Miracle-dz/UniFAR-FTUs huggingface dataset:
Tell Your Model Where to Attend: Post-hoc Attention Steering for LLMs

In human-written articles, we often leverage the subtleties of text style, such as bold and italics, to guide the attention of readers. These textual emphases are vital for the readers to grasp the conveyed information. When interacting with large language models (LLMs), we have a similar need - steering the model to pay closer attention to user-specified information, e.g., an instruction. Existing methods, however, are constrained to process plain text and do not support such a mechanism. This motivates us to introduce PASTA - Post-hoc Attention STeering Approach, a method that allows LLMs to read text with user-specified emphasis marks. To this end, PASTA identifies a small subset of attention heads and applies precise attention reweighting on them, directing the model attention to user-specified parts. Like prompting, PASTA is applied at inference time and does not require changing any model parameters. Experiments demonstrate that PASTA can substantially enhance an LLM's ability to follow user instructions or integrate new knowledge from user inputs, leading to a significant performance improvement on a variety of tasks, e.g., an average accuracy improvement of 22% for LLAMA-7B. Our code is publicly available at https://github.com/QingruZhang/PASTA .
A relaxation approach to the minimisation of the neo-Hookean energy in 3D

Despite its high significance in nonlinear elasticity, the neo-Hookean energy is still not known to admit minimisers in some appropriate admissible class. Using ideas from relaxation theory, we propose a larger minimisation space and a modified functional that coincides with the neo-Hookean energy on the original space. This modified energy is the sum of the neo-Hookean energy and a term penalising the singularities of the inverse deformation. The new functional attains its minimum in the larger space, so the initial question of existence of minimisers of the neo-Hookean energy is thus transformed into a question of regularity of minimisers of this new energy.
Collision Avoidance using Iterative Dynamic and Nonlinear Programming with Adaptive Grid Refinements

Nonlinear optimal control problems for trajectory planning with obstacle avoidance present several challenges. While general-purpose optimizers and dynamic programming methods struggle when adopted separately, their combination enabled by a penalty approach was found capable of handling highly nonlinear systems while overcoming the curse of dimensionality. Nevertheless, using dynamic programming with a fixed state space discretization limits the set of reachable solutions, hindering convergence or requiring enormous memory resources for uniformly spaced grids. In this work we solve this issue by incorporating an adaptive refinement of the state space grid, splitting cells where needed to better capture the problem structure while requiring less discretization points overall. Numerical results on a space manipulator demonstrate the improved robustness and efficiency of the combined method with respect to the single components.
Temporal Shift - Multi-Objective Loss Function for Improved Anomaly Fall Detection

Falls are a major cause of injuries and deaths among older adults worldwide. Accurate fall detection can help reduce potential injuries and additional health complications. Different types of video modalities can be used in a home setting to detect falls, including RGB, Infrared, and Thermal cameras. Anomaly detection frameworks using autoencoders and their variants can be used for fall detection due to the data imbalance that arises from the rarity and diversity of falls. However, the use of reconstruction error in autoencoders can limit the application of networks' structures that propagate information. In this paper, we propose a new multi-objective loss function called Temporal Shift, which aims to predict both future and reconstructed frames within a window of sequential frames. The proposed loss function is evaluated on a semi-naturalistic fall detection dataset containing multiple camera modalities. The autoencoders were trained on normal activities of daily living (ADL) performed by older adults and tested on ADLs and falls performed by young adults. Temporal shift shows significant improvement to a baseline 3D Convolutional autoencoder, an attention U-Net CAE, and a multi-modal neural network. The greatest improvement was observed in an attention U-Net model improving by 0.20 AUC ROC for a single camera when compared to reconstruction alone. With significant improvement across different models, this approach has the potential to be widely adopted and improve anomaly detection capabilities in other settings besides fall detection.
Error bounds for the approximation of matrix functions with rational Krylov methods

We obtain an expression for the error in the approximation of $f(A) \boldsymbol{b}$ and $\boldsymbol{b}^T f(A) \boldsymbol{b}$ with rational Krylov methods, where $A$ is a symmetric matrix, $\boldsymbol{b}$ is a vector and the function $f$ admits an integral representation. The error expression is obtained by linking the matrix function error with the error in the approximate solution of shifted linear systems using the same rational Krylov subspace, and it can be exploited to derive both a priori and a posteriori error bounds. The error bounds are a generalization of the ones given in [T. Chen, A. Greenbaum, C. Musco, C. Musco, SIAM J. Matrix Anal. Appl. , 43 (2022), pp. 787--811] (arXiv:2106.09806) for the Lanczos method for matrix functions. A technique that we employ in the rational Krylov context can also be applied to refine the bounds for the Lanczos case.
Feature Attribution Explanations for Spiking Neural Networks

Third-generation artificial neural networks, Spiking Neural Networks (SNNs), can be efficiently implemented on hardware. Their implementation on neuromorphic chips opens a broad range of applications, such as machine learning-based autonomous control and intelligent biomedical devices. In critical applications, however, insight into the reasoning of SNNs is important, thus SNNs need to be equipped with the ability to explain how decisions are reached. We present Temporal Spike Attribution (TSA), a local explanation method for SNNs. To compute the explanation, we aggregate all information available in model-internal variables: spike times and model weights. We evaluate TSA on artificial and real-world time series data and measure explanation quality w.r. t. multiple quantitative criteria. We find that TSA correctly identifies a small subset of input features relevant to the decision (i.e., is output-complete and compact) and generates similar explanations for similar inputs (i.e., is continuous). Further, our experiments show that incorporating the notion of absent spikes improves explanation quality. Our work can serve as a starting point for explainable SNNs, with future implementations on hardware yielding not only predictions but also explanations in a broad range of application scenarios. Source code is available at https://github.com/ElisaNguyen/tsa-explanations.
Nonlinear Modeling for Soft Pneumatic Actuators via Data-Driven Parameter Estimation

Precise modeling soft robots remains a challenge due to their infinite-dimensional nature governed by partial differential equations. This paper introduces an innovative approach for modeling soft pneumatic actuators, employing a nonlinear framework through data-driven parameter estimation. The research begins by introducing Ludwick's Law, providing a accurate representation of the large deflections exhibited by soft materials. Three key material properties, namely Young's modulus, tensile stress, and mixed viscosity, are utilized to estimate the parameters inside the nonlinear model using the least squares method. Subsequently, a nonlinear dynamic model for soft actuators is constructed by applying Ludwick's Law. To validate the accuracy and effectiveness of the proposed method, several experiments are performed demonstrating the model's capabilities in predicting the dynamic behavior of soft pneumatic actuators. In conclusion, this work contributes to the advancement of soft pneumatic actuator modeling that represents their nonlinear behavior.
Interlayer bias effect on time-reversal symmetry breaking in twisted bilayer cuprates

We study a one-band Hubbard model of twisted bilayer cuprates with a twist angle of $53.13^\circ$. By introducing an inter-layer bias, we simulate heterobilayers of different dopings. Using the variational cluster approximation (VCA) we probe the effect of this bias on the time-reversal-symmetry (TRS) breaking phase. Doping differences between layers affect the region where TRS-breaking occurs; we construct a phase diagram mapping out the TRS-breaking phase in the $n_1$-$n_2$ plane, $n_\ell$ being the electron density on layer $\ell$. We also map the spontaneous supercurrent on the same plane.
Approximation of Classical Two-Phase Flows of Viscous Incompressible Fluids by a Navier-Stokes/Allen-Cahn System

We show convergence of the Navier-Stokes/Allen-Cahn system to a classical sharp interface model for the two-phase flow of two viscous incompressible fluids with same viscosities in a smooth bounded domain in two and three space dimensions as long as a smooth solution of the limit system exists. Moreover, we obtain error estimates with the aid of a relative entropy method. Our results hold provided that the mobility $m_\varepsilon>0$ in the Allen-Cahn equation tends to zero in a subcritical way, i.e., $m_\varepsilon= m_0 \varepsilon^\beta$ for some $\beta\in (0,2)$ and $m_0>0$. The proof proceeds by showing via a relative entropy argument that the solution to the Navier-Stokes/Allen-Cahn system remains close to the solution of a perturbed version of the two-phase flow problem, augmented by an extra mean curvature flow term $m_\varepsilon H_{\Gamma_t}$ in the interface motion. In a second step, it is easy to see that the solution to the perturbed problem is close to the original two-phase flow.
PainSeeker: An Automated Method for Assessing Pain in Rats Through Facial Expressions

In this letter, we aim to investigate whether laboratory rats' pain can be automatically assessed through their facial expressions. To this end, we began by presenting a publicly available dataset called RatsPain, consisting of 1,138 facial images captured from six rats that underwent an orthodontic treatment operation. Each rat' facial images in RatsPain were carefully selected from videos recorded either before or after the operation and well labeled by eight annotators according to the Rat Grimace Scale (RGS). We then proposed a novel deep learning method called PainSeeker for automatically assessing pain in rats via facial expressions. PainSeeker aims to seek pain-related facial local regions that facilitate learning both pain discriminative and head pose robust features from facial expression images. To evaluate the PainSeeker, we conducted extensive experiments on the RatsPain dataset. The results demonstrate the feasibility of assessing rats' pain from their facial expressions and also verify the effectiveness of the proposed PainSeeker in addressing this emerging but intriguing problem. The RasPain dataset can be freely obtained from https://github.com/xhzongyuan/RatsPain.
Edge2Node: Reducing Edge Prediction to Node Classification

Despite the success of graph neural network models in node classification, edge prediction (the task of predicting missing or potential links between nodes in a graph) remains a challenging problem for these models. A common approach for edge prediction is to first obtain the embeddings of two nodes, and then a predefined scoring function is used to predict the existence of an edge between the two nodes. Here, we introduce a preliminary idea called Edge2Node which suggests to directly obtain an embedding for each edge, without the need for a scoring function. This idea wants to create a new graph H based on the graph G given for the edge prediction task, and then suggests reducing the edge prediction task on G to a node classification task on H. We anticipate that this introductory method could stimulate further investigations for edge prediction task.
Flexible Multi-Generator Model with Fused Spatiotemporal Graph for Trajectory Prediction

Trajectory prediction plays a vital role in automotive radar systems, facilitating precise tracking and decision-making in autonomous driving. Generative adversarial networks with the ability to learn a distribution over future trajectories tend to predict out-of-distribution samples, which typically occurs when the distribution of forthcoming paths comprises a blend of various manifolds that may be disconnected. To address this issue, we propose a trajectory prediction framework, which can capture the social interaction variations and model disconnected manifolds of pedestrian trajectories. Our framework is based on a fused spatiotemporal graph to better model the complex interactions of pedestrians in a scene, and a multi-generator architecture that incorporates a flexible generator selector network on generated trajectories to learn a distribution over multiple generators. We show that our framework achieves state-of-the-art performance compared with several baselines on different challenging datasets.
Interfacial Thermal Transport and Electrical Performances of Supercapacitors with Graphene/Carbon Nanotube Composite Electrodes

Advanced supercapacitors have great potential to transform how we store and utilize energy, leading to more efficient and sustainable energy systems. This study reveals the structural features influencing the interfacial thermal transport and electrical performances of supercapacitors, using the constant potential and constant charge molecular dynamics simulation techniques. Thermal and electrical properties were calculated for graphene/carbon nanotube composite electrodes and ionic liquid electrolytes with different nanotube diameter, number, layers, and alignments of the nanotubes. The effect of application of a constant potential on the Kapitza resistance is determined for the first time. The vertically aligned CNT structures exhibited higher electrical performance, while the horizontal arrangement showed better thermal performance. Optimum electrode configurations were identified by considering thermal and electrical performance, along with other design factors, such as structural stability, ease of manufacturing, and scalability. After considering all these factors, the horizontally stacked multi-layer CNT arrangement emerged as the optimal electrode structure. The insights gained from this study aid in comprehending the effects of variations in electrode structure, thereby enabling efficient supercapacitor electrode design.
Single-Source Shortest Paths with Negative Real Weights in Õ(mn8/9) Time

This paper presents a randomized algorithm for the problem of single-source shortest paths on directed graphs with real (both positive and negative) edge weights. Given an input graph with $n$ vertices and $m$ edges, the algorithm completes in $\tilde{O}(mn^{8/9})$ time with high probability. For real-weighted graphs, this result constitutes the first asymptotic improvement over the classic $O(mn)$-time algorithm variously attributed to Shimbel, Bellman, Ford, and Moore.
Suppression of water vapor condensation by glycerol droplets on hydrophobic surfaces

Vapor sink is an effective strategy to suppress the formation of water vapor condensation around hygroscopic materials, and consequently reduce frosting and icing. However, traditionally used materials, such as salt solutions, fibers, exhibit insufficient condensation inhibition or pose safety concerns, such as corrosiveness. In this study, we highlight the remarkable anti-condensation properties of glycerol droplets, attributed to their strong hygroscopicity. We compared the anti-condensation capabilities of glycerol droplets with commonly used salt solutions and hygroscopic alcohols. The results indicated that glycerol droplets establish a relatively expansive dry zone where the condensation is effectively inhibited, while also offering safety compared to other materials. Furthermore, we conducted a systematic study of the anti-condensation properties of glycerol droplets with experiments and theoretical analysis. We explored the varying trends of the dry zone ratio concerning temperature, cooling time, humidity, and droplet volume on hydrophobic surfaces. To provide a comprehensive understanding, we propose a straightforward yet robust theoretical model that elucidates the relationship between this ratio and temperature, aligning well with the experimental data. Our study not only sheds light on the superior anti-condensation qualities of glycerol but also offers insights and guidelines for the development of effective anti-icing and anti-frost materials.
Watermelon production on stored rainwater in Sahelian sandy soils

Watermelon [Citrullus lanatus (Thumb) Matsun and Nakai ] is an important cash crop in West Africa where it is cultivated under rainfed conditions. The objective of this work was to identify best cultural practices for production of watermelons in the Sahel on stored rainwater in acid sandy soils. The experiments were carried out at the ICRISAT Sadore research center in Niger during two consecutive dry seasons, 2003 - 2004 and 2004 - 2005. Three soil management treatments were applied: microcatchments (also called half-moons), planting pits (also called zai) and sowing on flat land. Each of these three treatments came with and without a soil amendment comprising of 500 g of manure mixed with 24 g of a complete (NPK) fertilizer (15-15-15) individually applied to each planting hill. Two watermelon cultivars were tested: ‘Malali’ and ‘Kaolack’. In each of the two years the experiments were sown on the 1st and on the 21st of September. Fruit and biomass yield, fruit Total Soluble Solids (TSS), days to fruiting and harvesting were determined. Soil fertility, root development and other physiological parameters were monitored to explain some of the differences between treatments. Soil amendments increased marketable yields from 1.3 to 3.5 tons ha-1 on average. Marketable yields at the first planting date were double the yields of the second planting date (3.2 vs. 1.6 tons ha-1). Yield differences were due to changes in fruit number not in fruit weight. Deep placement of soil amendments resulted in significant root development in deeper soil layers. Highest watermelon yields were achieved when sowing the Malali cultivar in amended planting pits on September 1st giving a yield of 8.2 tons ha-1.
Dissipative quadratizations of polynomial ODE systems

Quadratization refers to a transformation of an arbitrary system of polynomial ordinary differential equations to a system with at most quadratic right-hand side. Such a transformation unveils new variables and model structures that facilitate model analysis, simulation, and control and offers a convenient parameterization for data-driven approaches. Quadratization techniques have found applications in diverse fields, including systems theory, fluid mechanics, chemical reaction modeling, and mathematical analysis. In this study, we focus on quadratizations that preserve the stability properties of the original model, specifically dissipativity at given equilibria. This preservation is desirable in many applications of quadratization including reachability analysis and synthetic biology. We establish the existence of dissipativity-preserving quadratizations, develop an algorithm for their computation, and demonstrate it in several case studies.
Parity violation in gravitational waves and observational bounds from third-generation detectors

In this paper, we analyze parity-violating effects in the propagation of gravitational waves (GWs). For this purpose, we adopt a newly proposed parametrized post-Einstenian (PPE) formalism, which encodes modified gravity corrections to the phase and amplitude of GW waveforms. In particular, we focus our study on three well-known examples of parity-violating theories, namely Chern-Simons, Symmetric Teleparallel and Hor\v ava-Lishitz gravity. For each model, we identify the PPE parameters emerging from the inclusion of parity-violating terms in the gravitational Lagrangian. Thus, we use the simulated sensitivities of third-generation GW interferometers, such as the Einstein Telescope and Cosmic Explorer, to obtain numerical bounds on the PPE coefficients and the physical parameters of binary systems. In so doing, we find that deviations from General Relativity cannot be excluded within given confidence limits. Moreover, our results show an improvement of one order of magnitude in the relative accuracy of the GW parameters compared to the values inferred from the LIGO-Virgo-KAGRA network. In this respect, the present work demonstrates the power of next-generation GW detectors to probe fundamental physics with unprecedented precision.
FocusTune: Tuning Visual Localization through Focus-Guided Sampling

We propose FocusTune, a focus-guided sampling technique to improve the performance of visual localization algorithms. FocusTune directs a scene coordinate regression model towards regions critical for 3D point triangulation by exploiting key geometric constraints. Specifically, rather than uniformly sampling points across the image for training the scene coordinate regression model, we instead re-project 3D scene coordinates onto the 2D image plane and sample within a local neighborhood of the re-projected points. While our proposed sampling strategy is generally applicable, we showcase FocusTune by integrating it with the recently introduced Accelerated Coordinate Encoding (ACE) model. Our results demonstrate that FocusTune both improves or matches state-of-the-art performance whilst keeping ACE's appealing low storage and compute requirements, for example reducing translation error from 25 to 19 and 17 to 15 cm for single and ensemble models, respectively, on the Cambridge Landmarks dataset. This combination of high performance and low compute and storage requirements is particularly promising for applications in areas like mobile robotics and augmented reality. We made our code available at \url{https://github.com/sontung/focus-tune}.
DISCO-DJ I: a differentiable Einstein-Boltzmann solver for cosmology

We present the Einstein-Boltzmann module of the DISCO-DJ (DIfferentiable Simulations for COsmology - Done with JAX) software package. This module implements a fully differentiable solver for the linearised cosmological Einstein-Boltzmann equations in the JAX framework, and allows computing Jacobian matrices of all solver output with respect to all input parameters using automatic differentiation. This implies that along with the solution for a given set of parameters, the tangent hyperplane in parameter space is known as well, which is a key ingredient for cosmological inference and forecasting problems as well as for many other applications. We discuss our implementation and demonstrate that our solver agrees at the per-mille level with the existing non-differentiable solvers CAMB and CLASS, including massive neutrinos and a dark energy fluid with parameterised equation of state. We illustrate the dependence of various summary statistics in large-scale structure cosmology on model parameters using the differentiable solver, and finally demonstrate how it can be easily used for Fisher forecasting. Since the implementation is significantly shorter and more modular than existing solvers, it is easy to extend our solver to include additional physics, such as additional dark energy models, modified gravity, or other non-standard physics.