Miracle-dz/UniFAR-FTUs huggingface dataset:
HER2 EXPRESSION IN RENAL CELL CARCINOMA

Renal cell carcinoma (RCC) is the most common malignancy of the adult kidney. A variety of proteins have been investigated for their use as prognostic tumor markers. At present, there are conflicting reports concerning HER2 expression in RCC. PURPOSE: The aim of this study is to evaluate the expression of HER2 protein in patients with renal cell carcinoma (RCC) and to determine the relationship between the tumor grading, staging and the type of the tumor. METHODS: The expression of HER2 in renal cell carcinoma was studied using immunohistochemical methods in paraffin-embedded specimens from 34 patients. Of these cases, 19 patients had clear cell carcinoma, 6 had chromophobe, 5 had papillary, and 4 had urothelial renal carcinoma. Nine of the patients had metastases (in regional lymph nodes and distants), 25 patients were without metastases. The membranous staining of HER2 was assessed as positive when the score was higher than 2 (0, 1 – negative, 2, 3 – positive), according to the well accepted scoring system for breast cancer. RESULTS: The results showed that there were varying degrees of HER2 expression in different RCC’s histological types. The same changes in HER2 expression can be seen in the tumors with and without metastases. There was no significant association between HER2 overexpression and tumor stage, grade and the type of tumor. The results of the present study point out the necessity to discuss eventual beneficial effect of Herceptin therapy.
Selective Caching in Content-Centric Networking

Addressing the well known challenges of current Internet,great expectations are held on Content-Centric Networking(CCN). In CCN,in-networking caching is the key difference from other Internet architectures,so the researching of efficient caching strategy becomes increasingly attractive but remains some great challenges.To address the disorderly caching of current caching strategy,this paper proposes a novel scheme—Selective Caching(SC). In SC, following the consumer's potential demand and the rise and fall of content popularity,content is cached only by some necessary nodes instead of all nodes on the content transmission path.At the same time,in accordance with the bandwidth-for-cache,content can be pushed to the adjacent node with more cache.We firstly propose a theoretical model to evaluate cache size consumed. Finally,we evaluate the proposed scheme through extensive simulation experiments in terms of a wide range of performance metrics.The experiment results show that our proposed scheme can yield a steady improvement of network performance and caching efficiency over diverse operating environments.At the same time,SC buys caching performance improvement with negligible overhead.
Periodicity Detection and its Application in Lifelog Data

Wearable sensors are catching our attention not only in industry but also in the market. We can now acquire sensor data from different types of health tracking devices like smart watches, smart bands, lifelog cameras and most smart phones are capable of tracking and logging information using built-in sensors. As data is generated and collected from various sources constantly, researchers have focused on interpreting and understanding the semantics of this longitudinal multi-modal data. One challenge is the fusion of multi-modal data and achieving good performance on tasks such activity recognition, event detection and event segmentation. The classical approach to process the data generated by wearable sensors has three main parts: 1) Event segmentation 2) Event recognition 3) Event retrieval. Many papers have been published in each of the three fields. This thesis has focused on the longitudinal aspect of the data from wearable sensors, instead of concentrating on the data over a short period of time. The following aspects are several key research questions in the thesis. Does longitudinal sensor data have unique features than can distinguish the subject generating the data from other subjects ? In other words, from the longitudinal perspective, does the data from different subjects share more common structure/similarity/identical patterns so that it is difficult to identify a subject using the data. If this is the case, what are those common patterns ? If we are able to eliminate those similarities among all the data, does the data show more specific features that we can use to model the data series and predict the future values ? If there are repeating patterns in longitudinal data, we can use different methods to compute the periodicity of the recurring patterns and furthermore to identify and extract those patterns. Following that we could be able to compare local data over a short time period with more global patterns in order to show the regularity of the local data. Some case studies are included in the thesis to show the value of longitudinal lifelog data related to a correlation of health conditions and training performance.
Almost tight bound on the query complexity of generalized Simon's problem

Simon's problem played an important role in the history of quantum algorithms, as it inspired Shor to discover the celebrated quantum algorithm solving integer factorization in polynomial time. Besides, the quantum algorithm for Simon's problem has been recently applied to break symmetric cryptosystems. Generalized Simon's problem is a natural extension of Simon's problem: Given a function $f:\{0,1\}^n \to \{0,1\}^m$ with $n \le m$ and the promise that there exists a subgroup $S \le \mathbb{Z}_2^n$ of rank $k$ s.t. for any $s, x\in\{0,1\}^n, f(x) = f(x\oplus s)$ iff $s\in S$, the goal is to find $S$. It is not difficult to design a quantum algorithm for solving this problem exactly with query complexity of $O(n-k)$. However, so far it is not clear what is the classical deterministic query complexity of this problem. % Therefore, it is interesting and nontrivial to consider that, not only for clarifying the gap between quantum and classical computing on this problem, but also from the viewpoint of classical computing. In this paper, we first prove that any classical deterministic algorithm solving generalized Simon's problem has to query at least $\Omega(\sqrt{k \cdot 2^{n-k}})$ values, clarifying the gap between quantum and classical computing on this problem. On the other hand, we devise a deterministic algorithm with query complexity of $O(\sqrt{k \cdot 2^{n - k}})$ in most cases. Therefore, the obtained bound $\Theta(\sqrt{k \cdot 2^{n-k}})$ is almost optimal, which fills the blank of classical deterministic query complexity for generalized Simon's problem.
A Novel Recursive Bayesian Learning-Based Method for the Efficient and Accurate Segmentation of Video With Dynamic Background

Segmentation of video with dynamic background is an important research topic in image analysis and computer vision domains. In this paper, we present a novel recursive Bayesian learning-based method for the efficient and accurate segmentation of video with dynamic background. In the algorithm, each frame pixel is represented as the layered normal distributions which correspond to different background contents in the scene. The layers are associated with a confident term and only the layers satisfy the given confidence which will be updated via the recursive Bayesian estimation. This makes learning of background motion trajectories more accurate and efficient. To improve the segmentation quality, the coarse foreground is obtained via simple background subtraction first. Then, a local texture correlation operator is introduced to fill the vacancies and remove the fractional false foreground regions. Extensive experiments on a variety of public video datasets and comparisons with some classical and recent algorithms are used to demonstrate its improvements in both segmentation accuracy and efficiency.
Optimal Energy Allocation and Task Offloading Policy for Wireless Powered Mobile Edge Computing Systems

This paper studies a single-user wireless powered mobile edge computing (MEC) system, in which one multi-antenna energy transmitter (ET) employs energy beamforming for wireless power transfer (WPT) towards the user, and the user relies on the harvested energy to locally execute a portion of tasks and offload the other portion to an access point (AP) integrated with an MEC server for remote execution. Different from prior works considering static wireless channels and computation tasks at the user, this paper considers both energy and task causality constraints due to the channel fluctuations and dynamic task arrivals over time. Towards an energy-efficient joint-WPT-MEC design, we minimize the total transmission energy consumption at the ET over a particular finite horizon while ensuring the user’s successful task execution, by jointly optimizing the transmission energy allocation at the ET for WPT and the task allocation at the user for local computing and offloading over a particular finite horizon. First, in order to characterize the fundamental performance limit, we consider the offline optimization by assuming that the perfect knowledge of channel state information (CSI) and task state information (TSI) (i.e., task arrival timing and amounts) is known a-priori. In this case, we obtain the well-structured optimal solution to the energy minimization problem by using convex optimization techniques. The optimal solution shows that in the scenario with static channels, the ET should allocate the transmission energy uniformly over time, and the user should employ staircase task allocation for both local computing and offloading, with the number of executed task input-bits monotonically increasing over time. It also shows that in the scenario with time-varying channels, the ET should transmit energy sporadically at slots with causally dominating channel power gains, and the user should apply the staircase task allocation for local computing and staircase water-filling task allocation for offloading with monotonically increasing computation levels over time. Next, inspired by the structured offline solutions obtained above, we develop heuristic online designs for the joint energy and task allocation when the knowledge of CSI/TSI is only causally known. Finally, numerical results show that the proposed joint energy and task allocation designs achieve significantly smaller energy consumption than benchmark schemes with only local computing or full offloading at the user, and the proposed heuristic online designs perform close to the optimal offline solutions and considerably outperform the conventional myopic designs.
The Peculiar Volatile Composition of CO-dominated Comet C/2016 R2 (PanSTARRS)

Comet C/2016 R2 (PanSTARRS) has a peculiar volatile composition, with CO being the dominant volatile, as opposed to H2O, and one of the largest N2/CO ratios ever observed in a comet. Using observations obtained with the Spitzer Space Telescope, NASA’s Infrared Telescope Facility, the 3.5 m Astrophysical Research Consortium telescope at Apache Point Observatory, the Discovery Channel Telescope at Lowell Observatory, and the Arizona Radio Observatory 10 m Submillimeter Telescope, we quantified the abundances of 12 different species in the coma of R2 PanSTARRS: CO, CO2, H2O, CH4, C2H6, HCN, CH3OH, H2CO, OCS, C2H2, NH3, and N2. We confirm the high abundances of CO and N2 and heavy depletions of H2O, HCN, CH3OH, and H2CO compared to CO reported by previous studies. We provide the first measurements (or most sensitive measurements/constraints) on H2O, CO2, CH4, C2H6, OCS, C2H2, and NH3, all of which are depleted relative to CO by at least 1–2 orders of magnitude compared to values commonly observed in comets. The observed species also show strong enhancements relative to H2O, and, even when compared to other species like CH4 or CH3OH, most species show deviations from typical comets by at least a factor of 2–3. The only mixing ratios found to be close to typical are CH3OH/CO2 and CH3OH/CH4. The CO2/CO ratio is within a factor of 2 of those observed for C/1995 O1 (Hale-Bopp) and C/2006 W3 (Christensen) at a similar heliocentric distance, though it is at least an order of magnitude lower than many other comets observed with AKARI. While R2 PanSTARRS was located at a heliocentric distance of 2.8 au at the time of our observations in 2018 January/February, we argue, using sublimation models and comparison to other comets observed at similar heliocentric distance, that this alone cannot account for the peculiar observed composition of this comet and therefore must reflect its intrinsic composition. We discuss possible implications for this clear outlier in compositional studies of comets obtained to date and encourage future dynamical and chemical modeling in order to better understand what the composition of R2 PanSTARRS tells us about the early solar system.
An Overview of Attacks and Defences on Intelligent Connected Vehicles

Cyber security is one of the most significant challenges in connected vehicular systems and connected vehicles are prone to different cybersecurity attacks that endanger passengers' safety. Cyber security in intelligent connected vehicles is composed of in-vehicle security and security of inter-vehicle communications. Security of Electronic Control Units (ECUs) and the Control Area Network (CAN) bus are the most significant parts of in-vehicle security. Besides, with the development of 4G LTE and 5G remote communication technologies for vehicle-toeverything (V2X) communications, the security of inter-vehicle communications is another potential problem. After giving a short introduction to the architecture of next-generation vehicles including driverless and intelligent vehicles, this review paper identifies a few major security attacks on the intelligent connected vehicles. Based on these attacks, we provide a comprehensive survey of available defences against these attacks and classify them into four categories, i.e. cryptography, network security, software vulnerability detection, and malware detection. We also explore the future directions for preventing attacks on intelligent vehicle systems.
New physics in light of the 
H0
 tension: An alternative view

The strong discrepancy between local and early-time (inverse distance ladder) estimates of the Hubble constant H0 could be pointing towards new physics beyond the concordance ΛCDM model. Several attempts to address this tension through new physics rely on extended cosmological models, featuring extra free parameters beyond the six ΛCDM parameters. However, marginalizing over additional parameters has the effect of broadening the uncertainties on the inferred parameters (including H0), and it is often the case that within these models the H0 tension is addressed due to larger uncertainties rather than a genuine shift in the central value of H0. In this paper I consider an alternative viewpoint: what happens if a physical theory is able to fix the extra parameters to a specific set of nonstandard values? In this case, the degrees of freedom of the model are reduced with respect to the standard case where the extra parameters are free to vary. Focusing on the dark energy equation of state w and the effective number of relativistic species Neff, I find that physical theories able to fix w≈-1.3 or Neff≈3.95 would lead to an estimate of H0 from cosmic microwave background, baryon acoustic oscillation, and type Ia supernovae data in perfect agreement with the local distance ladder estimate, without broadening the uncertainty on the former. These two nonstandard models are, from a model-selection perspective, strongly disfavored with respect to the baseline ΛCDM model. However, models that predict Neff≈3.45 would be able to bring the tension down to 1. 5σ while only being weakly disfavored with respect to ΛCDM, whereas models that predict w≈-1.1 would be able to bring the tension down to 2σ (at the cost of the preference for ΛCDM being definite). Finally, I estimate dimensionless multipliers relating variations in H0 to variations in w and Neff, which can be used to swiftly repeat the analysis of this paper in light of future more precise local distance ladder estimates of H0, should the tension persist. As a caveat, these results were obtained from the 2015 Planck data release, but these findings would be qualitatively largely unaffected were I to use more recent data.
Iterative temporal differencing with random synaptic feedback weights support error backpropagation for deep learning

This work shows that a differentiable activation function is not necessary any more for error backpropagation. The derivative of the activation function can be replaced by an iterative temporal differencing using fixed random feedback alignment. Using fixed random synaptic feedback alignment with an iterative temporal differencing is transforming the traditional error backpropagation into a more biologically plausible approach for learning deep neural network architectures. This can be a big step toward the integration of STDP-based error backpropagation in deep learning.
Serre Intersection Multiplicity Conjecture and Hodge theory

We explain intersection multiplicity defined by J. P. Serre, in terms of the Poincare product in Hodge theory by a modification of the chern character map. We also discuss a formulation of the Euler characteristic via the action of correspondences on the Chow groups of projective varieties, assuming the Grothendieck Standard conjectures over $Q_l$.
A Neural Network Detector for Spectrum Sensing under Uncertainties

Spectrum sensing is of critical importance in any cognitive radio system. When the primary user's signal has uncertain parameters, the likelihood ratio test, which is the theoretically optimal detector, generally has no closed-form expression. As a result, spectrum sensing under parameter uncertainty remains an open question, though many detectors exploiting specific features of a primary signal have been proposed and have achieved reasonably good performance. In this paper, a neural network is trained as a detector for modulated signals. The result shows by training on an appropriate dataset, the neural network gains robustness under uncertainties in system parameters including the carrier frequency offset, carrier phase offset, and symbol time offset. The result displays the neural network's potential in exploiting implicit and incomplete knowledge about the signal's structure.
Style Transfer Applied to Face Liveness Detection with User-Centered Models

This paper proposes a face anti-spoofing user-centered model (FAS-UCM). The major difficulty, in this case, is obtaining fraudulent images from all users to train the models. To overcome this problem, the proposed method is divided in three main parts: generation of new spoof images, based on style transfer and spoof image representation models; training of a Convolutional Neural Network (CNN) for liveness detection; evaluation of the live and spoof testing images for each subject. The generalization of the CNN to perform style transfer has shown promising qualitative results. Preliminary results have shown that the proposed method is capable of distinguishing between live and spoof images on the SiW database, with an average classification error rate of 0.22.
Generalized High-Energy Thermionic Electron Injection at Graphene Interface

Graphene thermionic electron emission across high-interface-barrier involves energetic electrons residing far away from the Dirac point where the Dirac cone approximation of the band structure breaks down. Here we construct a full-band model beyond the simple Dirac cone approximation for the thermionic injection of high-energy electrons in graphene. We show that the thermionic emission model based on the Dirac cone approximation is valid only in the graphene/semiconductor Schottky interface operating near room temperature, but breaks down in the cases involving high-energy electrons, such as graphene/vacuum interface or heterojunction in the presence of photon absorption, where the full-band model is required to account for the band structure nonlinearity at high electron energy. We identify a critical barrier height, $\Phi_B^{(\text{c})} \approx 3.5$ eV, beyond which the Dirac cone approximation crosses over from underestimation to overestimation. In the high-temperature thermionic emission regime at graphene/vacuum interface, the Dirac cone approximation severely overestimates the electrical and heat current densities by more than 50\% compared to the more accurate full-band model. The large discrepancies between the two models are demonstrated using a graphene-based thermionic cooler. These findings reveal the fallacy of Dirac cone approximation in the thermionic injection of high-energy electrons in graphene. The full-band model developed here can be readily generalized to other 2D materials, and shall provide an improved theoretical avenue for the accurate analysis, modeling and design of graphene-based thermionic energy devices.
Double flat bands in kagome twisted bilayers

We have studied how a generic bilayer kagome lattice behave upon layer rotation. We employed a Tight Binding model with one orbital per site and found (i) for low rotational angles, and at low energies, the same flat bands structure like in twisted bilayer graphene; though, for a larger value of the magic angle. Moreover, (ii) at high energies, due to the superstructure symmetry regions, we found the characteristics three band dispersion of the kagome lattice. In the latter, its band width decreases for lower angles confining them within a few meV. Therefore, we found in twisted kagome lattice the coexistence of two sets of flat bands in different energies and lying in different spatial regions of the bilayer system.
Weakly regular Sturm-Liouville problems: a corrected spectral matrix method

In this paper, we consider weakly regular Sturm-Liouville eigenproblems with unbounded potential at both endpoints of the domain. We propose a Galerkin spectral matrix method for its solution and we study the error in the eigenvalue approximations it provides. The result of the convergence analysis is then used to derive a low-cost and very effective formula for the computation of corrected numerical eigenvalues. Finally, we present and discuss the results of several numerical experiments which confirm the validity of the approach.
A Multidimensional Dependence of the Substructure Evolution on the Tidal Coherence

We numerically explore how the subhalo mass-loss evolution is affected by the tidal coherences measured along different eigenvector directions. The mean virial-to-accretion mass ratios of the subhalos are used to quantify the severity of their mass-loss evolutions within the hosts, and the tidal coherence is expressed as an array of three numbers each of which quantifies the alignment between the tidal fields smoothed on the scales of $2$ and $30\,h^{-1}$Mpc in each direction of three principal axes. Using a Rockstar halo catalog retrieved from a N-body simulation, we investigate if and how the mass-loss evolutions of the subhalos hosted by distinct halos at fixed mass scale of [$1$-$3$]$10^{14}\,h^{-1}\,M_{\odot}$ are correlated with three components of the tidal coherence. The tides coherent along different eigenvector directions are found to have different effects on the subhalo mass-loss evolution, which cannot be ascribed to the differences in the densities and ellipticities of the local environments. It is shown that the substructures surrounded by the tides highly coherent along the first eigenvector direction and highly {\it incoherent} along the third eigenvector direction experience the least severe mass-loss evolution, while the tides highly {\it incoherent} only along the first eigenvector direction is responsible for the most severe mass-loss evolution of the subhalos. Explaining that the coherent tides have an obstructing effect on the satellite infalls onto their hosts and that the strength of the obstruction effect depends on which directions the tides are coherent or {\it incoherent} along, we suggest that the multidimensional dependence of the substructure evolution on the tidal coherence should be deeply related to the complex nature of the large-scale assembly bias.