Miracle-dz/UniFAR-FTUs huggingface dataset:
Connecting Artificial Brains to Robots in a Comprehensive Simulation Framework: The Neurorobotics Platform

Combined efforts in the fields of neuroscience, computer science, and biology allowed to design biologically realistic models of the brain based on spiking neural networks. For a proper validation of these models, an embodiment in a dynamic and rich sensory environment, where the model is exposed to a realistic sensory-motor task, is needed. Due to the complexity of these brain models that, at the current stage, cannot deal with real-time constraints, it is not possible to embed them into a real-world task. Rather, the embodiment has to be simulated as well. While adequate tools exist to simulate either complex neural networks or robots and their environments, there is so far no tool that allows to easily establish a communication between brain and body models. The Neurorobotics Platform is a new web-based environment that aims to fill this gap by offering scientists and technology developers a software infrastructure allowing them to connect brain models to detailed simulations of robot bodies and environments and to use the resulting neurorobotic systems for in silico experimentation. In order to simplify the workflow and reduce the level of the required programming skills, the platform provides editors for the specification of experimental sequences and conditions, environments, robots, and brain–body connectors. In addition to that, a variety of existing robots and environments are provided. This work presents the architecture of the first release of the Neurorobotics Platform developed in subproject 10 “Neurorobotics” of the Human Brain Project (HBP). 1 At the current state, the Neurorobotics Platform allows researchers to design and run basic experiments in neurorobotics using simulated robots and simulated environments linked to simplified versions of brain models. We illustrate the capabilities of the platform with three example experiments: a Braitenberg task implemented on a mobile robot, a sensory-motor learning task based on a robotic controller, and a visual tracking embedding a retina model on the iCub humanoid robot. These use-cases allow to assess the applicability of the Neurorobotics Platform for robotic tasks as well as in neuroscientific experiments.
A baseline for content-based blog classification

A content-based network representation of web logs (blogs) using a basic word-overlap similarity measure is presented. Due to a strong signal in blog data the approach is sufficient for accurately classifying blogs. Using Swedish blog data we demonstrate that blogs that treat similar subjects are organized in clusters that, in turn, are hierarchically organized in higher-order clusters. The simplicity of the representation renders it both computationally tractable and transparent. We therefore argue that the approach is suitable as a baseline when developing and analyzing more advanced content-based representations of the blogosphere.
Practical Annotation Scheme for an HPSG Treebank of Bulgarian

The paper presents an HPSG-based annotation scheme for constructing a Bulgarian treebank: BulTreeBank. It differs from other grammar-based annotation schemes in having a hybrid status with respect to the partial parsing component and the full parsing module. As the parsing complexity is handled preferably by the pre-processing step, the task of the HPSG module is maximally facilitated and simplified.
Robot assisted internal mammary artery detection for coronary revascularisation surgery

This paper presents a semi-automatic robotic system supporting a surgeon in the harvesting of the internal mammary artery (IMA) for an open chested intervention in coronary revascularisation surgery. The versatile surgical lightweight robot MIRO developed at DLR (German Aerospace Center) is used to detect and mark the path of the IMA at the inner side of the thoracic wall. The robot is equipped with a tool combining a Doppler ultrasonography (US) probe and a medical marker pen. The position of the IMA is extracted from the US-images to place the tool above the artery via visual servoing. Additionally, the robot moves the tool in direction of the artery to mark the location of the IMA on it's path. To achieve an ideal contact situation for US-imaging along the whole path the contact force between tissue and probe is controlled according to force measurements based on the internal torque sensors of the robot. The evaluation of the robotic system by an animal experiment shows that the system is capable of robustly detecting the IMA.
A Proinflammatory Cytokine Inhibits P53 Tumor Suppressor Activity

p53 has a key role in the negative regulation of cell proliferation, in the maintenance of genomic stability, and in the suppression of transformation and tumorigenesis. To identify novel regulators of p53, we undertook two functional screens to isolate genes which bypassed either p53-mediated growth arrest or apoptosis. In both screens, we isolated cDNAs encoding macrophage migration inhibitory factor (MIF), a cytokine that was shown previously to exert both local and systemic proinflammatory activities. Treatment with MIF overcame p53 activity in three different biological assays, and suppressed its activity as a transcriptional activator. The observation that a proinflammatory cytokine, MIF, is capable of functionally inactivating a tumor suppressor, p53, may provide a link between inflammation and tumorigenesis.
Bidirectional Parsing of Lexicalized Tree Adjoining Grammars

In this paper a bidirectional parser for Lexicalized Tree Adjoining Grammars will be presented. The algorithm takes advantage of a peculiar characteristic of Lexicalized TAGs, i.e. that each elementary tree is associated with a lexical item, called its anchor. The algorithm employs a mixed strategy: it works bottom-up from the lexical anchors and then expands (partial) analyses making top-down predictions. Even if such an algorithm does not improve the worst-case time bounds of already known TAGs parsing methods, it could be relevant from the perspective of linguistic information processing, because it employs lexical information in a more direct way.
Using Hardware Event Counters for Continuous , Online System Optimization : Lessons and Challenges

Most modern processors offer hardware support for monitoring performance events related to the interaction of applications with specific subunits of the processor [4, 7, 8, 9, 10]. The insight attained from performance monitoring counters is useful for both application programmers and processor manufacturers. Programmers typically employ them as a powerful tool for post-mortem analysis, identification and resolution of performance bottlenecks in their applications. Processor manufacturers, on the other hand, can collect valuable information on the performance of their products while the latter are used in production environments. This knowledge is then exploited during the design phase of future products. Our project, MOHCA (MOnitoring of Hardware for Continuous Adaptation) exploits performance monitoring counters in a different way. The counters are used for online monitoring of hardware events. The information collected is fed back to OS scheduling policies, providing them with awareness of the dynamically changing characteristics of the execution environment and allowing them to continuously adapt to these characteristics and reach more educated scheduling decisions. The scheduling policies have been implemented in the context of a processor manager, i.e. a server process which applies kernel-level scheduling decisions from user-level. Although this approach introduces practically negligible overhead, since counters do not need to be sampled frequently, it has been totaly neglected by current OS schedulers. We have already implemented a successful prototype in Linux, and used it to efficiently schedule workloads on SMPs consisting of multiple Intel HyperThreaded (HT) processors. The same prototype can be used on generic multi-SMT or future multi-CMP systems. The performance gains due to the use of feedback-driven scheduling policies are significant, even in current architectures. Moreover, the performance impact is projected to be huge in future multicore architectures, where a full-blown multiprogrammed/multiprocessor operating system with distributed functionality will be contained in a single chip. More information and results from this project are presented in recent publications [1, 2, 6]. The current implementation of HyperThreaded (HT) processors from Intel shares performance monitoring hardware among both execution contexts available on the same physical processor. As a result, conflicts may arise if both threads executing on the same processor attempt to use performance monitoring. The strategy typically adopted by system software to deal with the problem is to disallow the execution of two threads using performance counters on the same physical processor. This restriction has adverse effects on the usability of performance monitoring facilities on HT processors. In particular, disabling threads makes online performance monitoring for continuous adaptation infeasible. We have managed to overcome this limitation by using 2 sets of performance monitoring registers for each event
Design and Fabrication of Graphene Reinforced Polymer Conductive Patch-Based Inset Fed Microstrip Antenna

This work explores the design and fabrication of graphene reinforced polyvinylidene fluoride (PVDF) patch-based microstrip antenna. Primarily, antenna was designed at 6GHz frequency and simulation results were obtained using Ansoft HFSS tool. Later fabrication of antenna was carried out with graphene–PVDF films as conducting patch deposited on bakelite substrate and copper as ground plane. Graphene–PVDF films were prepared using solvent casting process. The radiation efficiency of fabricated microstrip patch antenna was 48% entailing it to be adapted as a practically functional antenna. Both simulated and the practical results were compared and analyzed.
New Passive Acoustic Monitoring in Monterey Bay National Marine Sanctuary

Understanding the marine soundscape is of growing importance to the National Marine Sanctuaries. In the center of Monterey Bay National Marine Sanctuary, a new Passive Acoustic Monitoring (PAM) project has begun. Using the power and communications infrastructure of the Monterey Accelerated Research System (MARS) cabled observatory, we deployed a broadband, digital, omnidirectional hydrophone on Smooth Ridge, near the MARS node (36°42.75'N, 122°11.21'W; depth 891 m). The system has been recording almost continuously since 28 July 2015. Natural biological sound (biophony) dominated long-term spectral average results at frequencies below 50 Hz, showing seasonal patterns in baleen whale vocalizations. Prevalence of blue whale vocalizations during August through October 2015 was succeeded by prevalence of fin whale vocalizations during November 2015 through January 2016. Diel variations (stronger signal at night) were evident for both species. In the high-frequency range, beaked whale clicks have been detected and represent a focus for advancing automated detection and classification methods. Biophonic richness in this soundscape was also indicated by human-expert analysis results for a one-week period, during which biological sound events were detected 86% of the time. Examination of natural physical sound (geophony) has included rainfall and the relationship between wind speed and ambient sound. The first 10 months of data showed that sound levels at 2 kHz followed Wenz curve predictions for wind speeds above 5 m/s. Examination of human-made sound (anthrophony) has included noise from vessel traffic and explosions detonated underwater during fishery operations. This new PAM project is providing extensive information on biophony, geophony, and anthrophony in this deep soundscape, information that is essential to understanding and managing acoustic habitat of the Sanctuary.
Posit: a lightweight approach for IP geolocation

Location-specific Internet services are predicated on the ability to identify the geographic position of IP hosts accurately. Fundamental to current state-of-the-art geolocation techniques is reliance on heavyweight traceroute-like probes that put a significant traffic load on networks. In this paper, we introduce a new lightweight approach to IP geolocation that we call Posit. This methodology requires only a small number of delay measurements conducted to end host targets in conjunction with a computationally-efficient statistical embedding technique. We demonstrate that Posit performs better than all existing geolocation tools across a wide spectrum of measurement infrastructures with varying geographic densities. Specifically, Posit is shown to geolocate hosts with median error improvements of over 55% with respect to all current measurement-based IP geolocation methodologies.
Random Cayley Graphs are Expanders: a Simple Proof of the Alon-Roichman Theorem

We give a simple proof of the Alon–Roichman theorem, which asserts that the Cayley graph obtained by selecting $c_\varepsilon \log |G|$ elements, independently and uniformly at random, from a finite group $G$ has expected second eigenvalue no more than $\varepsilon$; here $c_\varepsilon$ is a constant that depends only on $\varepsilon$. In particular, such a graph is an expander with constant probability. Our new proof has three advantages over the original proof: (i.) it is extremely simple, relying only on the decomposition of the group algebra and tail bounds for operator-valued random variables, (ii.) it shows that the $\log |G|$ term may be replaced with $\log D$, where $D \leq |G|$ is the sum of the dimensions of the irreducible representations of $G$, and (iii.) it establishes the result above with a smaller constant $c_\varepsilon$.
CODEF: a system level design space exploration tool

The increasing complexity of embedded applications combined with the advances in chip integration make the design process a very challenging task. Due to this rising complexity, the design under performance, area and consumption constraints of a system-on-a-chip (SOC) composed of mixed software-hardware units, becomes increasingly intricate. This paper presents a method and an associated tool (CODEF) which allow the designer to do an automatic and/or interactive system design space exploration in order to construct cost effective embedded real-time architectures dedicated to complex signal processing applications. The method is based on a recursive partitioning algorithm followed by a communication synthesis procedure.
A Machine Learning Approach to Ranging Error Mitigation for UWB Localization

Location-awareness is becoming increasingly important in wireless networks. Indoor localization can be enabled through wideband or ultra-wide bandwidth (UWB) transmission, due to its fine delay resolution and obstacle-penetration capabilities. A major hurdle is the presence of obstacles that block the line-of-sight (LOS) path between devices, affecting ranging performance and, in turn, localization accuracy. Many techniques have been proposed to address this issue, most of which make modifications to the localization algorithm. Since many localization algorithms work with distance or angle estimates, rather than received waveforms, information inherent in the wideband waveform is lost, leading to sub-optimal ranging error mitigation. To avoid this information loss, we present a novel approach to mitigate ranging errors directly in the physical layer. In contrast to existing techniques, which detect the non-line-of-sight (NLOS) condition, our approach directly mitigates the bias incurred in both LOS and non-LOS conditions. In particular, we apply two classes of non-parametric regressors to form an estimate of the ranging error. Our work is based on, and validated by, an extensive indoor measurement campaign with FCC-compliant UWB radios. The results show that the proposed regressors provide significant performance improvements in various practical localization scenarios, compared to conventional approaches.
Assessing Language through Computer Technology (Cambridge Language Assessment)

Chapelle and Douglas's book is a clear example of an introduction to the most important concepts in CALT (Computer Assisted Language Testing). The book efficiently describes the main aspects that influence the design, production and implementation of CALT systems for language learning, putting special emphasis on CALT's future impact. The authors show straightforwardly and concisely the latest advances in the CALT field, innovations that can serve as support for creators and developers of specific language tests using computer tools.
Decentralized Interference Aware Link Adaptation Using Busy Bursts

This paper presents a new cross-layer approach to achieve active link adaptation and QoS support in ad hoc networks. Upon data reception in a transmission slot, receivers broadcast a feedback signal in a time-multiplexed mini-slot. The level of this feedback or busy signal is made dependent on the received signal from the intended transmitter and the required SINR (signal-to-interference-plus-noise ratio). Potential new interfering transmitters adjust their data transmit power based on the received busy signal power. It is demonstrated that due to the channel reciprocity in TDD (time division duplex) the received busy signal power is sufficient information to be able to maintain the SINR at the already existing links. Since multiple links are coupled via the busy signal, convergence and stability issues are analyzed.
The Impact of Corporate Governance on Investment Returns in Developed and Developing Countries

We shed light on three conundrums in the literature on investment: why investments out of different sources of finance earn different returns, why different studies report different patterns of returns across sources of finance, and why companies in developing countries make greater use of external equity capital to finance their investment than do companies in developed countries. We show that the strength of corporate governance systems affects the preferred source of financing, which in turn helps to explain why investments financed in different ways exhibit significantly different rates of return. We find considerable differences between developed and developing countries in the effectiveness of corporate governance systems in aligning managers and shareholders' interests.
Serious Specification for Composing Components

We discuss the use of an industrial-strength specification language to specify component-level contracts for a product group within Microsoft. We outline how the specification language evolved to meet the needs of the component-based approach followed by that group. The specification language, AsmL, is executable which allows for testing to be done using runtime verification. Runtime verification dynamically monitors the behavior of a component to ensure that it conforms to its specification.
Development and in-vitro characterization of sustained release tramadol hydrochloride by film coating technique

Tramadol Hydrochloride is a centrally acting analgesic. Tramadol acts as a μ-opioid receptor agonist, serotoninnor epinephrine reuptake inhibitor (SNRI), NMDA receptor antagonist, 5-HT2C receptor antagonist, nicotinic acetylcholine receptor antagonist and M1 and M3 muscarinic acetylcholine receptor antagonist. The main objective of the present work was to develop sustained release film coated tablets of water soluble Tramadol hydrochloride by wet granulation. Various polymers and the combination of polymers were used for controlling the release of drug up to desired time. Sustaining the release of drug from the dosage form was useful for achieving controlled plasma level of the drug as well as improving bioavailability. The precompression parameters were evaluated for flow properties. After evaluation of post compression parameters of tablet, the in vitro release study was performed in 0.1N HCl pH 1.2 for 16 hrs. Among all formulations (F1-F10), formulation F5 was identified to be the best as it showed 99.39% drug release and sustained action for 16 hrs. The optimized formulation (F5) was kept at stability studies according to ICH guidelines for 3 months, which showed that the formulation was stable. FTIR studies showed no unacceptable extra peaks which confirm the absence of chemical interaction between the drug and polymers.
Unsupervised Scene Change Detection Techniques In Feature Domain Via Clustering And Elimination

The last generation video compression techniques such as MPEG-4 and H.263+, allow presence of shot-cut detection methods that are usually built into the encoders. Such methods are used to extract key-frames to be encoded as intra frames. This paper presents automated scene change detection techniques, which mainly work over these key-frames to extract scene frames. Proposed techniques aim at unsupervised or semi-automatic scene detection schemes. Preliminary results show that the proposed framework provides near optimal scene frames suitable for video summarization by choosing semantically important key-frames as the scene frames.
Characterization of the Ag Mediated Surface-Enhanced Raman Spectroscopy of Saxitoxin

The rapid detection and quantification of saxitoxin (STX) is reported using surface-enhanced Raman spectroscopy (SERS) with a colloidal hydrosol of silver nanoparticles. Under the conditions of our experiments, the limit of detection (LD) for STX using SERS is 3 nM, with a limit of quantification (LQ) of 20 nM. It is shown that the SERS method is rapid, with spectra being collected in as little as 5 seconds total integration time for a 40 nM STX sample. In order to improve the signal-to-noise ratio, SERS spectra were generally collected with a total integration time of 1 minute (6 accumulations of 10 seconds each), with no need for extensive sample work-up or substrate preparation. Based on these results, the SERS technique shows great promise for the future detection and quantification of STX molecules in aqueous solutions.
Framing Fuzzy Rules using Support Sets for Effective Heart Disease Diagnosis

Significance and relevance of certain features are obtained by various techniques. Feature subset selection involves summarizing mutual associations between class decisions and attribute values in a pre-classified database. In this paper genetic algorithm is used to find the relevant set of features by optimizing the fitness function and using the operators like crossover and mutation. Fuzzy logic is a form of knowledge representation suitable for notions that cannot be defined precisely, but which depend upon their contexts. In this work the fuzzy rules are framed with the help of support sets. The classification done using fuzzy inference system provides results that are better than other techniques.