According to Chomsky (1981, 1986), empty categories consist of PRO, pro, trace, and variable. However, some empty object positions seem to be incompatible with extant empty categories. Given this, Li (2007a, 2007b, 2014) and Li & Wei (2014) raise the true empty category hypothesis, which holds that true empty category is only an empty position with category and Case features. As a last resort option, it is used mainly to meet the subcatgorization of a verb. This assumption is ingenious, and if proved to be true, it will exert a great impact on the study of UG. In this paper, we evaluate their evidence from topicalization and demonstrate that it can be accounted for without invoking true empty category.

Thermoelectric coolers (TECs) offer a promising solution for direct cooling of local hotspots and active thermal management in advanced electronic systems. However, TECs present significant trade-offs among spatial cooling, heating and power consumption. The optimization of TECs requires extensive simulations, which are impractical for managing actual systems with multiple hotspots under spatial and temporal variations. In this study, we present a novel machine learning-assisted optimization algorithm for thermoelectric coolers that can achieve global optimal temperature by individually controlling TEC units based on real-time multi-hotspot conditions across the entire domain. We train a convolutional neural network (CNN) with a combination of the Inception module and multi-task learning (MTL) approach to comprehend the coupled thermal-electrical physics underlying the system and attain accurate predictions for both temperature and power consumption with and without TECs. Due to the intricate interaction among passive thermal gradient, Peltier effect and Joule effect, a local optimal TEC control experiences spatial temperature trade-off which may not lead to a global optimal solution. To address this issue, we develop a backtracking-based optimization algorithm using the machine learning model to iterate all possible TEC assignments for attaining global optimal solutions. For any m by n matrix with NHS hotspots (n, m <= 10, 0<= NHS <= 20), our algorithm is capable of providing 52.4% peak temperature reduction and its corresponding TEC array control within an average of 1.64 seconds while iterating through tens of temperature predictions behind-the-scenes. This represents a speed increase of over three orders of magnitude compared to traditional FEM strategies which take approximately 27 minutes.

Speech emotion recognition aims to identify and analyze emotional states in target speech similar to humans. Perfect emotion recognition can greatly benefit a wide range of human-machine interaction tasks. Inspired by the human process of understanding emotions, we demonstrate that compared to quantized modeling, understanding speech content from a continuous perspective, akin to human-like comprehension, enables the model to capture more comprehensive emotional information. Additionally, considering that humans adjust their perception of emotional words in textual semantic based on certain cues present in speech, we design a novel search space and search for the optimal fusion strategy for the two types of information. Experimental results further validate the significance of this perception adjustment. Building on these observations, we propose a novel framework called Multiple perspectives Fusion Architecture Search (MFAS). Specifically, we utilize continuous-based knowledge to capture speech semantic and quantization-based knowledge to learn textual semantic. Then, we search for the optimal fusion strategy for them. Experimental results demonstrate that MFAS surpasses existing models in comprehensively capturing speech emotion information and can automatically adjust fusion strategy.

This week NVIDIA, LXAI, and Tecnológico de Monterrey announced the upcoming launch of the AI Supercomputer Network in collaboration with Hub de IA del Tec de Monterrey. Many countries in Latin America including Mexico, Brazil, Perú, Chile, and Colombia, are developing their national AI strategies but many LATAM universities and research centers struggle to access high-performance computing due to high prices, low government investment in research, and limited international collaborations. This initiative aims to address these challenges by providing an international network of state of the art GPUs for LATAM access. The official launch will be in Guadalajara on August 9th. The objective of this initiative is to strengthen the capacities of the AI ecosystem in Latin America including developing and attracting AI experts, building technological infrastructure, boosting international collaboration, and providing easy access to public data.

Emotion classification in text is typically performed with neural network models which learn to associate linguistic units with emotions. While this often leads to good predictive performance, it does only help to a limited degree to understand how emotions are communicated in various domains. The emotion component process model (CPM) by Scherer (2005) is an interesting approach to explain emotion communication. It states that emotions are a coordinated process of various subcomponents, in reaction to an event, namely the subjective feeling, the cognitive appraisal, the expression, a physiological bodily reaction, and a motivational action tendency. We hypothesize that these components are associated with linguistic realizations: an emotion can be expressed by describing a physiological bodily reaction ("he was trembling"), or the expression ("she smiled"), etc. We annotate existing literature and Twitter emotion corpora with emotion component classes and find that emotions on Twitter are predominantly expressed by event descriptions or subjective reports of the feeling, while in literature, authors prefer to describe what characters do, and leave the interpretation to the reader. We further include the CPM in a multitask learning model and find that this supports the emotion categorization. The annotated corpora are available at https://www.ims.uni-stuttgart.de/data/emotion.

Tensor (multidimensional array) classification problem has become very popular in modern applications such as image recognition and high dimensional spatio-temporal data analysis. Support Tensor Machine (STM) classifier, which is extended from the support vector machine, takes CANDECOMP / Parafac (CP) form of tensor data as input and predicts the data labels. The distribution-free and statistically consistent properties of STM highlight its potential in successfully handling wide varieties of data applications. Training a STM can be computationally expensive with high-dimensional tensors. However, reducing the size of tensor with a random projection technique can reduce the computational time and cost, making it feasible to handle large size tensors on regular machines. We name an STM estimated with randomly projected tensor as Random Projection-based Support Tensor Machine (RPSTM). In this work, we propose a Tensor Ensemble Classifier (TEC), which aggregates multiple RPSTMs for big tensor classification. TEC utilizes the ensemble idea to minimize the excessive classification risk brought by random projection, providing statistically consistent predictions while taking the computational advantage of RPSTM. Since each RPSTM can be estimated independently, TEC can further take advantage of parallel computing techniques and be more computationally efficient. The theoretical and numerical results demonstrate the decent performance of TEC model in high-dimensional tensor classification problems. The model prediction is statistically consistent as its risk is shown to converge to the optimal Bayes risk. Besides, we highlight the trade-off between the computational cost and the prediction risk for TEC model. The method is validated by extensive simulation and a real data example. We prepare a python package for applying TEC, which is available at our GitHub.

Although it does most of its work behind the scenes, from car navigation to computer assistants to movie recommendations to your social media feeds, artificial intelligence (AI) is becoming a deeper part of our everyday lives. The public may not know exactly what it is, but they do think AI will have a big impact on their lives and the U.S. economy, a new poll finds. At a recent event hosted by the U.S. Chamber's Technology Engagement Center (C_TEC), TheBridge, and Dcode42, C_TEC shared the results of a poll they recently conducted with Morning Consult. More than half of the respondents admitted that they knew "not much" or "nothing at all" about Artificial Intelligence. One-in-three said they didn't know there was a difference between machine learning and AI. However, despite being unfamiliar with the technology's details, 68% of respondents indicated they thought AI would be at least somewhat common in the next 5 years, and 29% described AI as inevitable.