Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive Doppler frequency shift estimation for OFDM systems

Orthogonal Frequency Division Multiplexing (OFDM) is sensitive to the system frequency offset, while in high speed movement Doppler frequency shift is the main factor which causes the system frequency offset and result in inter-carrier interference (ICI) and degrade system performance. This paper proposes an adaptive Doppler frequency shift estimation method based on autocorrelation function of the time domain channel estimation. Compared with the conventional method, the estimation parameter in the proposed method is adjusted adaptively according to the velocity of mobile station, so the method has a larger estimation range. The simulation results include the performance comparison of this proposed method and the conventional method, NMSE comparison, and the performance comparison under different signal-to-noise ratio (SNR). Through investigating the simulation results, the proposed method is verified to be able to generate the accurate estimation results and robust in the noise.
Coarse-to-fine vision-based localization by indexing scale-Invariant features

This paper presents a novel coarse-to-fine global localization approach inspired by object recognition and text retrieval techniques. Harris-Laplace interest points characterized by scale-invariant transformation feature descriptors are used as natural landmarks. They are indexed into two databases: a location vector space model (LVSM) and a location database. The localization process consists of two stages: coarse localization and fine localization. Coarse localization from the LVSM is fast, but not accurate enough, whereas localization from the location database using a voting algorithm is relatively slow, but more accurate. The integration of coarse and fine stages makes fast and reliable localization possible. If necessary, the localization result can be verified by epipolar geometry between the representative view in the database and the view to be localized. In addition, the localization system recovers the position of the camera by essential matrix decomposition. The localization system has been tested in indoor and outdoor environments. The results show that our approach is efficient and reliable.
Age-Related Differences in Gross Motor Skills

Body-based interfaces have recently attracted much attention. In such interfaces, gross motor skills are critical in providing a safe and pleasant experience. However, little is known about gross motor performance, particularly on the age-related differences between the elderly and young adults. This study compares simple gross motor skills of the elderly with those of younger adults when performing arm and leg movements in different directions and different time intervals. By measuring participants' body movements during the task, we identified movement ranges, awkward movements, and the appropriate time interval between consecutive movements. We conclude by providing relevant HCI design implications.
A life course approach to chronic disease epidemiology: conceptual models, empirical challenges and interdisciplinary perspectives.

Over the last few years there has been increasing interest in conceptualizing disease aetiology within a life course framework. , This approach is not new to Public Health or unique to epidemiology (see below). However, its current resonance and interest within epidemiology reflects the challenging theoretical framework this approach provides. This issue of the International Journal of Epidemiology has several papers with a ‘life course theme’. This accompanying editorial is intended to highlight what we believe are the key conceptual issues around life course epidemiology. We have chosen to use examples from chronic disease epidemiology, but this approach is also applicable within the context of infectious diseases and wider notions of health and wellbeing. We have defined a life course approach to chronic disease epidemiology as the study of long-term effects on chronic disease risk of physical and social exposures during gestation, childhood, adolescence, young adulthood and later adult life. It includes studies of the biological, behavioural and psychosocial pathways that operate across an individual's life course, as well as across generations, to influence the development of chronic diseases.
Correlation of in Vitro Cytokine Responses with the Chemical Composition of Soil-Derived Particulate Matter

We treated human lung epithelial cells, type BEAS-2B, with 10–80 μg/cm2 of dust from soils and road surfaces in the western United States that contained particulate matter (PM) < 2.5 μm aerodynamic diameter. Cell viability and cytokine secretion responses were measured at 24 hr. Each dust sample is a complex mixture containing particles from different minerals mixed with biogenic and anthropogenic materials. We determined the particle chemical composition using methods based on the U.S. Environmental Protection Agency Speciation Trends Network (STN) and the National Park Service Interagency Monitoring of Protected Visual Environments (IMPROVE) network. The functionally defined carbon fractions reported by the ambient monitoring networks have not been widely used for toxicology studies. The soil-derived PM2.5 from different sites showed a wide range of potency for inducing the release of the proinflammatory cytokines interleukin-6 (IL-6) and IL-8 in vitro. Univariate regression and multivariate redundancy analysis were used to test for correlation of viability and cytokine release with the concentrations of 40 elements, 7 ions, and 8 carbon fractions. The particles showed positive correlation between IL-6 release and the elemental and pyrolyzable carbon fractions, and the strongest correlation involving crustal elements was between IL-6 release and the aluminum:silicon ratio. The observed correlations between low-volatility organic components of soil- and road-derived dusts and the cytokine release by BEAS-2B cells are relevant for investigation of mechanisms linking specific air pollution particle types with the initiating events leading to airway inflammation in sensitive populations.
Sabar, Nasar and Ayob, Masri and Kendall, Graham and Qu, Rong (2015) A dynamic multi-armed bandit-gene expression programming hyper-heuristic for combinatorial optimization problems. IEEE Transactions

Hyper-heuristics are search methodologies that aim to provide high quality solutions across a wide variety of problem domains, rather than developing tailor-made methodologies for each problem instance/domain. A traditional hyper-heuristic framework has two levels, namely, the high level strategy (heuristic selection mechanism and the acceptance criterion) and low level heuristics (a set of problem specific heuristics). Due to the different landscape structures of different problem instances, the high level strategy plays an important role in the design of a hyper-heuristic framework. In this work, we propose a new high level strategy for the hyper-heuristic framework. The proposed high level strategy utilizes the dynamic multi-armed bandit-extreme value based rewards as an online heuristic selection mechanism to select the appropriate heuristic to be applied at each iteration. In addition, we propose a gene expression programming framework to automatically generate the acceptance criterion for each problem instance, instead of using human designed criteria. Two well-known, and very different, combinatorial optimization problems, one static (exam timetabling) and one dynamic (dynamic vehicle routing) are used to demonstrate the generality of the proposed framework. Compared with various well-known acceptance criteria, state of the art of hyper-heuristics and other bespoke methods, empirical results demonstrate that the proposed framework is able to generalize well across both domains. We obtain competitive, if not better results, when compared to the best known results obtained from other methods that have been presented in the scientific literature. We also compare our approach against the recently released hyper-heuristic competition (CHeSC) test suite. We again demonstrate the generality of our approach when we compare against other methods that have utilized the same six benchmarks datasets from this test suite.
Blood-Brain Barrier Dysfunction in Epileptogenesis of the Temporal Lobe

Epilepsy of the temporal lobe (TLE) is the most common form of focal epilepsy, and in adults, it most frequently develops after injury. However, the mechanisms by which a normal functioning brain turns into an epileptic one still remain obscure. Recent studies point to vascular involvement and particularly blood-brain barrier (BBB) dysfunction in the development of epilepsy. The BBB is a specialized structure which functions to control the neuronal extracellular milieu. BBB dysfunction is found in many diseases of the central nervous system, including stroke, traumatic injuries, tumors and infections. Interestingly, all these insults may initiate an epileptogenic process which eventually leads to spontaneous, recurrent seizures. This epileptogenic time frame usually lasts weeks, months, or even years in man, and days to weeks in rodents and may serve as a “window of opportunity” for the prevention of epilepsy. However, no prevention strategy exists, stressing the importance of research into the mechanisms of epileptogenesis. Here, we will underscore recent experiments suggesting that BBB dysfunction directly induces epileptogenesis. We will provide new evidence to support the hypothesis that BBB breakdown and specifically exposure of temporal lobe structures to the most common serum protein, albumin, is sufficient to induce epileptogenesis.
"The end of innocence" revisited: resistance of herpesviruses to antiviral drugs

In the past 4 years, interest in drug-resistant herpesviruses has evolved from the realm of academic laboratory studies to that of great clinical importance. Recurrent and persistent infections due to the herpes simplex viruses, varicella-zoster virus, and human cytomegalovirus have been an unwelcome consequence of immunosuppression in graft recipients, cancer patients, and those suffering from AIDS. Treatment of these infections with the available antiviral drugs, such as acyclovir, ganciclovir, and foscarnet, has resulted in both clinical benefit and the emergence of drug-resistant variants. In addition, the role of Epstein-Barr virus is being clarified for an array of disease syndromes, and therapeutic approaches are beginning to emerge. In the present review, the emergence and clinical importance of drug resistance among the herpesviruses have been explored. Furthermore, particular attention has been focused on our understanding of the mechanisms of drug resistance and how that understanding will guide us in the development of more effective antiviral drugs and drug usage.
To appear in the International Journal of Control Learning Optimal Trajectories for Nonholonomic Systems

Many advanced robotic systems are subject to nonholonomic constraints, e.g., wheeled mobile robots, space manipulators and multifingered robot hands. Steering these mechanisms between configurations in the presence of perturbations is a difficult problem. In fact, the divide et impera strategy (first plan a trajectory, then track it by feedback) has a fundamental drawback in this case: due to the peculiar control properties of nonholonomic systems, smooth feedback cannot provide tracking of the whole trajectory. As a result, it would be necessary to give up either accuracy in the final positioning or predictability of the actual motion. We pursue here a different approach which does not rely on a separation between planning and control. Based on the learning control paradigm, a robust steering scheme is devised for systems which can be put in chained form, a canonical structure for nonholonomic systems. By overparameterizing the control law, other performance goals can be met, typically expressed as cost functions to be minimized along the trajectory. As a case study, we consider the generation of robust optimal trajectories for a car-like mobile robot, with criteria such as total length, maximum steering angle, distance from workspace obstacles, or error with respect to an off-line planned trajectory. ∗ Corresponding Author
Energy packet networks: smart electricity storage to meet surges in demand

When renewable energy is used either as a primary source, or as a back-up source to meet excess demand, energy storage becomes very useful. Simple examples of energy storage units include electric car batteries and uninterruptible power supplies. More sophisticated examples include dams into which water may be pumped when renewable sources of electricity such as wind or photovoltaic are plentiful. Drawing an analogy between computer networks which store and forward and download data, and electrical networks that furnish energy both from direct production and storage, we suggest a model of Energy Packet Networks (EPNs) which store and forward quantised energy units to and from a large range of devices. In addition to being a model for networks of energy storage units, energy suppliers and consumers, an EPN can be an appropriate framework for energy harvesting networks at the micro-scale, and bio-nano scale energy networks. A mathematical model for EPNs is suggested and we analyse two examples using a probability model. We first consider the case where stored energy and a fast ramp-up source are used to meet random energy surges assuming an unlimited storage unit and a random flow of renewable energy, and compute the probability that all requests are satisfied, or that some cannot be satisfied, and the spillover rate of renewable energy. Then we analyse the effect of an imperfect communication and computer-control system where message losses and delays degrade the energy system performance.
Story graphs: Tracking document set evolution using dynamic graphs

With the growing number of document sets accessible online, tracking their evolution over time story tracking became an increasingly interesting problem. In this paper we propose a story tracking method based on the dynamics of keyword-association graphs. We create a graph representation of the story evolution that we call story graphs, and investigate how graph structure can be used for detecting and discovering new developments in the story. First we investigate the possibly interesting graph properties for development detection. We continue by investigating how graph structure can be linked to the sentences representing developments. For this we create an evaluation framework which bridges the gap between temporal text mining patterns and sentences. We apply this framework to evaluate our method against other temporal text mining methods. Our experiments show that story graphs perform at similar levels overall, but provide distinctive advantages in some settings.
Evaluating and optimizing public policy

Taking a systematic approach, this text stresses the adequate formulation of policy problems as a crucial step in solving them. It emphasizes the need to understand the political contexts in which policy analysis is practiced, and the application of policy analysis to problems facing governments in increasingly complex societies. The Second Edition provides up-to-date information of problem structuring methods and a simplified format for presenting policy analysis.
Sense-and-predict: Opportunistic MAC based on spatial interference correlation for cognitive radio networks

Opportunity detection at secondary transmitters (TXs) is a key technique enabling cognitive radio (CR) networks. Such detection however cannot guarantee reliable communication at secondary receivers (RXs), especially when their association distance is long. To cope with the issue, this paper proposes a novel MAC called sense-and-predict (SaP), where each secondary TX decides whether to access or not based on the prediction of the interference level at RX. Firstly, we provide the spatial interference correlation in a probabilistic form using stochastic geometry, and utilize it to maximize the area spectral efficiency (ASE) for secondary networks while guaranteeing the service quality of primary networks. Through simulations and testbed experiments using USRP, SaP is shown to always achieve ASE improvement compared with the conventional TX based sensing.
Neuronal CC chemokines: the distinct roles of CCL21 and CCL2 in neuropathic pain

The development of neuropathic pain in response to peripheral nerve lesion for a large part depends on microglia located at the dorsal horn of the spinal cord. Thus the injured nerve initiates a response of microglia, which represents the start of a cascade of events that leads to neuropathic pain development. For long it remained obscure how a nerve injury in the periphery would initiate a microglia response in the dorsal horn of the spinal cord. Recently, two chemokines have been suggested as potential factors that mediate the communication between injured neurons and microglia namely CCL2 and CCL21. This assumption is based on the following findings. Both chemokines are not found in healthy neurons, but are expressed in response to neuronal injury. In injured dorsal root ganglion cells CCL2 and CCL21 are expressed in vesicles in the soma and transported through the axons of the dorsal root into the dorsal horn of the spinal cord. Finally, microglia in vitro are known to respond to CCL2 and CCL21. Whereas the microglial chemokine receptor involved in CCL21-induced neuropathic pain is not yet defined the situation concerning the receptors for CCL2 in microglia in vivo is even less clear. Recent results obtained in transgenic animals clearly show that microglia in vivo do not express CCR2 but that peripheral myeloid cells and neurons do. This suggests that CCL2 expressed by injured dorsal root neurons does not act as neuron-microglia signal in contrast to CCL21. Instead, CCL2 in the injured dorsal root ganglia (DRG) may act as autocrine or paracrine signal and may stimulate first or second order neurons in the pain cascade and/or attract CCR2-expressing peripheral monocytes/macrophages to the spinal cord.
Identification, classification, and partial characterization of genes in humans and other vertebrates homologous to a fish membrane progestin receptor

Recently we discovered a previously uncharacterized gene with the characteristics of a membrane progestin receptor (mPR) in a fish model, spotted seatrout. Here, we report the identification, cloning, and characteristics of other members of this hitherto unknown family of putative mPRs from several vertebrate species, including human, mouse, pig, Xenopus, zebrafish, and Fugu, with highly conserved nucleotide and deduced amino acid sequences and similar structures to the spotted seatrout mPR. The 13 vertebrate genes identified seem to belong to an unknown gene family. Phylogenetic analysis indicates these cDNAs comprise three distinct groups (named α, β, and γ) within this gene family. Structural analyses of the translated cDNAs suggest they encode membrane proteins with seven transmembrane domains. The transcript sizes of the human α, β, and γ putative mPR mRNAs varied from 2.8 to 5.8 kb and showed distinct distributions in reproductive, neural, kidney and intestinal tissues, respectively. Recombinant human α, γ, and mouse β proteins produced in an Escherichia coli expression system demonstrated high affinity (Kd = 20–30 nM) saturable binding for progesterone. Further analysis of binding to the γ-subtype revealed binding was specific for progestins and was displaceable, with rapid rates of association and dissociation (t1/2 = 2–8 min). These results suggest this is a new family of steroid receptors unrelated to nuclear steroid receptors, but instead having characteristics of G protein-coupled receptors.
Optimizing Data Freshness by Waiting

In this work we study how to manage the freshness of status updates sent from a source to a remote monitor via a network server. A proper metric of data freshness at the moni tor is the age-of-information, which is defined as how old the freshest update is since the moment this update was generated at the source. A logical policy is thezero-wait policy, i.e., the source submits a fresh update once the server is free, which achieve s the maximum throughput and the minimum average delay. Surprisingly, this zero-wait policy does not always minimi ze the average age. This motivates us to study how to optimally cont rol the status updates to keep data fresh and to understand when the zero-wait policy is optimal. We introduce a penalty function to characterize the level of “dissatisfaction” on data stal eness, and formulate the average age penalty minimization problem as a constrained semi-Markov decision process (SMDP) with a n uncountable state space. Despite of the difficulty of this pr oblem, we develop efficient algorithms to find the optimal status update policy. We show that, in many scenarios, the optimal policy i s to wait for a certain amount of time before submitting a new update. In particular, the zero-wait policy can be far from the optimum if (i) the penalty function grows quickly with respect to the age, and (ii) the update service times are highly rando m and positive correlated. To the best of our knowledge, this i s the first optimal control policy which is proven to minimize the age-of-information in status update systems. I. I NTRODUCTION In recent years, the proliferation of mobile devices and applications has significantly boosted the need for real-ti me information updates, such as news, weather reports, email notifications, stock quotes, social updates, mobile ads, et c. Timely status updates are also critical in network-based mo nitoring and control systems, including sensor networks used in temperature and air pollution monitoring, surround monito ring in autonomous vehicles, and phasor data updates in power gri d stabilization systems. A common need in these applications is to maximize the freshness of the data at the monitor. In light of this, a metri c called theage-of-information, or simply age, was defined in [1]. At time t, if the freshest update at the monitor has a time stampU(t), the age is∆(t) = t−U(t). Hence, the age is the time elapsed since the freshest packet was generated. Most existing research on the age-of-information focuses o n an “enqueue-and-forward” model [1]–[7], where status upda tes are randomly generated or arrive at a source node. The source This work was supported in part by DTRA grant HDTRA1-14-1-00 58, NSF grants CNS-1446582, CNS-1409336, CNS-1518829, CNS-15142 60, CNS1422988, and CNS-1054738, ARO grant W911NF-14-1-0368, and ONR grant N00014-15-1-2166. E. Uysal-Biyikoglu was supported in par t by TUBITAK and in part by a Science Academy BAGEP award. Source idle/busy
A critical role for IRAK4 kinase activity in Toll-like receptor–mediated innate immunity

IRAK4 is a member of IL-1 receptor (IL-1R)–associated kinase (IRAK) family and has been shown to play an essential role in Toll-like receptor (TLR)–mediated signaling. We recently generated IRAK4 kinase-inactive knock-in mice to examine the role of kinase activity of IRAK4 in TLR-mediated signaling pathways. The IRAK4 kinase–inactive knock-in mice were completely resistant to lipopolysaccharide (LPS)- and CpG-induced shock, due to impaired TLR-mediated induction of proinflammatory cytokines and chemokines. Although inactivation of IRAK4 kinase activity did not affect the levels of TLR/IL-1R–mediated nuclear factor κB activation, a reduction of LPS-, R848-, and IL-1–mediated mRNA stability contributed to the reduced cytokine and chemokine production in bone marrow–derived macrophages from IRAK4 kinase–inactive knock-in mice. Both TLR7- and TLR9-mediated type I interferon production was abolished in plasmacytoid dendritic cells isolated from IRAK4 knock-in mice. In addition, influenza virus–induced production of interferons in plasmacytoid DCs was also dependent on IRAK4 kinase activity. Collectively, our results indicate that IRAK4 kinase activity plays a critical role in TLR-dependent immune responses.