Tabular data is ubiquitous in many real-life systems. In particular,
time-dependent tabular data, where rows are chronologically related, is
typically used for recording historical events, e.g., financial transactions,
healthcare records, or stock history. Recently, hierarchical variants of the
attention mechanism of transformer architectures have been used to model
tabular time-series data. At first, rows (or columns) are encoded separately by
computing attention between their fields. Subsequently, encoded rows (or
columns) are attended to one another to model the entire tabular time-series.
While efficient, this approach constrains the attention granularity and limits
its ability to learn patterns at the field-level across separate rows, or
columns. We take a first step to address this gap by proposing Fieldy, a
fine-grained hierarchical model that contextualizes fields at both the row and
column levels. We compare our proposal against state of the art models on
regression and classification tasks using public tabular time-series datasets.
Our results show that combining row-wise and column-wise attention improves
performance without increasing model size. Code and data are available at
this https URL

Tabular data is commonly used for recording historical events, and this research proposes a fine-grained hierarchical model that contextualizes fields at both the row and column levels to improve performance on regression and classification tasks.

分层 Transformer 中的细粒度注意力用于表格时间序列

Fine-grained Attention in Hierarchical Transformers for Tabular  Time-series

Explainable Artificial Intelligence (XAI) is a paradigm that delivers
transparent models and decisions, which are easy to understand, analyze, and
augment by a non-technical audience. Fuzzy Logic Systems (FLS) based XAI can
provide an explainable framework, while also modeling uncertainties present in
real-world environments, which renders it suitable for applications where
explainability is a requirement. However, most real-life processes are not
characterized by high levels of uncertainties alone; they are inherently
time-dependent as well, i.e., the processes change with time. In this work, we
present novel Temporal Type-2 FLS Based Approach for time-dependent XAI (TXAI)
systems, which can account for the likelihood of a measurement's occurrence in
the time domain using (the measurement's) frequency of occurrence. In Temporal
Type-2 Fuzzy Sets (TT2FSs), a four-dimensional (4D) time-dependent membership
function is developed where relations are used to construct the inter-relations
between the elements of the universe of discourse and its frequency of
occurrence. The TXAI system manifested better classification prowess, with
10-fold test datasets, with a mean recall of 95.40\% than a standard XAI system
(based on non-temporal general type-2 (GT2) fuzzy sets) that had a mean recall
of 87.04\%. TXAI also performed significantly better than most non-explainable
AI systems between 3.95\%, to 19.04\% improvement gain in mean recall. In
addition, TXAI can also outline the most likely time-dependent trajectories
using the frequency of occurrence values embedded in the TXAI model; viz. given
a rule at a determined time interval, what will be the next most likely rule at
a subsequent time interval. In this regard, the proposed TXAI system can have
profound implications for delineating the evolution of real-life time-dependent
processes, such as behavioural or biological processes.

本研究提出了基于时域的模糊逻辑系统的可解释性人工智能方法，可以更好地解释现实世界中的不确定性，并在分类方面表现出更好的性能。此系统在处理时间依赖数据时，可以追踪时间相关的事件，从而可以在生物学和社会科学等真实世界的庞大过程中具有潜在的应用。

一种用于时依赖可解释人工智能的时间类型 - 2 模糊系统

A Temporal Type-2 Fuzzy System for Time-dependent Explainable Artificial Intelligence

Recovering an unknown Hamiltonian from measurements is an increasingly
important task for certification of noisy quantum devices and simulators.
Recent works have succeeded in recovering the Hamiltonian of an isolated
quantum system with local interactions from long-ranged correlators of a single
eigenstate. Here, we show that such Hamiltonians can be recovered from local
observables alone, using computational and measurement resources scaling
linearly with the system size. In fact, to recover the Hamiltonian acting on
each finite spatial domain, only observables within that domain are required.
The observables can be measured in a Gibbs state as well as a single
eigenstate; furthermore, they can be measured in a state evolved by the
Hamiltonian for a long time, allowing to recover a large family of
time-dependent Hamiltonians. We derive an estimate for the statistical recovery
error due to approximation of expectation values using a finite number of
samples, which agrees well with numerical simulations.

本研究提出了一种可以通过局部可观测量从吉布斯态或特定本征态中恢复出有限空间内的时变哈密顿量的方法，并且证明了该方法关于统计恢复误差的理论是正确的。

从本地测量中学习局部哈密顿量

Learning a local Hamiltonian from local measurements

Existing sequence prediction methods are mostly concerned with
time-independent sequences, in which the actual time span between events is
irrelevant and the distance between events is simply the difference between
their order positions in the sequence. While this time-independent view of
sequences is applicable for data such as natural languages, e.g., dealing with
words in a sentence, it is inappropriate and inefficient for many real world
events that are observed and collected at unequally spaced points of time as
they naturally arise, e.g., when a person goes to a grocery store or makes a
phone call. The time span between events can carry important information about
the sequence dependence of human behaviors. In this work, we propose a set of
methods for using time in sequence prediction. Because neural sequence models
such as RNN are more amenable for handling token-like input, we propose two
methods for time-dependent event representation, based on the intuition on how
time is tokenized in everyday life and previous work on embedding
contextualization. We also introduce two methods for using next event duration
as regularization for training a sequence prediction model. We discuss these
methods based on recurrent neural nets. We evaluate these methods as well as
baseline models on five datasets that resemble a variety of sequence prediction
tasks. The experiments revealed that the proposed methods offer accuracy gain
over baseline models in a range of settings.

本文提出了一组用于时间序列预测的方法，包括两种基于时间的事件表示方法和两种使用下一个事件持续时间作为正则化的方法，并通过基于 RNN 的实验评估，证明这些方法可以提高准确性。