Miracle-dz/UniFAR-FTUs huggingface dataset:
Distributed computation of symmetric functions with binary inputs

This paper considers the following network computation problem: n nodes are placed on a √n×√n grid, each node in the network is connected to every other node within distance r(n) of itself, and is given an arbitrary input bit. Connected nodes communicate with each other over independent binary symmetric channels of a given transition probability ε ≥ 0, and an arbitrarily designated node computes a symmetric target function ƒ of the input bits. We characterize up to order the minimum number of transmissions required to compute ƒ with a probability of error less than any given positive constant δ. As a side result, we answer an open question posed by El Gamal in 1987 [1] regarding the number of transmissions required to compute the parity function over ring and tree networks.
The use of Kernel PCA in evolutionary optimization for computationally demanding engineering applications

Two techniques to further enhance the efficiency of Evolutionary Algorithms (EAs), even those which have already been accelerated by implementing surrogate evaluation models or metamodels to overcome a great amount of costly evaluations, are presented. Both rely upon the use of a Kernel Principal Component Analysis (Kernel PCA or KPCA) of the design space, as this reflects upon the offspring population in each generation. The PCA determines a feature space where the evolution operators should preferably be applied. In addition, in Metamodel-Assisted EA (MAEAs), the PCA can reduce the number of sensory units of metamodels. Due to the latter, the metamodels yield better approximations to the objective function value. This paper extends previous work by the authors which was based on Linear PCA, used for the same purposes. In the present paper, the superiority of using the Kernel (rather than the Linear) PCA, especially in real-world applications, is demonstrated. The proposed methods are assessed in single- and two-objective mathematical optimization problems and, finally, showcased in aerodynamic shape optimization problems with computationally expensive evaluation software.
Human Behavior Based Predictive Brake Assistance

Driver assistance systems have both the potential to alert the driver to critical situations and distract or annoy the driver if the driver is already aware of the situation. As systems attempt to preemptively warn drivers more and more in advance, this problem becomes exacerbated. We present a predictive braking assistance system that identifies not only the need for braking action, but also whether or not a braking action is being planned by the driver. Our system uses a Bayesian framework to determine the criticality of the situation by assessing (1) the probability that braking should be performed given observations of the vehicle and surround and (2) the probability that the driver intends to perform a braking action. We train and evaluate our system using over 22 hours of data collected from real driving scenarios with 28 different drivers
Gravitational Microlensing in the Local Group

▪ Abstract The status of searches for gravitational microlensing events of the stars in our galaxy and in other galaxies of the Local Group, the interpretation of the results, some theory, and prospects for the future are reviewed. The searches have already unveiled ∼ 100 events, at least two of them caused by binaries, and have already proven to be useful for studies of the Galactic structure. The events detected so far are probably attributable to the effects of ordinary stars, and possibly to substellar brown dwarfs; however, a firm conclusion cannot be reached yet because the analysis published to date is based on a total of only 16 events. The current searches, soon to be upgraded, will probably allow determination of the mass function of stars and brown dwarfs in the next few years; these efforts will also provide good statistical information about binary systems, in particular their mass ratios. They may also reveal the nature of dark matter and allow us to detect planets and planetary mass objects.
Research on Virtual 3D Station based on Images

With the improvement of electric information, 3D visualization for the substation in the planning, designing, inspection, training and other aspects calls for a gradual increase. 3D rendering of the current technology is not yet mature, and equipment modelling is too complex, we propose a virtual 3D substation, which is an image-based system. The key issues include a sphere- based methods of fish-eye image mosaic, images of the plane and spherical mapping algorithm, a complete solution to achieve the system's construction, finally made some suggestions for improvement.
Awakening dynamics via passive coupling and synchronization mechanism in oscillatory cellular neural/nonlinear networks

We have studied synchronization mechanism in locally coupled nonlinear oscillators. Here, synchronization takes place by passive coupling based on a reaction–diffusion process. We will compare this mechanism with basic synchronization techniques, showing their similarities and specific properties. In addition to synchronization, passive and local coupling can also ‘awaken’ non‐oscillating cell circuits and trigger oscillation, provided that cells are locally active. This result resembles Turing's and Smale's works showing that locally communicating simple elements can produce very different patterns even if separate elements do not show any activity. This property will be demonstrated for two second‐order cells and also for a large ensemble of oscillatory cells. In latter case, the network of oscillatory cells exhibits very sophisticated spatio‐temporal waves, e.g. spiral waves. Copyright © 2008 John Wiley & Sons, Ltd.
The Correlation-Base-Selection Algorithm for Diagnostic Schizophrenia Based on Blood-Based Gene Expression Signatures

Microarray analysis of gene expression is often used to diagnose different types of disease. Many studies report remarkable achievements in nervous system disease. Clinical diagnosis of schizophrenia (SCZ) still depends on doctors' experience, which is unreliable and needs to be more objective and quantified. To solve this problem, we collected whole blood gene expression data from four studies, including 152 individuals with schizophrenia (SCZ) and 138 normal controls in different regions. The correlation-based feature selection (CFS, one of the machine learning methods) algorithm was applied in this study, and 103 significantly differentially expressed genes between patients and controls, called “feature genes,” were selected; then, a model for SCZ diagnosis was built. The samples were subdivided into 10 groups, and cross-validation showed that the model we constructed achieved nearly 100% classification accuracy. Mathematical evaluation of the datasets before and after data processing proved the effectiveness of our algorithm. Feature genes were enriched in Parkinson's disease, oxidative phosphorylation, and TGF-beta signaling pathways, which were previously reported to be associated with SCZ. These results suggest that the analysis of gene expression in whole blood by our model could be a useful tool for diagnosing SCZ.
Quantification of Rapid Myosin Regulatory Light Chain Phosphorylation Using High-Throughput In-Cell Western Assays: Comparison to Western Immunoblots

Background Quantification of phospho-proteins (PPs) is crucial when studying cellular signaling pathways. Western immunoblotting (WB) is commonly used for the measurement of relative levels of signaling intermediates in experimental samples. However, WB is in general a labour-intensive and low-throughput technique. Because of variability in protein yield and phospho-signal preservation during protein harvesting, and potential loss of antigen during protein transfer, WB provides only semi-quantitative data. By comparison, the “in-cell western” (ICW) technique has high-throughput capacity and requires less extensive sample preparation. Thus, we compared the ICW technique to WB for measuring phosphorylated myosin regulatory light chain (PMLC20) in primary cultures of uterine myocytes to assess their relative specificity, sensitivity, precision, and quantification of biologically relevant responses. Methodology/Principal Findings ICWs are cell-based microplate assays for quantification of protein targets in their cellular context. ICWs utilize a two-channel infrared (IR) scanner (Odyssey®) to quantify signals arising from near-infrared (NIR) fluorophores conjugated to secondary antibodies. One channel is dedicated to measuring the protein of interest and the second is used for data normalization of the signal in each well of the microplate. Using uterine myocytes, we assessed oxytocin (OT)-stimulated MLC20 phosphorylation measured by ICW and WB, both using NIR fluorescence. ICW and WB data were comparable regarding signal linearity, signal specificity, and time course of phosphorylation response to OT. Conclusion/Significance ICW and WB yield comparable biological data. The advantages of ICW over WB are its high-throughput capacity, improved precision, and reduced sample preparation requirements. ICW might provide better sensitivity and precision with low-quantity samples or for protocols requiring large numbers of samples. These features make the ICW technique an excellent tool for the study of phosphorylation endpoints. However, the drawbacks of ICW include the need for a cell culture format and the lack of utility where protein purification, concentration or stoichiometric analyses are required.
C New Approach for Color Image Compression

It is well-known that edges contain semantically important image information. In this paper, color image coding based on edge-detection is introduced. Modification of the JPEG technique to improve the color image compression is proposed. The process is based on the modification of the quantized value of the DCT coefficients. The efficiency of the proposed scheme is demonstrated by results, especially, when faced to the method presented in the recently published papers.
Regularized online learning of pseudometrics

We present a regularized approach for online learning of a pseudometric in the form of a Mahalanobis distance. We express the problem as an optimization that learns on the current labeled instance whilst favoring a solution of a predefined form. Our focus is on regularization. Our formulation takes up a flexible form allowing for scenarios ranging from traditional L2 regularization to regularization to a prior estimated from unsupervised data. We apply our method to an online content-based music retrieval scenario (e.g. personalized internet radio). Here the user provides information on his listening preferences via online feedback for each song that is played. By updating a pseudometric given this feedback, the algorithm optimizes a transformation that maps the user's preferred songs closer together and undesired songs far from these preferred songs.
Direct Interaction of the trans-Golgi Network Membrane Protein, TGN38, with the F-actin Binding Protein, Neurabin*

TGN38 is a type I integral membrane protein that constitutively cycles between the trans-Golgi network (TGN) and plasma membrane. The cytosolic domain of TGN38 interacts with AP2 clathrin adaptor complexes via the tyrosine-containing motif (-SDYQRL-) to direct internalization from the plasma membrane. This motif has previously been shown to direct both internalization and subsequent TGN targeting of TGN38. We have used the cytosolic domain of TGN38 in a two-hybrid screen, and we have identified the brain-specific F-actin binding protein neurabin-I as a TGN38-binding protein. We demonstrate a direct interaction between TGN38 and the ubiquitous homologue of neurabin-I, neurabin-II (also called spinophilin). We have used a combination of yeast two-hybrid and in vitro protein interaction assays to show that this interaction is dependent on the serine (but not tyrosine) residue of the known TGN38 trafficking motif. We show that TGN38 interacts with the coiled coil region of neurabinin vitro and binds preferentially with the dimeric form of neurabin. TGN38 and neurabin also interact in vivo as demonstrated by coimmunoprecipitation from stably transfected PC12 cells. These data suggest that neurabin provides a direct physical link between TGN38-containing membranes and the actin cytoskeleton.
Neurosphere Formation Is an Independent Predictor of Clinical Outcome in Malignant Glioma

Renewable neurosphere formation in culture is a defining characteristic of certain brain tumor initiating cells. This retrospective study was designed to assess the relationship among neurosphere formation in cultured human glioma, tumorigenic capacity, and patient clinical outcome. Tumor samples were cultured in neurosphere conditions from 32 patients with glioma, including a subpopulation of 15 patients with primary glioblastoma. A subsample of renewable neurosphere cultures was xenografted into mouse brain to determine if they were tumorigenic. Our study shows that both renewable neurosphere formation and tumorigenic capacity are significantly associated with clinical outcome measures. Renewable neurosphere formation in cultured human glioma significantly predicted an increased hazard of patient death and more rapid tumor progression. These results pertained to both the full population of glioma and the subpopulation of primary glioblastoma. Similarly, there was a significant hazard of progression for patients whose glioma had tumorigenic capacity. Multivariate analysis demonstrated that neurosphere formation remained a significant predictor of clinical outcome independent of Ki67 proliferation index. In addition, multivariate analysis of neurosphere formation, tumor grade and patient age, demonstrated that neurosphere formation was a robust, independent predictor of glioma tumor progression. Although the lengthy duration of this assay may preclude direct clinical application, these results exemplify how neurosphere culture serves as a clinically relevant model for the study of malignant glioma. Furthermore, this study suggests that the ability to propagate brain tumor stem cells in vitro is associated with clinical outcome. STEM CELLS 2009;27:980–987
Ecient Substring Traversal with Sux Arrays

The substring traversal problem is the problem of enumerating all branching substrings appearing in a given text. Although this problem is easily solvable with the su x tree of McCreight (1976), a space e cient and practically fast solution is important. We devise a simple and e cient algorithm that simulates the traversal of the su x tree for a given text with the su x array of Manber and Meyers (1993) and Gonnet, Baeza-Yates, Snider (1992). The algorithm runs in O(n) time and 5n B bulk I/O with the su x array and an additional structure called the height array, while the naive algorithm using binary search on the su x array requires O(n 2 ) time in the worst case. The space requirement 7N bytes of our algorithm is smaller than 15N bytes of the traversal algorithm with the su x tree. A linear time algorthm for computing the height array from the su x and the height arrays is also presented. Computer experiments on real datasets showed that our traversal algorithm with the su x array is an order of magnitude faster than the naive simulation method and comparable to the traversal algorithm with the su x tree.
4 design themes for skateboarding

Interactive technology can support exertion activities, with many examples focusing on improving athletic performance. We see an opportunity for technology to also support extreme sports such as skateboarding, which often focus primarily on the experience of doing tricks rather than on athletic performance. However, there is little knowledge on how to design for such experiences. In response, we designed 12 basic skateboarding prototypes inspired by skateboarding theory. Using an autoethnographical approach, we skated with each of these and reflected on our experiences in order to derive four design themes : location of feedback in relation to the skater's body, timing of feedback in relation to peaks in emotions after attempts, aspects of the trick emphasized by feedback, and aesthetic fittingness of feedback. We hope our work will guide designers of interactive systems for skateboarding, and extreme sports in general, and will therefore further our understanding of how to design for the active human body.
Adaptive agent generation using machine learning for dynamic difficulty adjustment

Player experience is a significant parameter in evaluating the overall success of a game, both technically and commercially. It is necessary to provide the player a game that provides: (1) satisfaction and (2) challenge. To enhance player experience, the game difficulty needs to be dynamically adjusted with respect to the player. Dynamic scripting is an importantlearning technique used for dynamic difficulty adjustment (DDA), already implemented successfully in commercial games of different genres. However the DDA systems are not sufficiently consistent in creating equally competent agents and do not provide equal opportunity to human players of different capabilities. This paper focuses on solving these issues by introducing these concepts: (a) dynamic weight clipping, (b) differential learning and (c) adrenalin rush. Experimental results indicate that dynamic scripting, in combination with these features, can implement an ideal DDA system for creating a equally competent computer agent who can engage the human player in absorbing games.
Public Expenditure and International Specialisation

It is widely recognised that public-sector purchasers tend to discriminate in favour of domestic suppliers. We study the consequences of home-biased public procurement on international specialisation. In the theoretical analysis we find two effects. First, a country will specialise in the sector for which it has relatively large home-biased procurement (the "pull" effect). Second, home-biased procurement can counter agglomeration forces and thereby attenuate the overall degree of international specialisation (the "spread" effect). Our empirical analysis, conducted on input-output data for the European Union, yields supporting evidence for the pull effect and some support for the spread effect.
A High-Capacity Separable Reversible Method for Hiding Multiple Messages in Encrypted Images

This work proposes a high-capacity scheme for separable reversible data hiding in encrypted images. At the sender side, the original uncompressed image is encrypted using an encryption key. One or several data hiders use the MSB of some image pixels to hide additional data. Given the encrypted image containing this additional data, with only one of those data hiding keys, the receiver can extract the corresponding embedded data, although the image content will remain inaccessible. With all of the embedding keys, the receiver can extract all of the embedded data. Finally, with the encryption key, the receiver can decrypt the received data and reconstruct the original image perfectly\ignore{ without the data embedding key(s) }by exploiting the spatial correlation of natural images. Based on the proposed method a receiver could recover the original image perfectly even when it does not have the data embedding key(s) and the embedding rate is high.
Identifying Potential Regulatory Sequences of Alternative Splicing

Alternative splicing is an important mechanism that contributes to expanding protein diversity by generating multiple protein isoforms from a single gene. We have previously reported computational approach to infer alternative splicing patterns from Mus musculus full-length cDNA clones and microarray data [4]. Although we have predicted a large number of unreported splice variants, general mechanisms that regulate those alternative splicing were yet to be understood. In the present study, we constructed datasets of putative alternatively spliced genes by use of full-length cDNA clones and genomic DNA sequences. Our putative datasets were then evaluated by comparing information contents of constitutive splice-sites and those of alternative splice-sites. Using phylogenetic footprinting approach, we identied the potential regulatory sequences of alternative splicing.
Preprint Typeset in Jhep Style -hyper Version

These are the unofficial notes for the Part III course given in Easter term 2009 in DAMTP, the University of Cambridge. The (official) books and references for this course are: Some examples given in the introduction section are omitted from these notes, but this should not affect understanding of subsequent material. Please direct any spotted errors,
Tax Cuts, Employment and Asset Prices: A Real Intertemporal Model

We determine the effects of a delayed or immediate tax cut with or without a “sunset” feature in a real customer-market, nonRicardian economy. Our model incorporates both the supply-sider channel, through which reduced wage income taxes stimulate work effort, as well as the Feldstein-Rubin-Summers channel, through which cuts in income tax, in widening the deficit and thus driving up future short real interest rates, has a chilling effect on present and future investment, so reducing growth and employment on that account. After establishing conditions under which fiscal policy is sustainable, we first show that a delayed tax cut may depress both the real asset price and employment in the period running up to the implementation of the tax cut; so this paradox is not restricted to Keynesian or other monetary models. Second, the same ambiguity may result even if the ∗Correspondence Address: Professor Edmund S. Phelps, Department of Economics, Columbia University, International Affairs Building, Room 1004, New York, NY 10027; tel: 1(212)-854-2060; e-mail: esp2@columbia.edu
Evaluation of Tumor Response after Short-Course Radiotherapy and Delayed Surgery for Rectal Cancer

Purpose Neoadjuvant therapy is able to reduce local recurrence in rectal cancer. Immediate surgery after short course radiotherapy allows only for minimal downstaging. We investigated the effect of delayed surgery after short-course radiotherapy at different time intervals before surgery, in patients affected by rectal cancer. Methods From January 2003 to December 2013 sixty-seven patients with the following characteristics have been selected: clinical (c) stage T3N0 ≤ 12 cm from the anal verge and with circumferential resection margin > 5 mm (by magnetic resonance imaging); cT2, any N, < 5 cm from anal verge; and patients facing tumors with enlarged nodes and/or CRM+ve who resulted unfit for chemo-radiation, were also included. Patients underwent preoperative short-course radiotherapy with different interval to surgery were divided in three groups: A (within 6 weeks), B (between 6 and 8 weeks) and C (after more than 8 weeks). Hystopatolgical response to radiotherapy was measured by Mandard’s modified tumor regression grade (TRG). Results All patients completed the scheduled treatment. Sixty-six patients underwent surgery. Fifty-three of which (80.3%) received a sphincter saving procedure. Downstaging occurred in 41 cases (62.1%). The analysis of subgroups showed an increasing prevalence of TRG 1–2 prolonging the interval to surgery (group A—16.7%, group B—36.8% and 54.3% in group C; p value 0.023). Conclusions Preoperative short-course radiotherapy is able to downstage rectal cancer if surgery is delayed. A higher rate of TRG 1–2 can be obtained if interval to surgery is prolonged to more than 8 weeks.