Symmetry Analysis and Exact Solutions to the Space-Dependent Coefficient PDEs in Finance

The variable-coefficients partial differential equations (vc-PDEs) in finance are investigated by Lie symmetry analysis and the generalized power series method. All of the geometric vector fields of the equations are obtained; the symmetry reductions and exact solutions to the equations are presented, including the exponentiated solutions and the similarity solutions. Furthermore, the exact analytic solutions are provided by the transformation technique and generalized power series method, which has shown that the combination of Lie symmetry analysis and the generalized power series method is a feasible approach to dealing with exact solutions to the variable-coefficients PDEs

Disentangled Generative Causal Representation Learning

This paper proposes a Disentangled gEnerative cAusal Representation (DEAR) learning method. Unlike existing disentanglement methods that enforce independence of the latent variables, we consider the general case where the underlying factors of interests can be causally correlated. We show that previous methods with independent priors fail to disentangle causally correlated factors. Motivated by this finding, we propose a new disentangled learning method called DEAR that enables causal controllable generation and causal representation learning. The key ingredient of this new formulation is to use a structural causal model (SCM) as the prior for a bidirectional generative model. The prior is then trained jointly with a generator and an encoder using a suitable GAN loss incorporated with supervision. We provide theoretical justification on the identifiability and asymptotic consistency of the proposed method, which guarantees disentangled causal representation learning under appropriate conditions. We conduct extensive experiments on both synthesized and real data sets to demonstrate the effectiveness of DEAR in causal controllable generation, and the benefits of the learned representations for downstream tasks in terms of sample efficiency and distributional robustness

Mitigating the Alignment Tax of RLHF

LLMs acquire a wide range of abilities during pre-training, but aligning LLMs under Reinforcement Learning with Human Feedback (RLHF) can lead to forgetting, which is also known as the alignment tax. To empirically verify this hypothesis, we conducted experiments with existing RLHF algorithms using OpenLLaMA-3B, which revealed a pronounced alignment tax in NLP tasks. On the other hand, despite various techniques to mitigate forgetting, they are often at odds with the RLHF performance, leading to a trade-off between reward maximization and forgetting mitigation. In light of the above pressing issue in aligning LLMs, in this paper we explore model averaging, which interpolates between pre and post RLHF model weights, to achieve a more efficient reward-tax Pareto front. To understand its effectiveness, We offer theoretical insights into model averaging, revealing that it enhances performance Pareto front by increasing feature diversity on the layers where tasks share overlapped feature spaces. Empirical evidence corroborates our analysis by showing the benefits of averaging low-level transformer layers. Building on the analysis and the observation that averaging different layers of the transformer leads to significantly different reward-tax trade-offs, we propose Adaptive Model Averaging (AMA) to adaptively find various combination ratios of model layers. AMA seeks to maximize the alignment reward while incurring minimal alignment tax. Moreover, we validate AMA's performance across a range of RLHF algorithms over OpenLLaMA-3B and further extend our findings to Mistral-7B.Comment: 28 Page

Spin torque resonant vortex core expulsion for an efficient radio-frequency detection scheme

Spin-polarised radio-frequency currents, whose frequency is equal to that of the gyrotropic mode, will cause an excitation of the core of a magnetic vortex confined in a magnetic tunnel junction. When the excitation radius of the vortex core is greater than that of the junction radius, vortex core expulsion is observed, leading to a large change in resistance, as the layer enters a predominantly uniform magnetisation state. Unlike the conventional spin-torque diode effect, this highly tunable resonant effect will generate a voltage which does not decrease as a function of rf power, and has the potential to form the basis of a new generation of tunable nanoscale radio-frequency detectors

Equivalent transformation and integrability of the nonlinear Schrödinger equations with time-dependent coefficients

The nonlinear Schrödinger (NLS) types of equations play a key role in quantum mechanics, Quantum communication and physical applications. However, how to deal with explicit solutions and other properties of the NLS equations, especially for the variable-coefficient NLS (vc-NLS) types of equations is a difficult problem. In this paper, we construct the form-preserving equivalent transformations (ETs) to transform the vc-NLS systems into constant-coefficient NLS (cc-NLS) systems, and the form-preserving ETs are given explicitly. Then, based on the equivalent transformation method, we deal with the integrability of the NLS equations, and the Lax pairs (LPs) are provided as verification of the integrability

Improving web search personalization using Luhn-Inspired vector re-weighting

Web search personalization has been studied as a way to tailor Web search results to individual users based on their interests and preferences. Commonly, document and personalization profile features are stored in vector space models using measures such as term frequency (TF) and term frequency-inverse document frequency (TF*IDF). Inspired by Luhn's model of term importance, a novel approach is proposed in this thesis to identify and re-weight significant terms in the vector-based personalization models. Evaluations with a set of ambiguous queries illustrate that the order of the search results using this approach is superior to the TF approach and comparable to the TF*IDF approach. However, it is based only on the information stored in the personalization profiles, rather than requiring access to the distribution of each term across the document collection. As such, it can be applied more broadly when only limited information regarding the collection being searched is available