional emotion modulation on electrophysiological correlates of emotional and cognitive processing. Seventeen participants received instructions to view, suppress, and enhance emotional responses to unpleasant stimuli. Results revealed significantly decreased electrophysiological activity during suppression of emotional responses beginning around 250 ms poststimulus and lasting several hundred milliseconds. These data suggest that ERPs are sensitive to emotion modulation/regulation processes.
A 32kb 10T Subthreshold SRAM Array with Bit-Interleaving and Differential Read Scheme in 90nm CMOS

The paper presents an SRAM array with bit interleaving and read scheme. The SRAM test-chip is fabricated in a 90nm CMOS technology. For leakage comparison, 49 kb arrays are implemented for both the conventional 6T cell and 10T cell. The leakage power consumption of this SRAM is close to that of the 6T cell (between 0.96times and 1.1times) even though it has extra transistors in a cell. This is because the subthreshold leakage from the bitline to the cell node is drastically reduced by the stacking of devices in the leakage path. The design operates at 31.25 kHz with a 0.18 V supply. With more aggressive wordline boosting of 80 mV, the VDD scales down to 0.16 V at 0.16 V VDD, the operating frequency is 500 Hz and power consumption is 0.123 muW.
Low parasitics planar transformer for LLC resonant battery chargers

Recently, high efficiency LLC resonant converters have gained popularity to implement battery chargers and employ traditional wire-wound transformers. Due to the height of traditional cores, the form factor of LLC chargers is often plump and bulky. In order to implement slim profile chargers for EVs and portable electronic applications, planar transformers can be used featuring low height, reproducibility, and low thermal resistance. Despite those advantages, planar magnetics suffer from very high parasitic capacitance resulting in severe regulation problems for LLC chargers that require wide voltage regulation. This paper analyzes LLC regulation issues with planar magnetics and proposes a novel strategy to provide wide regulation by reducing the effects of intra-winding capacitance. The proposed strategy employs a simplified capacitance network and achieves low parasitics to successfully design LLC chargers with wide regulation. Experimental results of a 1.2kW LLC converter confirms that the proposed winding layout not only provides better output voltage regulation in light load conditions, but also increases the overall efficiency of the power converter.
Bad Subsequences of Well-Known Linear Congruential Pseudo-Random Number Generators

We present a spectral test analysis of full-period subsequences with small step sizes generated by well-known linear congruential pseudorandom number generators. Subsequences may occur in certain simulation problems or as a method to get parallel streams of pseudorandom numbers. Applying the spectral test, it is possible to find bad subsequences with small step sizes for almost all linear pseudorandom number generators currently in use.
Project and Program Management Implications in the Portfolio Management of IT Projects in Applied R&D Organizations

Nowadays, projects represent a significantinvestment for organizations, their intrinsic role asorganizational change mechanisms is being increasinglyrecognized. In this context, project portfolio management effortsare considered as a main efficiency and effectiveness mechanismto align the projects execution with the organization strategy. Thecomponents of a project portfolio management should bemeasured, ranked, and prioritized according to criteria preestablished. The main objective of the PhD research work is todevelop a new method for portfolio management of Informationand Technology projects, by taking into account the implicationsfrom Project Management Institute and Office of GovernmentCommerce project and program management reference models.
Divide, Denoise, and Defend against Adversarial Attacks

Deep neural networks, although shown to be a successful class of machine learning algorithms, are known to be extremely unstable to adversarial perturbations. Improving the robustness of neural networks against these attacks is important, especially for security-critical applications. To defend against such attacks, we propose dividing the input image into multiple patches, denoising each patch independently, and reconstructing the image, without losing significant image content. This proposed defense mechanism is nondifferentiable which makes it non-trivial for an adversary to apply gradient-based attacks. Moreover, we do not fine-tune the network with adversarial examples, making it more robust against unknown attacks. We present a thorough analysis of the tradeoff between accuracy and robustness against adversarial attacks. We evaluate our method under black-box, grey-box, and white-box settings. The proposed method outperforms the state-ofthe-art by a significant margin on the ImageNet dataset under grey-box attacks while maintaining good accuracy on clean images. We also establish a strong baseline for a novel white-box attack.