Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of Backward Crosstalk in 2 × 2 MIMO Transmitters on NMSE and Spectral Efficiency

We consider backward crosstalk in $2\times 2$ transmitters, which is caused by crosstalk from the outputs of the transmitter to the inputs or by the combination of output crosstalk and impedance mismatch. We analyze its impact via feedback networks together with third-order power amplifier non-linearities. We utilize the Bussgang decomposition to express the distorted output signals of the transmitter as a linear transformation of the input plus uncorrelated distortion. The normalized mean-square errors (NMSEs) between the distorted and desired amplified signals are expressed analytically and the optimal closed-form power back-off that minimizes the worst NMSE of the two branches is derived. In the second part of the paper, an achievable spectral efficiency (SE) is presented for the communication from this “dirty” transmitter to another single-antenna receiver. The SE-maximizing precoder is optimally found by exploiting the hardware characteristics. Furthermore, the optimal power back-off is analyzed for two sub-optimal precoders, which either do not exploit any hardware knowledge or only partial knowledge. The simulation results show that the performance of these sub-optimal precoders is close-to-optimal. We also discuss how the analysis in this paper can be extended to transmitters with an arbitrary number of antenna branches.
Lecture notes on Generalised Hydrodynamics

These are lecture notes for a series of lectures given at the Les Houches Summer School on Integrability in Atomic and Condensed Matter Physics, 30 July to 24 August 2018. The same series of lectures has also been given at the Tokyo Institute of Technology, October 2019. I overview in a pedagogical fashion the main aspects of the theory of generalised hydrodynamics, a hydrodynamic theory for quantum and classical many-body integrable systems. Only very basic knowledge of hydrodynamics and integrable systems is assumed.
The effect study of nursing on the complications of cerebral hemorrhage and clinical symptoms

Objective To explore the effect of nursing on the complications of cerebral hemorrhage and the clinical symptoms.Methods One hundred and thirty-four patients with cerebral hemorrhage were randomly divided into control group and research group.Both groups were given routine nursing,and only the research group was given targeted nursing on complication additionally.The anxiety level,incidence of complications and patient satisfaction were investigated in both groups.Results(1)The research group's SAS score was lower than that of control group(P 0.05);the number of patients with mild anxiety in research group was more than that in control group(P 0.01),while the number of patients with severe anxiety in research group was less than that in control group(P 0.01). (2)The number of complications in research group was lower than that of control group(P 0.05),especially infection,central heating and secondary hard injury. (3)The satisfaction rate of research group was higher than that of control group(P 0.01). Conclusion The nursing targeting on the clinical effect and complications can reduce patients' anxiety and the incidence of complications.
Entanglement entropy growth in stochastic conformal field theory and the KPZ class

We introduce a model of effective conformal quantum field theory in dimension coupled to stochastic noise, where Kardar-Parisi-Zhang (KPZ) class fluctuations can be observed. The analysis of the quantum dynamics of the scaling operators reduces to the study of random trajectories in a random environment, modeled by Brownian vector fields. We use recent results on random walks in random environments to calculate the time-dependent entanglement entropy of a subsystem interval, starting from a factorized state. We find that the fluctuations of the entropy in the large deviation regime are governed by the universal Tracy-Widom distribution. This enlarges the KPZ class, previously observed in random circuit models, to a family of interacting many-body quantum systems.
Severe Back and Pelvic Pain in a 14-Year-Old Boy

CASE REPORT A 14-year-old boy with cerebral palsy who had been ambulatory presented to the rheumatology clinic with a 2-week history of progressively worsening right knee pain that had migrated to the lower back, left hip, and both proximal thighs. The onset of the pain was gradual and was not associated with fever, chills, rash, sore throat, nausea, vomiting, or diarrhea. He denied any trauma. He had a history of occasional urinary incontinence that had increased in frequency. His only medication was ibuprofen for pain, as needed. A radiograph of his knee obtained 7 days before presentation had been normal. The boy was carried into the clinic by his father and seemed to be in severe distress due to pain. He had a temperature of 36.7-C, pulse of 110/min, respiratory rate of 26/min, and blood pressure of 112/66 mm Hg. Pharyngeal, skin, lung, heart, and abdominal examination findings were normal. Tenderness was noted on palpation over the L4 to L5 vertebra. His legs were held in a ‘‘windswept’’ position: his right leg was adducted and internally rotated and his left leg was externally rotated and abducted. A small right knee effusion was noted. He was unable to completely extend either knee. Laboratory evaluations revealed a white blood cell count of 12,800/KL (with 81% neutrophils, 10% lymphocytes, and 9% monocytes), hemoglobin of 14.6 g/dL, hematocrit of 43.6%, and platelets of 401,000/KL. The erythrocyte sedimentation rate was 9 mm/h, and the C-reactive protein was 4.7 mg/dL (normal, e1 mg/dL). Clean-catch midstream urinalysis was remarkable for a specific gravity of 1.025, positive nitrite, 20 to 25 white blood cells per high-power field, and 2+ bacteria. Blood and urine cultures were sent. Radiographs of the pelvis and lumbar spine were normal. The boy’s pain failed to significantly improve with intravenous ketorolac. He was admitted for pain relief and further evaluations. He required intravenous fentanyl to position for a complete magnetic resonance imaging (MRI) of the lumbar spine. Left iliac osteomyelitis, sacroiliitis, and psoas myositis were noted. Because he also complained of knee pain, a bone scan was obtained, revealing only intense activity within the left sacroiliac joint. The following day, our interventional radiologist obtained computed tomography (CT)Yguided cultures and biopsy of the left iliac bone and left sacroiliac joint space (Figs. 1 and 2). A few white blood cells, but no organisms, were noted on Gram stains. The initial blood culture and a second blood culture obtained 12 hours after admission were positive for Grampositive cocci in clusters and the urine culture revealed greater than 100,000 colony forming units per milliliter of Gram-positive cocci. Intravenous vancomycin was initiated. All of these cultures subsequently grew methicillin-sensitive Staphylococcus aureus, and the biopsy was consistent with severe acute osteomyelitis. The antibiotic therapy was changed to nafcillin, and no growth was noted on all subsequent blood cultures. Additional imaging studies (Figs. 3 and 4) were ordered and identified an unexpected inciting cause.
Theoretical study of electron structure of fermium ($Z=100$): energy levels and transition rates

Calculation of the excited states and electromagnetic transition probabilities in atoms with open f-shell, like erbium and fermium, is a challenging problem due to a very large number of mixed configurations. We use recently developed version of the configuration interaction method for open shells to study electron structure of fermium atom. We calculate excitation energies of odd states connected to the ground state by the electric dipole transition and corresponding transition rates. The results are in good agreement with experiment and earlier calculations for seven previously studied states. Twenty eight new states are reported. We use similar calculations for erbium, which is lighter analog of fermium, to validate the method and estimate the accuracy of the calculations.
Progressive VAE Training on Highly Sparse and Imbalanced Data

In this paper, we present a novel approach for training a Variational Autoencoder (VAE) on a highly imbalanced data set. The proposed training of a high-resolution VAE model begins with the training of a low-resolution core model, which can be successfully trained on imbalanced data set. In subsequent training steps, new convolutional, upsampling, deconvolutional, and downsampling layers are iteratively attached to the model. In each iteration, the additional layers are trained based on the intermediate pretrained model - a result of previous training iterations. Thus, the resolution of the model is progressively increased up to the required resolution level. In this paper, the progressive VAE training is exploited for learning a latent representation with imbalanced, highly sparse data sets and, consequently, generating routes in a constrained 2D space. Routing problems (e.g., vehicle routing problem, travelling salesman problem, and arc routing) are of special significance in many modern applications (e.g., route planning, network maintenance, developing high-performance nanoelectronic systems, and others) and typically associated with sparse imbalanced data. In this paper, the critical problem of routing billions of components in nanoelectronic devices is considered. The proposed approach exhibits a significant training speedup as compared with state-of-the-art existing VAE training methods, while generating expected image outputs from unseen input data. Furthermore, the final progressive VAE models exhibit much more precise output representation, than the Generative Adversarial Network (GAN) models trained with comparable training time. The proposed method is expected to be applicable to a wide range of applications, including but not limited image impainting, sentence interpolation, and semi-supervised learning.
Reconciling Bayesian and Perimeter Regularization for Binary Inversion

A central theme in classical algorithms for the reconstruction of discontinuous functions from observational data is perimeter regularization via the use of the total variation. On the other hand, sparse or noisy data often demands a probabilistic approach to the reconstruction of images, to enable uncertainty quantification; the Bayesian approach to inversion, which itself introduces a form of regularization, is a natural framework in which to carry this out. In this paper the link between Bayesian inversion methods and perimeter regularization is explored. In this paper two links are studied: (i) the maximum a posteriori (MAP) objective function of a suitably chosen Bayesian phase-field approach is shown to be closely related to a least squares plus perimeter regularization objective; (ii) sample paths of a suitably chosen Bayesian level set formulation are shown to possess finite perimeter and to have the ability to learn about the true perimeter.
The performance evaluation of Multi-representation in the Deep Learning models for Relation Extraction Task

Single implementing, concatenating, adding or replacing of the representations has yielded significant improvements on many NLP tasks. Mainly in Relation Extraction where static, contextualized and others representations that are capable of explaining word meanings through the linguistic features that these incorporates. In this work addresses the question of how is improved the relation extraction using different types of representations generated by pretrained language representation models. We benchmarked our approach using popular word representation models, replacing and concatenating static, contextualized and others representations of hand-extracted features. The experiments show that representation is a crucial element to choose when DL approach is applied. Word embeddings from Flair and BERT can be well interpreted by a deep learning model for RE task, and replacing static word embeddings with contextualized word representations could lead to significant improvements. While, the hand-created representations requires is time-consuming and not is ensure a improve in combination with others representations.
Large Responsivity of Graphene Radiation Detectors with Thermoelectric Readout: Results of Simulations

Simple estimations show that the thermoelectric readout in graphene radiation detectors can be extremely effective even for graphene with modest charge-carrier mobility ∼1000 cm2/(Vs). The detector responsivity depends mostly on the residual charge-carrier density and split-gate spacing and can reach competitive values of ∼103–104 V/W at room temperature. The optimum characteristics depend on a trade-off between the responsivity and the total device resistance. Finding out the key parameters and their roles allows for simple detectors and their arrays, with high responsivity and sufficiently low resistance matching that of the radiation-receiving antenna structures.
ActiveMoCap: Optimized Drone Flight for Active Human Motion Capture

The accuracy of monocular 3D human pose estimation depends on the viewpoint from which the image is captured. While camera-equipped drones provide control over this viewpoint, automatically positioning them at the location which will yield the highest accuracy remains an open problem. This is the problem that we address in this paper. Specifically, given a short video sequence, we introduce an algorithm that predicts the where a drone should go in the future frame so as to maximize 3D human pose estimation accuracy. A key idea underlying our approach is a method to estimate the uncertainty of the 3D body pose estimates. We integrate several sources of uncertainty, originating from a deep learning based regressors and temporal smoothness. The resulting motion planner leads to improved 3D body pose estimates and outperforms or matches existing planners that are based on person following and orbiting.
CPGAN: Full-Spectrum Content-Parsing Generative Adversarial Networks for Text-to-Image Synthesis

Typical methods for text-to-image synthesis seek to design effective generative architecture to model the text-to-image mapping directly. It is fairly arduous due to the cross-modality translation. In this paper we circumvent this problem by focusing on parsing the content of both the input text and the synthesized image thoroughly to model the text-to-image consistency in the semantic level. Particularly, we design a memory structure to parse the textual content by exploring semantic correspondence between each word in the vocabulary to its various visual contexts across relevant images during text encoding. Meanwhile, the synthesized image is parsed to learn its semantics in an object-aware manner. Moreover, we customize a conditional discriminator to model the fine-grained correlations between words and image sub-regions to push for the text-image semantic alignment. Extensive experiments on COCO dataset manifest that our model advances the state-of-the-art performance significantly (from 35.69 to 52.73 in Inception Score).
Compositeness smears causality in effective theory and limits the maximum precision of some dispersive approaches.

Scattering off the edge of a composite particle or, generically, off a finite-range interaction, can precede that off its center. In an effective theory that takes the particle as pointlike and the interaction as contact, the scattered wave is slightly advanced in violation of causality (the fundamental theory underlying the compositeness is of course causal). In practice, partial-wave amplitudes exponentially grow for large imaginary energy, so that upper complex plane analyticity is not sufficient to obtain a dispersion relation for them, but only for a slightly modified function. This limits the maximum precision of certain dispersive approaches to compositeness based on Cauchy's theorem leading to partial-wave dispersion relations (the modified relations additionally connect different angular momenta). The introduced uncertainty may be of interest to some dispersive tests of the Standard Model with hadrons, and to unitarization methods used to extend electroweak effective theories. Interestingly, the Inverse Amplitude Method is safe because the underlying dispersion relation is built from the inverse amplitude which has the opposite behavior and converges to zero allowing to close the contour.
A Promising Method for Touch-typing Keyboard Rendering

—We have developed a novel button click rendering mechanism based on active lateral force feedback. The effect can be localized because electroadhesion between a ﬁnger and a surface can be localized. Psychophysical experiments were conducted to evaluate the quality of a rendered button click, which subjects judged to be acceptable. Both the experiment results and the subjects’ comments conﬁrm that this button click rendering mechanism has the ability to generate a range of realistic button click sensations that could match subjects’ different preferences. We can thus generate a button click on a ﬂat surface without macroscopic motion of the surface in the lateral or normal direction, and we can localize this haptic effect to an individual ﬁnger. This mechanism is promising for touch-typing keyboard rendering.
The Brier Score under Administrative Censoring: Problems and Solutions

The Brier score is commonly used for evaluating probability predictions. In survival analysis, with right-censored observations of the event times, this score can be weighted by the inverse probability of censoring (IPCW) to retain its original interpretation. It is common practice to estimate the censoring distribution with the Kaplan-Meier estimator, even though it assumes that the censoring distribution is independent of the covariates. This paper discusses the general impact of the censoring estimates on the Brier score and shows that the estimation of the censoring distribution can be problematic. In particular, when the censoring times can be identified from the covariates, the IPCW score is no longer valid. For administratively censored data, where the potential censoring times are known for all individuals, we propose an alternative version of the Brier score. This administrative Brier score does not require estimation of the censoring distribution and is valid even if the censoring times can be identified from the covariates.
Finite-Time Convergence of Continuous-Time Optimization Algorithms via Differential Inclusions

In this paper, we propose two discontinuous dynamical systems in continuous time with guaranteed prescribed finite-time local convergence to strict local minima of a given cost function. Our approach consists of exploiting a Lyapunov-based differential inequality for differential inclusions, which leads to finite-time stability and thus finite-time convergence with a provable bound on the settling time. In particular, for exact solutions to the aforementioned differential inequality, the settling-time bound is also exact, thus achieving prescribed finite-time convergence. We thus construct a class of discontinuous dynamical systems, of second order with respect to the cost function, that serve as continuous-time optimization algorithms with finite-time convergence and prescribed convergence time. Finally, we illustrate our results on the Rosenbrock function.
Spatial Segregation of Massive Clusters in Dwarf Galaxies

The relative average minimum projected separations of star clusters in the Legacy ExtraGalactic UV Survey (LEGUS) and in tidal dwarfs around the interacting galaxy NGC 5291 are determined as a function of cluster mass to look for cluster–cluster mass segregation. Class 2 and 3 LEGUS clusters, which have a more irregular internal structure than the compact and symmetric class 1 clusters, are found to be mass-segregated in low-mass galaxies, which means that the more massive clusters are systematically bunched together compared to the lower-mass clusters. This mass segregation is not present in high-mass galaxies or class 1 clusters. We consider possible causes for this segregation, including differences in cluster formation and scattering in the shallow gravitational potentials of low-mass galaxies.
Formula size games for modal logic and μ-calculus

We propose a new version of formula size game for modal logic. The game characterizes the equivalence of pointed Kripke models up to formulas of given numbers of modal operators and binary connectives. Our game is similar to the well-known Adler–Immerman game. However, due to a crucial difference in the definition of positions of the game, its winning condition is simpler, and the second player does not have a trivial optimal strategy. Thus, unlike the Adler–Immerman game, our game is a genuine two-person game. We illustrate the use of the game by proving a non-elementary succinctness gap between bisimulation invariant first-order logic $\textrm{FO}$ and (basic) modal logic $\textrm{ML}$. We also present a version of the game for the modal $\mu $-calculus $\textrm{L}_\mu $ and show that $\textrm{FO}$ is also non-elementarily more succinct than $\textrm{L}_\mu $.
The Maximin Share Dominance Relation

Given a finite set $X$ and an ordering $\succeq$ over its subsets, the $l$-out-of-$d$ maximin-share of $X$ is the maximal (by $\succeq$) subset of $X$ that can be constructed by partitioning $X$ into $d$ parts and picking the worst union of $l$ parts. A pair of integers $(l,d)$ dominates a pair $(l',d')$ if, for any set $X$ and ordering $\succeq$, the $l$-out-of-$d$ maximin-share of $X$ is at least as good (by $\succeq$) as the $l'$-out-of-$d'$ maximin-share of $X$. This note presents a necessary and sufficient condition for deciding whether a given pair of integers dominates another pair, and an algorithm for finding all non-dominated pairs. It compares the $l$-out-of-$d$ maximin-share to some other criteria for fair allocation of indivisible objects among people with different entitlements.
Enjoy the Untrusted Cloud: A Secure, Scalable and Efficient SQL-like Query Framework for Outsourcing Data

While the security of the cloud remains a concern, a common practice is to encrypt data before outsourcing them for utilization. One key challenging issue is how to efficiently perform queries over the ciphertext. Conventional crypto-based solutions, e.g. partially/fully homomorphic encryption and searchable encryption, suffer from low performance, poor expressiveness and weak compatibility. An alternative method that utilizes hardware-assisted trusted execution environment, i.e., Intel SGX, has emerged recently. On one hand, such work lacks of supporting scalable access control over multiple data users. On the other hand, existing solutions are subjected to the key revocation problem and knowledge extractor vulnerability. In this work, we leverage the newly hardware-assisted methodology and propose a secure, scalable and efficient SQL-like query framework named QShield. Building upon Intel SGX, QShield can guarantee the confidentiality and integrity of sensitive data when being processed on an untrusted cloud platform. Moreover, we present a novel lightweight secret sharing method to enable multi-user access control in QShield, while tackling the key revocation problem. Furthermore, with an additional trust proof mechanism, QShield guarantees the correctness of queries and significantly alleviates the possibility to build a knowledge extractor. We implemented a prototype for QShield and show that QShield incurs minimum performance cost.
Integrality and cuspidality of pullbacks of nearly holomorphic Siegel Eisenstein series

We study nearly holomorphic Siegel Eisenstein series of general levels and characters on $\mathbb{H}_{2n}$, the Siegel upper half space of degree $2n$. We prove that the Fourier coefficients of these Eisenstein series (once suitably normalized) lie in the ring of integers of $\mathbb{Q}_p$ for all sufficiently large primes $p$. We also prove that the pullbacks of these Eisenstein series to $\mathbb{H}_n \times \mathbb{H}_n$ are cuspidal under certain assumptions.