United States

Data uncertainty is commonly observed in the images for face
recognition (FR). However, deep learning algorithms often make
predictions with high confidence even for uncertain or irrelevant
inputs. Intuitively, FR algorithms can benefit from both the estimation of uncertainty and the detection of out-of-distribution (OOD)
samples. Taking a probabilistic view of the current classification
model, the temperature scalar is exactly the scale of uncertainty
noise implicitly added in the softmax function. Meanwhile, the
uncertainty of images in a dataset should follow a prior distribution. Based on the observation, a unified framework for uncertainty
modeling and FR, Random Temperature Scaling (RTS), is proposed
to learn a reliable FR algorithm. The benefits of RTS are two-fold.
(1) In the training phase, it can adjust the learning strength of clean
and noisy samples for stability and accuracy. (2) In the test phase, it
can provide a score of confidence to detect uncertain, low-quality
and even OOD samples, without training on extra labels. Extensive
experiments on FR benchmarks demonstrate that the magnitude of
variance in RTS, which serves as an OOD detection metric, is closely
related to the uncertainty of the input image. RTS can achieve top
performance on both the FR and OOD detection tasks. Moreover,
the model trained with RTS can perform robustly on datasets with
noise. The proposed module is light-weight and only adds negligible
computation cost to the training and test processes.

AAAI 2023

Improving Training and Inference of Face Recognition Models via Random Temperature Scaling

"ml: deep neural network algorithms"

gesture & pose"

"cv: biometrics

"ml: calibration & uncertainty quantification"

face

#### Welcome to the 62nd IEEE International Reliability Physics Symposium (IRPS)! 
**The 2024 IEEE International Reliability Physics Symposium (IRPS) was held in Dallas, Texas from April 14 to 18, 2024.** 

[![](https://assets.underline.io/uploads/markdown_image/1/image/9cc005f94b4ba4a81918eb49e0a51057.png)](https://underline.io/events/446/reception)
[![](https://assets.underline.io/uploads/markdown_image/1/image/acae953ba70ae2a57eed7b0250d6dd7d.png)](https://www.irps.org/)
[![](https://assets.underline.io/uploads/markdown_image/1/image/e1806748f2fc69f825e596872a7afdfd.png)](https://drive.google.com/drive/folders/1SmhpLZD9swYxrfP0TYF6327QTIciy3tV?usp=drive_link)
 

**[IRPS 2024 eGuide](https://drive.google.com/file/d/1DHxkWbafodK_D2HlDi7lwRK7KatgBgBD/view?usp=drive_link)** 
Please save the zip file locally and click the "start.pdf" to view the file. 
 
**IRPS 2024 General Chair Welcome Message**

On behalf of the Management Committee of the IEEE International Reliability Physics Symposium (IRPS) and the IRPS Board of Directors, it gives me great pleasure to extend a warm welcome to all who are planning to attend the IRPS 2024, scheduled to take place from April 14-18, 2024, in Dallas, TX. 
Over its 62-year history, IRPS has solidified its position as the premiere conference for engineers and scientists to present new and original work in the field of microelectronics reliability. Drawing participants from the United States, Europe, Asia, and other parts of the world, IRPS aims to deepen our understanding of reliability of semiconductor devices, integrated circuits, and microelectronic assemblies through an improved understanding of both the physics of failure as well as the application environment. IRPS offers diverse opportunities for attendees to enhance their knowledge and understanding of all aspects of microelectronics reliability. It is also an outstanding chance to meet and network with reliability colleagues from around the world.

The IRPS 2024 technical activities will begin with two days of a total of twenty tutorial sessions covering a broad spectrum of emerging reliability topics beyond CMOS device, circuit and system reliability. These sessions are devoted to both newcomers as well as experts. Three reliability Year in Review (YIR) talks focusing on neuromorphic computing, dielectric breakdown in leading edge devices, and GaN technology will be provided on Monday after the tutorials.

The IRPS 2024 technical program will feature four distinguished keynote talks in the plenary sessions delivered by Prof. Shinichi Takagi from The University of Tokyo and Dr. Su Jin Ahn from Samsung on Tuesday, Dr. Rajeev Malik from IBM on Wednesday, and Dr. Sameer Pendharkar from Texas Instruments on Thursday. The technical program, organized into three parallel tracks, consists of contributed and seventeen invited papers from leaders in industry and academia. Additionally, two invited talks by recipients of paper awards from our sister conferences, European Symposium on Reliability of Electron Device Failure Physics and Analysis (ESREF) 2023 and the International Integrated Reliability Workshop (IIRW) 2023, will be given in the technical program. 

Tuesday evening will host ten workshops, offering an opportunity for in-depth, face-to-face discussions on emerging reliability challenges, including the three IRPS 2024 focus topics. A poster session will be held in the Austin Ranch outside the symposium building on Wednesday evening. Both events are sure to be lively. The IRPS 2024 and the International ESD Workshop (IEW) will be co-hosted with IEW registrants participating in both IRPS and IEW programs.

The IRPS 2024 will adopt a hybrid conference style, allowing virtual access to live oral presentations hosted by the Underline. Real-time streaming of all technical events (excluding Workshops and Posters) will be available in the US Central Timezone, providing virtual attendees with Q&A opportunities. For those virtual attendees who cannot watch the live presentations, the material will be available on demand to all attendees after the symposium. All authors will be presenting in person.

Finally, I would like to extend my extreme gratitude to our patrons, exhibitors, and all participants for their invaluable support, which makes IRPS successful. 

Please enjoy the IRPS 2024!

Koji Eriguchi, General Chair 
of 2024 IRPS Management Committee
 
**About IRPS** 
The IEEE International Reliability Physics Symposium (IRPS) is the premiere conference for engineers and scientists to present new and original work in the area of microelectronics reliability. Drawing participants from the United States, Europe, Asia, and all parts of the world, IRPS seeks to understand the reliability of semiconductor devices, integrated circuits, and microelectronic assemblies through an improved understanding of both the physics of failure as well as the application environment. IRPS provides numerous opportunities for attendees to increase their knowledge and understanding of all aspects of microelectronics reliability, and to meet and network with reliability colleagues from around the world.

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IRPS 2024 Main Conference

The IEEE International Reliability Physics Symposium (IRPS) is the premiere conference for engineers and scientists to present new and original work in the area of microelectronics reliability. IRPS seeks to understand the reliability of semiconductor devices, integrated circuits, and microelectronic assemblies through an improved understanding of both the physics of failure as well as the application environment. 

poster

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
AAAI is providing you with a conference planner, which you can use to help organize your itinerary of activities. This includes talks to attend in person, talks to attend remotely, breaks with colleagues and your site seeing activities. To access this conference planner, please go to [https://aaai-2023.takemobi.io](https://aaai-2023.takemobi.io).

In order to access this site, you need to register. If you haven't already, please register [here](https://aaai.org/Conferences/AAAI-23/registration/).


The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

Previous remote sensing recognition approaches predomi-
nantly perform well on the training-testing dataset. Howev-
er, due to large style discrepancies not only among mul-
tidomain datasets but also within a single domain, they suf-
fer from obvious performance degradation when applied to
unseen domains. In this paper, we propose a style-content
metric learning framework to address the generalizable re-
mote sensing object recognition issue. Specifically, we first-
ly design an inter-class dispersion metric to encourage the
model to make decision based on content rather than the
style, which is achieved by dispersing predictions gener-
ated from the contents of both positive sample and nega-
tive sample and the style of input image. Secondly, we pro-
pose an intra-class compactness metric to force the model
to be less style-biased by compacting classifier’s prediction-
s from the content of input image and the styles of posi-
tive sample and negative sample. Lastly, we design an intra-
class interaction metric to improve model’s recognition ac-
curacy by pulling in classifier’s predictions obtained from
the input image and positive sample. Extensive experiments
on four datasets show that our style-content metric learn-
ing achieves superior generalization performance against the
state-of-the-art competitors. Code and model are available at:
https://github.com/wdzhao123/TSCM.

Style-Content Metric Learning for Multidomain Remote Sensing Object Recognition

Multi-view clustering has gained broad attention owing to its capacity to exploit complementary information across multiple data views. Although existing methods demonstrate delightful clustering performance, most of them are of high time complexity and cannot handle large-scale data. Matrix factorization-based models are a representative of solving this problem. However, they assume that the views share a dimension-fixed consensus coefficient matrix and view-specific base matrices, limiting their representability. Moreover, a series of large-scale algorithms that bear one or more hyperparameters are impractical in real-world applications. To address the two issues, we propose an auto-weighted multi-view clustering (AWMVC) algorithm. Specifically, AWMVC first learns coefficient matrices from corresponding base matrices of different dimensions, then fuses them to obtain an optimal consensus matrix. By mapping original features into distinctive low-dimensional spaces, we can attain more comprehensive knowledge, thus obtaining better clustering results. Moreover, we design a six-step alternative optimization algorithm proven to be convergent theoretically. Also, AWMVC shows excellent performance on various benchmark datasets compared with existing ones. The code of AWMVC is publicly available at https://github.com/wanxinhang/AAAI-2023-AWMVC.

Auto-weighted Multi-view Clustering for Large-scale Data

A core step of mining human mobility data is to learn accurate representations for user-generated check-in sequences. The learned representations should be able to fully describe the spatial-temporal mobility patterns of users and the high-level semantics of traveling. However, existing check-in sequence representation learning is usually implicitly achieved by end-to-end models designed for specific downstream tasks, resulting in unsatisfactory generalizable abilities and poor performance. Besides, although the sequence representation learning models that follow the contrastive learning pre-training paradigm have achieved breakthroughs in many fields like NLP, they fail to simultaneously consider the unique spatial-temporal characteristics of check-in sequences and need manual adjustments on the data augmentation strategies. So, directly applying them to check-in sequences cannot yield a meaningful pretext task. To this end, in this paper we propose a contrastive pre-training model with adversarial perturbations for check-in sequence representation learning (CACSR). Firstly, we design a novel spatial-temporal augmentation block for disturbing the spatial-temporal features of check-in sequences in the latent space to relieve the stress of designing manual data augmentation strategies. Secondly, to construct an effective contrastive pretext task, we generate “hard” positive and negative pairs for the check-in sequence by adversarial training. These two designs encourage the model to capture the high-level spatial-temporal patterns and semantics of check-in sequences while ignoring the noisy and unimportant details. We demonstrate the effectiveness and versatility of CACSR on two kinds of downstream tasks using three real-world datasets. The results show that our model outperforms both the state-of-the-art pre-training methods and the end-to-end models.

Contrastive Pre-training with Adversarial Perturbations for Check-in Sequence Representation Learning

Consistency and realness have always been the two critical issues of image super-resolution. While the realness has been dramatically improved with the use of GAN prior, the state-of-the-art methods still suffer inconsistencies in local structures and colors (e.g., tooth and eyes). In this paper, we show that these inconsistencies can be analytically eliminated by learning only the null-space component while fixing the range-space part. Further, we design a pooling-based decomposition (PD), a universal range-null space decomposition for super-resolution tasks, which is concise, fast, and parameter-free. PD can be easily applied to state-of-the-art GAN Prior based SR methods to eliminate their inconsistencies, neither compromise the realness nor bring extra parameters or computational costs. Besides, our ablation studies reveal that PD can replace pixel-wise losses for training and achieve better generalization performance when facing unseen downsamplings or even real-world degradation. Experiments show that the use of PD refreshes state-of-the-art SR performance and speeds up the convergence of training up to 2~10 times.

GAN Prior based Null-Space Learning for Consistent Super-Resolution

Many NLP tasks can be regarded as a selection problem from a set of options, such as classification tasks, multi-choice question answering, etc. Textual entailment (TE) has been shown as the state-of-the-art (SOTA) approach to dealing with those selection problems. TE treats input texts as premises (P), options as hypotheses (H), then handles the selection problem by modeling (P, H) pairwise. Two limitations: first, the pairwise modeling is unaware of other options, which is less intuitive since humans often determine the best options by comparing competing candidates; second, the inference process of pairwise TE is time-consuming, especially when the option space is large. To deal with the two issues, this work first proposes a contextualized TE model (Context-TE) by appending other k options as the context of the current (P, H) modeling. Context-TE is able to learn more reliable decision for the H since it considers various context. Second, we speed up Context-TE by coming up with Parallel-TE, which learns the decisions of multiple options simultaneously. Parallel-TE significantly improves the inference speed while keeping comparable performance with Context-TE. Our methods are evaluated on three tasks (ultra-fine entity typing, intent detection and multi-choice QA) that are typical selection problems with different sizes of options. Experiments show our models set new SOTA performance; particularly, Parallel-TE is faster than the pairwise TE by k times in inference.

Learning to Select from Multiple Options

Cake-cutting is a fundamental model of dividing a heterogeneous resource, such as land, broadcast time, and advertisement space. In this study, we consider the problem of dividing indivisible goods fairly under the connectivity constraints of a path. We prove that a connected division of indivisible items satisfying a discrete counterpart of envy-freeness, called {\em envy-freeness up to one good} (EF1), always exists for any number of agents $n$ with monotone valuations. Our result settles an open question raised by Bil\`o et al. (2019), who proved that an EF1 connected division always exists for the number of agents $n \le 4$. Moreover, the proof can be extended to show the following (1) ``secretive" and (2) ``extra" versions: (1) for $n$ agents with monotone valuations, the path can be divided into $n$ connected bundles such that an EF1 assignment of the remaining bundles can be made to the other agents for any selection made by the “secretive agent”; (2) for $n+1$ agents with monotone valuations, the path can be divided into $n$ connected bundles such that when any ``extra agent” leaves, an EF1 assignment of the bundles can be made to the remaining agents.  

How to cut a discrete cake fairly

Cross-domain named entity recognition (NER), aiming to address the limitation of labeled resources in the target domain, is a challenging yet important task. Most existing studies alleviate the data discrepancy across different domains at the coarse level via combing NER with language modelings or introducing domain-adaptive pre-training (DAPT). Notably, source and target domains tend to share more fine-grained local information within denser subsequences than global information within the whole sequence, such that subsequence features are easier to transfer, which has not been explored well. Besides, compared to token-level representation, subsequence-level information can help the model distinguish different meanings of the same word in different domains. In this paper, we propose to incorporate subsequence-level features for promoting the cross-domain NER. In detail, we first utilize a pre-trained encoder to extract the global information. Then, we re-express each sentence as a group of subsequences and propose a novel bidirectional memory recurrent unit (BMRU) to capture features from the subsequences. Finally, an adaptive coupling unit (ACU) is proposed to combine global information and subsequence features for predicting entity labels. Experimental results on several benchmark datasets illustrate the effectiveness of our model, which achieves the state of the art performance.

A Simple yet Effective Subsequence-Enhanced Approach for Cross-Domain NER

Federated Learning (FL) is a privacy-preserving distributed deep learning paradigm that involves substantial communication and computation effort, which is a problem for resource-constrained mobile and IoT devices. Model pruning/sparsification develops sparse models that could solve this problem, but existing sparsification solutions cannot satisfy at the same time the requirements for low bidirectional communication overhead between the server and the clients, low computation overhead at the clients, and good model accuracy, under the FL assumption that the server does not have access to raw data to fine-tune the pruned models. We propose Complement Sparsification (CS), a pruning mechanism that satisfies all these requirements through a complementary and collaborative pruning done at the server and the clients.  At each round, CS creates a global sparse model that contains the weights that capture the general data distribution of all clients, while the clients create local sparse models with the weights pruned from the global model to capture the local trends. For improved model performance, these two types of complementary sparse models are aggregated into a dense model in each round, which is subsequently pruned in an iterative process. CS requires little computation overhead on the top of vanilla FL for both the server and the clients. We demonstrate that CS is an approximation of vanilla FL and, thus, its models perform well. We evaluate CS experimentally with two popular FL benchmark datasets. CS achieves substantial reduction in bidirectional communication, while achieving performance comparable with vanilla FL. In addition, CS outperforms baseline pruning mechanisms for FL. 

Complement Sparsification: Low-Overhead Model Pruning for Federated Learning

In recent years, game-theoretic Shapley values have gained increasing attention with respect to local model explanation by feature attributions. While the approach using Shapley values is model-independent, their (exact) computation is usually intractable, so efficient model-specific algorithms have been devised including approaches for decision trees or their ensembles in general. Our work goes further in this direction by extending the interventional TreeSHAP algorithm to piecewise linear regression trees, which gained more attention in the past few years. To this end, we introduce a decomposition of the contribution function based on decision paths, which allows a more comprehensible formulation of SHAP algorithms for tree-based models. Our algorithm can also be readily applied to computing SHAP interaction values of these models. In particular, as the main contribution of this paper, we provide a more efficient approach of interventional SHAP for tree-based models by precomputing statistics of the background data based on the tree structure.

Interventional SHAP Values and Interaction Values for Piecewise Linear Regression Trees

The soundness of syntax is an important issue for the paraphrase generation task. 
Most methods control the syntax of paraphrases by embedding the syntax and semantics in the generation process, which cannot guarantee the syntactical correctness of the results. 
Different from them, in this paper we investigate the structural patterns of word usages termed as the word composable knowledge and integrate it into the paraphrase generation to control the syntax in an explicit way.
This syntax knowledge is pretrained on a large corpus with the dependency relationships and formed as the probabilistic functions on the word-level syntactical soundness.
For the sentence-level correctness, we design a hierarchical syntax structure loss to quantitatively verify the syntactical soundness of the paraphrase against the given dependency template. 
Thus, the generation process can select the appropriate words with consideration on both  semantics and syntax. 
The proposed method is evaluated on a few paraphrase datasets.
The experimental results show that the quality of paraphrases by our proposed method outperforms the compared methods, especially in terms of syntax correctness.

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Style-Content Metric Learning for Multidomain Remote Sensing Object Recognition

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