Miracle-dz/UniFAR-FTUs huggingface dataset:
Addressing Variability in Reuse Prediction for Last-Level Caches

Last-Level Cache (LLC) represents the bulk of a modern CPU processor's transistor budget and is essential for application performance as LLC enables fast access to data in contrast to much slower main memory. However, applications with large working set size often exhibit streaming and/or thrashing access patterns at LLC. As a result, a large fraction of the LLC capacity is occupied by dead blocks that will not be referenced again, leading to inefficient utilization of the LLC capacity. To improve cache efficiency, the state-of-the-art cache management techniques employ prediction mechanisms that learn from the past access patterns with an aim to accurately identify as many dead blocks as possible. Once identified, dead blocks are evicted from LLC to make space for potentially high reuse cache blocks. In this thesis, we identify variability in the reuse behavior of cache blocks as the key limiting factor in maximizing cache efficiency for state-of-the-art predictive techniques. Variability in reuse prediction is inevitable due to numerous factors that are outside the control of LLC. The sources of variability include control-flow variation, speculative execution and contention from cores sharing the cache, among others. Variability in reuse prediction challenges existing techniques in reliably identifying the end of a block's useful lifetime, thus causing lower prediction accuracy, coverage, or both. To address this challenge, this thesis aims to design robust cache management mechanisms and policies for LLC in the face of variability in reuse prediction to minimize cache misses, while keeping the cost and complexity of the hardware implementation low. To that end, we propose two cache management techniques, one domain-agnostic and one domain-specialized, to improve cache efficiency by addressing variability in reuse prediction.
Spherical Motion Dynamics of Deep Neural Networks with Batch Normalization and Weight Decay

We comprehensively reveal the learning dynamics of deep neural networks (DNN) with batch normalization (BN) and weight decay (WD), named as Spherical Motion Dynamics (SMD). Our theorem on SMD is based on the scale-invariant property of weights caused by BN, and regularization effect of WD. SMD shows the optimization trajectory of weights is like a spherical motion; and a new indicator, angular update is proposed to measure the update efficiency of DNN with BN and WD. We rigorously prove that the angular update is only determined by pre-defined hyper-parameters (i.e. learning rate, WD parameter and momentum coefficient), and provide their quantitative relationship. Most importantly, the quantitative result of SMD can perfectly match the empirical observation in complex and large scale computer vision tasks like ImageNet and COCO with standard training schemes. SMD can also yield reasonable interpretations on some phenomena about BN from an entirely new perspective, including avoidance of vanishing and exploding gradient, no risk of being trapped into sharp minima, and sudden drop of loss when shrinking learning rate. Further, to present the practical significance of SMD, we discuss the connection between SMD and commonly used learning rate tuning scheme: Linear Scaling Principle.
Convolutional Generation of Textured 3D Meshes

Recent generative models for 2D images achieve impressive visual results, but clearly lack the ability to perform 3D reasoning. This heavily restricts the degree of control over generated objects as well as the possible applications of such models. In this work, we leverage recent advances in differentiable rendering to design a framework that can generate triangle meshes and associated high-resolution texture maps, using only 2D supervision from single-view natural images. A key contribution of our work is the encoding of the mesh and texture as 2D representations, which are semantically aligned and can be easily modeled by a 2D convolutional GAN. We demonstrate the efficacy of our method on Pascal3D+ Cars and the CUB birds dataset, both in an unconditional setting and in settings where the model is conditioned on class labels, attributes, and text. Finally, we propose an evaluation methodology that assesses the mesh and texture quality separately.
Notes about the macroscopic fluctuating theory

The macroscopic fluctuating theory developed during the last 30 years is applied to generic systems described by continuum fields ϕ(x, t) that evolve by a Langevin equation that locally either conserves or does not conserve the field. This paper aims to review well-known basic concepts and results from a pedagogical point of view by following a general framework in a practical and self-consistent way. From the probability of a path, we study the general properties of the system’s stationary state. In particular, we focus on the study of the quasipotential that defines the stationary distribution at the small noise limit. To discriminate between equilibrium and non-equilibrium stationary states, the system’s adjoint dynamics are defined as the system’s time-reversal Markov process. The equilibrium is then defined as the unique stationary state that is dynamically time-reversible, and therefore its adjoint dynamics are equal to those of the original one. This property is confronted with the macroscopic reversibility that occurs when the most probable path to create a fluctuation from the stationary state is equal to the time-reversed path that relaxes it. The lack of this symmetry implies a nonequilibrium stationary state; however, the converse is not true. Finally, we extensively study the two-body correlations at the stationary state. We derive some generic properties at various situations, including a discussion about the equivalence of ensembles in nonequilibrium systems.
Current bounds and future prospects of light neutralino dark matter in the NMSSM

Unlike its minimal counterpart, the Next to Minimal supersymmetric Standard Model (NMSSM) allows the possibility that the lightest neutralino could have a mass as small as $\sim 1~{\rm GeV}$ while still providing a significant component of relic dark matter (DM). Such a neutralino can provide an invisible decay mode to the Higgs as well. Further, the observed SM-like Higgs boson ($H_{125}$) could also have an invisible branching fraction as high as $\sim 19\%$. Led by these facts, we first delineate the region of parameter space of the NMSSM with a light neutralino ($M_{{\tilde{\chi}}_{1}^{0}} < 62.5~{\rm GeV}$) that yields a thermal neutralino relic density smaller than the measured relic density of cold dark matter, and is also compatible with constraints from collider searches, searches for dark matter, and from flavor physics. We then examine the prospects for probing the NMSSM with a light neutralino via direct DM detection searches, via invisible Higgs boson width experiments at future $e^+e^-$ colliders, via searches for a light singlet Higgs boson in $2b2\mu$, $2b2\tau$ and $2\mu2\tau$ channels and via pair production of winos or doublet higgsinos at the high luminosity LHC and its proposed energy upgrade. For this last-mentioned electroweakino search, we perform a detailed analysis to map out the projected reach in the $3l+{\rm E{\! \! \! /}_T}$ channel, assuming that chargino decays to $W {\tilde{\chi}}_{1}^{0}$ and the neutralino(s) decay to $Z$ or $H_{125}$ + ${\tilde{\chi}}_{1}^{0}$. We find that the HL-LHC can discover SUSY in just part of the parameter space in each of these channels, which together can probe almost the entire parameter space. The HE-LHC probes essentially the entire region with higgsinos~(winos) lighter than 1~TeV~(2~TeV) independently of how the neutralinos decay, and leads to significantly larger signal rates.
Homology and $K$-Theory of Torsion-Free Ample Groupoids and Smale Spaces

Given an ample groupoid, we construct a spectral sequence with groupoid homology with integer coefficients on the second sheet, converging to the K-groups of the groupoid C*-algebra when the groupoid has torsion-free stabilizers and satisfies the strong Baum-Connes conjecture. The construction is based on the triangulated category approach to the Baum-Connes conjecture by Meyer and Nest. For the unstable equivalence relation of a Smale space with totally disconnected stable sets, this spectral sequence shows Putnam's homology groups on the second sheet.
The Effects of Rehabilitative Training in the Treatment of Patellofemoral Pain Syndrome

Objective: Inappropriate neural control of the quadriceps femoris muscle group has been implicated in patellofemoral pain syndrome (PFPS) and the methods of biomechanical assessment of PFPS has been developed recently. The aims of the present investigation were to evaluate the effects of therapeutic exercise in the alleviation of PFPS and to develope objective clinical test for PFPS. Method: We investigated the onset time of the isometric contraction of vastus medialis oblique muscle (VMO) and the vastus lateralis muscle (VL) under four different conditions: knee flexion and extension in weight bearing (standing) and non-weight bearing (sitting on chair) situations. For each condition, onset times of EMG activities and onset times for VMO and VL were determined from five trials of isometric contraction. In addition, we compared knee flexor and extensor torques in control group and subjects with PFPS. To evaluate the effects of the exercise we compared the onset time difference (onset time of VL-onset time of VM) and quadriceps muscle torque at preand postexercise in PFPS groups. Results: In PFPS group, onset time of VMO during knee extension was significantly longer than the onset time of VL and the knee extensor torque was considerably weaker in comparison with normal group. Although onset time difference was not changed after exercise program, there were significant increase in knee extensor torque in subjects with PFPS. Conclusion: The role of exercise in the rehabilitation of quadriceps functions is to reduce the pain, to strengthen the knee extensor, and further accurate diagnostic tools and methods for the result of therapeutic exercise in PFPS are needed. ꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏ
Accurate Anchor Free Tracking

Visual object tracking is an important application of computer vision. Recently, Siamese based trackers have achieved good accuracy. However, most of Siamese based trackers are not efficient, as they exhaustively search potential object locations to define anchors and then classify each anchor (i.e., a bounding box). This paper develops the first Anchor Free Siamese Network (AFSN). Specifically, a target object is defined by a bounding box center, tracking offset, and object size. All three are regressed by Siamese network with no additional classification or regional proposal, and performed once for each frame. We also tune the stride and receptive field for Siamese network, and further perform ablation experiments to quantitatively illustrate the effectiveness of our AFSN. We evaluate AFSN using five most commonly used benchmarks and compare to the best anchor-based trackers with source codes available for each benchmark. AFSN is 3-425 times faster than these best anchor based trackers. AFSN is also 5.97% to 12.4% more accurate in terms of all metrics for benchmark sets OTB2015, VOT2015, VOT2016, VOT2018 and TrackingNet, except that SiamRPN++ is 4% better than AFSN in terms of Expected Average Overlap (EAO) on VOT2018 (but SiamRPN++ is 3.9 times slower).
On Reducing EM Exposure to Large-scale BS Antenna Array Aided with CV in LOS Scenarios

Without any radiation control measures, a large-scale mmWave antenna array at close range may lead to a large amount of electromagnetic exposure of human. In this paper, with the aid of pose detection in computer vision, an exposure avoidance method is introduced in outdoor line of sight scenarios. Instead of reducing transmitted power, the proposed method can protect the vulnerable parts of human body from electromagnetic exposure by deviating the transmission beams from vulnerable parts. Besides, a finer beam management granularity is adopted to better balance the trade-off between exposure reduction and communication quality loss, because finer beams can provide more adjustability for finding the beam that reduces exposure without excessively reducing the link quality. The proposed exposure avoidance method is validated in simulations, and the results show that the finer beam management granularity can guarantee communication quality while reducing the electromagnetic exposure.
Team RUC_AIM3 Technical Report at Activitynet 2020 Task 2: Exploring Sequential Events Detection for Dense Video Captioning

Detecting meaningful events in an untrimmed video is essential for dense video captioning. In this work, we propose a novel and simple model for event sequence generation and explore temporal relationships of the event sequence in the video. The proposed model omits inefficient two-stage proposal generation and directly generates event boundaries conditioned on bi-directional temporal dependency in one pass. Experimental results show that the proposed event sequence generation model can generate more accurate and diverse events within a small number of proposals. For the event captioning, we follow our previous work to employ the intra-event captioning models into our pipeline system. The overall system achieves state-of-the-art performance on the dense-captioning events in video task with 9.894 METEOR score on the challenge testing set.
Defending Support Vector Machines against Poisoning Attacks: the Hardness and Algorithm

Adversarial machine learning has attracted a great amount of attention in recent years. In a poisoning attack, the adversary can inject a small number of specially crafted samples into the training data which make the decision boundary severely deviate and cause unexpected misclassification. Due to the great importance and popular use of support vector machines (SVM), we consider defending SVM against poisoning attacks in this paper. We consider two common strategies for defending: designing robust SVM algorithms and data sanitization. Though several robust SVM algorithms have been proposed before, most of them either are in lack of adversarial-resilience or rely on strong assumptions about the data distribution or the attacker's behavior. Moreover, the research on the complexities is still quite limited nowadays. We are the first, to the best of our knowledge, to prove that even the simplest hard-margin one-class SVM with outliers problem is NP-complete, and has no fully PTAS unless P=NP. For the data sanitization defense, we link it to the intrinsic dimensionality of data; in particular, we provide a sampling theorem in doubling metrics for explaining the effectiveness of DBSCAN (as a density-based outlier removal method) for defending against poisoning attacks. In our empirical experiments, we compare several defenses including the DBSCAN and robust SVM methods, and investigate the influences from the intrinsic dimensionality and data density to their performances.
Hidden Markov Models Applied To Intraday Momentum Trading With Side Information

A Hidden Markov Model for intraday momentum trading is presented which specifies a latent momentum state responsible for generating the observed securities' noisy returns. Existing momentum trading models suffer from time-lagging caused by the delayed frequency response of digital filters. Time-lagging results in a momentum signal of the wrong sign, when the market changes trend direction. A key feature of this state space formulation, is no such lagging occurs, allowing for accurate shifts in signal sign at market change points. The number of latent states in the model is estimated using three techniques, cross validation, penalized likelihood criteria and simulation-based model selection for the marginal likelihood. All three techniques suggest either 2 or 3 hidden states. Model parameters are then found using Baum-Welch and Markov Chain Monte Carlo, whilst assuming a single (discretized) univariate Gaussian distribution for the emission matrix. Often a momentum trader will want to condition their trading signals on additional information. To reflect this, learning is also carried out in the presence of side information. Two sets of side information are considered, namely a ratio of realized volatilities and intraday seasonality. It is shown that splines can be used to capture statistically significant relationships from this information, allowing returns to be predicted. An Input Output Hidden Markov Model is used to incorporate these univariate predictive signals into the transition matrix, presenting a possible solution for dealing with the signal combination problem. Bayesian inference is then carried out to predict the securities $t+1$ return using the forward algorithm. Simple modifications to the current framework allow for a fully non-parametric model with asynchronous prediction.
Match and Reweight Strategy for Generalized Target Shift

We address the problem of unsupervised domain adaptation under the setting of generalized target shift (both class-conditional and label shifts occur). We show that in that setting, for good generalization, it is necessary to learn with similar source and target label distributions and to match the class-conditional probabilities. For this purpose, we propose an estimation of target label proportion by blending mixture estimation and optimal transport. This estimation comes with theoretical guarantees of correctness. Based on the estimation, we learn a model by minimizing a importance weighted loss and a Wasserstein distance between weighted marginals. We prove that this minimization allows to match class-conditionals given mild assumptions on their geometry. Our experimental results show that our method performs better on average than competitors accross a range domain adaptation problems including digits,VisDA and Office.
Emotion Recognition in Audio and Video Using Deep Neural Networks

Humans are able to comprehend information from multiple domains for e.g. speech, text and visual. With advancement of deep learning technology there has been significant improvement of speech recognition. Recognizing emotion from speech is important aspect and with deep learning technology emotion recognition has improved in accuracy and latency. There are still many challenges to improve accuracy. In this work, we attempt to explore different neural networks to improve accuracy of emotion recognition. With different architectures explored, we find (CNN+RNN) + 3DCNN multi-model architecture which processes audio spectrograms and corresponding video frames giving emotion prediction accuracy of 54.0% among 4 emotions and 71.75% among 3 emotions using IEMOCAP[2] dataset.
Fine-grained Human Evaluation of Transformer and Recurrent Approaches to Neural Machine Translation for English-to-Chinese

This research presents a fine-grained human evaluation to compare the Transformer and recurrent approaches to neural machine translation (MT), on the translation direction English-to-Chinese. To this end, we develop an error taxonomy compliant with the Multidimensional Quality Metrics (MQM) framework that is customised to the relevant phenomena of this translation direction. We then conduct an error annotation using this customised error taxonomy on the output of state-of-the-art recurrent- and Transformer-based MT systems on a subset of WMT2019’s news test set. The resulting annotation shows that, compared to the best recurrent system, the best Transformer system results in a 31% reduction of the total number of errors and it produced significantly less errors in 10 out of 22 error categories. We also note that two of the systems evaluated do not produce any error for a category that was relevant for this translation direction prior to the advent of NMT systems: Chinese classifiers.
Relational reasoning and generalization using non-symbolic neural networks

The notion of equality (identity) is simple and ubiquitous, making it a key case study for broader questions about the representations supporting abstract relational reasoning. Previous work suggested that neural networks were not suitable models of human relational reasoning because they could not represent mathematically identity, the most basic form of equality. We revisit this question. In our experiments, we assess out-of-sample generalization of equality using both arbitrary representations and representations that have been pretrained on separate tasks to imbue them with structure. We find neural networks are able to learn (a) basic equality (mathematical identity), (b) sequential equality problems (learning ABA-patterned sequences) with only positive training instances, and (c) a complex, hierarchical equality problem with only basic equality training instances ("zero-shot" generalization). In the two latter cases, our models perform tasks proposed in previous work to demarcate human-unique symbolic abilities. These results suggest that essential aspects of symbolic reasoning can emerge from data-driven, nonsymbolic learning processes. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
VLA imaging of the XMM-LSS/VIDEO deep field at 1–2 GHz

Modern radio telescopes are routinely reaching depths where normal starforming galaxies are the dominant observed population. Realising the potential of radio as a tracer of star formation and black hole activity over cosmic time involves achieving such depths over representative volumes, with radio forming part of a larger multiwavelength campaign. In pursuit of this we used the Karl G. Jansky Very Large Array (VLA) to image $\sim$5 deg$^{2}$ of the VIDEO/XMM-LSS extragalactic deep field at 1--2 GHz. We achieve a median depth of 16 $\mu$Jy beam$^{-1}$ with an angular resolution of 4.5\arcsec. Comparisons with existing radio observations of XMM-LSS showcase the improved survey speed of the upgraded VLA: we cover 2.5 times the area and increase the depth by $\sim$20\% in 40\% of the time. Direction-dependent calibration and wide-field imaging were required to suppress the error patterns from off-axis sources of even modest brightness. We derive a catalogue containing 5,762 sources from the final mosaic. Sub-band imaging provides in-band spectral indices for 3,458 (60\%) sources, with the average spectrum becoming flatter than the canonical synchrotron slope below 1 mJy. Positional and flux-density accuracy of the observations, and the differential source counts are in excellent agreement with those of existing measurements. A public release of the images and catalogue accompanies this article.
DreamCoder: growing generalizable, interpretable knowledge with wake–sleep Bayesian program learning

Expert problem-solving is driven by powerful languages for thinking about problems and their solutions. Acquiring expertise means learning these languages—systems of concepts, alongside the skills to use them. We present DreamCoder, a system that learns to solve problems by writing programs. It builds expertise by creating domain-specific programming languages for expressing domain concepts, together with neural networks to guide the search for programs within these languages. A ‘wake–sleep’ learning algorithm alternately extends the language with new symbolic abstractions and trains the neural network on imagined and replayed problems. DreamCoder solves both classic inductive programming tasks and creative tasks such as drawing pictures and building scenes. It rediscovers the basics of modern functional programming, vector algebra and classical physics, including Newton’s and Coulomb’s laws. Concepts are built compositionally from those learned earlier, yielding multilayered symbolic representations that are interpretable and transferrable to new tasks, while still growing scalably and flexibly with experience. This article is part of a discussion meeting issue ‘Cognitive artificial intelligence’.