Miracle-dz/UniFAR-FTUs huggingface dataset:
Reflection of Thought: Inversely Eliciting Numerical Reasoning in Language Models via Solving Linear Systems

Numerical reasoning over natural language has been a long-standing goal for the research community. However, cutting-edge language models have proven difficult to reliably generalize to a broad range of numbers, although they have shown proficiency in reasoning over common and simple numbers. In this paper, we propose a novel method to elicit and exploit the numerical reasoning knowledge hidden in pre-trained language models using simple anchor numbers. Concretely, we first leverage simple numbers as anchors to probe the implicitly inferred arithmetic expressions from language models, and then explicitly apply the expressions on complex numbers to get corresponding answers. To inversely elicit arithmetic expressions, we transform and formulate the task as an analytically solvable linear system. Experimental results on several numerical reasoning benchmarks demonstrate that our approach significantly improves numerical reasoning capabilities of existing LMs. More importantly, our approach is training-free and simply works in the inference phase, making it highly portable and achieving consistent performance benefits across a variety of language models (GPT-3, T5, BART, etc) in all zero-shot, few-shot, and fine-tuning scenarios.
A Perception-Driven Approach To Immersive Remote Telerobotics

Virtual Reality (VR) interfaces are increasingly used as remote visualization media in telerobotics. Remote environments captured through RGB-D cameras and visualized using VR interfaces can enhance operators' situational awareness and sense of presence. However, this approach has strict requirements for the speed, throughput, and quality of the visualized 3D data.Further, telerobotics requires operators to focus on their tasks fully, requiring high perceptual and cognitive skills. This paper shows a work-in-progress framework to address these challenges by taking the human visual system (HVS) as an inspiration. Human eyes use attentional mechanisms to select and draw user engagement to a specific place from the dynamic environment. Inspired by this, the framework implements functionalities to draw users's engagement to a specific place while simultaneously reducing latency and bandwidth requirements.
Climate of High-obliquity Exoterrestrial Planets with a Three-dimensional Cloud System Resolving Climate Model

Planetary climates are strongly affected by planetary orbital parameters such as obliquity, eccentricity, and precession. In exoplanetary systems, exoterrestrial planets should have various obliquities. High-obliquity planets would have extreme seasonal cycles due to the seasonal change of the distribution of the insolation. Here, we introduce the Non-hydrostatic ICosahedral Atmospheric Model (NICAM), a global cloud-resolving model, to investigate the climate of high-obliquity planets. This model can explicitly simulate a three-dimensional cloud distribution and vertical transports of water vapor. We simulated exoterrestrial climates with high resolution using the supercomputer FUGAKU. We assumed aqua-planet configurations with 1 bar of air as a background atmosphere, with four different obliquities (0°, 23.5°, 45°, and 60°). We ran two sets of simulations: (1) low resolution (∼220 km mesh as the standard resolution of a general circulation model for exoplanetary science) with parameterization for cloud formation, and (2) high resolution (∼14 km mesh) with an explicit cloud microphysics scheme. Results suggest that high-resolution simulations with an explicit treatment of cloud microphysics reveal warmer climates due to less low cloud fraction and a large amount of water vapor in the atmosphere. It implies that treatments of cloud-related processes lead to a difference between different resolutions in climatic regimes in cases with high obliquities.
Glass transitions in 1, 2, 3, and 4 dimensional binary Lennard-Jones systems

We investigate the calorimetric liquid-glass transition by performing simulations of a binary Lennard-Jones mixture in one through four dimensions. Starting at a high temperature, the systems are cooled to T = 0 and heated back to the ergodic liquid state at constant rates. Glass transitions are observed in two, three and four dimensions as a hysteresis between the cooling and heating curves. This hysteresis appears in the energy and pressure diagrams, and the scanningrate dependence of the area and height of the hysteresis can be described by power laws. The one dimensional system does not experience a glass transition but its specific heat curve resembles the shape of the D ≥ 2 results in the supercooled liquid regime above the glass transition. As D increases, the radial distribution functions reflect reduced geometric constraints. Nearest-neighbor distances become smaller with increasing D due to interactions between nearest and next-nearest neighbors. Simulation data for the glasses are compared with crystal and melting data obtained with a Lennard-Jones system with only one type of particle and we find that with increasing D crystallization becomes increasingly more difficult.
Two-dimensional ferromagnetic semiconductors of rare-earth Janus 2 H -GdXY (X, Y=Cl, Br, I, X (cid:54) = Y) monolayers with large valley polarization

Two-dimensional (2D) ferromagnetic semiconductors show great potentials for their potential applications in next-generation information devices. In this work, using ﬁrst-principles calculations, the Janus 2 H -GdXY (X, Y = Cl, Br, I, X (cid:54) = Y) monolayers based on rare-earth element with 4 f 7 + 5 d 1 of Gd 2 + are predicted. The results suggest that the Janus 2 H -GdXY monolayers exhibit small exfoliation energy of less than 0.25 J/m 2 , excellent dynamical/thermal stabilities and ferromagnetic semiconductor characters with large spin-orbit coupling effect and high Curie temperatures (T c ) above 260 K based on the Monte Carlo simulations. Under proper tensile strains (5-6%), the transition from bipolar magnetic semiconductor (BMS) to half-semiconductor (HSC) phase is observed for the Janus 2 H -GdXY monolayers. In addition, both the 2 H -GdICl and 2 H -GdIBr monolayers show in-plane magnetic anisotropy (IMA) character, while the 2 H -GdBrCl monolayer exhibits perpendicular magnetic anisotropy (PMA) character, which can be attributed to the competition between Gd- p / d and halogen atom- p orbitals. Calculated valley optical actions of the Janus 2 H -GdXY monolayers exhibit distinguished valley-selective circular dichroisms, which is expected to realize the special valley excitation by polarized light. Spontaneously valley-Zeeman effect in the valance band for the Janus 2 H -GdXY monolayers induces a giant valley splitting of 60-120 meV, which is also robust against various external biaxial strains. Tunable valley degree of freedom in the Janus 2 H -GdXY systems is very necessary for encoding and processing information. This work paves the way to design ferro-valley materials for practical application in multifunctional valleytronic devices.
HiFECap: Monocular High-Fidelity and Expressive Capture of Human Performances

Monocular 3D human performance capture is indispensable for many applications in computer graphics and vision for enabling immersive experiences. However, detailed capture of humans requires tracking of multiple aspects, including the skeletal pose, the dynamic surface, which includes clothing, hand gestures as well as facial expressions. No existing monocular method allows joint tracking of all these components. To this end, we propose HiFECap, a new neural human performance capture approach, which simultaneously captures human pose, clothing, facial expression, and hands just from a single RGB video. We demonstrate that our proposed network architecture, the carefully designed training strategy, and the tight integration of parametric face and hand models to a template mesh enable the capture of all these individual aspects. Importantly, our method also captures high-frequency details, such as deforming wrinkles on the clothes, better than the previous works. Furthermore, we show that HiFECap outperforms the state-of-the-art human performance capture approaches qualitatively and quantitatively while for the first time capturing all aspects of the human.
Cost-Effectiveness Analysis for Therapy Sequence in Advanced Cancer: A Microsimulation Approach with Application to Metastatic Prostate Cancer

Purpose Patients with advanced cancer may undergo multiple lines of treatment, switching therapies as their disease progresses. We developed a general microsimulation framework to study therapy sequence and applied it to metastatic prostate cancer. Methods We constructed a discrete-time state transition model to study 2 lines of therapy. Using digitized published survival curves (progression-free survival, time to progression, and overall survival [OS]), we inferred event types (progression or death) and estimated transition probabilities using cumulative incidence functions with competing risks. We incorporated within-patient dependence over time; first-line therapy response informed subsequent event probabilities. Parameters governing within-patient dependence calibrated the model-based results to a target clinical trial. We applied these methods to 2 therapy sequences for metastatic prostate cancer, wherein both docetaxel (DCT) and abiraterone acetate (AA) are appropriate for either first- or second-line treatment. We assessed costs and quality-adjusted life-years (5-y QALYs) for 2 treatment strategies: DCT → AA versus AA → DCT. Results Models assuming within-patient independence overestimated OS time, which corrected with the calibration approach. With generic pricing, AA → DCT dominated DCT → AA, (higher 5-y QALYs and lower costs), consistent for all values of calibration parameters (including no correction). Model calibration increased the difference in 5-y QALYs between treatment strategies (0.07 uncorrected v. 0.15 with base-case correction). Applying the correction decreased the estimated difference in cost (−$5,360 uncorrected v. −$3,066 corrected). Results were strongly affected by the cost of AA. Under a lifetime horizon, AA → DCT was no longer dominant but still cost-effective (incremental cost-effectiveness ratio: $19,463). Conclusions We demonstrate a microsimulation approach to study the cost-effectiveness of therapy sequences for advanced prostate cancer, taking care to account for within-patient dependence. Highlights We developed a discrete-time state transition model for studying therapy sequence in advanced cancers. Results are sensitive to dependence within patients. A calibration approach can introduce dependence across lines of therapy and closely match simulation outcomes to target trial outcomes.
Bayesian analysis of mixtures of lognormal distribution with an unknown number of components from grouped data

This study proposes a reversible jump Markov chain Monte Carlo method for estimating parameters of lognormal distribution mixtures for income. Using simulated data examples, we examined the proposed algorithm's performance and the accuracy of posterior distributions of the Gini coefficients. Results suggest that the parameters were estimated accurately. Therefore, the posterior distributions are close to the true distributions even when the different data generating process is accounted for. Moreover, promising results for Gini coefficients encouraged us to apply our method to real data from Japan. The empirical examples indicate two subgroups in Japan (2020) and the Gini coefficients' integrity.
General relativistic simulations of collapsing binary neutron star mergers with Monte Carlo neutrino transport

Recent gravitational wave observations of neutron star-neutron star and neutron star-black hole binaries appear to indicate that massive neutron stars may not be too uncommon in merging systems. In this manuscript, we present a first set of evolution of massive neutron star binaries using Monte-Carlo radiation transport for the evolution of neutrinos. We study a range of systems, from nearly symmetric binaries that collapse to a black hole before forming a disk or ejecting material, to more asymmetric binaries in which tidal disruption of the lower mass star leads to the production of more interesting post-merger remnants. For the latter type of systems, we additionally study the impact of viscosity on the properties of the outflows, and compare our results to two recent simulations of identical binaries performed with the WhiskyTHC code. We find agreement on the black hole properties, disk mass, and mass and velocity of the outflows within expected numerical uncertainties, and some minor but noticeable differences in the evolution of the electron fraction when using a subgrid viscosity model, with viscosity playing a more minor role in our simulations. The method used to account for r-process heating in the determination of the outflow properties appears to have a larger impact on our result than those differences between numerical codes. We also use the simulation with the most ejected material to verify that our newly implemented Lagrangian tracers provide a reasonable sampling of the matter outflows as they leave the computational grid. We note that, given the lack of production of hot outflows in these mergers, the main role of neutrinos in these systems is to set the composition of the post-merger remnant. One of the main potential use of our simulations is thus as improved initial conditions for longer evolutions of such remnants.
Weakly-Supervised Optical Flow Estimation for Time-of-Flight

Indirect Time-of-Flight (iToF) cameras are a widespread type of 3D sensor, which perform multiple captures to obtain depth values of the captured scene. While recent approaches to correct iToF depths achieve high performance when removing multi-path-interference and sensor noise, little research has been done to tackle motion artifacts. In this work we propose a training algorithm, which allows to supervise Optical Flow (OF) networks directly on the reconstructed depth, without the need of having ground truth flows. We demonstrate that this approach enables the training of OF networks to align raw iToF measurements and compensate motion artifacts in the iToF depth images. The approach is evaluated for both single- and multi-frequency sensors as well as multi-tap sensors, and is able to outperform other motion compensation techniques.
Analog Cavity Emulators to Support LLRF Development

The goal of a LLRF system is to control an actual RF cavity with beam. While digital simulations have a place, having an analog circuit to stand in for the cavity can be tremen-dously helpful in validating hardware+firmware+software under development. A wide range of cavity emulators have been developed in collaboration with SLAC, and LBNL. Cavity emulators are typically based on quartz crystals and frequency conversion hardware. The choice of crystal frequency and coupling mechanism depends in part on the bandwidth and coupling of the cavity it’s intended to emulate. Examples of bandwidth range from 800 Hz (SLAC) as a stand-in for a SRF cavity, to 31 kHz (LBNL) for a room-temperature accumulator-ring cavity. An external LO is used to tune the emulated cavity frequency. The coupling properties are also of interest if the scope includes emulat-ing reverse power waveforms. LLRF system checks such as closed-loop bandwidth, and determining cavity detuning can be performed interactively and as part of a Continuous Integration (CI) process. This paper describes the design, implementation, and performance of the cavity emulators.
Natural Language Processing for Cognitive Analysis of Emotions

Emotion analysis in texts suffers from two major limitations: annotated gold-standard corpora are mostly small and homogeneous, and emotion identification is often simplified as a sentence-level classification problem. To address these issues, we introduce a new annotation scheme for exploring emotions and their causes, along with a new French dataset composed of autobiographical accounts of an emotional scene. The texts were collected by applying the Cognitive Analysis of Emotions developed by A. Finkel to help people improve on their emotion management. The method requires the manual analysis of an emotional event by a coach trained in Cognitive Analysis. We present a rule-based approach to automatically annotate emotions and their semantic roles (e.g. emotion causes) to facilitate the identification of relevant aspects by the coach. We investigate future directions for emotion analysis using graph structures.
Regret Analysis of the Stochastic Direct Search Method for Blind Resource Allocation

Motivated by programmatic advertising optimization, we consider the task of sequentially allocating budget across a set of resources. At every time step, a feasible allocation is chosen and only a corresponding random return is observed. The goal is to maximize the cumulative expected sum of returns. This is a realistic model for budget allocation across subdivisions of marketing campaigns, when the objective is to maximize the number of conversions. We study direct search (aka pattern search) methods for linearly constrained and derivative-free optimization in the presence of noise. Those algorithms are easy to implement and particularly suited to constrained optimization. They have not yet been analyzed from the perspective of cumulative regret. We provide a regret upper-bound of the order of T 2/3 in the general case. Our mathematical analysis also establishes, as a by-product, time-independent regret bounds in the deterministic, unconstrained case. We also propose an improved version of the method relying on sequential tests to accelerate the identification of descent directions.
Spectroscopic follow-up of statistically selected extremely metal-poor star candidates from GALAH DR3

The advent of large-scale stellar spectroscopic surveys naturally leads to the implementation of machine learning techniques to isolate, for example, small sub-samples of potentially interesting stars from the full data set. A recent example is the application of the t-SNE statistical method to ∼600,000 stellar spectra from the GALAH survey in order to identify a sample of candidate extremely metal-poor (EMP, [Fe/H] ≤ –3) stars. We report the outcome of low-resolution spectroscopic follow-up of 83 GALAH EMP candidates that lack any previous metallicity estimates. Overall, the statistical selection is found to be efficient (∼one-third of the candidates have [Fe/H] ≤ –2.75) with low contamination (<10 per cent have [Fe/H] > –2), and with a metallicity distribution function that is consistent with previous work. Five stars are found to have [Fe/H] ≤ –3.0, one of which is a main sequence turnoff star. Two other stars are revealed as likely carbon-enhanced metal-poor (CEMP) stars of type CEMP-s, and a known carbon star is re-identified. The results indicate that the statistical selection approach employed was successful, and therefore it can be applied to forthcoming even larger stellar spectroscopic surveys with the expectation of similar positive outcomes.
FreGAN: Exploiting Frequency Components for Training GANs under Limited Data

Training GANs under limited data often leads to discriminator overfitting and memorization issues, causing divergent training. Existing approaches mitigate the overfitting by employing data augmentations, model regularization, or attention mechanisms. However, they ignore the frequency bias of GANs and take poor consideration towards frequency information, especially high-frequency signals that contain rich details. To fully utilize the frequency information of limited data, this paper proposes FreGAN, which raises the model's frequency awareness and draws more attention to producing high-frequency signals, facilitating high-quality generation. In addition to exploiting both real and generated images' frequency information, we also involve the frequency signals of real images as a self-supervised constraint, which alleviates the GAN disequilibrium and encourages the generator to synthesize adequate rather than arbitrary frequency signals. Extensive results demonstrate the superiority and effectiveness of our FreGAN in ameliorating generation quality in the low-data regime (especially when training data is less than 100). Besides, FreGAN can be seamlessly applied to existing regularization and attention mechanism models to further boost the performance.
Distributed-Memory Randomized Algorithms for Sparse Tensor CP Decomposition

Sparse Candecomp / PARAFAC decomposition, a generalization of the matrix singular value decomposition to higher-dimensional tensors, is a popular tool for analyzing diverse datasets. On tensors with billions of nonzero entries, computing a CP decomposition is a computationally intensive task. We propose the first distributed-memory implementations of two randomized CP decomposition algorithms, CP-ARLS-LEV and STS-CP, that offer nearly an order-of-magnitude speedup at high decomposition ranks over well-tuned non-randomized decomposition packages. Both algorithms rely on leverage score sampling and enjoy strong theoretical guarantees, each with varying time and accuracy tradeoffs. We tailor the communication schedule for our random sampling algorithms, eliminating expensive reduction collectives and forcing communication costs to scale with the random sample count. Finally, we optimize the local storage format for our methods, switching between an analogue of compressed sparse column and compressed sparse row formats to facilitate both random sampling and efficient parallelization of sparse-dense matrix multiplication. Experiments show that our methods are fast and scalable, producing 11x speedup over SPLATT to compute a decomposition of the billion-scale Reddit tensor on 512 CPU cores in under 2 minutes.
Discovered Policy Optimisation

Tremendous progress has been made in reinforcement learning (RL) over the past decade. Most of these advancements came through the continual development of new algorithms, which were designed using a combination of mathematical derivations, intuitions, and experimentation. Such an approach of creating algorithms manually is limited by human understanding and ingenuity. In contrast, meta-learning provides a toolkit for automatic machine learning method optimisation, potentially addressing this flaw. However, black-box approaches which attempt to discover RL algorithms with minimal prior structure have thus far not outperformed existing hand-crafted algorithms. Mirror Learning, which includes RL algorithms, such as PPO, offers a potential middle-ground starting point: while every method in this framework comes with theoretical guarantees, components that differentiate them are subject to design. In this paper we explore the Mirror Learning space by meta-learning a"drift"function. We refer to the immediate result as Learnt Policy Optimisation (LPO). By analysing LPO we gain original insights into policy optimisation which we use to formulate a novel, closed-form RL algorithm, Discovered Policy Optimisation (DPO). Our experiments in Brax environments confirm state-of-the-art performance of LPO and DPO, as well as their transfer to unseen settings.
Regret Bounds for Risk-Sensitive Reinforcement Learning

In safety-critical applications of reinforcement learning such as healthcare and robotics, it is often desirable to optimize risk-sensitive objectives that account for tail outcomes rather than expected reward. We prove the first regret bounds for reinforcement learning under a general class of risk-sensitive objectives including the popular CVaR objective. Our theory is based on a novel characterization of the CVaR objective as well as a novel optimistic MDP construction.