Miracle-dz/UniFAR-FTUs huggingface dataset:
Predicting CSI Sequences With Attention-Based Neural Networks

—In this work, we consider the problem of multi-step channel prediction in wireless communication systems. In existing works, autoregressive (AR) models are either replaced or combined with feed-forward neural networks (NNs) or, alternatively, with recurrent neural networks (RNNs). This paper explores the possibility of using sequence-to-sequence (Seq2Seq) and transformer neural network (TNN) models for channel state information (CSI) prediction. Simulation results show that both, Seq2Seq and TNNs, represent an appealing alternative to RNNs and feed-forward NNs in the context of CSI prediction. Additionally, the TNN with a few adaptations can extrapolate better than other models to CSI sequences that are either shorter or longer than the ones the model saw during training.
Ideas for the future of Prolog inspired by Oz

Both Prolog and Oz are multiparadigm languages with a logic programming core. There is a significant subset of Oz that is a syntactic variant of Prolog: pure Prolog programs with green or blue cuts and bagof/3 or setof/3 can be translated directly to Oz. Because of this close relationship between Prolog and Oz, we propose that the extensions made by Oz to logic programming can be an inspiration for the future evolution of Prolog. We explain three extensions, namely deterministic logic programming, lazy concurrent functional programming, and purely functional distributed computing. We briefly present these extensions and we explain how they can help Prolog evolve in its next 50 years.
Mind the (optimality) Gap: A Gap-Aware Learning Rate Scheduler for Adversarial Nets

Adversarial nets have proved to be powerful in various domains including generative modeling (GANs), transfer learning, and fairness. However, successfully training adversarial nets using first-order methods remains a major challenge. Typically, careful choices of the learning rates are needed to maintain the delicate balance between the competing networks. In this paper, we design a novel learning rate scheduler that dynamically adapts the learning rate of the adversary to maintain the right balance. The scheduler is driven by the fact that the loss of an ideal adversarial net is a constant known a priori. The scheduler is thus designed to keep the loss of the optimized adversarial net close to that of an ideal network. We run large-scale experiments to study the effectiveness of the scheduler on two popular applications: GANs for image generation and adversarial nets for domain adaptation. Our experiments indicate that adversarial nets trained with the scheduler are less likely to diverge and require significantly less tuning. For example, on CelebA, a GAN with the scheduler requires only one-tenth of the tuning budget needed without a scheduler. Moreover, the scheduler leads to statistically significant improvements in model quality, reaching up to $27\%$ in Frechet Inception Distance for image generation and $3\%$ in test accuracy for domain adaptation.
Chance-Constrained Trajectory Optimization for High-DOF Robots in Uncertain Environments

Many practical applications of robotics require systems that can operate safely despite uncertainty. In the context of motion planning, two types of uncertainty are particularly important when planning safe robot trajectories. The first is environmental uncertainty -- uncertainty in the locations of nearby obstacles, stemming from sensor noise or (in the case of obstacles' future locations) prediction error. The second class of uncertainty is uncertainty in the robots own state, typically caused by tracking or estimation error. To achieve high levels of safety, it is necessary for robots to consider both of these sources of uncertainty. In this paper, we propose a risk-bounded trajectory optimization algorithm, known as Sequential Convex Optimization with Risk Optimization (SCORA), to solve chance-constrained motion planning problems despite both environmental uncertainty and tracking error. Through experiments in simulation, we demonstrate that SCORA significantly outperforms state-of-the-art risk-aware motion planners both in planning time and in the safety of the resulting trajectories.
Early Water Delivery to Terrestrial Planet Regions during the Stages of Jupiter’s Formation and Migration in the Grand Tack Model

The formation and subsequent migration of gas giants could significantly affect the material mixing in the solar system. In this study, we use N-body simulations to investigate how much water is transported into the region of the terrestrial planet formation during the growth and migration phases of Jupiter in the Grand Tack model. We found that Jupiter’s growth was accompanied by significant mass transport and that a substantial amount of water (about 10 times Earth’s ocean mass for the initial planetesimal distribution based on the minimum-mass solar nebula) was transported into the terrestrial planet region. The total amount delivered increased further during Jupiter’s migration phase (totaling about 10–40 times Earth’s ocean mass), which was less dependent on simulation parameters. In addition, at these stages, terrestrial planets were not fully grown. Therefore, water supplied during these early stages could interact with metallic iron during the core formation of protoplanets and/or growing Earth. Since hydrogen in water molecules can dissolve into their cores, this could explain the density deficit observed in the current Earth core. Notably, Jupiter could play an important role as a “barrier” in explaining the dichotomy of the isotopic compositions between noncarbonaceous (NC) and carbonaceous (CC) meteorites. This study’s results show that Jupiter’s growth necessitates some mixing of NC and CC materials.
Study of density independent scattering angle and energy loss for low- to high-Z material using Muon Tomography

Cosmic ray muon, as they pass through a material, undergoes Multiple Coulomb Scattering (MCS). The analysis of muon scattering angle in a material provides us with an opportunity to study the characteristics of material and its internal 3D structure as the scattering angle depends on the atomic number, the density of the material, and the thickness of the medium at a given energy. We have used the GEANT4 toolkit to study the scattering angle and utilize this information to identify the material. We have analyzed the density dependent $\&$ density independent scattering angle and observed various patterns for distinct periods in the periodic table.
Efficiency of ETA Prediction

Modern mobile applications such as navigation services and ride-sharing platforms rely heavily on geospatial technologies, most critically predictions of the time required for a vehicle to traverse a particular route, or the so-called estimated time of arrival (ETA). There are various methods used in practice, which differ in terms of the geographic granularity at which the predictive model is trained -- e.g., segment-based methods predict travel time at the level of road segments (or a combination of several adjacent road segments) and then aggregate across the route, whereas route-based methods use generic information about the trip, such as origin and destination, to predict travel time. Though various forms of these methods have been developed, there has been no rigorous theoretical comparison regarding their accuracies, and empirical studies have, in many cases, drawn opposite conclusions. We provide the first theoretical analysis of the predictive accuracy of various ETA prediction methods and argue that maintaining a segment-level architecture in predicting travel time is often of first-order importance. Our work highlights that the accuracy of ETA prediction is driven not just by the sophistication of the model but also by the spatial granularity at which those methods are applied.
Probabilistic Point Cloud Modeling via Self-Organizing Gaussian Mixture Models

This letter presents a continuous probabilistic modeling methodology for spatial point cloud data using finite Gaussian Mixture Models (GMMs) where the number of components are adapted based on the scene complexity. Few hierarchical and adaptive methods have been proposed to address the challenge of balancing model fidelity with size. Instead, state-of-the-art mapping approaches require tuning parameters for specific use cases, but do not generalize across diverse environments. To address this gap, we utilize a self-organizing principle from information-theoretic learning to automatically adapt the complexity of the GMM model based on the relevant information in the sensor data. The approach is evaluated against existing point cloud modeling techniques on real-world data with varying degrees of scene complexity.
Adaptive hedging horizon and hedging performance estimation

In this study, we constitute an adaptive hedging method based on empirical mode decomposition (EMD) method to extract the adaptive hedging horizon and build a time series cross-validation method for robust hedging performance estimation. Basing on the variance reduction criterion and the value-at-risk (VaR) criterion, we find that the estimation of in-sample hedging performance is inconsistent with that of the out-sample hedging performance. The EMD hedging method family exhibits superior performance on the VaR criterion compared with the minimum variance hedging method. The matching degree of the spot and futures contracts at the specific time scale is the key determinant of the hedging performance in the corresponding hedging horizon.
Quantum Phase Transitions in periodically quenched systems

Quantum phase transitions encompass a variety of phenomena that occur in quantum systems exhibiting several possible symmetries. Traditionally, these transitions are explored by continuously varying a control parameter that connects two different symmetry configurations. Here we propose an alternative approach where the control parameter undergoes abrupt and time-periodic jumps between only two values. This approach yields results surprisingly similar to those obtained by the traditional one and may prove experimentally useful in situations where accessing the control parameter is challenging.
Learning Optimal Fair Decision Trees: Trade-offs Between Interpretability, Fairness, and Accuracy

The increasing use of machine learning in high-stakes domains – where people’s livelihoods are impacted – creates an urgent need for interpretable, fair, and highly accurate algorithms. With these needs in mind, we propose a mixed integer optimization (MIO) framework for learning optimal classification trees – one of the most interpretable models – that can be augmented with arbitrary fairness constraints. In order to better quantify the “price of interpretability”, we also propose a new measure of model interpretability called decision complexity that allows for comparisons across different classes of machine learning models. We benchmark our method against state-of-the-art approaches for fair classification on popular datasets; in doing so, we conduct one of the first comprehensive analyses of the trade-offs between interpretability, fairness, and predictive accuracy. Given a fixed disparity threshold, our method has a price of interpretability of about 4.2 percentage points in terms of out-of-sample accuracy compared to the best performing, complex models. However, our method consistently finds decisions with almost full parity, while other methods rarely do.
Contact2Grasp: 3D Grasp Synthesis via Hand-Object Contact Constraint

3D grasp synthesis generates grasping poses given an input object. Existing works tackle the problem by learning a direct mapping from objects to the distributions of grasping poses. However, because the physical contact is sensitive to small changes in pose, the high-nonlinear mapping between 3D object representation to valid poses is considerably non-smooth, leading to poor generation efficiency and restricted generality. To tackle the challenge, we introduce an intermediate variable for grasp contact areas to constrain the grasp generation; in other words, we factorize the mapping into two sequential stages by assuming that grasping poses are fully constrained given contact maps: 1) we first learn contact map distributions to generate the potential contact maps for grasps; 2) then learn a mapping from the contact maps to the grasping poses. Further, we propose a penetration-aware optimization with the generated contacts as a consistency constraint for grasp refinement. Extensive validations on two public datasets show that our method outperforms state-of-the-art methods regarding grasp generation on various metrics.
Steady-state and time-resolved fluorescence investigation of 2-pyridone and 3-pyridone in solution and their specific binding to human serum albumin

2-pyridone (2Py) and 3-pyridone (3Py) were examined in different solvents and their binding to human serum albumin (HSA) was studied using steady-state spectroscopy and time-resolved fluorescence. Solvation of 2Py and 3Py by water was examined in binary mixtures of 1,4-dioxane and water. Analysis of the absorption and fluorescence data reveals the solvation of the hydrogen bonding center in 2Py by one water molecule and in 3Py by three water molecules. A zwitterionic tautomer of 3Py is formed in water and shows distinct absorption peaks from the absorption of the neutral tautomer. Fluorescence of 3Py was observed in polar solvents only, whereas 2Py is fluorescent in polar and nonpolar solvents. The absorption and fluorescence spectra of 2Py in different solvents indicate less solute-solvent interaction in nonpolar solvents. This observation was confirmed by the measured longer fluorescence lifetime of 2Py in cyclohexane compared to that in water. The mechanism of binding of 2Py and 3Py as probe ligands to HSA was investigated by following the intensity change and lifetime of HSA fluorescence after excitation at 280 nm. The presence of 2Py and 3Py causes a reduction in the fluorescence intensity and lifetime of HSA. This observation indicates that subdomain IIA binding site (Sudlow site I) is the host of the probes and the reduction in the fluorescence of HSA is due to energy transfer from the Trp-214 residue to the probe in each case. The distance between Trp-214 and each of the probes was calculated using Förster theory for energy transfer to be 1.99 nm for HSA/2Py and 2.44 nm for HSA/3Py. The shorter distance in the former complex indicates more efficient energy transfer than in the latter. This was confirmed by estimating the quenching rate constant (kq) in each complex. kq was calculated to be 1.44 x 1012 M-1s-1 for HSA/2Py and 3.45 x 1011 M-1s-1 for HSA/3Py. The calculated distances and the kq values indicate a static quenching mechanism operative in the two complexes. The binding constants were estimated to be K = (3.4 ± 0.4) x 104 M-1 for the HSA/2Py complex and K = (2.3 ± 0.3) x 104 M-1 for the HSA/3Py complex. The number of binding sites of HSA was calculated to be one in both complexes. The latter results, along with the quenching results, indicate that both probes, 2Py and 3Py, bind only in Sudlow site I in subdomain IIA.
Rotation and scale invariant texture classification by compensating for distribution changes using covariate shift in uniform local binary patterns

A novel rotation and scale invariant texture classification methodology is proposed based on distribution matching in higher dimensional space. Feature extraction is performed by using uniform local binary patterns (uLBPs) in which the rotation and scale changes in an image cause shifts in the underlying uLBP histograms. To compensate for these shifts at the classification layer, the distributions of training and testing data using kernel methods are estimated and means of the two distributions in the transformed domain using importance weights are matched. These calculated importance weights are used in the standard support vector machines to compensate for the shift in the distributions. The proposed method is used for classifying the images in the Brodatz texture database demonstrating the effectiveness of the proposed methodology.
Epigenetic modification of CSDE1 locus dictates immune recognition of nascent tumorigenic cells

Weak immunogenicity of tumor cells is a root cause for the ultimate failure of immunosurveillance and immunotherapy. Although tumor evolution can be shaped by immunoediting toward a less immunogenic phenotype, mechanisms governing the initial immunogenicity of primordial tumor cells or original cancer stem cells remain obscure. Here, using a single tumor-repopulating cell (TRC) to form tumors in immunodeficient or immunocompetent mice, we demonstrated that immunogenic heterogeneity is an inherent trait of tumorigenic cells defined by the activation status of signal transducer and activator of transcription 1 (STAT1) protein in the absence of immune pressure. Subsequent investigation identified that the RNA binding protein cold shock domain–containing protein E1 (CSDE1) can promote STAT1 dephosphorylation by stabilizing T cell protein tyrosine phosphatase (TCPTP). A methyltransferase SET and MYN domain–containing 3 (SMYD3) was further identified to mediate H3K4 trimethylation of CSDE1 locus, which was under the regulation of mechanotransduction by cell-matrix and cell-cell contacts. Thus, owing to the differential epigenetic modification and subsequent differential expression of CSDE1, nascent tumorigenic cells may exhibit either a high or low immunogenicity. This identified SMYD3-CSDE1 pathway represents a potential prognostic marker for cancer immunotherapy effectiveness that requires further investigation. Description The SMYD3-CSDE1 axis regulates the immunogenicity of nascent tumor cells and presents as a target for tumor immunotherapy. Shocking the immune system One way tumors overcome immunosurveillance and immunotherapy is through their inherent lack of immunogenicity, however, the mechanisms underlying this are not well understood. Here, Lv et al. have utilized a single tumor-repopulating cell model in mice to demonstrate that immunogenic heterogeneity is regulated by signal transducer and activator of transcription 1 (STAT1) and cold shock domain containing protein E1 (CSDE1). CSDE1 is additionally regulated epigenetically by mechanotransduction and has the propensity to exhibit either high or low immunogenicity. Thus, CSDE1 expression could be a potential marker for cancer immunotherapy response that requires further investigation. — DH
Lyman-Alpha Clouds as a Relic of Primordial Density Fluctuations

Primordial density fluctuations are studied using a CDM model and primordial clouds some of which are expanding, driven by pressure gradients created when the medium is photionized, and some of which are massive enough to continue collapsing in spite of the pressure. Normalization of CDM models to the clustering properties on large scales are used to predict the parameters of collapsing clouds of subgalactic mass at early epochs. It is shown that the abundance and dimensions of these clouds are comparable to those of the Lyman-alpha systems. The evolutionary history of the clouds is computed, utilizing a spherically symmetric hydrodynamics code with the dark matter treated as a collisionless fluid, and the H I column density distribution is evaluated as a function of N(H I) and redshift. The observed cloud parameters come out naturally in the CDM model and suggest that Lyman-alpha clouds are the missing link between primordial density fluctuations and the formation of galaxies. 31 references.
Cardiac sympathetic denervation prevents sudden cardiac arrest and improves cardiac function by enhancing mitochondrial-antioxidant capacity

RATIONALE Sudden cardiac arrest (SCA) and heart failure (HF) are leading causes of death. The underlying mechanisms are incompletely understood, limiting the design of new therapies. Whereas most autonomic modulation therapies have not shown clear benefit in HF patients, growing evidence indicates cardiac sympathetic denervation (CSD) exerts cardioprotective effects. The underlying molecular and cellular mechanisms remain unexplored. OBJECTIVE Based on the hypothesis that mitochondrial reactive oxygen species (mROS) drive the pathogenesis of HF and SCA, we investigated whether CSD prevents SCA and HF by improving mitochondrial antioxidant capacity and redox balance, to correct impaired Ca2+ handling and repolarization reserve. METHODS AND RESULTS We interrogated CSD-specific responses in pressure-overload HF models with spontaneous SCA using in vivo echocardiographic and electrocardiographic studies and in vitro biochemical and functional studies including ratiometric measures of mROS, Ca2+ and sarcomere dynamics in left ventricular myocytes. Pressure-overloaded HF reduced mitochondrial antioxidant capacity and increased mROS, which impaired β-adrenergic signaling and caused SR Ca2+ leak, reducing SR Ca2+ and increasing diastolic Ca2+, impaired myofilament contraction and further increased the sympathetic stress response. CSD improved contractile function and mitigated mROS-mediated diastolic Ca2+ overload, dispersion of repolarization, triggered activity and SCA by upregulating mitochondrial antioxidant and NADPH-producing enzymes. CONCLUSIONS Our findings support a fundamental role of sympathetic stress-induced downregulation of mROS scavenging enzymes and RyR-leak mediated diastolic Ca2+ overload in HF and SCA pathogenesis that are mitigated by CSD. This first report on the molecular and cellular mechanisms of CSD supports its evaluation in additional high-risk patient groups. BRIEF SUMMARY Cardiac sympathetic denervation (CSD) confers cardioprotective effects via unclear mechanisms. In a guinea pig model that uniquely mimics human pressure-overload heart failure (HF) with spontaneous sudden cardiac arrest (SCA), we interrogated CSD-specific responses using echocardiographic, electrocardiographic and biochemical measures, and ratiometric measures of mitochondrial reactive oxygen species (mROS), Ca2+ and sarcomere dynamics. Consistent with our hypothesis, CSD rescued cardioprotection by upregulating mitochondrial antioxidant and NADPH-producing enzymes, which mitigate mROS-mediated Ca2+ derangements, repolarization lability, triggered activity, HF and SCA. Our findings provide the first molecular and cellular mechanistic basis for evaluating CSD therapy in a broader group of high-risk patients.
In Vitro Validation of Computationally Predicted Oncogenic Driver Mutations in EGFR Tyrosine Kinase Domain

Epidermal Growth Factor Receptor (EGFR) signaling is known to play essential roles in growth and development; nevertheless, overexpression and mutation of EGFR have been reported in several cancers. Non-small cell lung cancer (NSCLC), the most observed type of lung cancer, harbors the highest number of EGFR tyrosine kinase mutations and therefore, EGFR has become an important therapeutic target for treatment of these tumors. Tyrosine Kinase Inhibitors (TKIs) are found to be effective in patients whose tumors contain activating mutations in the tyrosine kinase region of the receptor. This would seem to be beneficial in the treatment of EGFR mutation-positive NSCLC patients but the activating mutations should be sensitive to TKIs. Earlier, a machine learning approach was developed to classify single amino acid polymorphisms (SAPs) in EGFR into driver (cancer-causing) and passenger (neutral) mutations using structural and functional features (Anoosha et al., 2015). This study screened all possible point mutations in EGFR and predicted a list of mutations with high probability of being a driver or a passenger. From this list, we selected 2 mutations (G729E and G719F) with high evolutionary conservation score for in vitro validation. If proven to be oncogenic drivers and sensitive to EGFR TKIs, these mutations can aid in the early diagnosis and successful therapy of EGFR mutation-positive NSCLC.