Miracle-dz/UniFAR-FTUs huggingface dataset:
Relative Behavioral Attributes: Filling the Gap between Symbolic Goal Specification and Reward Learning from Human Preferences

Generating complex behaviors that satisfy the preferences of non-expert users is a crucial requirement for AI agents. Interactive reward learning from trajectory comparisons (a.k.a. RLHF) is one way to allow non-expert users to convey complex objectives by expressing preferences over short clips of agent behaviors. Even though this parametric method can encode complex tacit knowledge present in the underlying tasks, it implicitly assumes that the human is unable to provide richer feedback than binary preference labels, leading to intolerably high feedback complexity and poor user experience. While providing a detailed symbolic closed-form specification of the objectives might be tempting, it is not always feasible even for an expert user. However, in most cases, humans are aware of how the agent should change its behavior along meaningful axes to fulfill their underlying purpose, even if they are not able to fully specify task objectives symbolically. Using this as motivation, we introduce the notion of Relative Behavioral Attributes, which allows the users to tweak the agent behavior through symbolic concepts (e.g., increasing the softness or speed of agents' movement). We propose two practical methods that can learn to model any kind of behavioral attributes from ordered behavior clips. We demonstrate the effectiveness of our methods on four tasks with nine different behavioral attributes, showing that once the attributes are learned, end users can produce desirable agent behaviors relatively effortlessly, by providing feedback just around ten times. This is over an order of magnitude less than that required by the popular learning-from-human-preferences baselines. The supplementary video and source code are available at: https://guansuns.github. io/pages/rba.
Preferential Subsampling for Stochastic Gradient Langevin Dynamics

Stochastic gradient MCMC (SGMCMC) offers a scalable alternative to traditional MCMC, by constructing an unbiased estimate of the gradient of the log-posterior with a small, uniformly-weighted subsample of the data. While efficient to compute, the resulting gradient estimator may exhibit a high variance and impact sampler performance. The problem of variance control has been traditionally addressed by constructing a better stochastic gradient estimator, often using control variates. We propose to use a discrete, non-uniform probability distribution to preferentially subsample data points that have a greater impact on the stochastic gradient. In addition, we present a method of adaptively adjusting the subsample size at each iteration of the algorithm, so that we increase the subsample size in areas of the sample space where the gradient is harder to estimate. We demonstrate that such an approach can maintain the same level of accuracy while substantially reducing the average subsample size that is used.
Méthodes de surface de réponse basées sur la décomposition de la variance fonctionnelle et application à l'analyse de sensibilité

L'objectif de cette these est l'investigation de nouvelles methodes de surface de reponse afin de realiser l'analyse de sensibilite de modeles numeriques complexes et couteux en temps de calcul. Pour ce faire, nous nous sommes interesses aux methodes basees sur la decomposition ANOVA. Nous avons propose l'utilisation d'une methode basee sur les splines de lissage de type ANOVA, alliant procedures d'estimation et de selection de variables. L'etape de selection de variable peut devenir tres couteuse en temps de calcul, particulierement dans le cas d'un grand nombre de parametre d'entree. Pour cela nous avons developpe un algorithme de seuillage iteratif dont l'originalite reside dans sa simplicite d'implementation et son efficacite. Nous avons ensuite propose une methode directe pour estimer les indices de sensibilite. En s'inspirant de cette methode de surface de reponse, nous avons developpe par la suite une methode adaptee a l'approximation de modeles tres irreguliers et discontinus, qui utilise une base d'ondelettes. Ce type de methode a pour propriete une approche multi-resolution permettant ainsi une meilleure approximation des fonctions a forte irregularite ou ayant des discontinuites. Enfin, nous nous sommes penches sur le cas ou les sorties du simulateur sont des series temporelles. Pour ce faire, nous avons developpe une methodologie alliant la methode de surface de reponse a base de spline de lissage avec une decomposition en ondelettes. Afin d'apprecier l'efficacite des methodes proposees, des resultats sur des fonctions analytiques ainsi que sur des cas d'ingenierie de reservoir sont presentees.
High-harmonic generation in quantum spin systems

We theoretically study the high-harmonic generation (HHG) in one-dimensional spin systems. While in electronic systems the driving by ac electric fields produces radiation from the dynamics of excited charges, we consider here the situation where spin systems excited by a magnetic field pulse generate radiation via a time-dependent magnetization. Specifically, we study the magnetic dipole radiation in two types of ferromagnetic spin chain models, the Ising model with static longitudinal field and the XXZ model, and reveal the structure of the spin HHG and its relation to spin excitations. For weak laser amplitude, a peak structure appears which can be explained by time-dependent perturbation theory. With increasing amplitude, plateaus with well-defined cutoff energies emerge. In the Ising model with longitudinal field, the thresholds of the multiple plateaus in the radiation spectra can be explained by the annihilation of multiple magnons. In the XXZ model, which retains the Z2 symmetry, the laser magnetic field can induce a phase transition of the ground state when it exceeds a critical value, which results in a drastic change of the spin excitation character. As a consequence, the first cutoff energy in the HHG spectrum changes from a single-magnon to a two-magnon energy at this transition. Our results demonstrate the possibility of generating high-harmonic radiation from magnetically ordered materials and the usefulness of high-harmonic signals for extracting information on the spin excitation spectrum.
An OSSS-type inequality for uniformly drawn subsets of fixed size

The OSSS inequality [O'Donnell, Saks, Schramm and Servedio, 46th Annual IEEE Symposium on Foundations of Computer Science (FOCS'05), Pittsburgh (2005)] gives an upper bound for the variance of a function f of independent 0-1 valued random variables, in terms of the influences of these random variables and the computational complexity of a (randomised) algorithm for determining the value of f. Duminil-Copin, Raoufi and Tassion [Annals of Mathematics 189, 75-99 (2019)] obtained a generalization to monotonic measures and used it to prove new results for Potts models and random-cluster models. Their generalization of the OSSS inequality raises the question if there are still other measures for which a version of that inequality holds. We derive a version of the OSSS inequality for a family of measures that are far from monotonic, namely the k-out-of-n measures (these measures correspond with drawing k elements from a set of size n uniformly). We illustrate the inequality by studying the event that there is an occupied horizontal crossing of an R times R box on the triangular lattice in the site percolation model where exactly half of the vertices in the box are occupied.
Newton–Raphson Emulation Network for Highly Efficient Computation of Numerous Implied Volatilities

In finance, implied volatility is an important indicator that reflects the market situation immediately. Many practitioners estimate volatility by using iteration methods, such as the Newton–Raphson (NR) method. However, if numerous implied volatilities must be computed frequently, the iteration methods easily reach the processing speed limit. Therefore, we emulate the NR method as a network by using PyTorch, a well-known deep learning package, and optimize the network further by using TensorRT, a package for optimizing deep learning models. Comparing the optimized emulation method with the benchmarks, implemented in two popular Python packages, we demonstrate that the emulation network is up to 1000 times faster than the benchmark functions.
Are Leptons as elementary as Quarks?

Nucleons and electrons were once considered elementary particles, a role nowadays taken by quarks and leptons. Here, mainly at the group theoretical level, we examine the unorthodox idea that nucleons and electrons share the same level of compositeness after all. We do it by first trading color $SU(3)_C$ for color/leptocolor $SU(3)_C\times SU(3)_{\ell C}$ confining gauge symmetry. Standard model leptons, equipped with inherited Yukawa couplings, make then their appearance at the intermediate (gauge protected) $SU(3)_C\times SU(3)_L\times SU(3)_R$ trinification stage as three pre-lepton composites. The addition of an exclusively prescribed (on anomaly free and Pauli exclusion grounds) spontaneously broken $SU(3)_F$ horizontal link to the unification chain, in the spirit of"One three to rule them all", strikingly preserves the anti-symmetric structure of the single family fermionic wave function. The threefold quark/lepton (spectators + composites) flavor chiral representation is then necessarily supplemented by a trinification singlet composite Majorana neutrino. The scheme serendipitously predicts a novel anomaly free lepto/dark portal.
Completely Heterogeneous Federated Learning

Federated learning (FL) faces three major difficulties: cross-domain, heterogeneous models, and non-i.i. d. labels scenarios. Existing FL methods fail to handle the above three constraints at the same time, and the level of privacy protection needs to be lowered (e.g., the model architecture and data category distribution can be shared). In this work, we propose the challenging"completely heterogeneous"scenario in FL, which refers to that each client will not expose any private information including feature space, model architecture, and label distribution. We then devise an FL framework based on parameter decoupling and data-free knowledge distillation to solve the problem. Experiments show that our proposed method achieves high performance in completely heterogeneous scenarios where other approaches fail.
Target-Speaker Voice Activity Detection Via Sequence-to-Sequence Prediction

Target-speaker voice activity detection is currently a promising approach for speaker diarization in complex acoustic environments. This paper presents a novel Sequence-to-Sequence Target-Speaker Voice Activity Detection (Seq2Seq-TSVAD) method that can efficiently address the joint modeling of large-scale speakers and predict high-resolution voice activities. Experimental results show that larger speaker capacity and higher output resolution can significantly reduce the diarization error rate (DER), which achieves the new state-of-the-art performance of 4.55% on the VoxConverse test set and 10.77% on Track 1 of the DIHARD-III evaluation set under the widely-used evaluation metrics.
Provably training overparameterized neural network classifiers with non-convex constraints

: Training a classiﬁer under non-convex constraints has gotten increasing attention in the machine learning community thanks to its wide range of applications such as algorithmic fairness and class-imbalanced clas- siﬁcation. However, several recent works addressing non-convex constraints have only focused on simple models such as logistic regression or support vector machines. Neural networks, one of the most popular models for classiﬁcation nowadays, are precluded and lack theoretical guarantees. In this work, we show that overparameterized neural networks could achieve a near-optimal and near-feasible solution of non-convex constrained op- timization problems via the project stochastic gradient descent. Our key ingredient is the no-regret analysis of online learning for neural networks in the overparameterization regime, which may be of independent interest in online learning applications.
Mathematical model for coupling a quasi-unidimensional perfect flow

Nonlinear acoustics of wind instruments conducts to study unidimensional fluid flows. From physically relevant approximations that are modelized with the thin layer Navier Stokes equations, we propose a coupled model where perfect fluid flow is described by the Euler equations of gas dynamics and viscous and thermal boundary layer is modelized by a linear equation. We describe numerical discretization, validate the associated software by comparison with analytical solutions and consider musical application of strongly nonlinear waves in the trombone.
Magnetic fields in star-forming systems (I): idealized synthetic signatures of dust polarization and Zeeman splitting in filaments

We use the POLARIS radiative transport code to generate predictions of the two main observables directly sensitive to the magnetic field morphology and strength in filaments: dust polarization and gas Zeeman line splitting. We simulate generic gas filaments with power-law density profiles assuming two density-field strength dependencies, six different filament inclinations, and nine distinct magnetic field morphologies, including helical, toroidal, and warped magnetic field geometries. We present idealized spatially resolved dust polarization and Zeeman-derived field strengths and directions maps. Under the assumption that dust grains are aligned by radiative torques (RATs), dust polarization traces the projected plane-of-the-sky magnetic field morphology. Zeeman line splitting delivers simultaneously the intensity-weighted line-of-sight field strength and direction. We show that linear dust polarization alone is unable to uniquely constrain the 3D field morphology. We demonstrate that these ambiguities are ameliorated or resolved with the addition of the Zeeman directional information. Thus, observations of both the dust polarization and Zeeman splitting together provide the most promising means for obtaining constraints of the 3D magnetic field configuration. We find that the Zeeman-derived field strengths are at least a factor of a few below the input field strengths due to line-of-sight averaging through the filament density gradient. Future observations of both dust polarization and Zeeman splitting are essential for gaining insights into the role of magnetic fields in star and cluster forming filaments.
Gaia-DR2 extended kinematical maps

Context. The Gaia Collaboration has used Gaia-DR2 sources with six-dimensional (6D) phase space information to derive kinematical maps within 5 kpc of the Sun, which is a reachable range for stars with relative error in distance lower than 20%. Aims. Here we aim to extend the range of distances by a factor of two to three, thus adding the range of Galactocentric distances between 13 kpc and 20 kpc to the previous maps, with their corresponding error and root mean square values. Methods. We make use of the whole sample of stars of Gaia-DR2 including radial velocity measurements, which consists in more than seven million sources, and we apply a statistical deconvolution of the parallax errors based on the Lucy’s inversion method of the Fredholm integral equations of the first kind, without assuming any prior. Results. The new extended maps provide lots of new and corroborated information about the disk kinematics: significant departures of circularity in the mean orbits with radial Galactocentric velocities between −20 and +20 km s−1 and vertical velocities between −10 and +10 km s−1; variations of the azimuthal velocity with position; asymmetries between the northern and the southern Galactic hemispheres, especially towards the anticenter that includes a larger azimuthal velocity in the south; and others. Conclusions. These extended kinematical maps can be used to investigate the different dynamical models of our Galaxy, and we will present our own analyses in the forthcoming second part of this paper. At present, it is evident that the Milky Way is far from a simple stationary configuration in rotational equilibrium, but is characterized by streaming motions in all velocity components with conspicuous velocity gradients.
First-principles study of spontaneous polarization in multiferroic BiFeO 3

The ground-state structural and electronic properties of ferroelectric BiFeO 3 are calculated using density functional theory within the local spin-density approximation sLSDAd and the LSDA+U method. The crystal structure is computed to be rhombohedral with space group R3c, and the electronic structure is found to be insulating and antiferromagnetic, both in excellent agreement with available experiments. A large ferroelectric polarization of 90‐ 100 m C/c m 2 is predicted, consistent with the large atomic displacements in the ferroelectric phase and with recent experimental reports, but differing by an order of magnitude from early experiments. One possible explanation is that the latter may have suffered from large leakage currents. However, both past and contemporary measurements are shown to be consistent with the modern theory of polarization, suggesting that the range of reported polarizations may instead correspond to distinct switching paths in structural space. Modern measurements on well-characterized bulk samples are required to confirm this interpretation.
Divergence of the quadrupole-strain susceptibility of YbRu$_2$Ge$_2$; a local moment realization of electronic nematicity

Ferroquadrupole order associated with local $4f$ atomic orbitals of rare earth ions is a realization of electronic nematic order. However, there are relatively few examples of intermetallic materials which exhibit continuous ferroquadrupole phase transitions, motivating the search for additional materials that fall in to this category. Furthermore, it is not clear a priori whether experimental approaches based on transport measurements which have been successfully used to probe the nematic susceptibility in materials such as the Fe-based superconductors, will be as effective in the case of $4f$ intermetallic materials, for which the important electronic degrees of freedom are local rather than itinerant and are consequently less strongly coupled to the charge-carrying quasiparticles near the Fermi energy. In the present work, we demonstrate that the intermetallic compound YbRu$_2$Ge$_2$ exhibits a tetragonal-to-orthorhombic phase transition consistent with ferroquadrupole order of the Yb ions, and go on to show that elastoresistivity measurements can indeed provide a clear window on the diverging nematic susceptibility in this system. This material provides a new arena in which to study the causes and consequences of electronic nematicity.
Temperature Stress in Psychrophilic Green Microalgae: Mini-Review.

Photosynthetic algae are the main primary producers in polar regions, form the basis of polar food webs, and are responsible for a significant portion of global carbon fixation. Many cold-water algae are psychrophiles that thrive in the cold but cannot grow at moderate temperatures (≥20°C). Polar regions are at risk of rapid warming caused by climate change, and the sensitivity of psychrophilic algae to rising temperatures makes them, and the ecosystems they inhabit, particularly vulnerable. Recent research on the Antarctic psychrophile Chlamydomonas priscuii, an emerging algal model, has revealed unique adaptations to life in the permanent cold. Additionally, genome sequencing of C. priscuii and its relative Chlamydomonas sp. ICE-L has given rise to a plethora of computational tools that can help elucidate the genetic basis of psychrophily. This minireview summarizes new advances in characterizing the heat stress responses in psychrophilic algae and examines their extraordinary sensitivity to temperature increases. Further research in this field will help determine the impact of climate change on psychrophiles from threatened polar environments.
Kinetic time-curves can classify individuals in distinct levels of drop jump performance

ABSTRACT This study examined whether analysing kinetic features of drop jumps (DJ) as one-dimensional biomechanical curves can reveal specific patterns that are consistent and can cluster DJ performance. Hierarchical clustering analysis on DJ from 40 cm data performed by 128 physically active male participants (23.0 ± 4.5 yrs, 1.84 ± 0.07 m, 79.1 ± 10.8 kg) was performed on the derived time-normalised force, power and vertical stiffness curves to unmask the underlying patterns and to explore the dissimilarities identified from the subgroup (cluster) analysis. Results revealed poor, average and top DJ performers. Top performers exhibited larger peak force, power and vertical stiffness compared to the other two groups, and the poor performers had lower values compared to the average performers (p < .05). The time curves of force, power and vertical stiffness exhibited between cluster dissimilarities from ~25% to ~70%, and ~20% to 40% plus ~55% to 70% from the beginning of the ground contact, respectively. The force and power time-curves distinguished DJ ability similarly since they shared 69% of the cases in the top performers’ cluster. The content of cases (membership) for vertical stiffness was different from the membership for the force and power time-curve clusters. In conclusion, stiffness should be considered during plyometric training, but does not distinctly define DJ performance.
Challenging the 50‐50 rule for the basal‐bolus insulin ratio in patients with type 2 diabetes who maintain stable glycaemic control

For patients using basal‐bolus insulin therapy, it is widespread clinical practice to aim for a 50‐50 ratio between basal and total daily bolus. However, this practice was based on a small study of individuals without diabetes. To assess the rule in real‐world practice, we retrospectively analyzed patients on basal‐bolus therapy that was adjusted at least weekly by an artificial intelligence‐driven titration within the d‐Nav® Insulin Management Technology.
Reduction of heat stress pressure and activation of photosystem II repairing system are crucial for citrus tolerance to multiple abiotic stress combination

Abstract Drought, heat and high irradiance are abiotic stresses that negatively affect plant development and reduce crop productivity. The confluence of these three factors is common in nature, causing extreme situations for plants that compromise their viability. Drought and heat stresses increase the saturation of the photosystem reaction centers, increasing sensitivity to high irradiance. In addition, these stress conditions affect photosystem II (PSII) integrity, alter redox balance of the electron transport chain and decrease the photosynthetic rate. Here, we studied the effect of the stress combinations on the photosynthetic apparatus of two citrus genotypes, Carrizo citrange (Citrus sinensis × Poncirus trifoliata) and Cleopatra mandarin (Citrus reshni). Results obtained showed that physiological responses, such as modulation of stomatal aperture and transpiration rate, aimed to reduce leaf temperature, are key to diminishing heat impact on photosynthetic apparatus and increasing tolerance to double and triple combinations of drought, high irradiance and high temperatures. By using transcriptomic and proteomic analyses, we have demonstrated that under these abiotic stress combinations, Carrizo plants were able to increase expression of genes and proteins related to the photosystem repairing machinery (which better maintained the integrity of PSII) and other components of the photosynthetic apparatus. Our findings reveal crucial physiological and genetic responses in citrus to increase tolerance to the combination of multiple abiotic stresses that could be the basis for breeding programs that ensure a sustainable citrus production.