Miracle-dz/UniFAR-FTUs huggingface dataset:
The effect of adaptation on subjective brightness

Attempts have often been made to find a relation between stimulus and sensation magnitudes since the implications of the existence of absolute and differential threshold, and theories of intensity discrimination, flicker sensitivity, resolving power, and so forth must depend largely on the view taken of this relation. Some of the problems have been discussed by wright (1938). Two principal methods have been employed in the case of vision: first, the direct estimation of sensation intensities as numerical multiples of one another (Richardson 1929; Maxwell 1929;) Hopkinson 1939) or as intermediate between other sensation intensities (Gage 1934); and secondly, the integration of the I/∆I curve, on the assumption that just noticeable differences at all points of the sensation-intensity range represent arithmetically equal sensation steps (Fechner 1858; Abribat 1935). Both these methods have been criticized on the general ground that sensation intensity is not a measurable quantity, and that the numerical results obtained are therefore illusory. In the case of the first type of experiment it is held that the numbers are merely names applied by convention to sensory intensities which can only be ranked in an ordinal series; and in the case of integration of ∆I’s it is objected that there is no method of showing that j. n. d. ’s at different sensation levels represent equal increments in sensation intensity. It has also been shown by Gage that even if the assumption of the equality of j. n. d. ’s were correct the result obtained would be false, since the effect of adaptation in altering the sensation intensity after initial exposure to a changed stimulus is neglected.
A new approach for the removal of mixed noise based on wavelet transform

This paper proposed a new approach for the removal of mixed noise. There are many different ways in image denoising. Donoho et al have proposed a method for image de-noising by thresholding, and indeed, the application of their method to image denoising has been extremely successful. But the method of Donoho is based on the assumption that the type of noise is only additive Gaussian noise, which is not successful for impulse noise. Mallat has also presented a method for signal denoising by discriminating the noise and the signal singularities through an analysis of their wavelet transform modulus maxima (WTMM). Nevertheless, the tracing of WTMM is not just tedious procedure computationally; ambiguity is often resulted in determining the correspondence of a modulus maximum to a singularity. In the light, we combine the merits of the two techniques to form a new approach for the removal of mixed noise. At first, we used wavelet singularity detection (WSD) technique to analyze singularities of signal and noise. According to the characteristic that wavelet transform modulus maxima of impulse noise rapidly decreases as the scale increases in wavelet domain, it can be accurately located with multiscale space by going through dyadic orthogonal wavelet transform and removed. Furthermore the Gaussian noise is also removed through a level-dependent thresholding algorithm. The experimental results demonstrate that the proposed method can effectively detect impulse noise and remove almost all of the noise while preserve image details very well.
Additive noise channels with side information at the transmitter

Consider the optimum strategy for using channel state ("side") information in transmission over a modulo-additive noise channel, where the receiver does not have access to the side information. Previous work showed that capacity-wise, the optimum transmitter shifts each code letter by a "prediction" of the noise sample based on the side information. We show that this structure achieves also the random-coding error exponent, and therefore is optimum at some range of rates below capacity. Specifically, the optimum transmitter-predictor minimizes the Renyi entropy of the prediction error; the Renyi order depends on the rate, and goes to one (corresponding to Shannon (1958) entropy) for rates close to capacity. We also consider the problem of coding with side information at the transmitter subject to a power constraint.
An Efficient Method for the Experimental Characterization of Periodic Multilayer Mirrors: A Global Optimization Approach

This study proposes a new approach to periodic multilayer mirrors (PMMs) characterizations based on measured X-ray reflectivity (XRR) data. Here, XRR data are used to reconstruct the internal structure of PMMs using grazing incidence XRR (GIXR). A mathematical model of electromagnetic wave reflection by PMMs is employed to implement forward prediction, which will then be used iteratively in a global optimization framework in order to reconstruct the PMM unknown structure parameters. A typical simulation of PMM often includes tens to thousands of unknown structure parameters, rendering standard curve fitting methods cumbersome and impractical. To make the PMM characterization method computationally feasible, this study combines implementation of the Levy flight particle swarm optimization (LFPSO) algorithm with a parallelized version of the electromagnetic solver X-Ray Calc in order to simplify the model parameter reconstruction process. Levy flight, a random walk wherein the Levy distribution is used to determine step size, is a more efficient search strategy for global optimization because of the long jumps made by the particles. It is demonstrated that a PMM model with up to thousands of structure parameters can be reconstructed within several seconds on a regular workstation. The algorithm is tested with both measured and theoretical XRR data using in-house fabricated PMMs with known structures. Excellent agreement with the actual structures is observed, which is attained in short computation time. The new approach avoids manual curve fitting and simplified GIXR analysis and is observed to scale linearly with the size of the PMM structure, making it attractive for X-ray optics systems involving large-and-complex reflecting mirrors.
INCA: Input-stationary Dataflow at Outside-the-box Thinking about Deep Learning Accelerators

This paper first presents an input-stationary (IS) implemented crossbar accelerator (INCA), supporting inference and training for deep neural networks (DNNs). Processing-in-memory (PIM) accelerators for DNNs have been actively researched, specifically, with resistive random-access memory (RRAM), due to RRAM’s computing and memorizing capabilities and device merits. To the best of our knowledge, all previous PIM accelerators have saved weights into RRAMs and inputs (activations) into conventional memories—it naturally forms weight-stationary (WS) dataflow. WS has generally been considered the most optimized choice for high parallelism and data reuse. How-ever, WS-based PIM accelerators show fundamental limitations: first, remaining high dependency on DRAM and buffers for fetching and saving inputs (activations); second, a remarkable number of extra RRAMs for transposed weights and additional computational intermediates in training; third, coarse-grained arrays demanding high-bit analog-to-digital converters (ADCs) and introducing poor utilization in depthwise and pointwise convolution; last, degraded accuracy due to its sensitivity to weights which are affected by RRAM’s nonideality. On the other hand, we observe that IS dataflow, where RRAMs retain inputs (activations), can effectively address the limitations of WS, because of low dependency by only loading weights, no need for extra RRAMs, feasibility of fine-grained accelerator design, and less impact of input (activation) variance on accuracy. But IS dataflow is hardly achievable by the existing crossbar structure because it is difficult to implement kernel sliding and preserve the high parallelism. To support kernel movement, we constitute a cell structure with two-transistor-one-RRAM (2T1R). Based on the 2T1R cell, we design a novel three-dimensional (3D) architecture for high parallelism in batch training. Our experiment results prove the potential of INCA. Compared to the WS accelerator, INCA achieves up to 20.6× and 260× energy efficiency improvement in inference and training, respectively; 4.8× (inference) and 18.6× (training) speedup as well. While accuracy in WS drops to 15% in our high-noise simulation, INCA presents an even more robust result as 86% accuracy.
GRACE: Loss-Resilient Real-Time Video through Neural Codecs

In real-time video communication, retransmitting lost packets over high-latency networks is not viable due to strict latency requirements. To counter packet losses without retransmission, two primary strategies are employed -- encoder-based forward error correction (FEC) and decoder-based error concealment. The former encodes data with redundancy before transmission, yet determining the optimal redundancy level in advance proves challenging. The latter reconstructs video from partially received frames, but dividing a frame into independently coded partitions inherently compromises compression efficiency, and the lost information cannot be effectively recovered by the decoder without adapting the encoder. We present a loss-resilient real-time video system called GRACE, which preserves the user's quality of experience (QoE) across a wide range of packet losses through a new neural video codec. Central to GRACE's enhanced loss resilience is its joint training of the neural encoder and decoder under a spectrum of simulated packet losses. In lossless scenarios, GRACE achieves video quality on par with conventional codecs (e.g., H.265). As the loss rate escalates, GRACE exhibits a more graceful, less pronounced decline in quality, consistently outperforming other loss-resilient schemes. Through extensive evaluation on various videos and real network traces, we demonstrate that GRACE reduces undecodable frames by 95% and stall duration by 90% compared with FEC, while markedly boosting video quality over error concealment methods. In a user study with 240 crowdsourced participants and 960 subjective ratings, GRACE registers a 38% higher mean opinion score (MOS) than other baselines.
The Chemokine Receptor Ccr2 Mediates the Binding and Internalization of Monocyte Chemoattractant Protein-1 along Brain Microvessels

Previous results from this laboratory revealed the presence of high-affinity saturable binding sites for monocyte chemoattrac-tant protein-1 (MCP-1) along human brain microvessels (And-jelkovic et al., 1999; Andjelkovic and Pachter, 2000), which suggested that CC chemokine receptor 2 (CCR2), the recognized receptor for this chemokine, was expressed by the brain microvascular endothelium. To test the role of CCR2 directly in mediating MCP-1 interactions with the brain microvasculature, we assessed MCP-1 binding activity in murine brain microves-sels isolated from wild-type mice and from CCR2 (Ϫ/Ϫ) mice engineered to lack this receptor. Results demonstrate that MCP-1 binding is greatly attenuated in microvessels prepared from CCR2 (Ϫ/Ϫ) mice compared with wild-type controls. Moreover, microvessels from wild-type mice exhibited MCP-1-induced downmodulation in MCP-1 binding and a recovery of binding activity that was not dependent on de novo protein synthesis. Furthermore, MCP-1 was shown to be internalized within wild-type microvessels, but not within microvessels obtained from CCR2 (Ϫ/Ϫ) mice, additionally demonstrating that CCR2 is obligatory for MCP-1 endocytosis. Last, internalization of MCP-1, but not transferrin, was observed to be inhibited by disruption of caveolae. Internalized MCP-1 also colocalized at some sites with caveolin-1, a major protein of caveolae, implying that this chemokine is endocytosed, in part, via nonclathrin-coated vesicles. These results prompt consideration that MCP-1 signals may be relayed across the blood–brain barrier by highly specialized interactions of this chemokine with its cognate receptor, CCR2, along brain microvascular endo-thelial cells. Chemokines, which are specialized cytokines, belong to a super-family of proteins that is composed of four recognized classes refers to the number of amino acids between cysteines (Rossi and Zlotnick, 2000). As their name implies, chemokines act as che-motactic factors that direct the emigration of leukocytes to select tissue locales during inflammatory episodes. The ␤ chemokine monocyte chemoattractant protein-1 (MCP-1), newly termed CCL2 (Rossi and Zlotnick, 2000), has been implicated in mediating the infiltration of mononuclear leukocytes into the CNS during a variety of neuroinflammatory conditions (Ghirnikar et al. the mechanism or mechanisms by which this chemokine effects extravasation into the CNS remain unclear. Confounding this issue is that astrocytes, which project their foot processes onto the microvessels comprising the blood–brain barrier (BBB; Lass-mann et al., 1991), are the predominant source of MCP-1 in several neuroinflammatory conditions (Ransohoff et al. primarily originates from a source located behind the BBB begs the following question: How does MCP-1 reach leukocytes residing in the microvessel lumen? Because chemokines might …
Gaussian processes for temporal and spatial pattern analysis in the MISR satellite land-surface data

The Multi-Angle Imaging SpectroRadiometer (MISR) is an Earth observation instrument operated by NASA on its Terra satellite. The instrument is unique in imaging the Earth’s surface from nine cameras at different angles. An extended system MISR-HR, has been developed by the Joint Research Centre of the European Commission (JRC) and NASA, which derives many values describing the interaction between solar energy, the atmosphere and different surface characteristics. It also generates estimates of data at the native resolution of the instrument for 24 of the 36 camera bands for which on-board averaging has taken place prior to downloading of the data. MISR-HR data potentially yields high value information in agriculture, forestry, environmental studies, land management and other fields. The MISR-HR system and the data for the African continent have also been provided by NASA and the JRC to the South African National Space Agency (SANSA). Generally, satellite remote-sensing of the Earth’s surface is characterised by irregularity in the time-series of data due to atmospheric, environmental and other effects. Time-series methods, in particular for vegetation phenology applications, exist for estimating missing data values, filling gaps and discerning periodic structure in the data. Recent evaluations of the methods established a sound set of requirements that such methods should satisfy. Existing methods mostly meet the requirements, but choice of method would largely depend on the analysis goals and on the nature of the underlying processes. An alternative method for time-series exists in Gaussian Processes, a long established statistical method, but not previously a common method for satellite remote-sensing time-series. This dissertation asserts that Gaussian Process regression could also meet the aforementioned set of time-series requirements, and further provide benefits of a consistent framework rooted in Bayesian statistical methods. To assess this assertion, a data case study has been conducted for data provided by SANSA for the Kruger National Park in South Africa. The requirements have been posed as research questions and answered in the affirmative by analysing twelve years of historical data for seven sites differing in vegetation types, in and bordering the Park. A further contribution is made in that the data study was conducted using Gaussian Process software which was developed specifically for this project in the modern open language Julia. This software will be released in due course as open source.
Assessment of Intra-operative and Early Post-operative Complications of Laser in–situ Keratomileusis

Several Laser in-situ keratomileusis (LASIK) complications have been identified over the years. Reporting complications of LASIK surgery will help refine the approach to their management. LASIK was introduced in the late 1980s with the development of the ophthalmic excimer laser [1, 2]. To avoid the disadvantages of photorefractive keratectomy (PRK), LASIK was introduced [3]. LASIK rapidly increased in popularity and became the predominant form of refractive surgery in the late 1990s and continues till today [4]. Correcting different refractive errors, including myopia, hyperopia, and astigmatism, is the main aim of all laser refractive procedures. LASIK has been used to treat many degrees of myopia with promising results, and there are great expectations for treating farsightedness. Compared to PRK, LASIK is advantageous in causing minimal postoperative discomfort, rapid restoration of visual clarity and stability of refractive changes, less frequent opacification, and better ability to correct high levels of myopia. Intraoperative complications, although infrequent, include Microkeratome-related flap complications (flap buttonhole, free cap, and incomplete, short, or irregular flaps), corneal perforation, corneal epithelial defect, conjunctival bleeding, subconjunctival hemorrhage, and interface debris [6]. Post-operative Complications include inaccurate correction, visual aberrations, flap striae (macrostriae -microstriae) [6], flap dislocation, dry eye, diffuse lamellar keratitis (DLK), pressure-induced stromal keratitis (PISK), infectious keratitis [7
Correct-by-Construction Interactive Software : From Declarative Specifications to Efficient Implementations

Introduction. Today’s interactive software is wildly successful and pervasive, invading nearly every facet of our daily lives. An interactive system, like a GUI, is also particularly complex, due to the nature of providing a rich, responsive, interactive experience for users. Today’s software platforms further compound this complexity for humans and tools alike because they force the programmers to design their application behavior in terms of implementation concepts, rather than declarative specifications. Typically, framework models for applications are complex: they consist of global, higher-order state and controlflow that is rarely direct, but often contorted into callbacks or continuation-passing-style (e.g., to be event-driven, and/or asynchronous) [5]. Seemingly, this complexity is required for the framework to be responsive and flexible. However, designing user interaction based solely on such application models is inherently flawed, since the resulting design process omits a key element: a declarative specification. A declarative specification is most useful to its designers and users alike when it is not framed within the arcane ontology of the latest software framework, but rather given as a simple executable program that is independent from the underlying framework implementation. As an alternative to framework-driven design, this extended abstract advocates a design process that we call Correct-by-Construction Interaction. Many of the underlying principles behind this process are not new, going back to pioneers such as Dijkstra, who famously advocated that programs and specifications should be developed hand-inhand, and who proposed starting with a specification, and systematically deriving an implementation that is correct by construction. Our contribution to this tradition consists of the following insight: A promising bridge between declarative specifications and efficient, responsive applications are general-purpose programming language techniques for incremental computation. To explore this connection, we designed and implemented a prototype text editor, IC-Edit. After sketching a specification, we implemented it in about 400 lines of OCaml, without any special libraries1. We explored this
Women and Children Last: The Discursive Construction of Weblogs

Weblogs (“blogs”), frequently modified webpages containing individual entries displayed in reverse chronological sequence, are the latest mode of computer-mediated communication (CMC) to attain widespread popularity. As with other new CMC technologies, blogs have been hailed as democratizing—any literate person can self-publish content in a blog, and reach an audience of potentially millions, for little or no cost. Moreover, the success of individual blogs in attracting readers and influencing opinion depends less on their formal credentials than on the quality of their ideas and their writing (what Winer, 2003, calls their "voice"). Certainly blog authors are numerous: In the five years since the introduction of the first free web-based blogging tools (Pitas and Blogger; Blood, 2002b), the number of people creating and maintaining blogs has grown exponentially, from fewer than 100 to over four million (Henning, 2003). Anecdotal accounts also suggest that they are diverse: the mainstream media have reported on popular blogs authored by individuals as varied as university adjuncts, dark horse candidates for political office, and a gay Iraqi dissident (McCarthy, 2003). As yet, however, there has been little empirical examination of the claim that blogs are “democratic,” or that blog authors represent diverse demographic groups.
Identifying users' topical tasks in web search

A search task represents an atomic information need of a user in web search. Tasks consist of queries and their reformulations, and identifying tasks is important for search engines since they provide valuable information for determining user satisfaction with search results, predicting user search intent, and suggesting queries to the user. Traditional approaches to identifying tasks exploit either temporal or lexical features of queries. However, many query refinements are topical, which means that a query and its refinements may not be similar on the lexical level. Furthermore, multiple tasks in the same search session may interleave, which means we cannot simply order the searches by their timestamps and divide the session into multiple tasks. Thus, in order to identify tasks correctly, we need to be able to compare two queries at the semantic level. In this paper, we use a knowledgebase known as Probase to infer the conceptual meanings of queries, and automatically identify the topical query refinements in the tasks. Experimental results on real search log data demonstrate that Probase can indeed help estimate the topical affinity between queries, and thus enable us to merge queries that are topically related but dissimilar at the lexical level.
Prevalence of autism-spectrum conditions: UK school-based population study

Background Recent reports estimate the prevalence of autism-spectrum conditions in the UK to be 1%. Aims To use different methods to estimate the prevalence of autism-spectrum conditions, including previously undiagnosed cases, in Cambridgeshire. Method We carried out a survey of autism-spectrum conditions using the Special Educational Needs (SEN) register. A diagnosis survey was distributed to participating schools to be handed out to parents of all children aged 5–9 years. The mainstream primary school population was screened for unknown cases. Results The prevalence estimates generated from the SEN register and diagnosis survey were 94 per 10 000 and 99 per 10 000 respectively. A total of 11 children received a research diagnosis of an autism-spectrum condition following screening and assessment. The ratio of known:unknown cases is about 3:2 (following statistical weighting procedures). Taken together, we estimate the prevalence to be 157 per 10 000, including previously undiagnosed cases. Conclusions This study has implications for planning diagnostic, social and health services.