The Municipal Pardon Power

At the state and federal levels, the pardon power can be used to restore the dignity and legal rights lost by a criminal conviction. Unfortunately, those facing similar consequences from municipal convictions may not have access to a pardon. Although clemency is exceedingly rare at any level of government, municipal defendants face a unique structural problem that deprives them of the possibility of a pardon. Specifically, many cities have simply failed to create a local clemency power. This Note argues that the authority to grant pardons for municipal offenses is part of the toolbox of powers provided to cities through the doctrine of home rule. Accordingly, cities do not have to wait for the permission of their parent states to create a local clemency power. By failing to advocate for a local interpretation of clemency, cities are missing a valuable opportunity to help municipal defendants overcome the stigma and collateral consequences that accompany municipal convictions. While existing scholarship largely ignores the application of clemency to municipal law, this Note offers a legal framework for reimagining the next frontier of clemency

Differences in the genetic structure between and within two landlocked Ayu groups with different migration patterns in Lake Biwa revealed by environmental DNA analysis

Ayu (Plecoglossus altivelis) is largely an annual amphidromous fish, although a landlocked population lives in Lake Biwa, the largest lake in Japan. The landlocked population comprises two migrant groups, spring migrants and autumn migrants, which run to inlet rivers from the lake at different seasons. We used environmental DNA (eDNA) analysis, which is reported to be more sensitive and cost-effective than capture surveys, to clarify the genetic structure of this landlocked Ayu population with different migration patterns in Lake Biwa. We took water samples in 11 inlet rivers in the spring and autumn for 2 years in a row and quantitatively detected a total of 265 haplotypes of the mitochondrial D-loop region. The pairwise fixation index (FST) value and haplotype diversity indicated that there were genetic differences between the two migrant groups in their respective rivers, and the FST values were negatively related to latitude and the presence of artificial fish stocking. Additionally, isolation by distance within spring migrant group was observed when the lake was divided into the east and west sides. These findings show that the landlocked Ayu population in Lake Biwa has genetic structure associated with migration patterns and geographical distance. This study demonstrates that the eDNA approach will be effective for conducting a large-scale investigation of genetic structure beyond simple presence/absence tests

Decoder-only Architecture for Speech Recognition with CTC Prompts and Text Data Augmentation

Collecting audio-text pairs is expensive; however, it is much easier to access text-only data. Unless using shallow fusion, end-to-end automatic speech recognition (ASR) models require architecture modifications or additional training schemes to use text-only data. Inspired by recent advances in decoder-only language models (LMs), such as GPT-3 and PaLM adopted for speech-processing tasks, we propose using a decoder-only architecture for ASR with simple text augmentation. To provide audio information, encoder features compressed by CTC prediction are used as prompts for the decoder, which can be regarded as refining CTC prediction using the decoder-only model. Because the decoder architecture is the same as an autoregressive LM, it is simple to enhance the model by leveraging external text data with LM training. An experimental comparison using LibriSpeech and Switchboard shows that our proposed models with text augmentation training reduced word error rates from ordinary CTC by 0.3% and 1.4% on LibriSpeech test-clean and testother set, respectively, and 2.9% and 5.0% on Switchboard and CallHome. The proposed model had advantage on computational efficiency compared with conventional encoder-decoder ASR models with a similar parameter setup, and outperformed them on the LibriSpeech 100h and Switchboard training scenarios.Comment: Submitted to ICASSP202

Effect of soil profile structure on seasonal changes of soil temperature in urban forests

Field observations of soil temperature were carried out in an urban park to compare the characteristics of heat transfer in urban forest soils having different soil profile structures. The diurnal variation of soil temperature was significant down to 10 cm depth. Their patterns in the layers deeper than 30 cm differed by seasons and soil profile structures having contrasted soil properties. Temperature transmission to deeper layers was faster in the soil profile having stronger soil compaction and abundant artifacts than in the soil profile with weaker soil compaction and no artifacts. From February to April, the soil temperature was higher in the undisturbed profile, having lower soil pH (acidic), lower compaction, smaller bulk density, and larger carbon content, than in the lithological disturbed profile containing a large amount of concrete rubbles with higher soil pH (neutral to weak alkaline), higher compaction, larger bulk density, and smaller carbon content. The reverse trend appeared from mid-April to December. Moreover, the annual range of soil temperature was larger and occurred deeper in the lithological disturbed profile than in the undisturbed profile. Thermal diffusivity and thermal conductivity were 0.9-7.9 × 10^ cm^2 s^ and 0.20-1.85 Wm^K^ for the lithological disturbed profile, respectively. The values were smaller, 0.3-5.3 ×10^ cm^2 s^ and 0.02-0.45 Wm^K^, respectively for the undisturbed profile. Based on our two years observation, we conclude that the intensive soil compaction and lithological discontinuity regulate soil thermal properties of urban forests, by which soils may likely to be assigned to a higher soil temperature regime

Integrating Pretrained ASR and LM to Perform Sequence Generation for Spoken Language Understanding

There has been an increased interest in the integration of pretrained speech recognition (ASR) and language models (LM) into the SLU framework. However, prior methods often struggle with a vocabulary mismatch between pretrained models, and LM cannot be directly utilized as they diverge from its NLU formulation. In this study, we propose a three-pass end-to-end (E2E) SLU system that effectively integrates ASR and LM subnetworks into the SLU formulation for sequence generation tasks. In the first pass, our architecture predicts ASR transcripts using the ASR subnetwork. This is followed by the LM subnetwork, which makes an initial SLU prediction. Finally, in the third pass, the deliberation subnetwork conditions on representations from the ASR and LM subnetworks to make the final prediction. Our proposed three-pass SLU system shows improved performance over cascaded and E2E SLU models on two benchmark SLU datasets, SLURP and SLUE, especially on acoustically challenging utterances.Comment: Accepted at INTERSPEECH 202

Risk factors for anemia of prematurity among 30-35-week preterm infants

Background: The risk factors for anemia of prematurity (AOP) among late preterm infants are unelucidated. We identified risk factors for declining hemoglobin (Hb) concentration and triggering factors for AOP treatment in infants born at 30-35 gestational weeks. Methods: From 2012 to 2020, we conducted a single-center retrospective study of infants born at 30-35 weeks of gestation without congenital anomalies or severe hemorrhage. The primary outcome was AOP development, defined by initiation of treatments including red blood cell transfusion, subcutaneous injections of erythropoietin, and iron supplementation. A multivariable logistic regression model was used to investigate potential risk factors for AOP. Results: A total of 358 infants were included. Lower gestational age (odds ratio, 0.19; 95% confidence interval 0.11-0.32), small for gestational age (SGA; 7.17, 2.15-23.9), low maternal Hb level before birth (0.66, 0.49-0.87), low Hb at birth (0.71, 0.57-0.89), and multiple large blood samplings (1.79; 1.40-2.29) showed significantly higher odds for AOP development. Conclusions: Gestational age, SGA, low maternal Hb before birth, Hb at birth, and high number of large blood samplings were positively associated with AOP development in infants born at 30-35 gestational weeks

Electroencephalogram-Based Single-Trial Detection of Language Expectation Violations in Listening to Speech

We propose an approach for the detection of language expectation violations that occur in communication. We examined semantic and syntactic violations from electroencephalogram (EEG) when participants listened to spoken sentences. Previous studies have shown that such event-related potential (ERP) components as N400 and the late positivity (P600) are evoked in the auditory where semantic and syntactic anomalies occur. We used this knowledge to detect language expectation violation from single-trial EEGs by machine learning techniques. We recorded the brain activity of 18 participants while they listened to sentences that contained semantic and syntactic anomalies and identified the significant main effects of these anomalies in the ERP components. We also found that a multilayer perceptron achieved 59.5% (semantic) and 57.7% (syntactic) accuracies

The Pipeline System of ASR and NLU with MLM-based Data Augmentation toward STOP Low-resource Challenge

This paper describes our system for the low-resource domain adaptation track (Track 3) in Spoken Language Understanding Grand Challenge, which is a part of ICASSP Signal Processing Grand Challenge 2023. In the track, we adopt a pipeline approach of ASR and NLU. For ASR, we fine-tune Whisper for each domain with upsampling. For NLU, we fine-tune BART on all the Track3 data and then on low-resource domain data. We apply masked LM (MLM) -based data augmentation, where some of input tokens and corresponding target labels are replaced using MLM. We also apply a retrieval-based approach, where model input is augmented with similar training samples. As a result, we achieved exact match (EM) accuracy 63.3/75.0 (average: 69.15) for reminder/weather domain, and won the 1st place at the challenge.Comment: To be appeared at ICASSP202

Tensor decomposition for minimization of E2E SLU model toward on-device processing

Spoken Language Understanding (SLU) is a critical speech recognition application and is often deployed on edge devices. Consequently, on-device processing plays a significant role in the practical implementation of SLU. This paper focuses on the end-to-end (E2E) SLU model due to its small latency property, unlike a cascade system, and aims to minimize the computational cost. We reduce the model size by applying tensor decomposition to the Conformer and E-Branchformer architectures used in our E2E SLU models. We propose to apply singular value decomposition to linear layers and the Tucker decomposition to convolution layers, respectively. We also compare COMP/PARFAC decomposition and Tensor-Train decomposition to the Tucker decomposition. Since the E2E model is represented by a single neural network, our tensor decomposition can flexibly control the number of parameters without changing feature dimensions. On the STOP dataset, we achieved 70.9% exact match accuracy under the tight constraint of only 15 million parameters.Comment: Accepted by INTERSPEECH 202