Miracle-dz/UniFAR-FTUs huggingface dataset:
Myeloid-derived cells are key targets of tumor immunotherapy

Tumors are composed of heterogeneous cell populations recruited by cancer cells to promote growth and metastasis. Among cells comprising the tumor stroma, myeloid-derived cells play pleiotropic roles in supporting tumorigenesis at distinct stages of tumor development. The tumor-infiltrating myeloid cell contingent is composed of mast cells, neutrophils, dendritic cells, macrophages, and myeloid-derived suppressor cells. Such cells are capable of evading the hostile tumor environment typically prone to immune cell destruction and can even promote angiogenesis, chronic inflammation, and invasion. This paper briefly summarizes the different myeloid-derived subsets that promote tumor development and the strategies that have been used to counteract the protumorigenic activity of these cells. These strategies include myeloid cell depletion, reduction of recruitment, and inactivation or remodeling of cell phenotype. Combining drugs designed to target tumor myeloid cells with immunotherapies that effectively trigger antitumor adaptive immune responses holds great promise in the development of novel cancer treatments.
Multimodal decision-level fusion for person authentication

The use of clustering algorithms for decision-level data fusion is proposed. Person authentication results coming from several modalities (e.g., still image, speech), are combined by using fuzzy k-means (FKM) and fuzzy vector quantization (FVQ) algorithms, and a median radial basis function (MRBF) network. The quality measure of the modalities data is used for fuzzification. Two modifications of the FKM and FVQ algorithms, based on a fuzzy vector distance definition, are proposed to handle the fuzzy data and utilize the quality measure. Simulations show that fuzzy clustering algorithms have better performance compared to the classical clustering algorithms and other known fusion algorithms. MRBF has better performance especially when two modalities are combined. Moreover, the use of the quality via the proposed modified algorithms increases the performance of the fusion system.
Gesture modeling and animation based on a probabilistic re-creation of speaker style

Animated characters that move and gesticulate appropriately with spoken text are useful in a wide range of applications. Unfortunately, this class of movement is very difficult to generate, even more so when a unique, individual movement style is required. We present a system that, with a focus on arm gestures, is capable of producing full-body gesture animation for given input text in the style of a particular performer. Our process starts with video of a person whose gesturing style we wish to animate. A tool-assisted annotation process is performed on the video, from which a statistical model of the person's particular gesturing style is built. Using this model and input text tagged with theme, rheme and focus, our generation algorithm creates a gesture script. As opposed to isolated singleton gestures, our gesture script specifies a stream of continuous gestures coordinated with speech. This script is passed to an animation system, which enhances the gesture description with additional detail. It then generates either kinematic or physically simulated motion based on this description. The system is capable of generating gesture animations for novel text that are consistent with a given performer's style, as was successfully validated in an empirical user study.
A practical data flow framework for array reference analysis and its use in optimizations

Data flow analysis techniques have traditionally been restricted to the analysis of scalar variables. This retriction, however, imposes a limitation on the kinds of optimizations that can be performed in loops containing array references. We present a data flow framework for array reference analysis that provides the information needed in various optimizations targeted at sequential or fine-grained parallel architectures. The framework extends the traditional scalar framework by incorporating iteration distance values into the analysis to qualify the computed data flow solution during the fixed point iteration. Analyses phrased in this framework are capable of discovering recurrent access patterns among array references that evolve during the execution of a loop. Applications of our framework are discussed for register allocation, load/store optimizations, and controlled loop unrolling.
Cost-Effectiveness of Endovascular Stroke Therapy: A Patient Subgroup Analysis From a US Healthcare Perspective

Background and Purpose— Endovascular therapy in addition to standard care (EVT+SC) has been demonstrated to be more effective than SC in acute ischemic large vessel occlusion stroke. Our aim was to determine the cost-effectiveness of EVT+SC depending on patients’ initial National Institutes of Health Stroke Scale (NIHSS) score, time from symptom onset, Alberta Stroke Program Early CT Score (ASPECTS), and occlusion location. Methods— A decision model based on Markov simulations estimated lifetime costs and quality-adjusted life years (QALYs) associated with both strategies applied in a US setting. Model input parameters were obtained from the literature, including recently pooled outcome data of 5 randomized controlled trials (ESCAPE [Endovascular Treatment for Small Core and Proximal Occlusion Ischemic Stroke], EXTEND-IA [Extending the Time for Thrombolysis in Emergency Neurological Deficits–Intra-Arterial], MR CLEAN [Multicenter Randomized Clinical Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands], REVASCAT [Randomized Trial of Revascularization With Solitaire FR Device Versus Best Medical Therapy in the Treatment of Acute Stroke Due to Anterior Circulation Large Vessel Occlusion Presenting Within 8 Hours of Symptom Onset], and SWIFT PRIME [Solitaire With the Intention for Thrombectomy as Primary Endovascular Treatment]). Probabilistic sensitivity analysis was performed to estimate uncertainty of the model results. Net monetary benefits, incremental costs, incremental effectiveness, and incremental cost-effectiveness ratios were derived from the probabilistic sensitivity analysis. The willingness-to-pay was set to $50 000/QALY. Results— Overall, EVT+SC was cost-effective compared with SC (incremental cost: $4938, incremental effectiveness: 1.59 QALYs, and incremental cost-effectiveness ratio: $3110/QALY) in 100% of simulations. In all patient subgroups, EVT+SC led to gained QALYs (range: 0.47–2.12), and mean incremental cost-effectiveness ratios were considered cost-effective. However, subgroups with ASPECTS ⩽5 or with M2 occlusions showed considerably higher incremental cost-effectiveness ratios ($14 273/QALY and $28 812/QALY, respectively) and only reached suboptimal acceptability in the probabilistic sensitivity analysis (75.5% and 59.4%, respectively). All other subgroups had acceptability rates of 90% to 100%. Conclusions— EVT+SC is cost-effective in most subgroups. In patients with ASPECTS ⩽5 or with M2 occlusions, cost-effectiveness remains uncertain based on current data.
Transformation of hyperspectral data to improve classification by mitigating nonlinear effects

Non-linear effects in hyperspectral data are caused by varying illumination conditions, different viewing angles or multiple scattering of the incident light. These effects interfere with commonly used data analysis procedures. Manifold learning procedures are slow and require certain assumptions about the data structure that do not necessarily hold in real hyperspectral data. In this paper, a transformation is proposed that uses neighborhood distances to track the nonlinear structures of multiple classes simultaneously. The transformation is evaluated using a hyperspectral data set containing nonlinearities. A classification is performed and the results on the original and the transformed data are compared.
Biography-Dependent Collaborative Entity Archiving for Slot Filling

Knowledge Base Population (KBP) tasks, such as slot filling, show the particular importance of entity-oriented automatic relevant document acquisition. Rich, diverse and reliable relevant documents satisfy the fundamental requirement that a KBP system explores the nature of an entity. Towards the bottleneck problem between comprehensiveness and definiteness of acquisition, we propose a collaborative archiving method. In particular we introduce topic modeling methodologies into entity biography profiling, so as to build a bridge between fuzzy and exact matching. On one side, we employ the topics in a small-scale high-quality relevant documents (i.e., exact matching results) to summarize the life slices of a target entity (i.e., biography), and on the other side, we use the biography as a reliable reference material to detect new truly relevant documents from a large-scale partially complete pseudo-feedback (i.e., fuzzy matching results). We leverage the archiving method to enhance slot filling systems. Experiments on KBP corpus show significant improvement over stateof-the-art.
Application of optical unmanned aerial vehicle-based imagery for the inventory of natural regeneration and standing deadwood in post-disturbed spruce forests

ABSTRACT Disturbances caused by the European spruce bark beetle (Ips typographus L.) infestations are amongst the main drivers of forest ecosystem dynamics in stands dominated by Norway spruce (Picea abies [L.] Karst.). Monitoring the post-disturbance stand development including establishment of the new tree cohorts (regeneration) is of particular importance, and is conventionally done by time-intensive field surveys. Efficiency of techniques such as airborne light detection and ranging (LiDAR) or stereo photogrammetry is constrained due to their quality or costs in small-scaled and substantially heterogeneous post-disturbed areas. Small, multi-rotor unmanned aerial vehicles (UAVs) offer alternatives via their lower cost, temporal flexibility and high spatial resolution. We investigated the Digital Surface Models (DSM) derived from the UAV for inventories in post-disturbed sites of the Bavarian Forest National Park, Germany. We compared the numbers and structural attributes of detected living trees and snags from UAV data with standard stereo aerial photogrammetry using conventional field survey as a reference. Moreover, we processed the UAV data both by manual and automated tree recognitions. The results differentiated for individual tree classes (Living Spruce/Standing Deadwood and Individual/Grouped) showed varying performance with best results achieved for Standing Deadwood of moderate height. The UAV products were superior to aerial photography for the height retrieval: UAV-based data showed in average the root mean square error (RMSE) = 1.56 m, coefficient of determination R2 = 0.74 and bias = −0.73 m, compared to the aerial photogrammetry RMSE = 2.71 m, R2 = 0.17 and bias = −1.27 m. In particular, the heights of tall snags were more biased. Furthermore, the UAV data provided good results in crown diameter determination (RMSE = 0.13 m, R2 = 0.87, bias = 0.05 m). The automated recognition method was associated with qualitative and quantitative drawbacks compared to the manual method. Detection rates for trees and regeneration growing individually (60.7% and 39.1% for manual and automated method, respectively) were higher compared to regeneration in groups (28.6% and 17.8%). To conclude, the UAV-based inventory has clear advantages over aerial photogrammetry, especially for inventory of sites dominated by larger individual trees with sparse understorey. However, it cannot fully replace the field survey in post-disturbed sites with dense regeneration, where the performance can be augmented by combining UAVs with reduced fieldwork in different stand structural classes.
The INTEGRAL long monitoring of persistent ultra compact X-ray bursters

Context. The combination of compact objects, short period variability and peculiar chemical composition of the Ultra Compact X-ray Binaries make up a very interesting laboratory to study accretion processes and thermonuclear burning on the neutron star surface. The improved large optical telescopes and more sensitive X-ray satellites have increased the number of known Ultra Compact X-ray Binaries allowing their study with unprecedented detail. Aims. We analyze the average properties common to all ultra compact Bursters observed by INTEGRALfrom∼ 0.2 keV to∼ 150 keV. Methods. We have performed a systematic analysis of the INTEGRAL public data and Key-Program proprietary observations of a sample of the Ultra Compact X-ray Binaries. In order to study their average properties in a very broad energy band, we combined INTEGRAL with BeppoSAX and SWIFT data whenever possible. For sources not showing any signific ant flux variations along the INTEGRAL monitoring, we build the average spectrum by combining all available data; in the case of variable fluxes, we use simultaneous INTEGRAL and SWIFT observations when available. Otherwise we compared IBIS and PDS data to check the variability and combine BeppoSAX with INTEGRAL/IBIS data. Results. All spectra are well represented by a two component model consisting of a disk-blackbody and Comptonised emission. The majority of these compact sources spend most of the time in a canonical low/hard state, with dominating Comptonised component and accretion rate ˙ M lower than∼ 10 −9 M⊙/yr, not depending on the used model to fit the data.
Operationalising and Evaluating Sub-Optimal and Optimal Play Experiences through Challenge-Skill Manipulation

The study examines the relationship of challenge-skill balance and the player experience through evaluation of competence, autonomy, presence, interest/enjoyment, and positive and negative affect states. To manipulate challenge-skill balance, three video game modes -- boredom (low challenge), balance (medium challenge), and overload (high challenge) -- were developed and experimentally tested (n = 45). The study showed that self-reported positive affect, autonomy, presence, and interest/enjoyment differed between the levels. The balance condition generally performed well in terms of positive player experiences, confirming the key role challenge-skill balance plays in designing for optimal play experiences. Interestingly, the study found significantly lower negative affect scores when playing the boredom condition. Greater feelings of competence were also reported for the boredom condition than the balance and overload conditions. Finally, some measures point to overload as a more enjoyable experience than boredom, suggesting possible player preference for challenge > skill imbalance over skill > challenge imbalance. Implications for design and future research are presented.
Intraband Auger effect in InAs∕InGaAlAs∕InP quantum dot structures

InAs quantum dot structures grown on InGaAlAs have been investigated for midinfrared photodetection. Intraband photocurrent and absorption measurements, together with a full three-dimensional theoretical modeling revealed that a bound-to-bound optical transition, where the final state is about 200meV deep below the conduction band continuum, is responsible for the photogenerated current. The reported results strongly suggest that an Auger process plays a fundamental role in generating the observed intraband photocurrent. Photoluminescence and interband photocurrent spectra of the same structures further support the reached conclusions.
Kinematic Chains in Ski Jumping In-run Posture

Abstract The concept of kinematic chains has been systematically applied to biological systems since the 1950s. The course of a ski jump can be characterized as a change between closed and open kinematic chains. The purpose of this study was to determine a relationship between adjacent segments within the ski jumper’s body’s kinematic chain during the in-run phase of the ski jump. The in-run positions of 267 elite male ski jumpers who participated in the FIS World Cup events in Innsbruck, Austria, between 1992 and 2001 were analyzed (656 jumps). Two-dimensional (2-D) kinematic data were collected from the bodies of the subjects. Relationships between adjacent segments of the kinematic chain in the ski jumper’s body at the in-run position are greater nearer the chain’s ground contact. The coefficient of determination between the ankle and knee joint angles is 0.67. Changes in the segments’ positions in the kinematic chain of the ski jumper’s body are stable during longitudinal assessment. Changes in shank and thigh positions, in the sense of increase or decrease, are the same.
Time-Space Efficient Simulations of Quantum Computations

We give two time- and space-efficient simulations of quantum computations with intermediate measurements, one by classical randomized computations with unbounded error and the other by quantum computations that use an arbitrary fixed universal set of gates. Specifically, our simulations show that every language solvable by a bounded-error quantum algorithm running in time t and space s is also solvable by an unbounded-error randomized algorithm running in time O(t logt) and space O(s+ logt), as well as by a bounded-error quantum algorithm restricted to use an arbitrary universal set and running in time O(t polylogt) and space O(s+ logt), provided the universal set is closed under adjoint. We also develop a quantum model that is particularly suitable for the study of general computations with simultaneous time and space bounds. As an application of our randomized simulation, we obtain the first nontrivial lower bound for general quantum algorithms solving problems related to satisfiability. Our bound applies to MAJSAT and MAJMAJSAT, which are the problems of determining the truth
Abnormality Segmentation and Classification of Brain MR Images using Combined Edge, Texture Region Features and Radial basics Function

Magnetic Resonance Images (MRI) are widely used in the diagnosis of Brain tumor. In this study we have developed a new approach for automatic classification of the normal and abnormal non-enhanced MRI images. The proposed method consists of four stages namely Preprocessing, feature extraction, feature reduction and classification. In the first stage anisotropic filter is applied for noise reduction and to make the image suitable for extracting the features. In the second stage, Region growing base segmentation is used for partitioning the image into meaningful regions. In the third stage, combined edge and Texture based features are extracted using Histogram and Gray Level Co-occurrence Matrix (GLCM) from the segmented image. In the next stage PCA is used to reduce the dimensionality of the Feature space which results in a more efficient and accurate classification. Finally, in the classification stage, a supervised Radial Basics Function (RBF) classifier is used to classify the experimental images into normal and abnormal. The obtained experimental are evaluated using the metrics sensitivity, specificity and accuracy. For comparison, the performance of the proposed technique has significantly improved the tumor detection accuracy with other neural network based classifier SVM, FFNN and FSVM.
Modelling Trade-by-Trade Price Movements of Multiple Assets Using Multivariate Compound Poisson Processes

In this paper we extend Rydberg-Shephards acivity, direction and size decomposition of trade-by-trade price movements to the mulvariate case. We illustrate our ideas using a bivariate modelling problem - modelling the evolution of the prices of Ford and GM shares. Throughout we use the continuous record of trades made in the first five months of 1997 on the New York Stock Exchange (NYSE).
RELN gene polymorphisms and susceptibility to autism in Chinese Han population.

BACKGROUND Single nucleotide polymorphisms (SNPs) in the Reelin gene (RELN) are likely candidates to confer risk for autism. The objective of the present study is to investigate the association of RELN gene SNPs with autism. MATERIALS AND METHODS A total of 367 Chinese Han subjects were recruited, including 186 autism patients and 181 unrelated healthy controls. Polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) and DNA sequencing methods were used to detect RELN gene polymorphisms. The association between SNPs and autism was analyzed in this study. RESULTS The g.333509A>C in intron12 and g.504742G>A in exon60 were detected in the RELN gene and a significant association was found between the g.504742G>A polymorphism and autism. Allele and genotype frequencies for the g.504742G>A polymorphism in autistic patients were significantly different for healthy subjects. There was no significantly difference in g.333509A>C polymorphism and autism in the studied populations. CONCLUSIONS Our findings indicated that g.333509A>C was not significantly associated with autism. The g.504742G>A polymorphic variant in the RELN gene might affect subjects susceptibility toward autism in Chinese Han population.
Energy Management in LTE Networks

Wireless cellular networks have seen dramatic growth in number of mobile users. As a result, data requirements, and hence the base-station power consumption has increased significantly. It in turn adds to the operational expenditures and also causes global warming. The base station power consumption in long-term evolution (LTE) has, therefore, become a major challenge for vendors to stay green and profitable in competitive cellular industry. It necessitates novel methods to devise energy efficient communication in LTE. Importance of the topic has attracted huge research interests worldwide. Energy saving (ES) approaches proposed in the literature can be broadly classified in categories of energy efficient resource allocation, load balancing, carrier aggregation, and bandwidth expansion. Each of these methods has its own pros and cons leading to a tradeoff between ES and other performance metrics resulting into open research questions. This paper discusses various ES techniques for the LTE systems and critically analyses their usability through a comprehensive comparative study.
A Uni ed Framework for De ning and Identifying Causal E ¤ ects

This paper uni es three complementary approaches to de ning, identifying, and estimating causal e¤ects: the classical structural equations approach of the Cowles Commision; methods of the labor econometrics and related treatment e¤ects literatures; and the structural Directed Acyclic Graph (DAG) approach of the machine learning literature. The settable system framework nests these prior approaches, while a¤ording signi cant improvements to each. For example, the settable system approach permits identi cation of causal e¤ects without requiring exogenous instruments; instead, a weaker conditional exogeneity condition su¢ ces. It removes the stable unit treatment value assumption of the treatment e¤ect approach and provides signi cant insight into the selection of covariates. It generalizes the DAG approach by accommodating mutual causality and attributes. We provide a variety of results ensuring structural identi cation of general covariate-conditioned average causal e¤ects, laying the foundation for parametric and nonparametric estimation of e¤ects of interest and new tests for structural identi cation. Acknowledgments: The authors are indebted to Patrick Bajari, Clive W.J. Granger, Jinyong Hahn, Lars Hansen, James Heckman, Kei Hirano, Kevin Hoover, Meng Huang, Chuck Manski, Massimiliano Marinucci, Robert Marshall, Rosa Matzkin, Judea Pearl, Chris Taber, and Rosalind Wu for helpful discussions and suggestions. Any errors remain the authorsresponsibility. JEL Classi ciation Numbers: C10, C20, C30, C50.