Miracle-dz/UniFAR-FTUs huggingface dataset:
Sampling-based sublinear low-rank matrix arithmetic framework for dequantizing Quantum machine learning

We present an algorithmic framework for quantum-inspired classical algorithms on close-to-low-rank matrices, generalizing the series of results started by Tang’s breakthrough quantum-inspired algorithm for recommendation systems [STOC’19]. Motivated by quantum linear algebra algorithms and the quantum singular value transformation (SVT) framework of Gilyén et al. [STOC’19], we develop classical algorithms for SVT that run in time independent of input dimension, under suitable quantum-inspired sampling assumptions. Our results give compelling evidence that in the corresponding QRAM data structure input model, quantum SVT does not yield exponential quantum speedups. Since the quantum SVT framework generalizes essentially all known techniques for quantum linear algebra, our results, combined with sampling lemmas from previous work, suffices to generalize all recent results about dequantizing quantum machine learning algorithms. In particular, our classical SVT framework recovers and often improves the dequantization results on recommendation systems, principal component analysis, supervised clustering, support vector machines, low-rank regression, and semidefinite program solving. We also give additional dequantization results on low-rank Hamiltonian simulation and discriminant analysis. Our improvements come from identifying the key feature of the quantum-inspired input model that is at the core of all prior quantum-inspired results: ℓ2-norm sampling can approximate matrix products in time independent of their dimension. We reduce all our main results to this fact, making our exposition concise, self-contained, and intuitive.
Trajectory construction of Dirac evolution

We extend our programme of representing the quantum state through exact stand-alone trajectory models to the Dirac equation. We show that the free Dirac equation in the angular coordinate representation is a continuity equation for which the real and imaginary parts of the wave function, angular versions of Majorana spinors, define conserved densities. We hence deduce an exact formula for the propagation of the Dirac spinor derived from the self-contained first-order dynamics of two sets of trajectories in 3-space together with a mass-dependent evolution operator. The Lorentz covariance of the trajectory equations is established by invoking the ‘relativity of the trajectory label'. We show how these results extend to the inclusion of external potentials. We further show that the angular version of Dirac's equation implies continuity equations for currents with non-negative densities, for which the Dirac current defines the mean flow. This provides an alternative trajectory construction of free evolution. Finally, we examine the polar representation of the Dirac equation, which also implies a non-negative conserved density but does not map into a stand-alone trajectory theory. It reveals how the quantum potential is tacit in the Dirac equation.
Radiomic Feature Stability Analysis Based on Probabilistic Segmentations

Identifying image features that are robust with respect to segmentation variability and domain shift is a tough challenge in radiomics. So far, this problem has mainly been tackled in test-retest analyses. In this work we analyze radiomics feature stability based on probabilistic segmentations. Based on a public lung cancer dataset, we generate an arbitrary number of plausible segmentations using a Probabilistic U-Net. From these segmentations, we extract a high number of plausible feature vectors for each lung tumor and analyze feature variance with respect to the segmentations. Our results suggest that there are groups of radiomic features that are more (e.g. statistics features) and less (e.g. gray-level size zone matrix features) robust against segmentation variability. Finally, we demonstrate that segmentation variance impacts the performance of a prognostic lung cancer survival model and propose a new and potentially more robust radiomics feature selection workflow.
A Crowded Future: Working against Abstraction on Turker Nation

This paper examines digital labor and community through an ethnography of a discussion board supporting short-term digital contract workers on the Amazon Mechanical Turk (mTurk). First, we give a thorough overview of mTurk, the crowdsourcing marketplace, and Turker Nation, a discussion board for workers on mTurk. We trace the experience of interacting with this infrastructure on mTurk as worker and employer. Following, we look at scholarship on software infrastructure and autonomous Marxist theorizations of contemporary work. We demonstrate how the labor of participating on the discussion board Turker Nation helps to counter the abstraction the infrastructure provides. We show how workers on Turker Nation use the platform to structure time, build socializing spaces at work and initiate collective organizing. In doing so, we argue that workers’ labor belies conventional class classification, such as white-collar and blue-collar labor and instead lays the groundwork for how to structure future digital workplaces. We argue that this laboring resists the assumed logic of capitalism for digital labor that subsumes and takes over workers’ lives and conclude by looking at the limitations of the community’s collective organizing in terms of agreeing on points to communicated to the public.
The Berlin Big Data Center (BBDC)

Abstract The last decade has been characterized by the collection and availability of unprecedented amounts of data due to rapidly decreasing storage costs and the omnipresence of sensors and data-producing global online-services. In order to process and analyze this data deluge, novel distributed data processing systems resting on the paradigm of data flow such as Apache Hadoop, Apache Spark, or Apache Flink were built and have been scaled to tens of thousands of machines. However, writing efficient implementations of data analysis programs on these systems requires a deep understanding of systems programming, prohibiting large groups of data scientists and analysts from efficiently using this technology. In this article, we present some of the main achievements of the research carried out by the Berlin Big Data Cente (BBDC). We introduce the two domain-specific languages Emma and LARA, which are deeply embedded in Scala and enable declarative specification and the automatic parallelization of data analysis programs, the PEEL Framework for transparent and reproducible benchmark experiments of distributed data processing systems, approaches to foster the interpretability of machine learning models and finally provide an overview of the challenges to be addressed in the second phase of the BBDC.
An FPGA implementation of an investigative many-core processor, Fynbos : in support of a Fortran autoparallelising software pipeline

An FPGA implementation of an investigative many-core processor; Fynbos, in support of a Fortran autoparallelising software pipeline Janet Ruth Wyngaard June 2014 In light of the power, memory, ILP, and utilisation walls facing the computing industry, this work examines the hypothetical many-core approach to finding greater compute performance and efficiency. In order to achieve greater efficiency in an environment in which Moore’s law continues but TDP has been capped, a means of deriving performance from dark and dim silicon is needed. The many-core hypothesis is one approach to exploiting these available transistors efficiently. As understood in this work, it involves trading in hardware control complexity for hundreds to thousands of parallel simple processing elements, and operating at a clock speed sufficiently low as to allow the efficiency gains of near threshold voltage operation. Performance is therefore dependant on exploiting a new degree of fine-grained parallelism such as is currently only found in GPGPUs, but in a manner that is not as restrictive in application domain range. While removing the complex control hardware of traditional CPUs provides space for more arithmetic hardware, a basic level of control is still required. For a number of reasons this work chooses to replace this control largely with static scheduling. This pushes the burden of control primarily to the software and specifically the compiler, rather not to the programmer or to an application specific means of control simplification. An existing legacy tool chain capable of autoparallelising sequential Fortran code to the degree of parallelism necessary for many-core exists. This work implements a many-core architecture to match it. Prototyping the design on an FPGA, it is possible to examine the real world performance of the compiler-architecture system to a greater degree than simulation only would allow. Comparing theoretical peak performance and real performance in a case study application, the system is found to be more efficient than any other reviewed, but to also significantly under perform relative to current competing architectures. This failing is apportioned to taking the need for simple hardware too far, and an inability to implement static scheduling mitigating tactics due to lack of support for such in the compiler.
Phénoménologie de la perception

« C'est dans l'epreuve que je fais d'un corps explorateur voue aux choses et au monde, d'un sensible qui m'investit jusqu'au plus individuel de moi-meme et m'attire aussitot de la qualite a l'espace, de l'espace a la chose et de la chose a l'horizon des choses, c'est-a-dire a un monde deja la, que se noue ma relation avec l'etre. » M. Merleau-Ponty. Date de premiere edition : 1945.
Distributed Uplink Offloading for IoT in 5G Heterogeneous Networks Under Private Information Constraints

The expected influx of Internet of Things (IoT) in 5G will provide new opportunities for uplink traffic offloading. In general, base stations with proximity require lower transmission power of the IoT device (IoTD), thus saving energy consumption as spectral efficiency (SE) of the transmissions increase. By letting IoTDs send to base stations with better link conditions the IoTDs’ battery lifetime is prolonged. In this paper, we present a many-to-many offloading scheme for uplink traffic. The scheme works when link conditions are private information and gives incentives to all involved players to participate. We believe this approach is better suited for the expected complex ecosystem of 5G base station cells. The sensitivity analyses show that there is a limited gain by requiring that the link conditions are public knowledge. Further, the suggested market optimizes the SE for all involved players. Numerical results show that the IoTDs can on average increase their SE with 25% and their spectral energy efficiency with 40%. The networks which are offloaded to and from can both expect an increase in the SE of 1%–6%. Sensitivity analyses show that the market equilibrium’s benefits are robust as they stay positive for a range of different network configurations. Also, the work proves that market equilibrium is stable and unique. To derive the equilibrium, two approaches are presented, a closed form solution and a distributed algorithm, that both are solvable in polynomial time.
Graduate School Thesis Acceptance

Cabuk, Serdar Ph.D., Purdue University, December, 2006. Network Covert Channels: Design, Analysis, Detection, and Elimination. Major Professors: Carla E. Brodley and Eugene H. Spafford. Indirect communication channels have been effectively employed in the communications world to bypass mechanisms that do not permit direct communication between unauthorized parties. Such covert channels emerge as a threat to information-sensitive systems in which leakage to unauthorized parties may be unacceptable (e.g., military systems). In this dissertation, we show that traffic analysis can counter traditional event-based covert channels, which do not employ any additional scheme to obfuscate the channel further. For these channels, we introduce effective noiseless and noisy covert channel detection mechanisms that capture the anomalous traffic patterns. However, because a motivated user can potentially hide the channel further, we introduce a new family of covert channels that do not produce such anomaly. These IP time-replay covert channels transmit covert messages by adjusting packet timings consistent with inter-arrival time sequences that are extracts from recently recorded normal sequences. Under certain assumptions and lowered data rates, these channels generate output sequences that are equal in distribution to normal sequences allowing them to by-pass traffic anomaly detection schemes that are based on distribution analysis. Additionally, we illustrate that these channels can potentially survive channel elimination schemes such as jammers and network data pumps with lowered data rates. Thus, we discuss two types of transformations on packet inter-arrival times to increase the efficacy of existing elimination schemes.
Episodic Memory Multimodal Learning for Robot Sensorimotor Map Building and Navigation

In this paper, an unsupervised learning model of episodic memory is proposed. The proposed model, enhanced episodic memory adaptive resonance theory (EEM-ART), categorizes and encodes experiences of a robot to the environment and generates a cognitive map. EEM-ART consists of multilayer ART networks to extract novel events and encode spatio-temporal connection as episodes by incrementally generating cognitive neurons. The model connects episodes to construct a sensorimotor map for the robot to continuously perform path planning and goal navigation. Experimental results for a mobile robot indicate that EEM-ART can process multiple sensory sources for learning events and encoding episodes simultaneously. The model overcomes perceptual aliasing and robot localization by recalling the encoded episodes with a new anticipation function and generates sensorimotor map to connect episodes together to execute tasks continuously with little to no human intervention.
Work Order Informatics Platform for Planning Residential Building Projects and Maintenance Work

Building and facility maintenance is one of the important aspect of life cycle of a building or facility, and this has been done overtime with manual approach which tends to create an effectiveness gap between the time of ordering for work and the execution time users can request for maintenance work to be done, this would eliminates delay in work execution and request processing and aids maintenance planning scheduling and execution. The platform is compact and well guided with a view to facilitating and eliminating facility and building maintenance vices. Also the knowledge gap was filled based on data obtained through questionnaire survey that was conducted between 2017 and 2018 in Nigeria involving 60 residential accommodations with maintainable facility components, while the task of developing the web platform was achieved with tools like PhP and MySQL. Descriptive statistics, categorical regression and factor analysis, mean item scores, simple percentages were used to analyze the data. The study identified important factors, which are critical to the realization of the sustainable platform as, include high human-machine compatibility, good internet and intranet facility, high ergonomic component design, effective return time in request execution, eliminating delay in work execution among others. The above factors formed part of the contribution of the study to the body of knowledge in the field of ICT applications in building and facility maintenance operations. The study concludes by bringing to the fore parameters like access to regular power supply and computer literacy, technical intelligence or know-how among others as factors that influences the usefulness of the Web-based maintenance work order platform interface.
Data mining in foreign language learning

Educational data mining (EDM) combines the techniques of data mining with educational data in order to provide students, instructors, and researchers with knowledge that can benefit academic processes. Due to globalization, foreign language learning (FLL) has become increasingly important. This work seeks to gain insight as to how data mining (DM) is being used to benefit FLL. For this purpose, an advanced review of pertinent research published from 2012 to 2017 was performed. After applying our screening method, 208 papers were selected for the exhaustive analysis. This analysis was divided into four aspects: context (educational environments, educational level), number of items, DM methods, and DM applications. The results indicated that 54% of studies were conducted in traditional environments, while only 3% of studies were performed in an m‐learning environment. In addition, 25 and 72% of the research was conducted in either a primary or secondary level, or in tertiary or adult level, respectively. Likewise, 76% of studies contained datasets of less than 1,000 items. The most utilized EDM methods were: factor analysis, regression, text mining, correlation mining, and causal DM. In addition, the studies analyzed showed that DM is mainly employed to predict the performance of students, to check learners' motivation, and to provide feedback for instructors. These results seem to indicate that although DM has much to offer the increasing number of language students, it is not being used to its full potential.
Machine Learning Priority Rule (MLPR) For Solving Resource-Constrained Project Scheduling Problems

This paper introduces a machine learning priority rule for solving non-preemptive resource-constrained project scheduling problems (RCPSP). The objective is to find a schedule of the project’s tasks that minimizes the total completion time of the project satisfying the precedence and resource constraints. Priority rule based scheduling technique is a scheduling method for constructing feasible schedules of the jobs of projects. This approach is made up of two parts: a priority rule to determine the activity list and a schedule generation scheme which constructs the feasible schedule of the constructed activity list. Different scheduling methods use one of these schemes to construct schedules to obtain the overall project completion time. Quite a number of priority rules are available; selecting the best one for a particular input problem is extremely difficult. We present a machine learning priority rule which assembles a set of priority rules, and uses machine learning strategies to choose the one with the best performance at every point in time to construct an activity list of a project. The one with better performance is used most frequently. This removes the problem of manually searching for the best priority rule amongst the dozens of rules that are available. We used our approach to solve a fictitious project with 11 activities from Pm Knowledge Center. Four priority rules were combined. We used serial schedule generation scheme to generate our schedules. Our result showed that the total completion time of the project obtained with our approach competes favorably with the completion times gotten with the component priority rules. We then went further and compared our algorithm with 9 other available priority rules. Our results showed that the completion time got using our algorithm compete favorably with the total 13 priority rules available in the literature.
Rancang Bangun Robot Bawah Air

Abstract — Underwater robot is one of the most indispensable robotic technologies for underwater exploration. In this research, underwater robots are made to have arduino control centers, the input device is a wireless remote control and 4 pieces of brushless motor controlled by ESC (electronic speed control). This underwater robot can move well in the water and can display the result of shooting in water. Testing of this robot includes testing the robot velocity moving forward, backward, dive, rise to the surface, turn left or right, testing the depth of the robot can dive and test the camera showing the video directly. This robot research gets the deepest diving result is 1.5 meters in the pool and display the result of observation in the water in the form of video displayed on the monitor directly. This underwater robot still needs some development to used in the sea. Keywords : Arduino, ESC (electronic speed control) , remote control, Technologies of Robot, Underwater robot. Abstrak — Robot bawah air merupakan salah satu teknologi robot yang sangat diperlukan untuk ekplorasi bawah air. Pada penelitian ini, robot bawah air dibuat memiliki pusat pengendali arduino, perangkat masukan berupa remot e control tanpa kabel dan 4 buah motor brushless yang dikendalikan oleh ESC ( electronic speed control ). Robot bawah air ini dapat bergerak dengan baik di dalam air dan dapat menampilkan hasil pengambilan gambar dalam air. Pengujian robot ini meliputi pengujian kecepatan robot bergerak maju, mundur, menyelam, naik ke permukaan, berbelok kekiri atau kekanan, pengujian kedalaman robot dapat menyelam dan pengujian kamera menampilkan video secara langsung. Penelitian robot ini mendapatkan hasil penyelaman terdalam yaitu 1.5 meter dalam kolam renang dan menampilkan hasil pengamatan dalam air berupa video yang ditampilkan pada monitor secara langsung. Robot bawah air ini masih memerlukan beberapa pengembangan agar dapat digunakan dalam laut. Kata kunci : Arduino, ESC ( electronic speed control ), Remote Control , Robot Bawah Air, Teknologi Robot.
Estudio biofarmacéutico comparativo de marcas comerciales de tabletas de ciprofloxacino disponibles en el mercado colombiano

Objetivo: Determinar la equivalencia biofarmaceutica de marcas comerciales de Ciprofloxacino 500 mg disponibles en el mercado colombiano. Metodos: Se tomaron 12 productos comerciales de Ciprofloxacino tabletas 500 mg, adquiridos en droguerias y farmacias de cuatro de las principales ciudades del pais, a los cuales se les realizaron los siguientes ensayos: Identificacion del principio activo por HPLC, contenido de ingrediente activo, uniformidad de dosificacion, pruebas de desintegracion y disolucion; ademas se compararon los perfiles de disolucion de los productos frente a uno tomado como referencia. Los resultados se analizaron a fin de establecer diferencias estadisticamente significativas y posible intercambiabilidad entre los productos evaluados. Resultados: El analisis comparativo de los productos, permitio evidenciar marcadas diferencias en cuanto a la liberacionin vitrodel principio activo, con uno de los productos incumpliendo este importante parametro de calidad. Todos los productos evaluados cumplen con las especificaciones oficiales de identificacion y valoracion del principio activo, uniformidad de dosificacion y tiempo de desintegracion. En cuanto a la cinetica de disolucion se encontraron diferencias entre las formulaciones, con productos de deficiente Eficiencia de Disolucion (ED) y que a pesar de cumplir con la especificacion a Q30, se disuelven muy lentamente. Conclusiones: Once productos cumplen con todas las especificaciones establecidas en la USP-33/NF28. Los resultados de este trabajo constituyen un valioso aporte para en un futuro cercano y en funcion de las politicas nacionales, poder establecer bioequivalencia entre estos productos.