Periodic input response of a second-order digital filter with two’s complement arithmetic

The dynamic behaviors of a nonlinear second-order digital filter with two’s complement arithmetic under periodic inputs are explored. The conditions for avoiding overflow are derived. Various dynamic periodic responses are analyzed, accompanied by numerous simulation examples

Fertilizer Use in China: The Role of Agricultural Support Policies

Using a decomposition method, this paper proposes an analytical framework to investigate the mechanisms by which agricultural support policies affect farmers’ use of fertilizers in agriculture in China. The mechanisms are decomposed into “three effects” (structural, scale, and technological effects). It is found that China’s agricultural support polices have significantly contributed to the increased use of agricultural fertilizers through encouraging farmers to bring more land under cultivation (the scale effect). Meanwhile, some policies have also helped reduce fertilizer consumption when farmers were motivated to increase the area of grains crops (the structural effect). The role of technological progress in affecting fertilizer consumption (the technological effect) appears to be minimal and uncertain. Compared to direct subsidies, indirect subsidies play a much greater role in affecting farmers’ production decision making and are more environmentally consequential. This paper argues that some of China’s agricultural support policies are not well aligned with one key objective of the country’s rural policies—improving environmental sustainability. It is recommended that the government takes measures to reform agricultural support policies and to reconcile agricultural and rural policies in order to achieve sustainable rural development

Transfer Learning and Bias Correction with Pre-trained Audio Embeddings

Deep neural network models have become the dominant approach to a large variety of tasks within music information retrieval (MIR). These models generally require large amounts of (annotated) training data to achieve high accuracy. Because not all applications in MIR have sufficient quantities of training data, it is becoming increasingly common to transfer models across domains. This approach allows representations derived for one task to be applied to another, and can result in high accuracy with less stringent training data requirements for the downstream task. However, the properties of pre-trained audio embeddings are not fully understood. Specifically, and unlike traditionally engineered features, the representations extracted from pre-trained deep networks may embed and propagate biases from the model's training regime. This work investigates the phenomenon of bias propagation in the context of pre-trained audio representations for the task of instrument recognition. We first demonstrate that three different pre-trained representations (VGGish, OpenL3, and YAMNet) exhibit comparable performance when constrained to a single dataset, but differ in their ability to generalize across datasets (OpenMIC and IRMAS). We then investigate dataset identity and genre distribution as potential sources of bias. Finally, we propose and evaluate post-processing countermeasures to mitigate the effects of bias, and improve generalization across datasets.Comment: 7 pages, 3 figures, accepted to the conference of the International Society for Music Information Retrieval (ISMIR 2023

Differentially Private Stochastic Convex Optimization in (Non)-Euclidean Space Revisited

In this paper, we revisit the problem of Differentially Private Stochastic Convex Optimization (DP-SCO) in Euclidean and general $\ell_p^d$ spaces. Specifically, we focus on three settings that are still far from well understood: (1) DP-SCO over a constrained and bounded (convex) set in Euclidean space; (2) unconstrained DP-SCO in $\ell_p^d$ space; (3) DP-SCO with heavy-tailed data over a constrained and bounded set in $\ell_p^d$ space. For problem (1), for both convex and strongly convex loss functions, we propose methods whose outputs could achieve (expected) excess population risks that are only dependent on the Gaussian width of the constraint set rather than the dimension of the space. Moreover, we also show the bound for strongly convex functions is optimal up to a logarithmic factor. For problems (2) and (3), we propose several novel algorithms and provide the first theoretical results for both cases when $1<p<2$ and $2\leq p\leq \infty$

Rational Interface Design for High-Performance All-Solid-State Lithium Batteries

All-solid-state lithium batteries (ASSLBs) have gained substantial attention owing to their excellent safety and high energy density. However, the development of ASSLBs has been hindered by large interfacial resistance originating from the detrimental interfacial reactions, poor solid-solid contact, and lithium dendrite growth. The research in this thesis aims at achieving high-performance ASSLBs via rational interface design and understanding the interfacial reaction mechanisms. At the cathode interface, an ideal dual core-shell nanostructure was first designed. Moreover, single-crystal LiNi0.5Mn0.3Co0.2O2 (SC-NMC532) cathode was compared with polycrystalline NMC532, the former exhibits much enhanced Li+ diffusion kinetics in ASSLIBs. Besides, it is found that the interfacial structural degradation significantly impedes interfacial Li+ transport in ASSLIBs. Fortunately, the interfacial coating is demonstrated to be effective in suppressing interfacial degradation. Furthermore, the ionic conductivity of interfacial layer LNTO was purposely tuned to investigate the effect of interfacial ionic conductivity on ASSLIBs, it is revealed that enhancing the interfacial ionic conductivity is very crucial for high-performance ASSLBs. The conclusion was confirmed by the in-situ growth of Li3InCl6. The high Li+-conductive Li3InCl6 coated LCO demonstrates an ultra-small interfacial resistance of 0.13 W.cm-2 and excellent electrochemical performance. At the anode interface, an inorganic-organic hybrid interlayer and a solid-state plastic crystal electrolyte were successfully engineered to prevent the interfacial reactions and lithium dendrite formation. Last but not least, a solid-liquid hybrid electrolyte was developed as interfacial solid-liquid electrolyte interphase (SLEI) to achieve high-performance ASSLBs. In summary, the discoveries in this thesis provide important guidance to achieve high-performance ASSLBs via rational interface design

Scattering Transform for Playing Technique Recognition

Playing techniques are expressive elements in music performances that carry important information about music expressivity and interpretation. When displaying playing techniques in the time–frequency domain, we observe that each has a distinctive spectro-temporal pattern. Based on the patterns of regularity, we group commonly-used playing techniques into two families: pitch modulation-based techniques (PMTs) and pitch evolution-based techniques (PETs). The former are periodic modulations that elaborate on stable pitches, including vibrato, tremolo, trill, and flutter-tongue; while the latter contain monotonic pitch changes, such as acciaccatura, portamento, and glissando. In this thesis, we present a general framework based on the scattering transform for playing technique recognition. We propose two variants of the scattering transform, the adaptive scattering and the direction-invariant joint scattering. The former provides highly-compact representations that are invariant to pitch transpositions for representing PMTs. The latter captures the spectro-temporal patterns exhibited by PETs. Using the proposed scattering representations as input, our recognition system achieves start-of-the-art results. We provide a formal interpretation of the role of each scattering component confirmed by explanatory visualisations. Whereas previously published datasets for playing technique analysis focused primarily on techniques recorded in isolation, we publicly release a new dataset to evaluate the proposed framework. The dataset, named CBFdataset, is the first dataset on the Chinese bamboo flute (CBF), containing full-length CBF performances and expert annotations of playing techniques. To provide evidence on the generalisability of the proposed framework, we test it over three additional datasets with a variety of playing techniques. Finally, to explore the applicability of the proposed scattering representations to general audio classification problems, we introduce two additional applications: one applies the adaptive scattering for identifying performers in polyphonic orchestral music and the other uses the joint scattering for detecting and classifying chick calls