Miracle-dz/UniFAR-FTUs huggingface dataset:
Progressive Augmentation of GANs

Training of Generative Adversarial Networks (GANs) is notoriously fragile, requiring to maintain a careful balance between the generator and the discriminator in order to perform well. To mitigate this issue we introduce a new regularization technique - progressive augmentation of GANs (PA-GAN). The key idea is to gradually increase the task difficulty of the discriminator by progressively augmenting its input or feature space, thus enabling continuous learning of the generator. We show that the proposed progressive augmentation preserves the original GAN objective, does not compromise the discriminator's optimality and encourages a healthy competition between the generator and discriminator, leading to the better-performing generator. We experimentally demonstrate the effectiveness of PA-GAN across different architectures and on multiple benchmarks for the image synthesis task, on average achieving ~3 point improvement of the FID score.
Cavity quantum electrodynamics of strongly correlated electron systems: A no-go theorem for photon condensation

In spite of decades of work it has remained unclear whether or not superradiant quantum phases, referred to here as photon condensates, can occur in equilibrium. In this Letter, we first show that when a non-relativistic quantum many-body system is coupled to a cavity field, gauge invariance forbids photon condensation. We then present a microscopic theory of the cavity quantum electrodynamics of an extended Falicov-Kimball model, showing that, in agreement with the general theorem, its insulating ferroelectric and exciton condensate phases are not altered by the cavity and do not support photon condensation.
Connecting galaxy structure and star formation: the role of environment in formation of S0 galaxies

In this work, we investigate the reason behind the increased occurrence of S0 galaxies in high-density environments. Our sample comprises of ∼2500 spiral and ∼2000 S0 galaxies spanning a wide range of environments. Dividing the galaxies into categories of classical and pseudo-bulge hosting spiral and S0 galaxies, we have studied their properties as a function of the environment. We find that the fraction of pseudo-bulge hosting disc galaxies decreases with increase in density. The classical bulge hosting spirals and S0 galaxies follow a similar trend in less dense environments but towards higher densities, we observe an increase in the fraction of classical bulge host S0 galaxies at the expense of spirals. Comparing the structural and the star formation properties of galaxies on the size–mass and NUV − r colour–mass planes, respectively, we infer that classical bulge hosting spirals are likely to get transformed into S0 morphology. We notice a trend of galaxy structure with environment such that the fraction of classical bulge hosting spiral galaxies is found to increase with environment density. We also find that among classical bulge hosting spirals, the fraction of quenched galaxies increases in denser environments. We surmise that the existence of more classical bulge hosting spirals galaxies and more efficient quenching leads to the observed increased occurrence of S0 galaxies in high-density environments. The relation between galaxy structure and environment also exists for the disc galaxies irrespective of their visual morphology, which is driven mainly by halo mass.
ON THE EULER CHARACTERISTICS OF SIGNED SELMER GROUPS

Let $p$ be an odd prime number and $E$ an elliptic curve defined over a number field $F$ with good reduction at every prime of $F$ above $p$. We compute the Euler characteristics of the signed Selmer groups of $E$ over the cyclotomic $\mathbb{Z}_{p}$-extension. The novelty of our result is that we allow the elliptic curve to have mixed reduction types for primes above $p$ and mixed signs in the definition of the signed Selmer groups.
Overexpression of CLIC1 in human gastric carcinoma and its clinicopathological significance

Gastric cancer is the second most common cancer worldwide and the fifth leading cause of cancer‐related death in Taiwan. Identification of biomarkers is essential to improve patient survival. Fifty aberrantly expressed proteins were identified using 2‐DE combined with MALDI TOF MS and were grouped based on their function. The overexpression of proteins was confirmed using real‐time quantitative RT‐PCR, Western blot, and immunohistochemical analysis. The clinicopathological correlations and prognostic significance of these aberrantly expressed proteins were evaluated to determine the novel gastric cancer biomarkers. In this study, expression of chloride intracellular channel 1 (CLIC1) is significantly up‐regulated in 67.9% of gastric patients and was selected for further study. The CLIC1 expression in tumor tissues was increased by 1.95‐fold (range, 0.01–6.19‐fold) compared with that expressed by adjacent noncancerous mucosa. Elevated CLIC1 expression was strongly correlated with lymph node metastasis, lymphatic invasion, perineural invasion, and pathological staging. Additionally, the 5‐year survival rate for the low CLIC1 expression group (n = 28; <1.72‐fold) was higher than that for the high CLIC1 expression group (n = 28; ≥1.72‐fold) (log rank, p = 0.0300). Experimental results indicate that overexpression of CLIC1 is a potential prognostic marker for gastric cancer.
Purifying Adversarial Perturbation with Adversarially Trained Auto-encoders

Machine learning models are vulnerable to adversarial examples. Iterative adversarial training has shown promising results against strong white-box attacks. However, adversarial training is very expensive, and every time a model needs to be protected, such expensive training scheme needs to be performed. In this paper, we propose to apply iterative adversarial training scheme to an external auto-encoder, which once trained can be used to protect other models directly. We empirically show that our model outperforms other purifying-based methods against white-box attacks, and transfers well to directly protect other base models with different architectures.
Resisting Adversarial Attacks by k-Winners-Take-All

We propose a simple change to the current neural network structure for defending against gradient-based adversarial attacks. Instead of using popular activation functions (such as ReLU), we advocate the use of $k$-Winners-Take-All ($k$-WTA) activation, a $C^0$ discontinuous function that purposely invalidates the neural network model's gradient at densely distributed input data points. Our proposal is theoretically rationalized. We show why the discontinuities in $k$-WTA networks can largely prevent gradient-based search of adversarial examples and why they at the same time remain innocuous to the network training. This understanding is also empirically backed. Even without notoriously expensive adversarial training, the robustness performance of our networks is comparable to conventional ReLU networks optimized by adversarial training. Furthermore, after also optimized through adversarial training, our networks outperform the state-of-the-art methods under white-box attacks on various datasets that we experimented with.
Loss-of-function mutations reveal that the Drosophila nautilus gene is not essential for embryonic myogenesis or viability.

nautilus (nau), the single Drosophila member of the bHLH-containing myogenic regulatory family of genes, is expressed in a subset of muscle precursors and differentiated fibers. It is capable of inducing muscle-specific transcription as well as myogenic transformation, and plays a role in the differentiation of a subset of muscle precursors into mature muscle fibers. In previous studies, the nau zygotic loss-of-function phenotype was determined using genetic deficiencies in which the gene is deleted. We note that this genetic loss-of-function phenotype differs from the loss-of-function phenotype determined using RNA interference (L. Misquitta and B. M. Paterson, 1999, Proc. Natl. Acad. Sci. USA 96, 1451-1456). The present study re-examines this loss-of-function phenotype using EMS-induced mutations that specifically alter the nau gene, and extends the genetic analysis to include the loss of both maternal and zygotic nau function. In brief, embryos lacking nau both maternally and zygotically are missing a distinct subset of muscle fibers, consistent with its apparent expression in a subset of muscle fibers. The muscle loss is tolerated, however, such that the loss of nau both maternally and zygotically does not result in lethality at any stage of development.
SOFIA/EXES Observations of Warm H2 at High Spectral Resolution: Witnessing Para-to-ortho Conversion behind a Molecular Shock Wave in HH7

Spectrally resolved observations of three pure rotational lines of H2, conducted with the EXES instrument on SOFIA toward the classic bow shock HH7, reveal systematic velocity shifts between the S(5) line of ortho-H2 and the two para-H2 lines [S(4) and S(6)] lying immediately above and below it on the rotational ladder. These shifts, reported here for the first time, imply that we are witnessing the conversion of para-H2 to ortho-H2 within a shock wave driven by an outflow from a young stellar object. The observations are in good agreement with the predictions of models for nondissociative, C-type molecular shocks. They provide a clear demonstration of the chemical changes wrought by interstellar shock waves, in this case the conversion of para-H2 to ortho-H2 in reactive collisions with atomic hydrogen, and provide among the most compelling evidence yet obtained for C-type shocks in which the flow velocity changes continuously.
Fair Decisions Despite Imperfect Predictions

Consequential decisions are increasingly informed by sophisticated data-driven predictive models. However, to consistently learn accurate predictive models, one needs access to ground truth labels. Unfortunately, in practice, labels may only exist conditional on certain decisions---if a loan is denied, there is not even an option for the individual to pay back the loan. Hence, the observed data distribution depends on how decisions are being made. In this paper, we show that in this selective labels setting, learning a predictor directly only from available labeled data is suboptimal in terms of both fairness and utility. To avoid this undesirable behavior, we propose to directly learn decision policies that maximize utility under fairness constraints and thereby take into account how decisions affect which data is observed in the future. Our results suggest the need for a paradigm shift in the context of fair machine learning from the currently prevalent idea of simply building predictive models from a single static dataset via risk minimization, to a more interactive notion of "learning to decide". In particular, such policies should not entirely neglect part of the input space, drawing connections to explore/exploit tradeoffs in reinforcement learning, data missingness, and potential outcomes in causal inference. Experiments on synthetic and real-world data illustrate the favorable properties of learning to decide in terms of utility and fairness.
An Open-Source Benchmark Suite for Cloud and IoT Microservices

Cloud services have recently started undergoing a major shift from monolithic applications, to graphs of hundreds of loosely-coupled microservices. Microservices fundamentally change a lot of assumptions current cloud systems are designed with, and present both opportunities and challenges when optimizing for quality of service (QoS) and utilization. In this paper we explore the implications microservices have across the cloud system stack. We first present DeathStarBench, a novel, open-source benchmark suite built with microservices that is representative of large end-to-end services, modular and extensible. DeathStarBench includes a social network, a media service, an e-commerce site, a banking system, and IoT applications for coordination control of UAV swarms. We then use DeathStarBench to study the architectural characteristics of microservices, their implications in networking and operating systems, their challenges with respect to cluster management, and their trade-offs in terms of application design and programming frameworks. Finally, we explore the tail at scale effects of microservices in real deployments with hundreds of users, and highlight the increased pressure they put on performance predictability.
Comparison of TFETs and CMOS Using Optimal Design Points for Power–Speed Tradeoffs

Tunnel transistors are one of the most attractive steep subthreshold slope devices currently being investigated as a means of overcoming the power density and energy inefficiency limitations of CMOS technology. In this paper, the evaluation and the comparison of the performance of distinct fan-in logic gates, using a set of widely accepted power–speed metrics, are addressed for five projected tunnel transistor (TFET) technologies and four mosfet and FinFET transistors. The impact of logic depth, switching activity, and minimum supply voltage has been also included in our analysis. Provided results suggest that benefits in terms of a certain metric, in which a higher weight is placed on power or delay, are strongly determined by the selected device. Particularly, the suitability of two of the explored TFET technologies to improve CMOS performance for different metrics is pointed out. A circuit level benchmark is evaluated to validate our analysis.
Social Cards Probably Provide For Better Understanding Of Web Archive Collections

Used by a variety of researchers, web archive collections have become invaluable sources of evidence. If a researcher is presented with a web archive collection that they did not create, how do they know what is inside so that they can use it for their own research? Search engine results and social media links are represented as surrogates, small easily digestible summaries of the underlying page. Search engines and social media have a different focus, and hence produce different surrogates than web archives. Search engine surrogates help a user answer the question "Will this link meet my information need? " Social media surrogates help a user decide "Should I click on this? " Our use case is subtly different. We hypothesize that groups of surrogates together are useful for summarizing a collection. We want to help users answer the question of "What does the underlying collection contain? " But which surrogate should we use? With Mechanical Turk participants, we evaluate six different surrogate types against each other. We find that the type of surrogate does not influence the time to complete the task we presented the participants. Of particular interest are social cards, surrogates typically found on social media, and browser thumbnails, screen captures of web pages rendered in a browser. At p=0.0569, and p=0.0770, respectively, we find that social cards and social cards paired side-by-side with browser thumbnails probably provide better collection understanding than the surrogates currently used by the popular Archive-It web archiving platform. We measure user interactions with each surrogate and find that users interact with social cards less than other types. The results of this study have implications for our web archive summarization work, live web curation platforms, social media, and more.
Efficient Neural Task Adaptation by Maximum Entropy Initialization

Transferring knowledge from one neural network to another has been shown to be helpful for learning tasks with few training examples. Prevailing fine-tuning methods could potentially contaminate pre-trained features by comparably high energy random noise. This noise is mainly delivered from a careless replacement of task-specific parameters. We analyze theoretically such knowledge contamination for classification tasks and propose a practical and easy to apply method to trap and minimize the contaminant. In our approach, the entropy of the output estimates gets maximized initially and the first back-propagated error is stalled at the output of the last layer. Our proposed method not only outperforms the traditional fine-tuning, but also significantly speeds up the convergence of the learner. It is robust to randomness and independent of the choice of architecture. Overall, our experiments show that the power of transfer learning has been substantially underestimated so far.
Doubling Phase Shifters for Efficient Hybrid Precoder Design in Millimeter-Wave Communication Systems

Hybrid precoding is a cost-effective approach to support directional transmissions for millimeter-wave (mm-wave) communications, but its precoder design is highly complicated. In this paper, we propose a new hybrid precoder implementation, namely the double phase shifter (DPS) implementation, which enables highly tractable hybrid precoder design. Efficient algorithms are then developed for two popular hybrid precoder structures, i.e., the fully- and partially-connected structures. For the fully-connected one, the RF-only precoding and hybrid precoding problems are formulated as a least absolute shrinkage and selection operator (LASSO) problem and a low-rank matrix approximation problem, respectively. In this way, computationally efficient algorithms are provided to approach the performance of the fully digital one with a small number of radio frequency (RF) chains. On the other hand, the hybrid precoder design in the partially-connected structure is identified as an eigenvalue problem. To enhance the performance of this cost-effective structure, dynamic mapping from RF chains to antennas is further proposed, for which a greedy algorithm and a modified K-means algorithm are developed. Simulation results demonstrate the performance gains of the proposed hybrid precoding algorithms over existing ones. It shows that, with the proposed DPS implementation, the fully-connected structure enjoys both satisfactory performance and low design complexity while the partially-connected one serves as an economic solution with low hardware complexity.
Exploring Temporal Information for Improved Video Understanding

Author(s): Zhu, Yi | Advisor(s): Newsam, Shawn | Abstract: In this dissertation, I present my work towards exploring temporal information for better video understanding. Specifically, I have worked on two problems: action recognition and semantic segmentation. For action recognition, I have proposed a framework, termed hidden two-stream networks, to learn an optimal motion representation that does not require the computation of optical flow. My framework alleviates several challenges faced in video classification, such as learning motion representations, real-time inference, multi-framerate handling, generalizability to unseen actions, etc. For semantic segmentation, I have introduced a general framework that uses video prediction models to synthesize new training samples. By scaling up the training dataset, my trained models are more accurate and robust than previous models even without modifications to the network architectures or objective functions. Along these lines of research, I have worked on several related problems. I performed the first investigation into depth for large-scale video action recognition where the depth cues are estimated from the videos themselves. I further improved my hidden two-stream networks for action recognition through several strategies, including a novel random temporal skipping data sampling method, an occlusion-aware motion estimation network and a global segment framework. For zero-shot action recognition, I proposed a pipeline using a large-scale training source to achieve a universal representation that can generalize to more realistic cross-dataset unseen action recognition scenarios. To learn better motion information in a video, I introduced several techniques to improve optical flow estimation, including guided learning, DenseNet upsampling and occlusion-aware estimation. I believe videos have much more potential to be mined, and temporal information is one of the most important cues for machines to perceive the visual world better.
ShrinkTeaNet: Million-scale Lightweight Face Recognition via Shrinking Teacher-Student Networks

Large-scale face recognition in-the-wild has been recently achieved matured performance in many real work applications. However, such systems are built on GPU platforms and mostly deploy heavy deep network architectures. Given a high-performance heavy network as a teacher, this work presents a simple and elegant teacher-student learning paradigm, namely ShrinkTeaNet, to train a portable student network that has significantly fewer parameters and competitive accuracy against the teacher network. Far apart from prior teacher-student frameworks mainly focusing on accuracy and compression ratios in closed-set problems, our proposed teacher-student network is proved to be more robust against open-set problem, i.e. large-scale face recognition. In addition, this work introduces a novel Angular Distillation Loss for distilling the feature direction and the sample distributions of the teacher's hypersphere to its student. Then ShrinkTeaNet framework can efficiently guide the student's learning process with the teacher's knowledge presented in both intermediate and last stages of the feature embedding. Evaluations on LFW, CFP-FP, AgeDB, IJB-B and IJB-C Janus, and MegaFace with one million distractors have demonstrated the efficiency of the proposed approach to learn robust student networks which have satisfying accuracy and compact sizes. Our ShrinkTeaNet is able to support the light-weight architecture achieving high performance with 99.77% on LFW and 95.64% on large-scale Megaface protocols.
Deep Multi-Index Hashing for Person Re-Identification

Traditional person re-identification (ReID) methods typically represent person images as real-valued features, which makes ReID inefficient when the gallery set is extremely large. Recently, some hashing methods have been proposed to make ReID more efficient. However, these hashing methods will deteriorate the accuracy in general, and the efficiency of them is still not high enough. In this paper, we propose a novel hashing method, called deep multi-index hashing (DMIH), to improve both efficiency and accuracy for ReID. DMIH seamlessly integrates multi-index hashing and multi-branch based networks into the same framework. Furthermore, a novel block-wise multi-index hashing table construction approach and a search-aware multi-index (SAMI) loss are proposed in DMIH to improve the search efficiency. Experiments on three widely used datasets show that DMIH can outperform other state-of-the-art baselines, including both hashing methods and real-valued methods, in terms of both efficiency and accuracy.
Unsupervised Learning of Anomaly Detection from Contaminated Image Data using Simultaneous Encoder Training

Unsupervised learning of anomaly detection in high-dimensional data, such as images, is a challenging problem recently subject to intense research. Through careful modelling of the data distribution of normal samples, it is possible to detect deviant samples, so called anomalies. Generative Adversarial Networks (GANs) can model the highly complex, high-dimensional data distribution of normal image samples, and have shown to be a suitable approach to the problem. Previously published GAN-based anomaly detection methods often assume that anomaly-free data is available for training. However, this assumption is not valid in most real-life scenarios, a.k. a. in the wild. In this work, we evaluate the effects of anomaly contaminations in the training data on state-of-the-art GAN-based anomaly detection methods. As expected, detection performance deteriorates. To address this performance drop, we propose to add an additional encoder network already at training time and show that joint generator-encoder training stratifies the latent space, mitigating the problem with contaminated data. We show experimentally that the norm of a query image in this stratified latent space becomes a highly significant cue to discriminate anomalies from normal data. The proposed method achieves state-of-the-art performance on CIFAR-10 as well as on a large, previously untested dataset with cell images.