Miracle-dz/UniFAR-FTUs huggingface dataset:
Quasi-Delay-Insensitive Implementation of Approximate Addition

Asynchronous quasi-delay-insensitive (QDI) implementation of approximate addition is described in this article. The objective is to provide an insight into the optimization in design metrics that can be achieved with approximate addition compared to accurate addition based on a QDI implementation by considering a practical digital image processing application. For the QDI implementation, some approximate adder architectures are considered which are deemed suitable for both ASIC and FPGA based implementations. The accurate and approximate adders considered are of size 32-bits. The delay-insensitive dual-rail code was used for data encoding, and four-phase return-to-zero (RTZ) and return-to-one (RTO) handshake protocols were used separately for data communication. The implementations used a 32/28-nm complementary metal oxide semiconductor (CMOS) technology. The simulation results show that an approximate adder HOERAA achieves a 19.7% reduction in cycle time, a 12.5% reduction in area, and an 17.7% reduction in energy compared to the accurate adder for RTZ handshaking. For RTO handshaking, HOERAA achieves an 18.7% reduction in cycle time, a 12.4% reduction in area, and a 16.6% reduction in energy compared to the accurate adder. Another approximate adder HEAA achieves a 19.7% reduction in cycle time, a 12.9% reduction in area, and a 20.2% reduction in energy, compared to the accurate adder for RTZ handshaking. For RTO handshaking, HEAA achieves an 18.7% reduction in cycle time, a 12.9% reduction in area, and a 19.2% reduction in energy compared to the accurate adder. Nevertheless, the RTO handshaking is preferable to RTZ handshaking as the former facilitates slightly better optimizations in design metrics compared to the latter. The mean absolute error (MAE) and the root mean square error (RMSE), which are popular error metrics used in approximate computing, were calculated for the approximate adders and are given for a comparison. While the MAE of HOERAA and HEAA are comparable, HOERAA has 8.6% reduced RMSE compared to HEAA. Digital image processing results based on accurate and approximate additions are also given, to substantiate the usefulness of approximate addition.
Impact of losses on the negative index behavior in a chiral dielectric and tradeoffs between dielectric and magnetic losses toward zero effective attenuation

Abstract. Negative refractive index is introduced in metamaterials whereby the Poynting vector of the electromagnetic (EM) wave is in opposition to the group velocity, or alternatively, when the group and phase velocities in the medium are in opposition. In recent years, considerable research has been carried out relative to the EM propagation and refraction characteristics in metamaterials with emphasis on the origins of negative refractive index. The latter phenomenon has been extensively investigated in the literature, including recent work involving chiral metamaterials with material dispersion up to the first and second orders. An EM propagation in a chiral, dispersive material up to the first order is examined for possible emergence of negative refractive index under non-conductive losses. Three loss scenarios are considered, viz., dielectric losses (complex permittivity), magnetic losses (complex permeability), and a combination of both types of losses. A spectral approach combined with a slowly time-varying phasor analysis is applied, leading to the analytic derivation of EM phase and group velocities and corresponding indices. The velocities and indices are examined by selecting arbitrary dispersion parameters based on published practical values. The results indicate the emergence of negative index within specific radio frequency modulation bands. The resulting polarization states of the fields are examined under loss conditions. Finally, a method is suggested whereby the effects of dielectric and magnetic losses may be combined via appropriate Taylor coefficients such that the effective attenuation constant may be driven to zero via induced gain mechanisms arising from the dielectric and magnetic loss models.
Radiance and the Power of Erasure in an Obsidian Lamaštu Amulet

cuneiform ritual tablets in The Metropolitan Museum of Art’s collection of ancient Near Eastern art is a nearly pristine obsidian amulet of the first millennium b.c. This amulet, small enough to fit in the palm of one’s hand, provides protection from the Mesopotamian demon Lamaštu. On one side is a representation of the demon surrounded by various ritual paraphernalia (fig. 1), and on the other, a ritual incantation carved in cuneiform script (fig. 2). In its current display—mounted flat against a beige cloth support—the opacity of the obsidian’s dark color makes it difficult to see the image and also precludes any observation of the text. When the amulet is examined at close range, however, one is able to see how brilliantly light reflects off the surface and gets a M I R I A M S A I D Radiance and the Power of Erasure in an Obsidian Lamaštu Amulet
On the Convergence of Smooth Regularized Approximate Value Iteration Schemes

Entropy regularization, smoothing of Q-values and neural network function ap-proximator are key components of the state-of-the-art reinforcement learning (RL) algorithms, such as Soft Actor-Critic [1]. Despite the widespread use, the impact of these core techniques on the convergence of RL algorithms is not yet fully understood. In this work, we analyse these techniques from error propagation perspective using the approximate dynamic programming framework. In particular, our analysis shows that (1) value smoothing results in increased stability of the algorithm in exchange for slower convergence, (2) entropy regularization reduces overestimation errors at the cost of modifying the original problem, (3) we study a combination of these techniques that describes the Soft Actor-Critic algorithm
GCOMB: Learning Budget-constrained Combinatorial Algorithms over Billion-sized Graphs

There has been an increased interest in discovering heuristics for combinatorial problems on graphs through machine learning. While existing techniques have primarily focused on obtaining high-quality solutions, scalability to billion-sized graphs has not been adequately addressed. In addition, the impact of budget-constraint, which is necessary for many practical scenarios, remains to be studied. In this paper, we propose a framework called G COMB to bridge these gaps. G COMB trains a Graph Convolutional Network (GCN) using a novel probabilistic greedy mechanism to predict the quality of a node. To further facilitate the combinatorial nature of the problem, G COMB utilizes a Q -learning framework, which is made efﬁcient through importance sampling . We perform extensive experiments on real graphs to benchmark the efﬁciency and efﬁcacy of G COMB . Our results establish that G COMB is 100 times faster and marginally better in quality than state-of-the-art algorithms for learning combinatorial algorithms. Additionally, a case-study on the practical combinatorial problem of Inﬂuence Maximization (IM) shows G COMB is 150 times faster than the specialized IM algorithm IMM with similar quality.
A graph similarity for deep learning

Graph neural networks (GNNs) have been successful in learning representations from graphs. Many popular GNNs follow the pattern of aggregate-transform : they aggregate the neighbors’ attributes and then transform the results of aggregation with a learnable function. Analyses of these GNNs explain which pairs of non-identical graphs have different representations. However, we still lack an understanding of how similar these representations will be. We adopt kernel distance and propose transform-sum-cat as an alternative to aggregate-transform to reﬂect the continuous similarity between the node neighborhoods in the neighborhood aggregation. The idea leads to a simple and efﬁcient graph similarity, which we name Weisfeiler–Leman similarity (WLS). In contrast to existing graph kernels, WLS is easy to implement with common deep learning frameworks. In graph classiﬁcation experiments, transform-sum-cat signiﬁcantly outperforms other neighborhood aggregation methods from popular GNN models. We also develop a simple and fast GNN model based on transform-sum-cat, which obtains, in comparison with widely used GNN models, (1) a higher accuracy in node classiﬁcation, (2) a lower absolute error in graph regression, and (3) greater stability in adversarial training of graph generation.
Rethinking Learnable Tree Filter for Generic Feature Transform

The Learnable Tree Filter presents a remarkable approach to model structure-preserving relations for semantic segmentation. Nevertheless, the intrinsic geometric constraint forces it to focus on the regions with close spatial distance, hindering the effective long-range interactions. To relax the geometric constraint, we give the analysis by reformulating it as a Markov Random Field and introduce a learnable unary term. Besides, we propose a learnable spanning tree algorithm to replace the original non-differentiable one, which further improves the flexibility and robustness. With the above improvements, our method can better capture long-range dependencies and preserve structural details with linear complexity, which is extended to several vision tasks for more generic feature transform. Extensive experiments on object detection/instance segmentation demonstrate the consistent improvements over the original version. For semantic segmentation, we achieve leading performance (82.1% mIoU) on the Cityscapes benchmark without bells-and-whistles. Code is available at https://github.com/StevenGrove/LearnableTreeFilterV2.
A review of current trends on visual perception studies in virtual and augmented reality

In the development of novel algorithms and techniques in virtual and augmented reality (VR/AR), it is crucial to take human visual perception into account. For example, when hardware resources are a restraining factor, the limitations of the human visual system can be exploited in the creation and evaluation of new effective techniques. Over the last decades, visual perception evaluation studies have become a vital part of the design, development, and evaluation of immersive computer graphics applications. This course aims at introducing the attendees to the basic concepts of visual perception applied to computer graphics and it offers an overview of recent perceptual evaluation studies that have been conducted with head-mounted displays (HMDs) in the context of VR and AR applications. During this course, we call attention to the latest published courses and surveys on visual perception applied to computer graphics and interaction techniques. Through an extensive search in the literature, we have identified six main areas in which recent visual perceptual evaluation studies have been focused on: distance perception, avatar perception, image quality, interaction, motion perception, and cybersickness. Trends, main results, and open challenges are discussed for each area and accompanied with relevant references offering the attendees a wide introduction and perspective on the topic.
Three-Saddle-Foci Chaotic Behavior of a Modified Jerk Circuit with Chua's Diode

This paper investigates the chaotic behavior of a modified jerk circuit with Chua’s diode. The Chua’s diode considered here is a nonlinear resistor having a symmetric piecewise linear voltage-current characteristic. To describe the system, we apply fundamental laws in electrical circuit theory to formulate a mathematical model in terms of a third-order (jerk) nonlinear differential equation, or equivalently, a system of three first-order differential equations. The analysis shows that this system has three collinear equilibrium points. The time waveform and the trajectories about each equilibrium point depend on its associated eigenvalues. We prove that all three equilibrium points are of type saddle focus, meaning that the trajectory of (x(t),y(t)) diverges in a spiral form but z(t) converges to the equilibrium point for any initial point (x(0),y(0),z(0)). Numerical simulation illustrates that the oscillations are dense, have no period, are highly sensitive to initial conditions, and have a chaotic hidden attractor.
Adapting to Misspecification in Contextual Bandits

A major research direction in contextual bandits is to develop algorithms that are computationally efficient, yet support flexible, general-purpose function approximation. Algorithms based on modeling rewards have shown strong empirical performance, but typically require a well-specified model, and can fail when this assumption does not hold. Can we design algorithms that are efficient and flexible, yet degrade gracefully in the face of model misspecification? We introduce a new family of oracle-efficient algorithms for $\varepsilon$-misspecified contextual bandits that adapt to unknown model misspecification -- both for finite and infinite action settings. Given access to an online oracle for square loss regression, our algorithm attains optimal regret and -- in particular -- optimal dependence on the misspecification level, with no prior knowledge. Specializing to linear contextual bandits with infinite actions in $d$ dimensions, we obtain the first algorithm that achieves the optimal $O(d\sqrt{T} + \varepsilon\sqrt{d}T)$ regret bound for unknown misspecification level $\varepsilon$. On a conceptual level, our results are enabled by a new optimization-based perspective on the regression oracle reduction framework of Foster and Rakhlin, which we anticipate will find broader use.
Stochastic Normalization

Fine-tuning pre-trained deep networks on a small dataset is an important component in the deep learning pipeline. A critical problem in ﬁne-tuning is how to avoid over-ﬁtting when data are limited. Existing efforts work from two aspects: (1) impose regularization on parameters or features; (2) transfer prior knowledge to ﬁne-tuning by reusing pre-trained parameters. In this paper, we take an alternative approach by refactoring the widely used Batch Normalization (BN) module to mitigate over-ﬁtting. We propose a two-branch design with one branch normalized by mini-batch statistics and the other branch normalized by moving statistics. During training, two branches are stochastically selected to avoid over-depending on some sample statistics, resulting in a strong regularization effect, which we interpret as “architecture regularization. ” The resulting method is dubbed stochastic normalization ( StochNorm ). With the two-branch architecture, it naturally incorporates pre-trained moving statistics in BN layers during ﬁne-tuning, exploiting more prior knowledge of pre-trained networks. Extensive empirical experiments show that StochNorm is a powerful tool to avoid over-ﬁtting in ﬁne-tuning with small datasets. Besides, StochNorm is readily pluggable in modern CNN backbones. It is complementary to other ﬁne-tuning methods and can work together to achieve stronger regularization effect.
On the Use of Jumping Gestures for Immersive Teleportation in VR

Virtual environments can be inﬁnitely large, but users only have a limited amount of space in the physical world. One way to navigate within large virtual environments is through teleportation. Teleportation requires two steps: targeting a place and sudden shifting. Conventional teleportation uses a controller to point to a target position and a button press or release to immediately teleport the user to the position. Since the teleportation does not require physical movement, the user can explore the entire virtual environment. However, as this is supernatural and can lead to momentary disorientation, it can break the sense of presence, and thus degrade the overall virtual reality experience. To compensate for the downside of this technique, we explore the effects of a jumping gesture as a teleportation trigger. We conducted a study with two factors: 1) triggering method (Jumping and Standing), and 2) targeting method (Head-direction and Controller). We found that the conventional way of using a controller while standing showed better efﬁciency, the highest usability and lower cybersickness. Nevertheless, Jumping+Controller invoked a high sense of engagement and fun, and therefore provides an interesting new technique, especially for VR games.
Online Decision Based Visual Tracking via Reinforcement Learning

A deep visual tracker is typically based on either object detection or template matching while each of them is only suitable for a particular group of scenes. It is straightforward to consider fusing them together to pursue more reliable tracking. However, this is not wise as they follow different tracking principles. Unlike previous fusion-based methods, we propose a novel ensemble framework, named DTNet, with an online decision mechanism for visual tracking based on hierarchical reinforcement learning. The decision mechanism substantiates an intelligent switching strategy where the detection and the template trackers have to compete with each other to conduct tracking within different scenes that they are adept in. Besides, we present a novel detection tracker which avoids the common issue of incorrect proposal. Extensive results show that our DTNet achieves state-of-the-art tracking performance as well as a good balance between accuracy and efﬁciency. The project website is available at https://vsislab.github. io/DTNet/ .
On Graph-Orthogonal Arrays by Mutually Orthogonal Graph Squares

During the last two centuries, after the question asked by Euler concerning mutually orthogonal Latin squares (MOLS), essential advances have been made. MOLS are considered as a construction tool for orthogonal arrays. Although Latin squares have numerous helpful properties, for some factual applications these structures are excessively prohibitive. The more general concepts of graph squares and mutually orthogonal graph squares (MOGS) offer more flexibility. MOGS generalize MOLS in an interesting way. As such, the topic is attractive. Orthogonal arrays are essential in statistics and are related to finite fields, geometry, combinatorics and error-correcting codes. Furthermore, they are used in cryptography and computer science. In this paper, our current efforts have concentrated on the definition of the graph-orthogonal arrays and on proving that if there are k MOGS of order n, then there is a graph-orthogonal array, and we denote this array by G-OA(n2,k,n,2). In addition, several new results for the orthogonal arrays obtained from the MOGS are given. Furthermore, we introduce a recursive construction method for constructing the graph-orthogonal arrays.
Curriculum Learning by Dynamic Instance Hardness

A good teacher can adjust a curriculum based on students’ learning history. By analogy, in this paper, we study the dynamics of a deep neural network’s (DNN) performance on individual samples during its learning process. The observed properties allow us to develop an adaptive curriculum that leads to faster learning of more accurate models. We introduce dynamic instance hardness ( DIH ), the exponential moving average of a sample’s instantaneous hardness (e.g., a loss, or a change in output) over the training history. A low DIH indicates that a model retains knowledge about a sample over time. For DNNs, we ﬁnd that a sample’s DIH early in training predicts its DIH in later stages. Hence, we can train a model using samples mostly with higher DIH and safely deprioritize those with lower DIH. This motivates a DIH guided curriculum learning ( DIHCL ) procedure. Compared to existing CL methods: (1) DIH is more stable over time than using only instantaneous hardness, which is noisy due to stochastic training and DNN’s non-smoothness; (2) DIHCL is computationally inexpensive since it uses only a byproduct of back-propagation and thus does not require extra inference. On 11 datasets, DIHCL signiﬁcantly outperforms random mini-batch SGD and recent CL methods in terms of efﬁciency and ﬁnal performance. The code of DIHCL is available at https://github.com/tianyizhou/DIHCL .
Group Contextual Encoding for 3D Point Clouds

Global context is crucial for 3D point cloud scene understanding tasks. In this work, we extend the contextual encoding layer that was originally designed for 2D tasks to 3D point cloud scenarios. The encoding layer learns a set of code words in feature space of the 3D point cloud to characterize the global semantic context, and then based on these code words, the method learns a global contextual descriptor to reweight the feature maps accordingly. Moreover, compared to 2D scenarios, data sparsity becomes a major issue in 3D point cloud scenarios, and the performance of contextual encoding quickly saturates when the number of code words increases. To mitigate this problem, we further propose a group contextual encoding method, which divides the channel into groups and then performs encoding on group-divided feature vectors. This method facilitates learning of global context in grouped subspace for 3D point clouds. We evaluate the effectiveness and generalizability of our method on three widely-studied 3D point cloud tasks. Experimental results have shown that the proposed method outperformed the VoteNet remarkably with 3 mAP on the benchmark of SUN-RGBD, with the metrics of mAP@0.25, and a much greater margin of 6.57 mAP on ScanNet with the metrics of mAP@0.5. Compared to the baseline of PointNet++, the proposed method leads to an accuracy of 86%, outperforming the baseline by 1.5%.
Effect of antioxidants and latex vulcanising agents on the environmental degradation of latex films.

The role of antioxidants and latex vulcanising agents on the environmental degradation of NR latex gloves and films was studied. In soil burial tests, acetone-treated glove pieces degraded significantly more than the untreated samples after 24 weeks, whereas polychloroprene and nitrile pieces remained unaffected. This solvent-induced rapid degradation rate of NR glove pieces was due to the leaching out of the organic antioxidants and dithiocarbamate accelerators. Under the scanning electron microscope, the particulate-structured surface of the treated NR samples displayed wide cracks and cavities in the degraded areas which indicates bulk disintegration in the film. In agar plate culture studies, more than 80% of bacteria could grow at the highest level (10 000 μg/mL) of the phenolic antioxidants (Irganox, Wingstay L). With the dithiocarbamate accelerators, ZDEC was more inhibitory than ZDBC as 50% of the bacteria grew at the highest level of ZDBC (10 000 μg/mL) but only 16% grew on ZDEC. The growth of fungi except yeast was unaffected by the presence of the antioxidants or accelerators. When latex films of varying curing systems were used, increasing the level of crosslink concentrations increased their resistance to environmental degradation. As a group, the non-sulphur prevulcanised films degraded much faster than the sulphur-vulcanised films. The singular presence of sulphur, ZDEC, ZnO or Irganox did not significantly affect the resistance of the non-sulphur prevulcanised films to environmental degradation whereas the combined effect of these additives did. The study confirmed that antioxidants and accelerators affect the rate of latex film degradation via their role in influencing the crosslink efficiency of the rubber.