Miracle-dz/UniFAR-FTUs huggingface dataset:
The Past, Present, and Future of Cyberdyne

Cyberdyne is a distributed system that discovers vulnerabilities in third-party, off-the-shelf binary programs. It competed in all rounds of DARPA’s Cyber Grand Challenge (CGC). In the qualifying event, Cyberdyne was the second most effective bug-finding system. In the final event, it was the bug-finding arm of the fourth-place team. Since then, Cyberdyne has been successfully applied during commercial code audits. The first half of this article describes the evolution and implementation of Cyberdyne and its bug-finding tools. The second half of the article looks at what it took to have Cyberdyne audit real applications and how we performed the first paid automated security audit for the Mozilla Secure Open Source Fund. We conclude with a discussion about the future of automated security audits.
From LIF to AdEx neuron models: Accelerated analog 65 nm CMOS implementation

Here we present analog circuits emulating an Adaptive Exponential I&F (AdEx) neuron model developed for our second generation 65-nm CMOS neuromorphic hardware. Designed for an existing accelerated Leaky Integrate and Fire (LIF) circuit, the modular circuit architecture allows us to switch between LIF and AdEx neuron models and further to multiple-compartments. We describe our circuit implementation and the simulation results for adaptation and exponential sub-circuits. The neuron circuit specifications are compared with a targeted set of computational models. We show how addition of analog AdEx circuits let us qualitatively reproduce spike patterns known from cortical neurons.
Data mining of pediatric medulloblastoma microarray expression reveals a novel potential subdivision of the Group 4 molecular subgroup

Medulloblastoma is the most common type of solid brain tumor in children. This type of embryonic tumor is highly heterogeneous and has been classified into 4 molecular subgroups based on their gene expression profiles: WNT, SHH, Group 3 (G3) and Group 4 (G4). WNT and SHH tumors exhibit the specific dysregulation of genes and pathways, whereas G3 and G4 tumors, two of the more frequent subtypes, are the least characterized. Thus, novel markers to aid in the diagnosis, prognosis and management of medulloblastoma are required. In the present study, microarray gene expression data was downloaded from the Gene Expression Omnibus database, including data from the 4 subgroups of medulloblastoma and healthy cerebellum tissue (CT). The data was utilized in an in silico analysis to characterize each subgroup at a transcriptomic level. Using Partek Genomics Suite software, the data were visualized via hierarchical clustering and principal component analysis. The differentially expressed genes were uploaded to the MetaCore portal to perform enrichment analysis using CT gene expression as baseline, with fold change thresholds of <-5 and >5 for differential expression. The data mining analysis of microarray gene expression data enabled the identification of a range of dysregulated molecules associated with each subgroup of medulloblastoma. G4 is the most heterogeneous subgroup, as no definitive pathway defines its pathogenesis; analysis of the gene expression profiles were associated with the G4α and G4β subcategories. TOX high mobility group box family member 3, synuclein α interacting protein and, potassium voltage-gated channel interacting protein 4 were identified as three novel potential markers for distinguishing the α and β subcategories of G4. These genes may be associated with medulloblastoma pathogenesis, and thus may provide a basis for researching novel targeted treatment strategies for G4 medulloblastoma.
On the Use of Normalized Compression Distances for Image Similarity Detection

This paper investigates the usefulness of the normalized compression distance (NCD) for image similarity detection. Instead of the direct NCD between images, the paper considers the correlation between NCD based feature vectors extracted for each image. The vectors are derived by computing the NCD between the original image and sequences of translated (rotated) versions. Feature vectors for simple transforms (circular translations on horizontal, vertical, diagonal directions and rotations around image center) and several standard compressors are generated and tested in a very simple experiment of similarity detection between the original image and two filtered versions (median and moving average). The promising vector configurations (geometric transform, lossless compressor) are further tested for similarity detection on the 24 images of the Kodak set subject to some common image processing. While the direct computation of NCD fails to detect image similarity even in the case of simple median and moving average filtering in 3 × 3 windows, for certain transforms and compressors, the proposed approach appears to provide robustness at similarity detection against smoothing, lossy compression, contrast enhancement, noise addition and some robustness against geometrical transforms (scaling, cropping and rotation).
Guidelines for treatment naming in radiation oncology

Safety concerns may arise from a lack of standardization and ambiguity during the treatment planning and delivery process in radiation therapy. A standardized target and organ‐at‐risk naming convention in radiation therapy was developed by a task force comprised of several Radiation Oncology Societies. We present a nested‐survey approach in a community setting to determine the methodology for radiation oncology departments to standardize their practice. Our Institution's continuous quality improvement (CQI) committee recognized that, due to growth from one to three centers, significant variability existed within plan parameters specific to patients’ treatment. A multidiscipline, multiclinical site consortium was established to create a guideline for standard naming. Input was gathered using anonymous, electronic surveys from physicians, physicists, dosimetrists, chief therapists, and nurse managers. Surveys consisted of several primary areas of interest: anatomical sites, course naming, treatment plan naming, and treatment field naming. Additional concepts included capitalization, specification of laterality, course naming in the event of multiple sites being treated within the same course of treatment, primary versus boost planning, the use of bolus, revisions for plans, image‐guidance field naming, forbidden characters, and standard units for commonly used physical quantities in radiation oncology practice. Guidelines for standard treatment naming were developed that could be readily adopted. This multidisciplinary study provides a clear, straightforward, and easily implemented protocol for the radiotherapy treatment process. Standard nomenclature facilitates the safe means of communication between team members in radiation oncology. The guidelines presented in this work serve as a model for radiation oncology clinics to standardize their practices. PACS number(s): 87.56. bd, 87.56. Fc, 87.55. Qr, 87.55. ‐x, 87.55. N‐, 87.55. T‐, 87.55. D‐
Non-Conventional Thermodynamics and Models of Gradient Elasticity

We consider material bodies exhibiting a response function for free energy, which depends on both the strain and its gradient. Toupin–Mindlin’s gradient elasticity is characterized by Cauchy stress tensors, which are given by space-like Euler–Lagrange derivative of the free energy with respect to the strain. The present paper aims at developing a first version of gradient elasticity of non-Toupin–Mindlin’s type, i.e., a theory employing Cauchy stress tensors, which are not necessarily expressed as Euler–Lagrange derivatives. This is accomplished in the framework of non-conventional thermodynamics. A one-dimensional boundary value problem is solved in detail in order to illustrate the differences of the present theory with Toupin–Mindlin’s gradient elasticity theory.
Pristionchus Scratchpads--an online platform for taxonomy, systematics and phylogeny.

The availability of molecular tools has transformed animal taxonomy and systematics in the last two decades. In particular, small animals that have few morphological diagnostic attributes can be identified and diagnosed accurately by adding molecular markers to those already used. In nematodes, molecular sequence information relevant for taxonomy and systematics is currently transforming the resolution of phylogenetic studies. The extensive use of molecular tools is associated with a large increase in the number of publications. This increase can make problematic accessing current knowledge comprehensively. Similarly, old publications in very specialized journals are often hard to access as well. To overcome some of these issues for the nematode genus Pristionchus , we introduce Pristionchus Scratchpads as an online platform for taxonomy, systematics and phylogeny. As an online database, Pristionchus Scratchpads is community driven, searchable, comprehensive and dynamic. Given the substantial effort in using Pristionchus pacificus and other Pristionchus species as model systems in evolutionary biology, Pristionchus Scratchpads is intended to facilitate the development of new online tools in nematode taxonomy and systematics.
INCARNATE: An interference aware spatial scheme for tightly coupled LTE-Wi-Fi networks

Stochastic geometry has been used in the literature to develop amenable models to delineate and apprehend the performance of wireless networks. In this paper, we develop a tractable and flexible model for heterogeneous wireless networks consisting of tightly coupled long-term evolution (LTE) Small cell eNodeBs (SeNBs) and wireless fidelity (Wi-Fi) access points (APs). We leverage stochastic geometry to characterize the key performance metrics of LTE-Wi-Fi Aggregation (LWA) system. The positions for SeNBs and APs are modeled as two independent homogeneous Poisson Point Processes (PPPs) in a non co-located LWA scenario. Enabling LWA operation with arbitrary number of Wi-Fi APs in a given region may not ensure maximum rate and coverage. Hence we propose a scheme, InterfereNCe Aware matéRN hArd-core poinT procEss (INCARNATE) to increase the performance of LWA system by allowing LWA operation with a chosen set of Wi-Fi APs. We derive Signal-to-Interference-plus-Noise Ratio (SINR) distribution of UE which is associated to SeNB, AP or LWA. INCARNATE is modelled as a modified Matérn Hard-Core Point Process (MHCPP). This further helps to find joint coverage probability and average data rate over the network. We then verify the accuracy of the analytical results with empirical outcomes. INCARNATE scheme outperforms the traditional MHCPP scheme by 73% and 17% in terms of data rate and coverage probability, respectively. Similarly, LWA with INCARNATE scheme excels by 51% and 6.23% as compared to regular LWA in terms of data rate and coverage probability, respectively.
Percentage depth dose evaluation in heterogeneous media using thermoluminescent dosimetry

The purpose of this study is to investigate the influence of lung heterogeneity inside a soft tissue phantom on percentage depth dose (PDD). PDD curves were obtained experimentally using LiF:Mg,Ti (TLD‐100) thermoluminescent detectors and applying Eclipse treatment planning system algorithms Batho, modified Batho (M‐Batho or BMod), equivalent TAR (E‐TAR or EQTAR), and anisotropic analytical algorithm (AAA) for a 15 MV photon beam and field sizes of 1×1,2×2,5×5, and 10×10cm2. Monte Carlo simulations were performed using the DOSRZnrc user code of EGSnrc. The experimental results agree with Monte Carlo simulations for all irradiation field sizes. Comparisons with Monte Carlo calculations show that the AAA algorithm provides the best simulations of PDD curves for all field sizes investigated. However, even this algorithm cannot accurately predict PDD values in the lung for field sizes of 1×1 and 2×2cm2. An overdosage in the lung of about 40% and 20% is calculated by the AAA algorithm close to the interface soft tissue/lung for 1×1 and 2×2cm2 field sizes, respectively. It was demonstrated that differences of 100% between Monte Carlo results and the algorithms Batho, modified Batho, and equivalent TAR responses may exist inside the lung region for the 1×1cm2 field. PACS number: 87.55. kd
Automatic paral ellizati on of Simulink models for multicore embedded systems development

The embedded software systems are first designed and validated by high level models such as MATLAB/Simulink models. The Simulink is a model-based design tool, widely used in the field of control and signal processing systems for ranging from algorithm development to code generation for embedded systems. However, with the increased use of multicore technology, designers are now required to consider this technology that the software might deployed on. For instance, it is very important to parallelize software in order to exploit the inherent performance of multicore processors. This paper proposes a method to parallelize models developed on Simulink. Under the parallelization process, the proposed method uses the flattening technique to extract task-parallelism embedded in the model's hierarchy and then measure their parallel execution time when running on multicore architecture. On the experiment of applying the method for a MIMO-OFDM application model, the execution time of the parallelized model could be reduced successfully to 50.75% on 2-cores and to 25.07% on a 4-core processor, compared with that running on single core.
Animal Models in Nutrition Research 1,2

Current knowledge in nutrition is based largely on the use of appropriate animal models together with defined diets. Numerous examples are cited where animal models have been used to solve nutrient 3 nutrient interactions, to evaluate bioavailability of nutrients and nutrient precursors, and to test for nutrient tolerances and toxicities. Advantages, disadvantages, and idiosyncrasies of various animal species are discussed. J. Nutr. 138: 391‐396, 2008.
A behavioural perspective on the effects of using performance measurement systems in the companies

The effects of Performance Measurement Systems (PMS) on people’s behaviour are relevant for an organization performance and success. Yet motivational and behavioural consequences of PMS are far from being understood. This study aims to go further regarding the consequences on people’s behaviour of using PMS in organizations. In order to collect data researchers conducted a case study in a Portuguese multi-national company. Evidence shows that the way in which managers understand a PMS influences in a significant way how they behave. The data also lends support to the claim that PMS influence motivation, perceptions, participation and job-related stress of managers.
ReLoC: A Mechanised Relational Logic for Fine-Grained Concurrency

We present ReLoC: a logic for proving refinements of programs in a language with higher-order state, fine-grained concurrency, polymorphism and recursive types. The core of our logic is a judgement e ⪯ e': τ, which expresses that a program e refines a program e' at type τ. In contrast to earlier work on refinements for languages with higher-order state and concurrency, ReLoC provides type- and structure-directed rules for manipulating this judgement, whereas previously, such proofs were carried out by unfolding the judgement into its definition in the model. These more abstract proof rules make it simpler to carry out refinement proofs. Moreover, we introduce logically atomic relational specifications: a novel approach for relational specifications for compound expressions that take effect at a single instant in time. We demonstrate how to formalise and prove such relational specifications in ReLoC, allowing for more modular proofs. ReLoC is built on top of the expressive concurrent separation logic Iris, allowing us to leverage features of Iris such as invariants and ghost state. We provide a mechanisation of our logic in Coq, which does not just contain a proof of soundness, but also tactics for interactively carrying out refinements proofs. We have used these tactics to mechanise several examples, which demonstrates the practicality and modularity of our logic.
SEVered: Subverting AMD's Virtual Machine Encryption

AMD SEV is a hardware feature designed for the secure encryption of virtual machines. SEV aims to protect virtual machine memory not only from other malicious guests and physical attackers, but also from a possibly malicious hypervisor. This relieves cloud and virtual server customers from fully trusting their server providers and the hypervisors they are using. We present the design and implementation of SEVered, an attack from a malicious hypervisor capable of extracting the full contents of main memory in plaintext from SEV-encrypted virtual machines. SEVered neither requires physical access nor colluding virtual machines, but only relies on a remote communication service, such as a web server, running in the targeted virtual machine. We verify the effectiveness of SEVered on a recent AMD SEV-enabled server platform running different services, such as web or SSH servers, in encrypted virtual machines. With these examples, we demonstrate that SEVered reliably and efficiently extracts all memory contents even in scenarios where the targeted virtual machine is under high load.
A free space technique for dielectrics and building materials characterization for future 5G applications

In this paper, a simple and fast time domain technique is proposed to determine simultaneously the complex permittivity and thickness of the material under test (MUT) for future 5G applications. The proposed free space time domain technique is calibration independent, noninvasive and uses only the magnitude of power corresponding to the reflected multiple signals from the MUT. The method is validated experimentally for various samples under different background materials and the error in the extracted permittivity and thickness is found less than 5%.
Detection of Cardiopulmonary Activity and Related Abnormal Events Using Microsoft Kinect Sensor

Monitoring of cardiopulmonary activity is a challenge when attempted under adverse conditions, including different sleeping postures, environmental settings, and an unclear region of interest (ROI). This study proposes an efficient remote imaging system based on a Microsoft Kinect v2 sensor for the observation of cardiopulmonary-signal-and-detection-related abnormal cardiopulmonary events (e.g., tachycardia, bradycardia, tachypnea, bradypnea, and central apnoea) in many possible sleeping postures within varying environmental settings including in total darkness and whether the subject is covered by a blanket or not. The proposed system extracts the signal from the abdominal-thoracic region where cardiopulmonary activity is most pronounced, using a real-time image sequence captured by Kinect v2 sensor. The proposed system shows promising results in any sleep posture, regardless of illumination conditions and unclear ROI even in the presence of a blanket, whilst being reliable, safe, and cost-effective.
Increasing Vaccination: Putting Psychological Science Into Action

Vaccination is one of the great achievements of the 20th century, yet persistent public-health problems include inadequate, delayed, and unstable vaccination uptake. Psychology offers three general propositions for understanding and intervening to increase uptake where vaccines are available and affordable. The first proposition is that thoughts and feelings can motivate getting vaccinated. Hundreds of studies have shown that risk beliefs and anticipated regret about infectious disease correlate reliably with getting vaccinated; low confidence in vaccine effectiveness and concern about safety correlate reliably with not getting vaccinated. We were surprised to find that few randomized trials have successfully changed what people think and feel about vaccines, and those few that succeeded were minimally effective in increasing uptake. The second proposition is that social processes can motivate getting vaccinated. Substantial research has shown that social norms are associated with vaccination, but few interventions examined whether normative messages increase vaccination uptake. Many experimental studies have relied on hypothetical scenarios to demonstrate that altruism and free riding (i.e., taking advantage of the protection provided by others) can affect intended behavior, but few randomized trials have tested strategies to change social processes to increase vaccination uptake. The third proposition is that interventions can facilitate vaccination directly by leveraging, but not trying to change, what people think and feel. These interventions are by far the most plentiful and effective in the literature. To increase vaccine uptake, these interventions build on existing favorable intentions by facilitating action (through reminders, prompts, and primes) and reducing barriers (through logistics and healthy defaults); these interventions also shape behavior (through incentives, sanctions, and requirements). Although identification of principles for changing thoughts and feelings to motivate vaccination is a work in progress, psychological principles can now inform the design of systems and policies to directly facilitate action.
The Effect of Process Parameters on the Mechanical Properties of A356 Al-Alloy/ZrO2 Nanocomposite

In this study the effect of zirconia (ZrO2) nanoparticles (40 nm) in reinforcing A356 aluminum alloys as a base metal matrix were investigated. Zirconia nanopowders were stirred in the A356 matrix with different fraction ratios ranging from (0, 1, 2, 3, 5%) by weight at variable stirring speeds ranging from (270, 800, 1500, 2150 r.p.m) at a mushy zone (600°C) and liquid state (700°C) using a constant stirring time for one minute. The microstructure revealed the change of grains from dendritic to spherical shape with increasing stirring speed. The Scanning Electron Microscopy of the fractured surface revealed the presence of nanoparticles at the interdendritic spacing of the fracture surface and was confirmed with EDX analysis of these particles.The results of the study showed that the mechanical properties (strength, elongation and hardness) using ZrO2 as reinforcements were increased at the following parameters: 1500 r.p.m stirring speed in semi-solid state (600oC) and adding 3 wt. % of ZrO2.
Estimation of Markov Chain via Rank-constrained Likelihood

This paper studies the estimation of low-rank Markov chains from empirical trajectories. We propose a non-convex estimator based on rank-constrained likelihood maximization. Statistical upper bounds are provided for the Kullback-Leiber divergence and the $\ell_2$ risk between the estimator and the true transition matrix. The estimator reveals a compressed state space of the Markov chain. We also develop a novel DC (difference of convex function) programming algorithm to tackle the rank-constrained non-smooth optimization problem. Convergence results are established. Experiments show that the proposed estimator achieves better empirical performance than other popular approaches.