Miracle-dz/UniFAR-FTUs huggingface dataset:
Forecasting Survival Probabilities After Radiotherapy of Metastatic Epidural Spinal Cord Compression from Colorectal Cancer in the Elderly.

AIM To develop a tool forecasting survival of elderly patients with metastatic epidural spinal cord compression (MESCC) from colorectal cancer (CRC). PATIENTS AND METHODS Fifty-seven patients were retrospectively evaluated. Eleven characteristics were investigated for survival. Independent characteristics were used for the tool. Scores were obtained from dividing 6-month survival rates by 10. From summing these points, patient scores were obtained. RESULTS On survival analysis (Cox regression model), organ metastases (p=0.006), performance status (p<0.001), pre-radiotherapy walking ability (p<0.001) and the dynamic of developing motor weakness (p=0.033) were significant factors affecting survival and were incorporated into the tool. Possible patient scores were 5, 9, 10, 13, 14, 16, 20 or 24 points. Three groups were created with scores of 5-10, 13-16 and 20-24 points, with 6-month survival rates of 4%, 23% and 79%, respectively (p<0.001). CONCLUSION By applying this tool, it is possible to forecast the survival of elderly patients experiencing MESCC from CRC, which is important for optimal treatment personalization.
A role for Q/N-rich aggregation-prone regions in P-body localization

P-bodies are cytoplasmic foci that are sites of mRNA degradation and translational repression. It is not known what causes the accumulation of RNA-degradation factors in P-bodies, although RNA is required. The yeast Lsm1-7p complex (comprising Lsm1p to Lsm7p) is recruited to P-bodies under certain stress conditions. It is required for efficient decapping and degradation of mRNAs, but not for the assembly of P-bodies. Here we show that the Lsm4p subunit and its asparagine-rich C-terminus are prone to aggregation, and that this tendency to aggregate promotes efficient accumulation of Lsm1-7p in P-bodies. The presence of glutamine- and/or asparagine-rich (Q/N-rich) regions in other P-body components suggests a more general role for aggregation-prone residues in P-body localization and assembly. This is supported by reduced P-body accumulation of Ccr4p, Pop2p and Dhh1p after deletion of these domains, and by the observed aggregation of the Q/N-rich region from Ccr4p.
Channel model simulation technique for Power Line Communications

Power distribution networks are designed for electrical power supply. In Power Line Communication (PLC) system, reflections caused by impedance mismatches deteriorate transmission characteristics in the high frequency. Estimation of Channel characteristics of power lines for PLC makes more efficient for design and operation of PLC networks. The authors propose a method to estimate transmission characteristics between a transmitter and a receiver installed on a distribution network. Propagation characteristics of PLC signals of each distribution equipment is represented using two-port networks as a component model. A channel model is automatically generated from the integration of component models along the topology of distribution network and the locations of a transmitter and a receiver. The proposed method was evaluated using the results of SPICE (Simulation Program with Integrated Circuit Emphasis) simulation and field data and was proved to be effective.
Learning a Nonlinear Controller From Data: Theory, Computation, and Experimental Results

The problem of learning a nonlinear controller directly from experimental data is considered. It is assumed that an existing, unknown controller, able to stabilize the plant, is available, and that input-output measurements can be collected during closed loop operations. A theoretical analysis shows that the error between the input issued by the existing controller and the input given by the learned one shall have low variability in order to achieve closed loop stability. This result is exploited to derive a $\ell_{1}$-norm regularized learning algorithm that achieves the stability condition for a finite number of data points. The approach is completely based on convex optimization. The presented technique is finally tested in real-world experiments to control the flight of a tethered flexible wing, which is characterized by highly nonlinear, unstable and uncertain dynamics and is subject to external disturbances.
Rapid Evolution of Novel Traits in Microorganisms

ABSTRACT The use of natural microorganisms in biotransformations is frequently constrained by their limited tolerance to the high concentrations of metabolites and solvents required for effective industrial production. In many cases, more robust strains have to be generated by random mutagenesis and selection. This process of directed evolution can be accelerated in mutator strains, which carry defects in one or more of their DNA repair genes. However, in order to use mutator strains, it is essential to restore the normal low mutation rate of the selected organisms immediately after selection to prevent the accumulation of undesirable spontaneous mutations. To enable this process, we constructed temperature-sensitive plasmids that temporarily increase the mutation frequency of their hosts by 20- to 4,000-fold. Under appropriate selection pressure, microorganisms transformed with mutator plasmids can be quickly evolved to exhibit new, complex traits. By using this approach, we were able to increase the tolerance of three bacterial strains to dimethylformamide by 10 to 20 g/liter during only two subsequent transfers. Subsequently, the evolved strains were returned to their normal low mutation rate by curing the cells of the mutator plasmids. Our results demonstrate a new and efficient method for rapid strain improvement based on in vivo mutagenesis.
Maximum Likelihood TDOA Estimation From Compressed Sensing Samples Without Reconstruction

One application for time-difference-of-arrival (TDOA) estimation is in emitter localization. A signal from an emitter reaching a group of sensors, each in a separate location, will have different arrival times. Finding the TDOAs between the output of pairs of sensors will provide the necessary measurements for the hyperbolic localization of the emitter. When the sensors acquire the signal by compressed sensing (CS), their outputs are reduced dimension linear transformation of the time samples of the signal. This shuffling of the time samples breaks up their time relation. Thus, a cross correlation of the CS output of two sensors cannot determine the TDOA. To apply cross correlation, it is necessary to reconstruct the time samples. This letter proposes an alternative that uses only the coefficients of the discrete Fourier transform (DFT) of the CS samples. It begins with the derivation of the maximum likelihood (ML) equation and the ML estimator. This estimator requires known values of signal and noise powers. Substituting these values by their estimates lead to the approximate ML estimator. The phase of the product of two DFT coefficients from each sensor is proportional to the unknown TDOA. Hence, these coefficients can provide an estimation of the TDOA. Simulation results show that although ML is the best, as expected, all these estimators have very close performance.
FORECAST: skimming off the malware cream

To handle the large number of malware samples appearing in the wild each day, security analysts and vendors employ automated tools to detect, classify and analyze malicious code. Because malware is typically resistant to static analysis, automated dynamic analysis is widely used for this purpose. Executing malicious software in a controlled environment while observing its behavior can provide rich information on a malware's capabilities. However, running each malware sample even for a few minutes is expensive. For this reason, malware analysis efforts need to select a subset of samples for analysis. To date, this selection has been performed either randomly or using techniques focused on avoiding re-analysis of polymorphic malware variants [41, 23]. In this paper, we present a novel approach to sample selection that attempts to maximize the total value of the information obtained from analysis, according to an application-dependent scoring function. To this end, we leverage previous work on behavioral malware clustering [14] and introduce a machine-learning-based system that uses all statically-available information to predict into which behavioral class a sample will fall, before the sample is actually executed. We discuss scoring functions tailored at two practical applications of large-scale dynamic analysis: the compilation of network blacklists of command and control servers and the generation of remediation procedures for malware infections. We implement these techniques in a tool called ForeCast. Large-scale evaluation on over 600,000 malware samples shows that our prototype can increase the amount of potential command and control servers detected by up to 137% over a random selection strategy and 54% over a selection strategy based on sample diversity.
On the study of registration methods for multiple depth cameras in free-viewpoint videos

In this paper, we investigate the best approach for estimating the pose between two or more depth cameras. After an overview and a comparison of the existing methods, we first experiment on the accuracy of the calibration when using features from corresponding colour/depth images. Next, we discuss those approaches by considering the interference produced by the presence of several depth cameras, and computational time issues. Finally, we propose our own implementation resulting from those studies. The main aim of this paper is then to evaluate the colour image based feature detectors for helping in the choice of a calibration method in future developments. Keywordsdepth camera; point cloud; state-of-the-art; alignment; registration; free viewpoint videos;
AN AGENDA-BASED DIALOG MANAGEMENT ARCHITECTURE FOR SPOKEN LANGUAGE SYSTEMS

Dialog management can be seen as a solution to two specific problems: (1) providing a coherent overall structure to interaction that extends beyond the single turn, (2) correctly manage mixed-initiative interaction, allowing users to guide interaction as per their (not necessarily explicitly shared) goals while allowing the system to guide interaction towards successful completion. We propose a dialog management architecture based on the following elements: handlers that manage interaction focussed on tightly coupled sets of information, a product that reflects mutually agreed-upon information and an agenda that orders the topics relevant to task completion.
Statistical block protection coding for DPCM-encoded speech

Blocks of speech-carrying dpcm bits are protected from transmission errors by means of explicit communication of two block statistics–the maximum and the root-mean-square (rms) values of the adjacent-sample differences in the dpcm-quantized speech. At the receiver, the maximum value is used as a cue for error-detection, white the rms value is used for a partial waveform correction procedure that provides intelligible speech at bit error rates as high as 10 percent.
Relationships among running performance, aerobic physiology and organ mass in male Mongolian gerbils

SUMMARY Relationships among individual variation in exercise capacity, resting metabolism and morphology may offer insights into the mechanistic basis of whole-animal performance, including possible performance trade-offs (e.g. burst versus sustainable exercise, resting `maintenance' costs versus maximal power output). Although there have been several studies of correlations between performance, metabolism and morphology in fish, birds and squamate reptiles, relatively little work has been done with mammals. We measured several aspects of forced and voluntary locomotor performance in Mongolian gerbils (Meriones unguiculatus), along with minimal and maximal aerobic metabolic rates and organ sizes (mainly visceral organs and the musculoskeletal system). Maximal sprint and aerobic speeds and maximal oxygen consumption (V̇O2max) during forced exercise were similar to those of other small rodents; basal metabolic rate was below allometric predictions. At all tested speeds, voluntary running had a lower energy cost than forced treadmill running, due primarily to a higher zero-speed intercept of the speed-versus-power (oxygen consumption) relationship during forced running. Incremental costs of transport (slopes of speed-versus-power regressions) were slightly higher during voluntary exercise. Few of the correlations among performance variables, or between performance and organ morphology, were statistically significant. These results are consistent with many other studies that found weak correlations between organismal performance (e.g. V̇O2max) and putatively relevant subordinate traits, thus supporting the idea that some components within a functional system may exhibit excess capacity at various points in the evolutionary history of a population, while others constitute limiting factors.
Constitutional Failure: Carl Schmitt in Weimar

Constitutional Failure is a major contribution to studies of the German political philosopher Carl Schmitt (1888–1985), the Weimar Republic, and the relationship of constitutionalism, political economy, and democracy. An internationally renowned scholar of Weimar legal theory, Ellen Kennedy brought Schmitt’s neglected work to the attention of English-speaking readers with her highly regarded translations of his work and studies of its place in twentieth-century political theory. In this eagerly awaited book, she tracks Schmitt’s contribution to the canon of Western political philosophy during its most difficult and dangerous moment—the time of Weimar Germany and the Third Reich—demonstrating the centrality of his thought to understandings of the modern constitutional state and its precarious economic and social foundations. Kennedy reveals how Schmitt’s argument for a strong but neutral state supported the maximization of market freedom at the cost of the political constitution. She argues that the major fault lines of Weimar liberalism—emergency powers, the courts as “defenders of the constitution,” mass mobilization of anti-liberal politics, ethnic-identity politics, a culture of resentment and contested legitimacy—are not exceptions within the liberal-democratic orders of the West, but central to them. Contending that Schmitt’s thought remains vital today because liberal norms are inadequate to the political challenges facing constitutional systems as diverse as those of Eastern Europe and the United States, Kennedy develops a compelling, rigorous argument that unsettles many assumptions about liberalism, democracy, and dictatorship.
Random Peptide Bacteriophage Display as a Probe for Urokinase Receptor Ligands

Abstract The urokinase receptor is a multifunctional protein that plays a central role in cell surface plasminogen activation, cell migration, and cell adhesion. We previously demonstrated that high affinity peptide ligands for the urokinase receptor, which are urokinase competitors, can be obtained from a 15mer peptide library (Goodson et al., 1994). In order to probe for additional urokinase receptor binding sites we affinity selected the same bacteriophage library on complexes of soluble urokinase receptor (suPAR) and the receptor binding domain of urokinase, residues 1 48 (uPA1 48). Bacteriophage were isolated which bound to suPAR and suPAR:uPA1 48 complexes with high yield. The peptide sequences encoded by these bacteriophage were distinct from those obtained previously on urokinase receptor expressing cells, and comprise two groups based upon effects on su PAR:1-anilino-8-napthalene sulfonate (ANS) fluorescence, and vitronectin binding competition. Alanine scanning mutagensis of the soluble peptides was used to define minimal regions and key residues for suPAR binding by competition with the parent bacteriophage. A comparison of these results with sequences of domains of both vitronectin and integrin αchains, which have been reported to be important for urokinase receptor binding, suggests that the homology with the peptide sequences selected is functionally significant.
Insurance Company Capital Structure Swaps and Shareholder Wealth

A new model is developed for optimal capital structure swaps for insurance companies under conditions of asymmetric information or comparative advantage. We examine the opportunity for insurers to increase shareholder wealth by issuing equity or selling policies when managers’ private information reveals intrinsic firm values that differ from market values. In such situations, managers may use the proceeds of an equity or debt issue to purchase reinsurance or retire equity with cash generated from selling new policies. Indeed, the model suggests that the optimal decision may involve issuing equity when private information suggests that equity prices are low. The model can be used to find the optimal capital structure that maximizes the increase in intrinsic wealth of the long-term or retaining shareholders when market values deviate from fundamentals.
Heuristics and Biases : Implications for Security

How can heuristics and biases improve the design of security technologies to leverage end-user behaviors? This position paper argues both for the importance of this question, and the specific identified examples. We discuss the limitations and criticisms of a heuristics and biases approach to understanding human behavior. We introduce some emerging theories in social-psychology that are more quantitative, and thus can be both predictive as well as descriptive. Our work is in response to the use of a rational actor model in computer security, which prescribes normative outcomes to individual preferences. However, observed behaviors consistently deviate from these normative prescriptions. These errors are typically attributed to biases that under-lie a decision-making paradigm based in quick and dirty heuristics compared to an algorithmic and accurate approach. Typical investigations based in bounded rationality have focused on perverse incentives for end users to underinvest in security and privacy. Here we argue for a different perspective to bounded rationality, one that build upon it as a foundational tool rather than an obstacle that must be avoided.1
Knowing in Organizations: A Practice-Based Approach

This is a collection of essays by older established scholars (the older voices) and younger scholars (the new voices) on important aspects of Japanese education and society. The topics range across historical and contemporary issues that have defined Japanese education as we know it today. Beauchamp, a leading authority in the field, has specially commissioned the papers for this volume, and has attracted the best of both generations.
An Improved Bound for the Nystrom Method for Large Eigengap

We develop an improved bound for the approximation error of the Nystr\"{o}m method under the assumption that there is a large eigengap in the spectrum of kernel matrix. This is based on the empirical observation that the eigengap has a significant impact on the approximation error of the Nystr\"{o}m method. Our approach is based on the concentration inequality of integral operator and the theory of matrix perturbation. Our analysis shows that when there is a large eigengap, we can improve the approximation error of the Nystr\"{o}m method from $O(N/m^{1/4})$ to $O(N/m^{1/2})$ when measured in Frobenius norm, where $N$ is the size of the kernel matrix, and $m$ is the number of sampled columns.
A usability evaluation of Neuromender's upper limb game-based rehabilitation system for stroke survivors

Game-based technologies have been widely used as part of stroke rehabilitation. The Neuromender system utilises game-based technologies and consists of serious games that are designed and developed for the purpose of rehabilitation of stroke survivors. In this paper, one of the modules in the Neuromender system which is the “upper limb” module is described and tested for its usability. The upper limb module primarily focuses on the rehabilitation of the upper body extremities of stroke survivors. An experimental study is designed to test the usability of the upper limb module. Various metrics including the optimal distance between the 3D depth sensor device and the survivor, the optimal position of the 3D depth sensor with respect to the survivor, and the response time of the gestures made by the survivors based on their distance to the sensor are evaluated. At the end of the experiments, the optimal distance and optimal position for the survivors to utilise the upper limb module is determined.
A novel hybrid architecture for real-time omnidirectional image reconstruction

The Panoptic camera is an omnidirectional multi-aperture visual system which is realized by mounting multiple imaging sensors on a hemispherical frame. It is a spherical light-field camera system that records light information from any direction around its center. Omnidirectional light field reconstruction algorithm, its centralized and distributed real-time hardware implementations were previously presented by the authors. In this work, we analyze advantages and disadvantages of previous approaches and propose a novel high performance hybrid architecture based on a tree based network topology for real-time omnidirectional image reconstruction. The novel hybrid architecture increases the scalability of the Panoptic camera systems while utilizing fewer resources. Furthermore, the tree based structure allows implementing further signal processing applications such as omnidirectional feature extraction which was not possible in centralized and distributed implementations.
International Networking of Large Amounts of Primary Biodiversity Data

Primary Biodiversity Data is a general term for information documenting the planet’s biodiversity, where each record represents the existence of a particular organism at a given location at a point in time. These data are scattered throughout numerous collections and databases worldwide, making it difficult to find all information available on, for instance, a certain species or a particular region. Several international networks and initiatives share a vision of free and open access to these resources and are working together to connect these heterogeneous data sources. This article provides an overview of the basic system architecture of these networks and some of the efforts aimed at solving the issues arising from the explosion of the amount of biodiversity data in recent years. It discusses the concept of “special interest networks” aimed at facilitating the setup of thematic, useror subject-specific biodiversity data portals; describes SYNTHESYS’ data portal software and its query expansion mechanism; and finally, outlines the duplicate detection system for identifying possible duplicate records in biodiversity networks.
On multilabel classification and ranking with bandit feedback

We present a novel multilabel/ranking algorithm working in partial information settings. The algorithm is based on 2nd-order descent methods, and relies on upper-confidence bounds to trade-off exploration and exploitation. We analyze this algorithm in a partial adversarial setting, where covariates can be adversarial, but multilabel probabilities are ruled by (generalized) linear models. We show O(T1/2 log T) regret bounds, which improve in several ways on the existing results. We test the effectiveness of our upper-confidence scheme by contrasting against full-information baselines on diverse real-world multilabel data sets, often obtaining comparable performance.