Miracle-dz/UniFAR-FTUs huggingface dataset:
Statistical inference for efficient microarchitectural analysis

The transition to multiprocessors expands the space of viable core designs and requires sophisticated optimization over multiple design metrics. However, microarchitectural design space exploration is often inefficient and ad hoc due to the significant computational costs of hardware simulators. Long simulation times cause designers to subjectively constrain the design space considered. However, by pruning the design space with intuition before a study, the designer risks obtaining conclusions that simply reinforce prior intuition, thereby limiting the study’s value. Addressing these fundamental challenges in microarchitectural analysis becomes increasingly urgent as the semiconductor industry moves into new domains where tried and tested intuition becomes less effective. This dissertation presents the case for statistical inference in microarchitectural design, proposing a simulation paradigm that (1) defines a comprehensive design space, (2) simulates sparse samples from that space, and (3) derives inferential regression models to reveal salient trends. These regression models accurately capture performance and power associations for comprehensive multi-billion point design spaces. Moreover, they are capable of thousand’s of predictions per second. Used as computationally efficient surrogates for detailed simulation, regression models enable previously intractable analyses of performance and power. Leverag-
Size Generalizability of Graph Neural Networks on Biological Data: Insights and Practices from the Spectral Perspective

We investigate the question of whether the knowledge learned by graph neural networks (GNNs) from small graphs is generalizable to large graphs in the same domain. Prior works suggest that the distribution shift, particularly in the degree distribution, between graphs of different sizes can lead to performance degradation in the graph classification task. However, this may not be the case for biological datasets where the degrees are bounded and the distribution shift of degrees is small. Even with little degree distribution shift, our observations show that GNNs’ performance on larger graphs from the same datasets still degrades, suggesting other causes. In fact, there has been a lack of exploration in real datasets to understand the types and properties of distribution shifts caused by various graph sizes. Furthermore, previous analyses of size generalizability mostly focus on the spatial domain. To fill these gaps, we take the spectral perspective and study the size generalizability of GNNs on biological data. We identify a distribution shift between small and large graphs in the eigenvalues of the normalized Laplacian/adjacency matrix, indicating a difference in the global node connectivity, which is found to be correlated with the node closeness centrality. We further find that despite of the variations in global connectivity, graphs of different sizes share similar local connectivity, which can be utilized to improve the size generalizability of GNNs. Based on our spectral insights and empirical observations, we propose a model-agnostic strategy, SIA, which uses size-irrelevant local structural features, i.e., the local closeness centrality of a node, to guide the learning process. Our empirical results demonstrate that our strategy improves the graph classification performance of various GNNs on small and large graphs when training with only small graphs.
Latent Structures Mining with Contrastive Modality Fusion for Multimedia Recommendation

Multimedia content is of predominance in the modern Web era. Recent years have witnessed growing research interests in multimedia recommendation, which aims to predict whether a user will interact with an item with multimodal contents. Most previous studies focus on modeling user-item interactions with multimodal features included as side information. However, this scheme is not well-designed for multimedia recommendation. Firstly, only collaborative item-item relationships are implicitly modeled through high-order item-user-item co-occurrences. Considering that items are associated with rich contents in multiple modalities, we argue that the latent semantic item-item structures underlying these multimodal contents could be beneficial for learning better item representations and assist the recommender models to comprehensively discover candidate items. Secondly, previous studies disregard the fine-grained multimodal fusion. Although having access to multiple modalities might allow us to capture rich information, we argue that the simple coarse-grained fusion by linear combination or concatenation in previous work is insufficient to fully understand content information of items and item relationships.To this end, we propose a latent structure MIning with ContRastive mOdality fusion method, which we term MICRO for brevity. To be specific, in the proposed MICRO model, we devise a novel modality-aware structure learning module, which learns item-item relationships for each modality. Based on the learned modality-aware latent item relationships, we perform graph convolutions which explicitly inject item affinities to modality-aware item representations. Additionally, we design a novel multi-modal contrastive framework to facilitate fine-grained multimodal fusion by forcing the modality-aware representation and multimodal fused representation to be close. Finally, these enriched item representations can be plugged into existing collaborative filtering methods to make more accurate recommendations. Extensive experiments on three real-world datasets demonstrate the superiority of our method over state-of-the-art multimedia recommendation methods and ablation studies validate the efficacy of mining latent item-item relationships and the contrastive multimodal fusion framework.
A Mixer Layer is Worth One Graph Convolution: Unifying MLP-Mixers and GCNs for Human Motion Prediction

The past few years has witnessed the dominance of Graph Convolutional Networks (GCNs) over human motion prediction, while their performance is still far from satisfactory. Recently, MLP-Mixers show competitive results on top of being more efﬁcient and simple. To extract features, GCNs typically follow an aggregate-and-update paradigm, while Mixers rely on token mixing and channel mixing operations. The two research paths have been independently established in the community. In this paper, we develop a novel perspective by unifying Mixers and GCNs. We show that a mixer layer can be seen as a graph convolutional layer applied to a fully-connected graph with parameterized adjacency. Extending this theoretical ﬁnding to the practical side, we propose Meta-Mixing Network (M 2 -Net). Assisted with a novel zero aggregation operation, our network is capable of capturing both the structure-agnostic and the structure-sensitive dependencies in a collaborative manner. Not only is it computationally efﬁcient, but most importantly, it also achieves state-of-the-art performance. An extensive evaluation on the Human3.6M, AMASS, and 3DPW datasets shows that M 2 -Net consistently outperforms all other approaches. We hope our work brings the community one step further towards truly predictable human motion. Our code will be publicly available.
Privacy-Preserving Data Synthetisation for Secure Information Sharing

We can protect user data privacy via many approaches, such as statistical transformation or generative models. However, each of them has critical drawbacks. On the one hand, creating a transformed data set using conventional techniques is highly time-consuming. On the other hand, in addition to long training phases, recent deep learning-based solutions require signiﬁcant computational resources. In this paper, we propose PrivateS-MOTE, a technique designed for competitive effectiveness in protecting cases at maximum risk of re-identiﬁcation while requiring much less time and computational resources. It works by synthetic data generation via interpolation to obfuscate high-risk cases while minimizing data utility loss of the original data. Compared to multiple conventional and state-of-the-art privacy-preservation methods on 20 data sets, PrivateSMOTE demonstrates competitive results in re-identiﬁcation risk. Also, it presents similar or higher predictive performance than the baselines, including generative adversarial networks and variational autoencoders, reducing their energy consumption and time requirements by a minimum factor of 9 and 12, respectively.
Ensemble Monte-Carlo Planning: An Empirical Study

Monte-Carlo planning algorithms, such as UCT, select actions at each decision epoch by intelligently expanding a single search tree given the available time and then selecting the best root action. Recent work has provided evidence that it can be advantageous to instead construct an ensemble of search trees and to make a decision according to a weighted vote. However, these prior investigations have only considered the application domains of Go and Solitaire and were limited in the scope of ensemble configurations considered. In this paper, we conduct a more exhaustive empirical study of ensemble Monte-Carlo planning using the UCT algorithm in a set of six additional domains. In particular, we evaluate the advantages of a broad set of ensemble configurations in terms of space and time efficiency in both parallel and singlecore models. Our results demonstrate that ensembles are an effective way to improve performance per unit time given a parallel time model and performance per unit space in a single-core model. However, contrary to prior isolated observations, we did not find significant evidence that ensembles improve performance per unit time in a single-core model.
Optimisation with Zeroth-Order Oracles in Formation

In this paper, we consider the optimisation of time varying functions by a network of agents with no gradient information. We propose a novel method to estimate the gradient at each agent’s position using only neighbour information. The gradient estimation is coupled with a formation controller, to minimise gradient estimation error and prevent agent collisions. Convergence results for the algorithm are provided for functions which satisfy the Polyak-Łojasiewicz inequality. Simulations and numerical results are provided to support the theoretical results.
Numerical approximations of one-point large deviations rate functions of stochastic differential equations with small noise

In this paper, we study the numerical approximation of the one-point large deviations rate functions of nonlinear stochastic diﬀerential equations (SDEs) with small noise. We show that the stochastic θ -method satisﬁes the one-point large deviations principle with a discrete rate function for suﬃciently small step-size, and present a uniform error estimate between the discrete rate function and the continuous one on bounded sets in terms of step-size. It is proved that the convergence orders in the cases of multiplicative noises and additive noises are 1 / 2 and 1 respectively. Based on the above results, we obtain an eﬀective approach to numerically approximating the large deviations rate functions of nonlinear SDEs with small time. To the best of our knowledge, this is the ﬁrst result on the convergence rate of discrete rate functions for approximating the one-point large deviations rate functions associated with nonlinear SDEs with small noise.
Cobaltkieserite, CoSO4·H2O, a new mineral species from Bastnäs, Skinnskatteberg, Sweden

Abstract Cobaltkieserite, ideally CoSO4·H2O, is a new member of the kieserite mineral group. It is monoclinic, C2/c; the unit-cell parameters are a = 6.980(1), b = 7.588(2), c = 7.639(2) Å and β = 118.65(3)°, with Z = 4. The five most intense X-ray powder lines (in Å) are 4.83 (111, 33%), 3.40 (112, 100%), 3.34 (111, 34%), 3.06 (200, 56%) and 2.51 (022, 49%). An empirical chemical formula obtained for the type material is (Co0.98Fe0.01)S0.95As0. 06Si0.01O4·H2O. Cobaltkieserite occurs as pink, fine-grained aggregates associated with scorodite and erythrite on a cobaltite-bearing, quartz-dominated rock from the Bastnäs deposit, Skinnskatteberg, Västmanland, in south-central Sweden.
A double layer theory for mixed ion systems as applied to the moisture content of clays under restraint

A double layer equation (after Gouy) for heterovalent ionic systems is applied to the thickness of the water films associated with the ionic double layer of clay particles. The equation is tested against a Ca/Na/Cl montmorillonite described by Smiles and Rosenthal (1968) in terms of moisture content versus pressure restraint. The equation reduces to simpler exact formulae for the case of unconstrained clays and gives useful approximations for the situations of small interactions and of large interactions which cover most situations to be encountered in soil systems. The restrictions of the application of the double layer model to swelling clay systems are considered.
About Voice: A Longitudinal Study of Speaker Recognition Dataset Dynamics

Likefacerecognition,speakerrecognitioniswidelyusedforvoice-basedbiometricidentificationinabroadrangeofindustries,including banking, education, recruitment, immigration, law enforcement, healthcare, and well-being. However, while dataset evaluations and audits have improved data practices in computer vision and face recognition, the data practices in speaker recognition have gone largely unquestioned. Our research aims to address this gap by exploring how dataset usage has evolved over time and what implications this has on bias and fairness in speaker recognition systems. Previous studies have demonstrated the presence of historical, representation, and measurement biases in popular speaker recognition benchmarks. In this paper, we present a longitudinal study of speaker recognition datasets used for training and evaluation from 2012 to 2021. We survey close to 700 papers to investigate community adoption of datasets and changes in usage over a crucial time period where speaker recognition approaches transitioned to the widespread adoption of deep neural networks. Our study identifies the most commonly used datasets in the field, examines their usage patterns, and assesses their attributes that affect bias, fairness, and other ethical concerns. Our findings suggest areas for further research on the ethics and fairness of speaker recognition technology.
Meta-analysis of time series studies and panel studies of particulate matter (PM) and ozone (O3). Report of a WHO task group

Quantitative health impact assessment has become increasingly important in the development of air quality policy. For such analysis it is important to have accurate information on the concentration-response relationships for the effects investigated, for example on the relationship between changes in daily air pollution and its impact on health. Therefore, a quantitative meta-analysis of peer reviewed studies was conducted to obtain summary estimates for certain health effects linked to the exposure to particulate matter (PM) and ozone. This work was done as part of the WHO project “Systematic review of health aspects of air pollution in Europe”, which is funded by the European Commission and is intended to provide input to the Clean Air For Europe (CAFE) programme. The data for these analyses came from a database of time-series studies (ecological and individual) developed at St. George’s Hospital Medical School at the University of London. The meta-analysis was also performed at St. George’s Hospital according to a protocol that was agreed upon by a WHO Task Group in advance of the work. This analysis confirmed statistically significant relationships between levels of PM and ozone in ambient air with mortality, using data from several European cities. Updated risk coefficients in relation to ambient exposure to PM and ozone were obtained for all-cause (relative risk for a 10 μg/m increase in PM10: 1.006 (1.004, 1.008) and ozone: 1.003 (1.001, 1.004), respectively) and cause-specific mortality and hospital admissions for respiratory and cardiovascular causes. In addition, possible publication bias was investigated and revised summary estimates were calculated. Also panel studies were analyzed to derive summary estimates for coughs and medication use in individuals with underlying respiratory disease.
UniMorph 3.0: Universal Morphology

The Universal Morphology (UniMorph) project is a collaborative effort providing broad-coverage instantiated normalized morphological paradigms for hundreds of diverse world languages. The project comprises two major thrusts: a language-independent feature schema for rich morphological annotation and a type-level resource of annotated data in diverse languages realizing that schema. We have implemented several improvements to the extraction pipeline which creates most of our data, so that it is both more complete and more correct. We have added 66 new languages, as well as new parts of speech for 12 languages. We have also amended the schema in several ways. Finally, we present three new community tools: two to validate data for resource creators, and one to make morphological data available from the command line. UniMorph is based at the Center for Language and Speech Processing (CLSP) at Johns Hopkins University in Baltimore, Maryland. This paper details advances made to the schema, tooling, and dissemination of project resources since the UniMorph 2.0 release described at LREC 2018.
Computer Vision and Natural Language Processing : Recent Approaches in Multimedia and Robotics

Languages are common tools to describe the world for human-human communication. There are many forms of languages which may be verbal or nonverbal but all are assistants for understanding. Some examples are texts, gestures, sign languages, face expressions, etc. Languages provide meaning and meaning is grounded in human perception of the world. This is usually referred to as the symbol grounding problem [86]. If it is a language without perception, it is a fantasy which is not based on the real world. If it is a pure perception without language, there is no movement and retention of any object or knowledge by the mind. In human perception, visual information is the dominant modality for acquiring knowledge since a big part of the human brain is dedicated to visual processing. Whether or not there are languages involved in the visual process is still an ongoing argument. However, for an intelligent system that tries to achieve AI, having languages provides interpretability and creates a way for human-machine interaction which gives rises to a lot of interesting applications. To bridge language and vision, we first revisit the major tasks in both language and vision.
Discovering cis-Regulatory RNAs in Shewanella Genomes by Support Vector Machines

An increasing number of cis-regulatory RNA elements have been found to regulate gene expression post-transcriptionally in various biological processes in bacterial systems. Effective computational tools for large-scale identification of novel regulatory RNAs are strongly desired to facilitate our exploration of gene regulation mechanisms and regulatory networks. We present a new computational program named RSSVM (RNA Sampler+Support Vector Machine), which employs Support Vector Machines (SVMs) for efficient identification of functional RNA motifs from random RNA secondary structures. RSSVM uses a set of distinctive features to represent the common RNA secondary structure and structural alignment predicted by RNA Sampler, a tool for accurate common RNA secondary structure prediction, and is trained with functional RNAs from a variety of bacterial RNA motif/gene families covering a wide range of sequence identities. When tested on a large number of known and random RNA motifs, RSSVM shows a significantly higher sensitivity than other leading RNA identification programs while maintaining the same false positive rate. RSSVM performs particularly well on sets with low sequence identities. The combination of RNA Sampler and RSSVM provides a new, fast, and efficient pipeline for large-scale discovery of regulatory RNA motifs. We applied RSSVM to multiple Shewanella genomes and identified putative regulatory RNA motifs in the 5′ untranslated regions (UTRs) in S. oneidensis, an important bacterial organism with extraordinary respiratory and metal reducing abilities and great potential for bioremediation and alternative energy generation. From 1002 sets of 5′-UTRs of orthologous operons, we identified 166 putative regulatory RNA motifs, including 17 of the 19 known RNA motifs from Rfam, an additional 21 RNA motifs that are supported by literature evidence, 72 RNA motifs overlapping predicted transcription terminators or attenuators, and other candidate regulatory RNA motifs. Our study provides a list of promising novel regulatory RNA motifs potentially involved in post-transcriptional gene regulation. Combined with the previous cis-regulatory DNA motif study in S. oneidensis, this genome-wide discovery of cis-regulatory RNA motifs may offer more comprehensive views of gene regulation at a different level in this organism. The RSSVM software, predictions, and analysis results on Shewanella genomes are available at http://ural.wustl. edu/resources.html#RSSVM.
Neighborhood Filtering Strategies for Overlay Construction in P2P-TV Systems: Design and Experimental Comparison

Peer-to-peer live-streaming (P2P-TV) systems' goal is disseminating real-time video content using peer-to-peer technology. Their performance is driven by the overlay topology, i.e., the virtual topology that peers use to exchange video chunks. Several proposals have been made in the past to optimize it, yet few experimental studies have corroborated results. The aim of this paper is to provide a comprehensive experimental comparison based on PeerStreamer in order to benchmark different strategies for the construction and maintenance of the overlay topology in P2P-TV systems. We present only experimental results in which fully distributed strategies are evaluated in both controlled experiments and the Internet using thousands of peers. Results confirm that the topological properties of the overlay have a deep impact on both user quality of experience and network load. Strategies based solely on random peer selection are greatly outperformed by smart yet simple and actually implementable strategies. The most performing strategy we devise guarantees to deliver almost all chunks to all peers with a playout delay as low as 6 s even when system load approaches 1, and in almost adversarial network scenarios. PeerStreamer is open-source to make results reproducible and allow further research by the community.
Deep Reflection Prior

Reflections are very common phenomena in our daily photography, which distract people's attention from the scene behind the glass. The problem of removing reflection artifacts is important but challenging due to its ill-posed nature. Recent learning-based approaches have demonstrated a significant improvement in removing reflections. However, these methods are limited as they require a large number of synthetic reflection/clean image pairs for supervision, at the risk of overfitting in the synthetic image domain. In this paper, we propose a learning-based approach that captures the reflection statistical prior for single image reflection removal. Our algorithm is driven by optimizing the target with joint constraints enhanced between multiple input images during the training stage, but is able to eliminate reflections only from a single input for evaluation. Our framework allows to predict both background and reflection via a one-branch deep neural network, which is implemented by the controllable latent code that indicates either the background or reflection output. We demonstrate superior performance over the state-of-the-art methods on a large range of real-world images. We further provide insightful analysis behind the learned latent code, which may inspire more future work.