Nacije i brojevi: osnovno matematičko obrazovanje kao instrument nacionalizacije

One of the central elements of the nation-building process in the 19th century was the attempt to homogenize the citizenry, i.e. to fabricate national citizens. Besides the military and church, schools were considered to be the main agencies capable of achieving this national homogenization. In this paper, focusing on the education in Switzerland and France, I argue that elementary mathematics education was also used for this particular purpose. I make the case that throughout the 19th century mathematics education became a way to familiarize the people with a standardized language – a language that was supposed to help them master their specific social, cultural and political realities.Jedan od središnjih elemenata procesa izgradnje nacije u 19. stoljeću bio je pokušaj homogenizacije građanstva, tj. stvaranja nacionalnih građana. Osim vojske i crkve, škole su smatrane glavnim sredstvom u postizanju nacionalne homogenizacije. U ovom radu, koji se fokusira na obrazovanje u Švicarskoj i Francuskoj, tvrdim da je elementarno matematičko obrazovanje također korišteno za ovu posebnu svrhu. Dokazujem da je tijekom 19. stoljeća matematičko obrazovanje postalo način upoznavanja ljudi sa standardiziranim jezikom - jezikom koji im je trebao pomoći pri svladavanju vlastitih specifičnih socijalnih, kulturoloških i političkih stvarnosti

Monolithic ultrasound fingerprint sensor.

This paper presents a 591×438-DPI ultrasonic fingerprint sensor. The sensor is based on a piezoelectric micromachined ultrasonic transducer (PMUT) array that is bonded at wafer-level to complementary metal oxide semiconductor (CMOS) signal processing electronics to produce a pulse-echo ultrasonic imager on a chip. To meet the 500-DPI standard for consumer fingerprint sensors, the PMUT pitch was reduced by approximately a factor of two relative to an earlier design. We conducted a systematic design study of the individual PMUT and array to achieve this scaling while maintaining a high fill-factor. The resulting 110×56-PMUT array, composed of 30×43-μm2 rectangular PMUTs, achieved a 51.7% fill-factor, three times greater than that of the previous design. Together with the custom CMOS ASIC, the sensor achieves 2 mV kPa-1 sensitivity, 15 kPa pressure output, 75 μm lateral resolution, and 150 μm axial resolution in a 4.6 mm×3.2 mm image. To the best of our knowledge, we have demonstrated the first MEMS ultrasonic fingerprint sensor capable of imaging epidermis and sub-surface layer fingerprints

Nation-building by education statistics and data: a comparative perspective on school surveys in Switzerland, France, and Scotland

Survey-generated data and numbers displayed in statistics play a relevant role in nation-building. They do not simply reflect logical or nationally relevant knowledge related to the topic of the survey but are used for educational policy decisions and political governance and follow specific cultural concepts and categories, containing ideologies of social order accordingly. The Swiss example illustrates the importance attached to the “hard facts” during the planning phase of school reforms. The French case shows that the means of getting a statistical picture of the state of French primary education represented an important political gesture to create national awareness and mobilization in support of primary education. The Scottish educational statistics from the 1820s and 1830s show how education not only was used as an identifying feature for the Scottish nation, but also involved the danger of putting this nation at risk. Overall, the chapter illustrates how differently and context-dependently numbers and surveys were used to approach the common goal of strengthening national identity through schooling

Crowd Behavior Understanding through SIOF Feature Analysis

Realizing the automated and online detection of crowd anomalies from surveillance CCTVs is a research-intensive and application-demanding task. This research proposes a novel technique for detecting crowd abnormalities through analyzing the spatial and temporal features of the input video signals. This integrated solution defines an image descriptor that reflects the global motion information over time. A non-linear SVM has then been adopted to classify dominant or large-scale crow d abnormal behaviors. The work reported has focused on: 1) online (or near real-time) detection of moving objects through a background subtraction model, namely ViBe; and to identify the saliency information as a spatial feature in addition to the optical flow of the motion foreground as the temporal feature; 2) to combine the extracted spatial and temporal features into a novel SIOF descriptor that encapsulates the global movement characteristic of a crowd; 3) the optimization of a nonlinear support vector machine (SVM) as classifier to detect suspicious crowd behaviors. The test and evaluation of the devised models and techniques have selected the BEHAVE database as the primary experimental data sets. Results against benchmarking models and systems have shown promising advancements in terms of the accuracy and efficiency for detecting crowd anomalies

A dicing free SOI process for MEMS devices

This paper presents a full wafer, dicing free, dry release process for MEMS silicon-on-insulator (SOI) sensors and actuators. The developed process is particularly useful for inertial sensors that benefit from a large proof mass, for example accelerometers and gyroscopes. It involves consecutive front and backside deep reactive ion etching (DRIE) of the substrate to define the device features, release holes, and trenches. This is followed by hydrofluoric acid vapor phase etching (HF VPE) to release the proof mass and the handle wafer underneath to allow vertical displacements of the proof mass. The release process also allows the devices to be detached from each other and the substrate without the need of an extra dicing step that may damage the delicate device features or create debris. In the work described here, the process is demonstrated for the full wafer release of a high performance accelerometer with a large proof mass measuring 4 × 7 mm2. The sensor was successfully fabricated with a yield of over 95

The age of data-driven proteomics : how machine learning enables novel workflows

A lot of energy in the field of proteomics is dedicated to the application of challenging experimental workflows, which include metaproteomics, proteogenomics, data independent acquisition (DIA), non-specific proteolysis, immunopeptidomics, and open modification searches. These workflows are all challenging because of ambiguity in the identification stage; they either expand the search space and thus increase the ambiguity of identifications, or, in the case of DIA, they generate data that is inherently more ambiguous. In this context, machine learning-based predictive models are now generating considerable excitement in the field of proteomics because these predictive models hold great potential to drastically reduce the ambiguity in the identification process of the above-mentioned workflows. Indeed, the field has already produced classical machine learning and deep learning models to predict almost every aspect of a liquid chromatography-mass spectrometry (LC-MS) experiment. Yet despite all the excitement, thorough integration of predictive models in these challenging LC-MS workflows is still limited, and further improvements to the modeling and validation procedures can still be made. In this viewpoint we therefore point out highly promising recent machine learning developments in proteomics, alongside some of the remaining challenges