Miracle-dz/UniFAR-FTUs huggingface dataset:
ET-1 Promotes Epithelial–Mesenchymal Transition in Oral Squamous Cell Carcinoma Cells via the microRNA-489-3p /TWIST Axis

Objective Oral squamous cell carcinoma (OSCC) constitutes almost 90% of head and neck malignancies and has a poor prognosis. To improve the efficacy of OSCC therapy, it is of great significance to explore other therapy for OSCC. Endothelin-1 (ET-1), a potent vasoconstrictor peptide, is implicated in cancer pathogenesis. Moreover, ET-1 promotes epithelial-mesenchymal transition (EMT) during the development of human cancers. We further to found that ET-1 exposure induced EMT in human squamous cell carcinoma cell lines SCC4 and SAS, by enhancing the expression of EMT biomarkers N-cadherin and vimentin and reducing E-cadherin expression via upregulation of the transcription factor TWIST. Materials and Methods Cell motility was examined by migration, invasion and wound-healing assays. Quantitative real time polymerase chain reaction (q-PCR), and promoter assays confirmed the inhibitory effects of ET-1 on miRNAs expression in oral cancer cells. We demonstrate an intravenous injection model of lung metastasis followed by an advanced method for quantifying metastatic tumor using image analysis software. Results In addition, ET-1/ETAR reduced levels of microRNA-489-3p (miR-489-3p), a transcriptional repressor of TWIST. We have identified a novel bypass mechanism through which ET-1/ETAR are involved in TWIST signaling and downregulate miR-489-3p expression, enabling OSCC cells to acquire the EMT phenotype. Notably, ET-1 knockdown dramatically decreased levels of EMT markers and cell migration potential. Conclusion The role of ET-1 in OSCC progression is supported by our findings from an in vivo murine model of OSCC. ET-1 may therefore represent a novel molecular therapeutic target in OSCC metastasis.
“Let me know when I’m needed”: Exploring the gendered nature of digital technology use for health information seeking during the transition to parenting

This paper presents results of a qualitative descriptive study conducted to understand parents’ experiences with digital technologies during their transition to parenting (i.e. the period from pre-conception through postpartum). Individuals in southwest Ontario who had become a new parent within the previous 24 months were recruited to participate in a focus group or individual interview. Participants were asked to describe the type of technologies they/their partner used during their transition to parenthood, and how such technologies were used to support their own and their family's health. Focus group and interview transcripts were then subjected to thematic analysis using inductive coding. Ten focus groups and three individual interviews were conducted with 26 heterosexual female participants. Participants primarily used digital technologies to: (1) seek health information for a variety of reproductive health issues, and (2) establish social and emotional connections. The nature of such health information work was markedly gendered and was categorized by 2 dominant themes. First, “‘Let me know when I’m needed’”, characterizes fathers’ apparent avoidance of health information seeking and resultant creation of mothers as lay information mediaries. Second, “Information Curation”, captures participants’ belief that gender biases built-in to popular parenting apps and resources reified the gendered nature of health and health information work during the transition to parenting. Overall, findings indicate that digital technology tailored to new and expecting parents actively reinforced gender norms regarding health information seeking, which creates undue burden on new mothers to become the sole health information seeker and interpreter for their family.
NetRep: Automatic Repair for Network Programs

Debugging imperative network programs is a challenging task for developers because understanding various network modules and complicated data structures is typically time-consuming. To address the challenge, this paper presents an automated technique for repairing network programs from unit tests. Specifically, given as input a faulty network program and a set of unit tests, our approach localizes the fault through symbolic reasoning, and synthesizes a patch such that the repaired program can pass all unit tests. It applies domain-specific abstraction to simplify network data structures and utilizes modular analysis to facilitate function summary reuse for symbolic analysis. We implement the proposed techniques in a tool called NetRep and evaluate it on 10 benchmarks adapted from real-world software-defined networking controllers. The evaluation results demonstrate the effectiveness and efficiency of NetRep for repairing network programs.
E-RAFT: Dense Optical Flow from Event Cameras

We propose to incorporate feature correlation and sequential processing into dense optical flow estimation from event cameras. Modern frame-based optical flow methods heavily rely on matching costs computed from feature correlation. In contrast, there exists no optical flow method for event cameras that explicitly computes matching costs. Instead, learning-based approaches using events usually resort to the U-Net architecture to estimate optical flow sparsely. Our key finding is that the introduction of correlation features significantly improves results compared to previous methods that solely rely on convolution layers. Compared to the state-of-the-art, our proposed approach computes dense optical flow and reduces the end-point error by 23% on MVSEC. Furthermore, we show that all existing optical flow methods developed so far for event cameras have been evaluated on datasets with very small displacement fields with maximum flow magnitude of 10 pixels. Based on this observation, we introduce a new real-world dataset that exhibits displacement fields with magnitudes up to 210 pixels and 3 times higher camera resolution. Our proposed approach reduces the end-point error on this dataset by 66%.
More Engineering, No Silos: Rethinking Processes and Interfaces in Collaboration between Interdisciplinary Teams for Machine Learning Projects

The introduction of machine learning (ML) components in software projects has created the need for software engineers to collaborate with data scientists and other specialists. While collaboration can always be challenging, ML introduces additional challenges with its exploratory model development process, additional skills and knowledge needed, difficulties testing ML systems, need for continuous evolution and monitoring, and non-traditional quality requirements such as fairness and explainability. Through inter-views with 45 practitioners from 28 organizations, we identified key collaboration challenges that teams face when building and deploying ML systems into production. We report on common collaboration points in the development of production ML systems for requirements, data, and integration, as well as corresponding team patterns and challenges. We find that most of these challenges center around communication, documentation, engineering, and process and collect recommendations to address these challenges.
A TinyML Platform for On-Device Continual Learning With Quantized Latent Replays

In the last few years, research and development on Deep Learning models & techniques for ultra-low-power devices– in a word, <italic>TinyML</italic> – has mainly focused on a <italic>train-then-deploy</italic> assumption, with static models that cannot be adapted to newly collected data without cloud-based data collection and fine-tuning. Latent Replay-based Continual Learning (CL) techniques (Pellegrini <italic>et al.</italic>, 2020) enable online, serverless adaptation in principle, but so far they have still been too computation- and memory-hungry for ultra-low-power TinyML devices, which are typically based on microcontrollers. In this work, we introduce a HW/SW platform for end-to-end CL based on a 10-core <italic>FP32</italic> -enabled parallel ultra-low-power (PULP) processor. We rethink the baseline Latent Replay CL algorithm, leveraging quantization of the frozen stage of the model and Latent Replays (LRs) to reduce their memory cost with minimal impact on accuracy. In particular, 8-bit compression of the LR memory proves to be almost lossless (−0.26% with 3000LR) compared to the full-precision baseline implementation, but requires <inline-formula> <tex-math notation="LaTeX">$4\times $ </tex-math></inline-formula> less memory, while 7-bit can also be used with an additional minimal accuracy degradation (up to 5%). We also introduce optimized primitives for forward and backward propagation on the PULP processor, together with data tiling strategies to fully exploit its memory hierarchy, while maximizing efficiency. Our results show that by combining these techniques, continual learning can be achieved in practice using less than 64MB of memory – an amount compatible with embedding in TinyML devices. On an advanced 22nm prototype of our platform, called <italic>VEGA</italic>, the proposed solution performs on average <inline-formula> <tex-math notation="LaTeX">$65 \times $ </tex-math></inline-formula> faster than a low-power STM32 L4 microcontroller, being <inline-formula> <tex-math notation="LaTeX">$37\times $ </tex-math></inline-formula> more energy efficient – enough for a lifetime of 535h when learning a new mini-batch of data once every minute.
Hierarchical Aspect-guided Explanation Generation for Explainable Recommendation

Explainable recommendation systems provide explanations for recommendation results to improve their transparency and persuasiveness. The existing explainable recommendation methods generate textual explanations without explicitly considering the user's preferences on different aspects of the item. In this paper, we propose a novel explanation generation framework, named Hierarchical Aspect-guided explanation Generation (HAG), for explainable recommendation. Specifically, HAG employs a review-based syntax graph to provide a unified view of the user/item details. An aspect-guided graph pooling operator is proposed to extract the aspect-relevant information from the review-based syntax graphs to model the user's preferences on an item at the aspect level. Then, a hierarchical explanation decoder is developed to generate aspects and aspect-relevant explanations based on the attention mechanism. The experimental results on three real datasets indicate that HAG outperforms state-of-the-art explanation generation methods in both single-aspect and multi-aspect explanation generation tasks, and also achieves comparable or even better preference prediction accuracy than strong baseline methods.
A Survey on Deep-Learning Approaches for Vehicle Trajectory Prediction in Autonomous Driving

With the rapid development of machine learning, autonomous driving has become a hot issue, making urgent demands for more intelligent perception and planning systems. Self-driving cars can avoid traffic crashes with precisely predicted future trajectories of surrounding vehicles. In this work, we review and categorize existing learning-based trajectory forecasting methods from perspectives of representation, modeling, and learning. Moreover, we make our implementation of Target-driveN Trajectory Prediction publicly available at https://github.com/Henryliu/TNT-Trajectory-Predition, demonstrating its outstanding performance whereas its original codes are withheld. Enlightenment is expected for researchers seeking to improve trajectory prediction performance based on the achievement we have made.
The CoRa Tensor Compiler: Compilation for Ragged Tensors with Minimal Padding

There is often variation in the shape and size of input data used for deep learning. In many cases, such data can be represented using tensors with non-uniform shapes, or ragged tensors. Due to limited and non-portable support for efficient execution on ragged tensors, current deep learning frameworks generally use techniques such as padding and masking to make the data shapes uniform and then offload the computations to optimized kernels for dense tensor algebra. Such techniques can, however, lead to a lot of wasted computation and therefore, a loss in performance. This paper presents CoRa, a tensor compiler that allows users to easily generate efficient code for ragged tensor operators targeting a wide range of CPUs and GPUs. Evaluating CoRa on a variety of operators on ragged tensors as well as on an encoder layer of the transformer model, we find that CoRa (i)performs competitively with hand-optimized implementations of the operators and the transformer encoder and (ii) achieves, over PyTorch, a 1.6X geomean speedup for the encoder on an Nvidia GPU and a 1.86X geomean speedup for the multi-head attention module used in transformers on an ARM CPU.
Ensemble of Averages: Improving Model Selection and Boosting Performance in Domain Generalization

In Domain Generalization (DG) settings, models trained independently on a given set of training domains have notoriously chaotic performance on distribution shifted test domains, and stochasticity in optimization (e.g. seed) plays a big role. This makes deep learning models unreliable in real world settings. We first show that this chaotic behavior exists even along the training optimization trajectory of a single model, and propose a simple model averaging protocol that both significantly boosts domain generalization and diminishes the impact of stochasticity by improving the rank correlation between the in-domain validation accuracy and out-domain test accuracy, which is crucial for reliable early stopping. Taking advantage of our observation, we show that instead of ensembling unaveraged models (that is typical in practice), ensembling moving average models (EoA) from independent runs further boosts performance. We theoretically explain the boost in performance of ensembling and model averaging by adapting the well known Bias-Variance trade-off to the domain generalization setting. On the DomainBed benchmark, when using a pre-trained ResNet-50, this ensemble of averages achieves an average of $68.0\%$, beating vanilla ERM (w/o averaging/ensembling) by $\sim 4\%$, and when using a pre-trained RegNetY-16GF, achieves an average of $76.6\%$, beating vanilla ERM by $6\%$. Our code is available at https://github.com/salesforce/ensemble-of-averages.
Decentralised Trustworthy Collaborative Intrusion Detection System for IoT

Intrusion Detection Systems (IDS) have been the industry standard for securing IoT networks against known attacks. To increase the capability of an IDS, researchers proposed the concept of blockchain-based Collaborative-IDS (CIDS), wherein blockchain acts as a decentralised platform allowing collaboration between CIDS nodes to share intrusion related information, such as intrusion alarms and detection rules. However, proposals in blockchain-based CIDS overlook the importance of continuous evaluation of the trustworthiness of each node and generally work based on the assumption that the nodes are always honest. In this paper, we propose a decentralised CIDS that emphasises the importance of building trust between CIDS nodes. In our proposed solution, each CIDS node exchanges detection rules to help other nodes detect new types of intrusion. Our architecture offloads the trust computation to the blockchain and utilises a decentralised storage to host the shared trustworthy detection rules, ensuring scalability. Our implementation in a lab-scale testbed shows that the our solution is feasible and performs within the expected benchmarks of the Ethereum platform.
Enhanced Recurrent Neural Tangent Kernels for Non-Time-Series Data

Kernels derived from deep neural networks (DNNs) in the infinite-width regime provide not only high performance in a range of machine learning tasks but also new theoretical insights into DNN training dynamics and generalization. In this paper, we extend the family of kernels associated with recurrent neural networks (RNNs), which were previously derived only for simple RNNs, to more complex architectures including bidirectional RNNs and RNNs with average pooling. We also develop a fast GPU implementation to exploit the full practical potential of the kernels. Though RNNs are typically only applied to time-series data, we demonstrate that classifiers using RNN-based kernels outperform a range of baseline methods on 90 non-time-series datasets from the UCI data repository.
Optimizing strongly interacting fermionic Hamiltonians

The fundamental problem in much of physics and quantum chemistry is to optimize a low-degree polynomial in certain anticommuting variables. Being a quantum mechanical problem, in many cases we do not know an efficient classical witness to the optimum, or even to an approximation of the optimum. One prominent exception is when the optimum is described by a so-called “Gaussian state”, also called a free fermion state. In this work we are interested in the complexity of this optimization problem when no good Gaussian state exists. Our primary testbed is the Sachdev–Ye–Kitaev (SYK) model of random degree-q polynomials, a model of great current interest in condensed matter physics and string theory, and one which has remarkable properties from a computational complexity standpoint. Among other results, we give an efficient classical certification algorithm for upper-bounding the largest eigenvalue in the q=4 SYK model, and an efficient quantum certification algorithm for lower-bounding this largest eigenvalue; both algorithms achieve constant-factor approximations with high probability.
FlexTOE: Flexible TCP Offload with Fine-Grained Parallelism

FlexTOE is a flexible, yet high-performance TCP offload engine (TOE) to SmartNICs. FlexTOE eliminates almost all host data-path TCP processing and is fully customizable. FlexTOE interoperates well with other TCP stacks, is robust under adverse network conditions, and supports POSIX sockets. FlexTOE focuses on data-path offload of established connections, avoiding complex control logic and packet buffering in the NIC. FlexTOE leverages fine-grained parallelization of the TCP data-path and segment reordering for high performance on wimpy SmartNIC architectures, while remaining flexible via a modular design. We compare FlexTOE on an Agilio-CX40 to host TCP stacks Linux and TAS, and to the Chelsio Terminator TOE. We find that Memcached scales up to 38% better on FlexTOE versus TAS, while saving up to 81% host CPU cycles versus Chelsio. FlexTOE provides competitive performance for RPCs, even with wimpy SmartNICs. FlexTOE cuts 99.99th-percentile RPC RTT by 3.2$\times$ and 50% versus Chelsio and TAS, respectively. FlexTOE's data-path parallelism generalizes across hardware architectures, improving single connection RPC throughput up to 2.4$\times$ on x86 and 4$\times$ on BlueField. FlexTOE supports C and XDP programs written in eBPF. It allows us to implement popular data center transport features, such as TCP tracing, packet filtering and capture, VLAN stripping, flow classification, firewalling, and connection splicing.
Mixer-based lidar lane detection network and dataset for urban roads

Accurate lane detection under various road conditions is a critical function for autonomous driving. Generally, when detected lane lines from a front camera image are projected into a bird’s-eye view (BEV) for motion planning, the resulting lane lines are often distorted. And convolutional neural network (CNN)-based feature extractors often lose resolution when increasing the receptive ﬁeld to detect global features such as lane lines. However, Lidar point cloud has little image distortion in the BEV-projection. Since lane lines are thin and stretch over entire BEV image while occupying only a small portion, lane lines should be detected as a global feature with high resolution. In this paper, we propose Lane Mixer Network (LMN) that extracts local features from Lidar point cloud, recognizes global features, and detects lane lines us-ing a BEV encoder, a Mixer-based global feature extractor, and a detection head, respectively. In addition, we provide a world-ﬁrst large urban lane dataset for Lidar, K-Lane, which has maximum 6 lanes under various urban road conditions. We demonstrate that the proposed LMN achieves the state-of-the-art performance, an F1 score of 91.67%, with K-Lane. The K-Lane, LMN training code, pre-trained models, and total dataset development platform are available at [github].
Evolving Transferable Pruning Functions

Channel pruning has made major headway in the design of efﬁcient deep learning models. Conventional approaches adopt human-made pruning functions to score channels’ importance for channel pruning, which requires domain knowledge and could be sub-optimal. In this work, we pro-pose an end-to-end framework to automatically discover strong pruning metrics. Speciﬁcally, we craft a novel design space for expressing pruning functions and leverage an evolution strategy, genetic programming, to evolve high-quality and transferable pruning functions. Unlike prior methods, our approach can not only provide compact pruned networks for efﬁcient inference, but also novel closed-form pruning metrics that are mathematically explainable and thus generalizable to different pruning tasks. The evolution is conducted on small datasets while the learned functions are transferable to larger datasets without any manual mod-iﬁcation. Compared to direct evolution on a large dataset, our strategy shows better cost-effectiveness. When applied to more challenging datasets, different from those used in the evolution process, e.g., ILSVRC-2012, an evolved function achieves state-of-the-art pruning results.
CNewSum: A Large-scale Chinese News Summarization Dataset with Human-annotated Adequacy and Deducibility Level

Automatic text summarization aims to produce a brief but crucial summary for the input documents. Both extractive and abstractive methods have witnessed great success in English datasets in recent years. However, there has been a minimal exploration of text summarization in Chinese, limited by the lack of large-scale datasets. In this paper, we present a large-scale Chinese news summarization dataset CNewSum, which consists of 304,307 documents and human-written summaries for the news feed. It has long documents with high-abstractive summaries, which can encourage document-level understanding and generation for current summarization models. An additional distinguishing feature of CNewSum is that its test set contains adequacy and deducibility annotations for the summaries. The adequacy level measures the degree of summary information covered by the document, and the deducibility indicates the reasoning ability the model needs to generate the summary. These annotations can help researchers analyze and target their model performance bottleneck. We examine recent methods on CNewSum and release our dataset to provide a solid testbed for automatic Chinese summarization research.
Generalized Bures-Wasserstein Geometry for Positive Definite Matrices

This paper proposes a generalized Bures-Wasserstein (BW) Riemannian geometry for the manifold of symmetric positive definite matrices. We explore the generalization of the BW geometry in three different ways: 1) by generalizing the Lyapunov operator in the metric, 2) by generalizing the orthogonal Procrustes distance, and 3) by generalizing the Wasserstein distance between the Gaussians. We show that they all lead to the same geometry. The proposed generalization is parameterized by a symmetric positive definite matrix M such that when M = I, we recover the BW geometry. We derive expressions for the distance, geodesic, exponential/logarithm maps, Levi-Civita connection, and sectional curvature under the generalized BW geometry. We also present applications and experiments that illustrate the efficacy of the proposed geometry.