Miracle-dz/UniFAR-FTUs huggingface dataset:
Microstructure and Mechanical Properties of Microalloyed Multiphase Steel

Multiphase steels were obtained by using Gleeble-1500 simulator and TMCP, and were characterized by optical microscopy, SEM, TEM, EBSD (electron back-scattered diffraction) and other tests to investigate its microstructure and mechanical properties. During the simulation, the deformation temperature is 850°C, and the steels are air cooled to 750-600°C and then quenched to room temperature. The results indicate that the microstructure of the specimen is composed of ferrite and bainite. With the lowering of quenching temperature, the proportion of ferrite increases and the proportion of bainite decreases, and the bainite laths is shorter. The fine (Nb, Ti) C particles and dislocations appear in ferrite and lath bainite, and the amount of high angle grain boundary decrease after the initial increasing. The microaolloyed hot-rolled multiphase steel plate was developed by two-stage rolling, subsequently quenching to room temperature or air cooling to 600°C, then quenching to room temperature. Two typical microstructures: acicular ferrite and ferrite-bainite multiphase were obtained. The ferrite-bainite multiphase steel showed better mechanical properties, and the yield strength, tensile strength, yield ratio, uniform elongation and percentage elongation were 488Mpa, 845Mpa, 0.58, 10.3% and 21% respectively. The refinement of bainite structures, fine (Nb, Ti) C particles and the dislocations in bainite increase the strength.
A Rate-Distortion Approach to Caching

In this paper, we consider a lossy single-user caching problem with correlated sources. We first describe the fundamental interplay between the source correlations, the capacity of the user’s cache, the user’s reconstruction distortion requirements, and the final delivery-phase (compression) rate. We then illustrate this interplay using a multivariate Gaussian source example and a binary symmetric source example. To fully explore the effect of the user’s distortion requirements, we formulate the caching problem using <inline-formula> <tex-math notation="LaTeX">${ {\mathsf {f}}}$ </tex-math></inline-formula>-separable distortion functions recently introduce by Shkel and Verdú. The class of <inline-formula> <tex-math notation="LaTeX">${ {\mathsf {f}}}$ </tex-math></inline-formula>-separable distortion functions includes separable distortion functions as a special case, and our analysis covers both the expected- and excess-distortion settings in detail. We also determine what “common information” should be placed in the cache, and what information should be transmitted during the delivery phase. To this end, two new common-information measures are introduced for caching, and their relationship to the common-information measures of Wyner, Gács, and Körner is discussed in detail.
Adaptation to numerosity requires only brief exposures, and is determined by number of events, not exposure duration

Exposure to a patch of dots produces a repulsive shift in the perceived numerosity of subsequently viewed dot patches. Although a remarkably strong effect, in which the perceived numerosity can be shifted by up to 50% of the actual numerosity, very little is known about the temporal dynamics. Here we demonstrate a novel adaptation paradigm that allows numerosity adaptation to be rapidly induced at several distinct locations simultaneously. We show that not only is this adaptation to numerosity spatially specific, with different locations of the visual field able to be adapted to high, low, or neutral stimuli, but it can occur with only very brief periods of adaptation. Further investigation revealed that the adaptation effect was primarily driven by the number of unique adapting events that had occurred and not by either the duration of each event or the total duration of exposure to adapting stimuli. This event-based numerosity adaptation appears to fit well with statistical models of adaptation in which the dynamic adjustment of perceptual experiences, based on both the previous experience of the stimuli and the current percept, acts to optimize the limited working range of perception. These results implicate a highly plastic mechanism for numerosity perception, which is dependent on the number of discrete adaptation events, and also demonstrate a quick and efficient paradigm suitable for examining the temporal properties of adaptation.
An evidential framework for pedestrian detection in high-density crowds

This paper addresses the problem of pedestrian detection in high-density crowd images, characterized by strong homogeneity and clutter. We propose an evidential fusion algorithm which is able to exploit multiple detectors based on different gradient, texture and orientation descriptors. The evidential framework allows us to model the spatial imprecision arising from each of the detectors. A first result of our study is that the fusion results underline clearly the good complementarity among the four descriptors we considered for this specific context. Moreover, the proposed algorithm outperforms a fusion solution based on Multiple Kernel Learning on difficult high-density crowd images acquired at Makkah at the height of the Muslim pilgrimage.
Enterotoxigenic Escherichia coli Elicits Immune Responses to Multiple Surface Proteins

ABSTRACT Enterotoxigenic Escherichiacoli (ETEC) causes considerable morbidity and mortality due to diarrheal illness in developing countries, particularly in young children. Despite the global importance of these heterogeneous pathogens, a broadly protective vaccine is not yet available. While much is known regarding the immunology of well-characterized virulence proteins, in particular the heat-labile toxin (LT) and colonization factors (CFs), to date, evaluation of the immune response to other antigens has been limited. However, the availability of genomic DNA sequences for ETEC strains coupled with proteomics technology affords opportunities to examine novel uncharacterized antigens that might also serve as targets for vaccine development. Analysis of whole or fractionated bacterial proteomes with convalescent-phase sera can potentially accelerate identification of secreted or surface-expressed targets that are recognized during the course of infection. Here we report results of an immunoproteomics approach to antigen discovery with ETEC strain H10407. Immunoblotting of proteins separated by two-dimensional electrophoresis (2DE) with sera from mice infected with strain H10407 or with convalescent human sera obtained following natural ETEC infections demonstrated multiple immunoreactive molecules in culture supernatant, outer membrane, and outer membrane vesicle preparations, suggesting that many antigens are recognized during the course of infection. Proteins identified by this approach included established virulence determinants, more recently identified putative virulence factors, as well as novel secreted and outer membrane proteins. Together, these studies suggest that existing and emerging proteomics technologies can provide a useful complement to ongoing approaches to ETEC vaccine development.
The construction of a common objective function for analytical infrastructures

The paper deals with the increasing growth of embedded systems and their role within structures similar to the Internet (Internet of Things) as those that provide calculating power and are more or less appropriate for analytical tasks. Faced with the example of a cyber-physical manufacturing system, a common objective function is developed with the intention to measure efficient task processing within analytical infrastructures. A first validation is realized on base of an expert panel.
Family food practices: relationships, materiality and the everyday at the end of life

Abstract This article draws on data from a research project that combined participant observation with in‐depth interviews to explore family relationships and experiences of everyday life during life‐threatening illness. In it I suggest that death has often been theorised in ways that make its ‘mundane’ practices less discernible. As a means to foreground the everyday, and to demonstrate its importance to the study of dying, this article explores the (re)negotiation of food and eating in families facing the end of life. Three themes that emerged from the study's broader focus on family life are discussed: ‘food talk’ and making sense of illness; food, family and identity; and food ‘fights’. Together the findings illustrate the material, social and symbolic ways in which food acts relationally in the context of dying, extending conceptual work on materiality in death studies in novel directions. The article also contributes new empirical insights to a limited sociological literature on food, families and terminal illness, building on work that theorises the entanglements of materiality, food, bodies and care. The article concludes by highlighting the analytical value of everyday materialities such as food practices for future research on dying as a relational experience.
Risk of new-onset diabetes mellitus in primary aldosteronism: a population study over 5 years

Objective: Abnormal glucose metabolism due to insulin resistance has been linked to aldosterone overproduction. However, the long-term incidence of new-onset diabetes mellitus (NODM) among patients with primary aldosteronism after targeted treatment has not been well documented. Methods: The diagnosis of primary aldosteronism and essential hypertension were identified, and then the occurrence of NODM, all-cause mortality among these patients, was ascertained by a validated algorithm from a 23-million population insurance registry. Results: From 1999 to 2007, 2367 primary aldosteronism patients without previously diabetes mellitus were identified and propensity score-matched with 9468 patients with essential hypertension. Among those primary aldosteronism patients, 754 aldosterone-producing adenomas patients were identified and matched with 3016 essential hypertension controls. After a mean 5.2 years of follow-up, primary aldosteronism patients who underwent adrenalectomy had an attenuated NODM incidence (hazard ratio = 0.60, P < 0.01, versus essential hypertension); whereas those treated with mineralocorticoid receptor antagonist had augmented risk of NODM (hazard ratio = 1.16, P < 0.001, versus essential hypertension). Among the aldosterone-producing adenoma patients, adrenalectomy is also protective from developing NODM (hazard ratio = 0.61, P < 0.001, versus essential hypertension), however, mineralocorticoid receptor antagonist treatment did not alter the risk of NODM (P = 0.10, versus essential hypertension). Adjusted hazard ratios for long-term risk of mortality from this analysis revealed that adrenalectomy is protective, but NODM and major cardiovascular disease are deleterious. Conclusion: The primary aldosteronism patients who underwent adrenalectomy had reduced risk for incident NODM and all-cause of mortality, compared with matched hypertensive controls. This observation adds more evidence on the association of primary aldosteronism with a higher risk of metabolic syndrome and long-term mortality.
Non-Malleable Codes for Small-Depth Circuits

We construct efficient, unconditional non-malleable codes that are secure against tampering functions computed by small-depth circuits. For constant-depth circuits of polynomial size (i.e.~AC^0 tampering functions), our codes have codeword length n = k^1+o(1) for a k-bit message. This is an exponential improvement of the previous best construction due to Chattopadhyay and Li (STOC 2017), which had codeword length 2^O(√k). Our construction remains efficient for circuit depths as large as Θ(log(n)/loglog(n)) (indeed, our codeword length remains n ≤ k^1+ε), and extending our result beyond this would require separating P from NC^1. We obtain our codes via a new efficient non-malleable reduction from small-depth tampering to split-state tampering. A novel aspect of our work is the incorporation of techniques from unconditional derandomization into the framework of non-malleable reductions. In particular, a key ingredient in our analysis is a recent pseudorandom switching lemma of Trevisan and Xue (CCC 2013), a derandomization of the influential switching lemma from circuit complexity; the randomness-efficiency of this switching lemma translates into the rate-efficiency of our codes via our non-malleable reduction.
Development of Surface Roughness Prediction for Steel in CNC Turning by Using Response Surface Method

This research presents the development of the surface roughness prediction in the turning process of the plain carbon steel with the coated carbide tool by using the response surface analysis with the Box-Behnken design. The effects of cutting parameters on the cutting force and the cutting temperature are investigated. The cutting force and the cutting temperature are measured to help analyze the relation between the surface roughness and the cutting conditions. The models of cutting force ratio and the cutting temperature are also proposed based on the experimental data. The surface plots are constructed to determine the optimum cutting condition referring to the minimum surface roughness.
Incentive Mechanism and Rating System Design for Crowdsourcing Systems: Analysis, Tradeoffs and Inference

Macrotasking crowdsourcing systems like Elance and Fiverr serve as efficient platforms for requesters to outsource challenging and innovative tasks that require special skills to workers. It is widely practiced in such systems that requesters reward workers based on requesters’ assessment on solution quality. The challenge is that requesters’ assessment may not be accurate to reflect the intrinsic quality of a solution due to human factors like personal preferences or biases. In this work, we consider answering the following questions: How to design a mechanism to incentivize workers provide high quality solutions in the presence of such human factors? How to formally study the impact of human factors on workers’ financial incentive to participate? We design a mechanism to incentivize workers to provide high-quality contributions, which is robust to human factors. Our incentive mechanism consists of a “task bundling scheme” and a “rating system”, which reward workers based on requesters’ rating on the solution quality. We propose a probabilistic model to capture human factors, and quantify their impact on the incentive mechanism. We formulate an optimization framework to select appropriate rating system parameters, which can be viewed as a tradeoff between “system efficiency”, i.e., the total number of tasks can be solved given a fixed reward budget, and the “rating system complexity”, which determines the human cognitive cost and time in expressing ratings. We also formulate an optimization framework to select appropriate bundling size, which can tradeoff system efficiency against service delay (i.e., the waiting time to form a task bundle). Finally, we conduct experiments on a dataset from Elance. Experimental results show that our incentive mechanism achieves at least 99.95 percent of the theoretical maximum system efficiency with a service delay of at most 2.3639 hours. Furthermore, we discover that the rating system in Elance is too complex, and it should be simplified to a binary rating system (i.e., two rating points).
Poly(N‐isopropylacrylamide)‐Clay Nanocomposite Hydrogels with Responsive Bending Property as Temperature‐Controlled Manipulators

Novel poly(N‐isopropylacrylamide)‐clay (PNIPAM‐clay) nanocomposite (NC) hydrogels with both excellent responsive bending and elastic properties are developed as temperature‐controlled manipulators. The PNIPAM‐clay NC structure provides the hydrogel with excellent mechanical property, and the thermoresponsive bending property of the PNIPAM‐clay NC hydrogel is achieved by designing an asymmetrical distribution of nanoclays across the hydrogel thickness. The hydrogel is simply fabricated by a two‐step photo polymerization. The thermoresponsive bending property of the PNIPAM‐clay NC hydrogel is resulted from the unequal forces generated by the thermoinduced asynchronous shrinkage of hydrogel layers with different clay contents. The thermoresponsive bending direction and degree of the PNIPAM‐clay NC hydrogel can be adjusted by controlling the thickness ratio of the hydrogel layers with different clay contents. The prepared PNIPAM‐clay NC hydrogels exhibit rapid, reversible, and repeatable thermoresponsive bending/unbending characteristics upon heating and cooling. The proposed PNIPAM‐clay NC hydrogels with excellent responsive bending property are demonstrated as temperature‐controlled manipulators for various applications including encapsulation, capture, and transportation of targeted objects. They are highly attractive material candidates for stimuli‐responsive “smart” soft robots in myriad fields such as manipulators, grippers, and cantilever sensors.
reMinMin: A novel static energy-centric list scheduling approach based on real measurements

Heterogeneous compute nodes in form of CPUs with attached GPU and FPGA accelerators have strongly gained interested in the last years. Applications differ in their execution characteristics and can therefore benefit from such heterogeneous resources in terms of performance or energy consumption. While performance optimization has been the only goal for a long time, nowadays research is more and more focusing on techniques to minimize energy consumption due to rising electricity costs. This paper presents reMinMin, a novel static list scheduling approach for optimizing the total energy consumption for a set of tasks executed on a heterogeneous compute node. reMinMin bases on a new energy model that differentiates between static and dynamic energy components and covers effects of accelerator tasks on the host CPU. The required energy values are retrieved by measurements on the real computing system. In order to evaluate reMinMin, we compare it with two reference implementations on three task sets with different degrees of heterogeneity. In our experiments, MinMin is consistently better than a scheduler optimizing for dynamic energy only, which requires up to 19.43% more energy, and very close to optimal schedules.
Revealing the distribution of transmembrane currents along the dendritic tree of a neuron from extracellular recordings

Revealing the current source distribution along the neuronal membrane is a key step on the way to understanding neural computations; however, the experimental and theoretical tools to achieve sufficient spatiotemporal resolution for the estimation remain to be established. Here, we address this problem using extracellularly recorded potentials with arbitrarily distributed electrodes for a neuron of known morphology. We use simulations of models with varying complexity to validate the proposed method and to give recommendations for experimental applications. The method is applied to in vitro data from rat hippocampus.
Monoclonal Antibodies MAM-2B11, MAM-6E10, and MAM-8G12 Against Aflatoxins.

Aflatoxins (AFs) are hepatotoxic mycotoxins produced from Aspergilus spp. (1) They are small molecular weight compounds, that is, haptens that cannot generate a humoral response in a mammalian immune system. They need to be introduced to the immune system conjugated to immunogenic molecules to stimulate antibody production. In this study, we conjugated AFB1 (Fermentek) to cationized ovalbumin (OVA), human apo transferrin (hTF), and bovine serum albumin (BSA) with Mannich-type reaction. AFB1-OVA and AFB1-TF were used as immunogens and AFB1-BSA was used as solid phase antigen in enzymelinked immunosorbent assay (ELISA) tests.
Feedback Control of Real-Time Display Advertising

Real-Time Bidding (RTB) is revolutionising display advertising by facilitating per-impression auctions to buy ad impressions as they are being generated. Being able to use impression-level data, such as user cookies, encourages user behaviour targeting, and hence has significantly improved the effectiveness of ad campaigns. However, a fundamental drawback of RTB is its instability because the bid decision is made per impression and there are enormous fluctuations in campaigns' key performance indicators (KPIs). As such, advertisers face great difficulty in controlling their campaign performance against the associated costs. In this paper, we propose a feedback control mechanism for RTB which helps advertisers dynamically adjust the bids to effectively control the KPIs, e.g., the auction winning ratio and the effective cost per click. We further formulate an optimisation framework to show that the proposed feedback control mechanism also has the ability of optimising campaign performance. By settling the effective cost per click at an optimal reference value, the number of campaign's ad clicks can be maximised with the budget constraint. Our empirical study based on real-world data verifies the effectiveness and robustness of our RTB control system in various situations. The proposed feedback control mechanism has also been deployed on a commercial RTB platform and the online test has shown its success in generating controllable advertising performance.
Designing interaction, not interfaces

Although the power of personal computers has increased 1000-fold over the past 20 years, user interfaces remain essentially the same. Innovations in HCI research, particularly novel interaction techniques, are rarely incorporated into products. In this paper I argue that the only way to significantly improve user interfaces is to shift the research focus from designing interfaces to designing interaction. This requires powerful interaction models, a better understanding of both the sensory-motor details of interaction and a broader view of interaction in the context of use. It also requires novel interaction architectures that address reinterpretability, resilience and scalability.
Drum Transcription via Classification of Bar-Level Rhythmic Patterns

We propose a novel method for automatic drum transcription from audio that achieves the recognition of individual drums by classifying bar-level drum patterns. Automatic drum transcription has to date been tackled by recognising individual drums or drum combinations. In high-level tasks such as audio similarity, statistics of longer rhythmic patterns have been used, reflecting that musical rhythm emerges over time. We combine these two approaches by classifying bar-level drum patterns on sub-beat quantised timbre features using support vector machines. We train the classifier using synthesised audio and carry out a series of experiments to evaluate our approach. Using six different drum kits, we show that the classifier generalises to previously unseen drum kits when trained on the other five (80% accuracy). Measures of precision and recall show that even for incorrectly classified patterns many individual drum events are correctly transcribed. Tests on 14 acoustic performances from the ENST-Drums dataset indicate that the system generalises to real-world recordings. Limited by the set of learned patterns, performance is slightly below that of a comparable method. However, we show that for rock music, the proposed method performs as well as the other method and is substantially more robust to added polyphonic accompaniment.