Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust RFID-Based Respiration Monitoring in Dynamic Environments

Respiration monitoring (RM) is crucial for tracking various health problems. Recently, RFID has been widely employed for lightweight and low-cost RM. However, existing RFID-based RM systems are designed for static environments where no people move around the monitored person. While, in practice, most environments are dynamic with people moving nearby, which introduces dynamic multipath signals and significantly distorts the respiration signal, leading to inaccurate RM. In this paper, we aim to realize accurate RFID-based RM in dynamic environments. Our observations show that multipath signals can result in a similar pattern to respiration, which leads to mis-detection of apnea and inaccurate respiration rate estimation. To address this issue, we first measure the respiration anomaly in the signal spectrogram to detect apnea. Second, we successfully remove the multipath effect for respiration rate estimation inspired by the intrinsic features of human respiration. Specifically, compared with people’s moving pattern, respiration pattern is regular and periodic. By transforming a normal respiration cycle into a matched filter, real respiration cycles can be extracted from the noisy RFID signal, which can be applied to estimate the respiration rate via peak detection scheme. The experiments show that our system achieves the average error of 4.2% and <inline-formula><tex-math notation="LaTeX">$0.51\ bpm$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>0</mml:mn><mml:mo>. </mml:mo><mml:mn>51</mml:mn><mml:mspace width="4pt"/><mml:mi>b</mml:mi><mml:mi>p</mml:mi><mml:mi>m</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="yang-ieq1-3106954.gif"/></alternatives></inline-formula> for apnea detection and respiration rate estimation in dynamic environments, respectively.
Protein kinase D1 inhibition interferes with mitosis progression

Protein kinase D1 (PKD1) plays a vital role in signal transduction, cell proliferation, membrane trafficking, and cancer; however, the majority of the studies up to date had centered primarily on PKD1 functions in interphase, very little is known about its role during cell division. We previously demonstrated that during mitosis PKD1 is activated and associated with centrosomes, spindles, and midbodies. However, these observations did not address whether PKD1 was associated with mitosis regulation. Accordingly, we used rapidly acting PKD‐specific inhibitors to examine the contribution of PKD1 the sequence of events in mitosis. We found that although PKD1 overexpression did not affect mitosis progression, suppression of its catalytic activity by two structurally unrelated inhibitors (kb NB 142‐70 and CRT 0066101) induced a significant delay in metaphase to anaphase transition time. PKD1 inhibition during mitosis also produced the appearance of abnormal spindles, defects in chromosome alignment, and segregation as well as apoptosis. Thus, these observations indicate that PKD1 activity is associated with mitosis regulation.
A Branch Elimination-Based Efficient Algorithm for Large-Scale Multiple Longest Common Subsequence Problem

It is a key issue to find out all longest common subsequences of multiple sequences over a set of finite alphabets, namely MLCS problem, in computational biology, pattern recognition and information retrieval, to name a few. However, it is very challenging to tackle the large-scale MLCS problem effectively and efficiently due to the high complexity of time and space. To this end, this paper will therefore propose a Branch Elimination-based Space and Time efficient algorithm called BEST-MLCS, which includes the following four key strategies: 1) Estimation scheme for the lower bound of the length of MLCS. 2) Estimation scheme for the upper bound of the length of the paths through the current match point. 3) Branch elimination strategy by finding all useless match points and removing the branches not on the longest paths. 4) A new Directed Acyclic Graph (DAG) construction method for constructing the smallest DAG among the existing ones. As a result, the proposed algorithm BEST-MLCS can save a lot of space and time and can handle much larger scale MLCS problems than the existing algorithms. Extensive experiments conducted on biological DNA sequences show that the performance of the proposed algorithm BEST-MLCS outperforms three state-of-the-art algorithms in terms of run-time and memory consumption.
Synthesizing Locally Symmetric Parameterized Protocols from Temporal Specifications

Scalable protocols and web services are typically parameterized: that is, each instance of the system is formed by linking together isomorphic copies of a representative process. Verification of such systems is difficult due to state explosion for large instances and the undecidability of verifying properties over all instances at once. This work turns instead to the derivation of a parameterized protocol from its specification. We exploit a reduction theorem showing that it suffices to construct a representative process $P$ that meets a local specification under interference by neighboring copies of P. Every instance of the parameterized protocol is built by deploying replicated instances of P. While the reduction from the original to a local specification is done by hand, the construction of $P$ is fully automated. This is a new and challenging synthesis question, as one must synthesize an unknown process $P$ while simultaneously considering interference by copies of this unknown process. We present two algorithms: an eager reduction to the synthesis of a transformed specification, and a lazy, iterative, tableau construction which incorporates fresh interference at each step. The tableau method has worst-case complexity that is exponential in the length of the local specification. We have implemented the tableau construction and show that it is capable of synthesizing parameterized protocols for mutual exclusion, leader election, and dining philosophers.
Unified and Incremental SimRank: Index-Free Approximation With Scheduled Principle

SimRank is a popular link-based similarity measure on graphs. It enables a variety of applications with different modes of querying (e.g., single-pair, single-source and all-pair modes). In this paper, we propose UISim, a unified and incremental framework for all SimRank modes based on a scheduled approximation principle. UISim processes queries with incremental and prioritized exploration of the entire computation space, and thus allows flexible tradeoff of time and accuracy. On the other hand, it creates and shares common “building blocks” for online computation without relying on indexes, and thus is efficient to handle both static and dynamic graphs. Our experiments on various real-world graphs show that to achieve the same accuracy, UISim runs faster than its respective state-of-the-art baselines, and scales well on larger graphs.
Re-Think Before You Share: A Comprehensive Study on Prioritizing Check-Worthy Claims

The massive amount of misinformation spreading on the internet on a daily basis has enormous negative impacts on societies. Therefore, we need systems to help fact-checkers to combat misinformation and to raise public awareness of this important problem. In this article, we propose a hybrid model which combines bidirectional encoder representations from transformer (BERT) model with various features to prioritize claims based on their check-worthiness. Features we use include domain-specific controversial topics (CT), word embeddings (WE), part-of-speech (POS) tags, and others. In addition, we explore various ways of increasing labeled data size to effectively train the models, such as increasing positive (IncPos) samples, active learning (AL), and utilizing labeled data in other languages. In our extensive experiments, we show that our model outperforms all state-of-the-art models in test collections of Conference and Labs of Evaluation Forum (CLEF) CheckThat! Lab (CTL) 2018 and 2019. In addition, when positive samples are increased in the training set, our model achieves the best mean average precision (MAP) score reported so far for the test collection of CTL 2020. Furthermore, we show that cross-lingual training is effective for prioritizing Arabic and Turkish claims, but not for English.
Smart Zero-Touch Management of UAV-Based Edge Network

The next generation of wireless communications networks, namely 6G, will be aimed at realizing a fully connected world, and at providing ubiquitous connectivity to people and objects even in remote areas that are very far from the structured Internet core network. These goals include the definition and the design of intelligent communications environments mainly characterized by pervasive artificial intelligence and large-scale automation. The target of this paper is the design of a management framework for edge networks realized with Flying Ad-Hoc Networks (FANET) consisting of a set of Unmanned Aerial Vehicles (UAVs) to provide a remote geographic area with computing and networking facilities for delay-sensitive applications. To this purpose, each UAV is equipped with a Computing Element (CE) to process jobs received through vertical offloading from ground devices. In addition, horizontal offload among UAVs of the FANET is introduced for load balancing purposes, to guarantee that the FANET computation delay for each received job is minimized and is almost independent of the activity state of the area covered by the UAV receiving that job. The proposed FANET management framework is based on Deep Reinforcement Learning (DRL) to allow zero-touch adaptation to the time-variant activity state of the area covered by each UAV. Numerical results demonstrate the power of the proposed framework and the enhancements achieved with respect to the current literature.
Sterile triggers drive joint inflammation in TNF and IL-1β dependent mouse arthritis models

Arthritis is the most common extra-intestinal complication in inflammatory bowel disease (IBD). Conversely, arthritis patients are at risk for developing IBD and often display subclinical gut inflammation. These observations suggest a shared disease etiology, commonly termed ‘the gut-joint-axis’. The clinical association between gut and joint inflammation is further supported by the success of common therapeutic strategies and microbiota dysbiosis in both conditions. Most data however support a correlative relationship between gut & joint inflammation, while causative evidence is lacking. Using two independent transgenic mouse arthritis models, either TNF or IL1β dependent, we demonstrate that arthritis develops independently of the microbiota and intestinal inflammation, since both lines develop full-blown articular inflammation under germ-free conditions. In contrast, TNF-driven gut inflammation is fully rescued in germ-free conditions indicating that the microbiota is driving TNF-induced gut inflammation. Together, our study demonstrates that, although common inflammatory pathways may drive both gut and joint inflammation, the molecular triggers initiating such pathways are distinct in these tissues.
Confidence Intervals Based on Rank Statistics in Simple Linear Models

Consider the simple linear models with non-normal errors. A method based on rank statistics has been proposed in our earlier work for constructing confidence interval for the slope parameter. When the skewness of the distributions of the errors is large, and the values of the explanatory variable are in equal steps, the method based on rank statistics has been shown to produce confidence interval of which the expected length is shorter than those of the bootstrap confidence interval, and the classical confidence interval which is derived by assuming that the errors are normally distributed. An important step in the method based on rank statistics is the determination of the acceptance region for testing the null hypothesis that the slope parameter is zero. Presently we show that the acceptance region depends basically on the skewness and kurtosis of the values of the explanatory variable. This finding suggests that when the values of the explanatory variable are given, we may construct a confidence interval for the slope parameter by using an acceptance region which has been determined for other set of values of the explanatory variable having the same measures of skewness and kurtosis. Thus in implementing the method in practice, we may use pre-determined acceptance regions. We also investigate the performance of the confidence interval based on rank statistics
Study on Detection of Human Motion Using a RGB Color Space Shadow Method with Mechanics Properties

The aim of human mechanics is to reveal the mechanics properties of human motion. Especially, the purpose of human motion detection is detecting the moving people from continuous image sequences, extracting human body segments and then getting motion feature. The paper presents a shadow detection algorithm based on covariance difference operator based RGB color space and discusses its mechanics properties. The presented algorithm includes four steps: object detection, suspected shadow detection, shadow detection and post processing. The presented algorithm of adaptive shadow detection threshold is adopted to suppress the effect of shadow in moving object detection more effectively. The experiment results show the algorithm presented in this paper can detect shadow effectively.
Densely packed decimal encoding

Chen-Ho encoding is a lossless compression of three binary coded decimal digits into 10 bits using an algorithm which can be applied or reversed using only simple Boolean operations. An improvement to the encoding which has the same advantages but is not limited to multiples of three digits is described. The new encoding allows arbitrary-length decimal numbers to be coded efficiently while keeping decimal digit boundaries accessible. This in turn permits efficient decimal arithmetic and makes the best use of available resources such as storage or hardware registers.
Nascent Starbursts in Synchrotron-deficient Galaxies with Hot Dust

Three nearby galaxies that have abnormally high infrared-to-radio continuum ratios, NGC 1377, IC 1953, and NGC 4491, are investigated with a view to understanding the physical origin of their peculiarity. We review the existing data and present new radio continuum measurements along with near-infrared integral-field spectroscopy and molecular gas observations. The three galaxies have low luminosities but starburst-like infrared colors; in NGC 1377, no synchrotron emission is detected at any wavelength; in IC 1953, the observed synchrotron component is attributable to the spiral disk alone and is lacking in the central regions; and the radio spectrum of NGC 4491 is unusually flat. We also compare and contrast them with NGC 4418, a heavily extinguished galaxy that shares some attributes with them. After examining various scenarios, we conclude that these galaxies are most likely observed within a few megayears of the onset of an intense star formation episode after being quiescent for at least ≈100 Myr. This starburst, while heating the dust, has not produced optical signatures or a normal amount of cosmic rays yet. We briefly discuss the statistics of such galaxies and what they imply for star formation surveys.
Thermal variation apparatus for X-ray diffraction experiments up to 3000 K

Describes a thermal variation device for an X-ray diffractometer with a horizontal positioned goniometer. This experimental set-up allows one to observe X-ray patterns between room temperature and 3000 K; the sample temperature is regulated with an accuracy of the order of +or-5 degrees C by using an infrared pyrometer linked to a furnace stabilizing circuit.
Storage mass‐curve analysis in a systems‐analytic perspective

During the past decade, the systems approach to storage reservoir problems has been heralded as something of a jump from the stone age of mass-curve analysis into the modern era of science. In reality, however, no such jump ever occurred. There were a number of small ones but, contrary to the common belief, many of them were confined to the staircase of mass-curve analysis and not all of them were in the upward direction. This paper attempts to put the mass-curve technique into a proper perspective by clearing out some undesirable semantic underbrush accumulated over the past decades and by showing an intrinsic identity of some mass-curve and systems-analytic formulations. It demonstrates that, for the important special case of convex loss functions, both the dynamic and the linear programming formulations of optimum reservoir operation policies as developed over the past decade still have a long way to go to match a 55-year old mass-curve technique in terms of exactness, accuracy, as well as computational efficiency. Last but not least, it shows that the mass-curve technique provides insights into the problems of storage reservoir operation which are entirely out of reach of the systems-analytic methods and can significantly enhance the art of reservoir design and operation.
Climate and climate change sensitivity to model configuration in the Canadian RCM over North America

Climate simulations performed with Regional Climate Models (RCMs) have been found to show sensitivity to parameter settings. The origin, consequences and interpretations of this sensitivity are varied, but it is generally accepted that sensitivity studies are very important for a better understanding and a more cautious manipulation of RCM results. In this work we present sensitivity experiments performed on the simulated climate produced by the Canadian Regional Climate Model (CRCM). In addition to climate sensitivity to parameter variation, we analyse the impact of the sensitivity on the climate change signal simulated by the CRCM.These studies are performed on 30-year long simulated present and future seasonal climates, and we have analysed the effect of seven kinds of configuration modifications: CRCM initial conditions, lateral boundary condition (LBC), nesting update interval, driving Global Climate Model (GCM), driving GCM member, large-scale spectral nudging, CRCM version, and domain size. Results show that large changes in both the driving model and the CRCM physics seem to be the main sources of sensitivity for the simulated climate and the climate change. Their effects dominate those of configuration issues, such as the use or not of large-scale nudging, domain size, or LBC update interval. Results suggest that in most cases, differences between simulated climates for different CRCM configurations are not transferred to the estimated climate change signal: in general, these tend to cancel each other out.
Real hypersurfaces in complex two-plane Grassmannians with parallel Ricci tensor

We introduce the full expression of the curvature tensor of a real hypersurface M in complex two-plane Grassmannians G2(ℂm+2) from the Gauss equation. We then derive a new formula for the Ricci tensor of M in G2(ℂm+2). Finally, we prove that there does not exist any Hopf real hypersurface in complex two-plane Grassmannians G2(ℂm+2) with parallel Ricci tensor.
Distribution system state estimation

A three-phase distribution system state estimation algorithm is proposed in this paper. The normal equation method is used to compute the real-time states of distribution systems modeled by their actual a-b-c phases. A current based formulation is introduced and compared with other formulations. Observability analysis for the proposed distribution system state estimation is discussed. Test results indicate that the normal equation method is applicable to the distribution system state estimation and the current based rectangular form formulation is suitable for this application. >
Nested sampling for Bayesian computations: A discussion

The approximation of marginal densities is central to the Bayesian approach to testing of hypotheses since ratios m1(x)/m2(x) of those marginals are providing Bayes factors. It is thus of interest to see the emergence of a novel proposal for the approximative computation, although we are less confident than the author about the applicability of nested sampling in realistic Bayesian problems.
Convergence of Brownian motion with a scaled Dirac delta potential

Abstract The method of deriving scaling limits using Dirichlet-form techniques has already been successfully applied to a number of infinite-dimensional problems. However, extracting the key tools from these papers is a rather difficult task for non-experts. This paper meets the need for a simple presentation of the method by applying it to a basic example, namely the convergence of Brownian motions with potentials given by n multiplied by the Dirac delta at 0 to Brownian motion with absorption at 0.
DETAILED NUMERICAL INVESTIGATION OF THE BOHM LIMIT IN COSMIC RAY DIFFUSION THEORY

A standard model in cosmic ray diffusion theory is the so-called Bohm limit in which the particle mean free path is assumed to be equal to the Larmor radius. This type of diffusion is often employed to model the propagation and acceleration of energetic particles. However, recent analytical and numerical work has shown that standard Bohm diffusion is not realistic. In the present paper, we perform test-particle simulations to explore particle diffusion in the strong turbulence limit in which the wave field is much stronger than the mean magnetic field. We show that there is indeed a lower limit of the particle mean free path along the mean field. In this limit, the mean free path is directly proportional to the unperturbed Larmor radius like in the traditional Bohm limit, but it is reduced by the factor δB/B0 where B0 is the mean field and δB the turbulent field. Although we focus on parallel diffusion, we also explore diffusion across the mean field in the strong turbulence limit.
DETERMINING THE NUCLEOLUS OF COMPROMISE STABLE GAMES

The main goal is to illustrate that the so-called indirect function of a cooperative game in characteristic function form is applicable to determine the nucleolus for a subclass of coalitional games called compromise stable transferable utility (TU) games. In accordance with the Fenchel–Moreau theory on conjugate functions, the indirect function is known as the dual representation of the characteristic function of the coalitional game. The key feature of a compromise stable TU game is the coincidence of its core with a box prescribed by certain upper and lower core bounds. For the purpose of the determination of the nucleolus, we benefit from the interrelationship between the indirect function and the prekernel of coalitional TU games. The class of compromise stable TU games contains the subclasses of clan games, big boss games and $1$- and $2$-convex $n$-person TU games. As an adjunct, this paper reports the indirect function of clan games for the purpose of determining their nucleolus.