Miracle-dz/UniFAR-FTUs huggingface dataset:
Mitigating network noise on Dragonfly networks through application-aware routing

System noise can negatively impact the performance of HPC systems, and the interconnection network is one of the main factors contributing to this problem. To mitigate this effect, adaptive routing sends packets on non-minimal paths if they are less congested. However, while this may mitigate interference caused by congestion, it also generates more traffic since packets traverse additional hops, causing in turn congestion on other applications and on the application itself. In this paper, we first describe how to estimate network noise. By following these guidelines, we show how noise can be reduced by using routing algorithms which select minimal paths with a higher probability. We exploit this knowledge to design an algorithm which changes the probability of selecting minimal paths according to the application characteristics. We validate our solution on microbenchmarks and real-world applications on two systems relying on a Dragonfly interconnection network, showing noise reduction and performance improvement.
The Formation of Subdwarf A-type Stars

Subdwarf A-type stars (sdAs) are objects that have hydrogen-rich spectra with surface gravity similar to that of hot subdwarf stars but effective temperature below the zero-age horizontal branch. They are considered to be metal-poor main-sequence (MS) stars or extremely low-mass white dwarfs (ELM WDs). In this work, using the stellar evolution code Modules for Experiments in Stellar Astrophysics, we investigate the sdAs formed both by the evolution of (pre-)ELM WDs in double-degenerate systems and metal-poor MS stars with single evolution models. We find that both of the evolutionary tracks of ELM WDs and metal-poor MS stars can explain the observation properties of sdAs. However, the proportions between these two populations are uncertain. In this work, we adopt the method of binary population synthesis of both ELM WDs in the disk and metal-poor MS stars in the halo to obtain their populations at different stellar population ages and calculate their proportions. We find that the proportion of metal-poor MS stars to sdAs for a stellar population of 10 Gyr is ∼98.5%, which is consistent with the conclusion that most sdAs (>95%) are metal-poor MS stars. And the proportion of ELM WDs (metal-poor MS stars) to sdAs increases (decreases) from 0.1% (99.9%) to 20% (80%) with stellar population ages from 5 to 13.7 Gyr.
Learning Explicit and Implicit Structures for Targeted Sentiment Analysis

Targeted sentiment analysis is the task of jointly predicting target entities and their associated sentiment information. Existing research efforts mostly regard this joint task as a sequence labeling problem, building models that can capture explicit structures in the output space. However, the importance of capturing implicit global structural information that resides in the input space is largely unexplored. In this work, we argue that both types of information (implicit and explicit structural information) are crucial for building a successful targeted sentiment analysis model. Our experimental results show that properly capturing both information is able to lead to better performance than competitive existing approaches. We also conduct extensive experiments to investigate our model’s effectiveness and robustness.
Cosmic-ray-driven outflow from the Galactic Centre and the origin of magnetized radio filaments

Radio, X-ray, and infrared observations of the inner few hundred parsecs of the Galactic Centre have highlighted two characteristics of the interstellar medium. The cosmic-ray ionization rate derived from molecular ions such as H$^+_3$ is at least two to three orders of magnitude higher than in the Galactic disc. The other is bipolar X-ray and radio emission away from the Galactic plane. These features are consistent with a scenario in which high cosmic-ray pressure drives large-scale winds away from the Galactic plane. The interaction of such a wind with stellar wind bubbles may explain the energetic non-thermal radio filaments found throughout the Galactic Centre. Some of the implications of this scenario is the removal of gas driven by outflowing winds, acting as a feedback to reduce the star formation rate in the central molecular zone (CMZ), and the distortion of azimuthal magnetic field lines in the CMZ to vertical direction away from the plane. The combined effects of the wind and the vertical magnetic field can explain why most magnetized filaments run perpendicular to the galactic plane. This proposed picture suggests our Milky Way nucleus has recently experienced starburst or black hole activity, as recent radio and X-ray observations indicate.
Document classification methods

Information on different fields which are collected by users requires appropriate management and organization to be structured in a standard way and retrieved fast and more easily. Document classification is a conventional method to separate text based on their subjects among scientific text, web pages and digital library. Different methods and techniques are proposed for document classifications that have advantages and deficiencies. In this paper, several unsupervised and supervised document classification methods are studied and compared.
Modeling Traffic Congestion with Spatiotemporal Big Data for An Intelligent Freeway Monitoring System

Traffic congestion is a complex, nonlinear spatiotemporal modeling problem. By collecting and analyzing a vast quantity and different categories of information, traffic flow, and road congestion can be predicted and controlled on an intelligent transportation system. This report provides an analysis of traveling time across Taiwan from North to South, vice versa. We analyze traffic in a national freeway between Tainan and Kaohsiung section, which represents the common trip of the population in Southern Taiwan. The data is recorded using the Electronic Toll Collection System (ETC) provided by Ministry of Transportation in Taiwan. We use MapReduce framework to process data into a smaller task which can be distributed on several computer clusters to speed up the process. The results show that the spatiotemporal model of traffic flow is strongly influenced by direction, working hour, and holidays with a recurring pattern for each week. The distinctive pattern inside the spatiotemporal dataset can be used on an AI-powered decision-making system for future development.
TAAL

Due to the globalization of semiconductor manufacturing and test processes, the system-on-a-chip (SoC) designers no longer design the complete SoC and manufacture chips on their own. This outsourcing of the design and manufacturing of Integrated Circuits (ICs) has resulted in several threats, such as overproduction of ICs, sale of out-of-specification/rejected ICs, and piracy of Intellectual Properties (IPs). Logic locking has emerged as a promising defense strategy against these threats. However, various attacks about the extraction of secret keys have undermined the security of logic locking techniques. Over the years, researchers have proposed different techniques to prevent existing attacks. In this article, we propose a novel attack that can break any logic locking techniques that rely on the stored secret key. This proposed TAAL attack is based on implanting a hardware Trojan in the netlist, which leaks the secret key to an adversary once activated. As an untrusted foundry can extract the netlist of a design from the layout/mask information, it is feasible to implement such a hardware Trojan. All three proposed types of TAAL attacks can be used for extracting secret keys. We have introduced the models for both the combinational and sequential hardware Trojans that evade manufacturing tests. An adversary only needs to choose one hardware Trojan out of a large set of all possible Trojans to launch the TAAL attack.
Two‐sample test based on classification probability

Robust classification algorithms have been developed in recent years with great success. We take advantage of this development and recast the classical two‐sample test problem in the framework of classification. Based on the estimates of classification probabilities from a classifier trained from the samples, a test statistic is proposed. We explain why such a test can be a powerful test and compare its performance in terms of the power and efficiency with those of some other recently proposed tests with simulation and real‐life data. The test proposed is nonparametric and can be applied to complex and high‐dimensional data wherever there is a classifier that provides consistent estimate of the classification probability for such data.
High-Throughput In-Memory Computing for Binary Deep Neural Networks With Monolithically Integrated RRAM and 90-nm CMOS

Deep neural network (DNN) hardware designs have been bottlenecked by conventional memories, such as SRAM due to density, leakage, and parallel computing challenges. Resistive devices can address the density and volatility issues but have been limited by peripheral circuit integration. In this work, we present a resistive RAM (RRAM)-based in-memory computing (IMC) design, which is fabricated in 90-nm CMOS with monolithic integration of RRAM devices. We integrated a <inline-formula> <tex-math notation="LaTeX">$128\times 64$ </tex-math></inline-formula> RRAM array with CMOS peripheral circuits, including row/column decoders and flash analog-to-digital converters (ADCs), which collectively become a core component for scalable RRAM-based IMC for large DNNs. To maximize IMC parallelism, we assert all 128 wordlines of the RRAM array simultaneously, perform analog computing along the bitlines, and digitize the bitline voltages using ADCs. The resistance distribution of low-resistance states is tightened by an iterative write-verify scheme. Prototype chip measurements demonstrate high binary DNN accuracy of 98.5% for MNIST and 83.5% for CIFAR-10 data sets, with 24 TOPS/W and 158 GOPS. This represents <inline-formula> <tex-math notation="LaTeX">$22.3\times $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$10.1\times $ </tex-math></inline-formula> improvements in throughput and energy–delay product (EDP), respectively, compared with the state-of-the-art literature, which can enable intelligent functionalities for area-/energy-constrained edge computing devices.
Time Scale Design for Network Resilience

In this paper we consider the ℋ2-norm of networked systems with multi-time scale consensus dynamics. We develop a general framework for such systems that allows for edge weighting, independent agent-based time scales, as well as measurement and process noise. From this general system description, we highlight an interesting case where the influences of the weighting and scaling can be separated in the design problem. We then consider the design of the time scale parameters for minimizing the ℋ2-norm for the purpose of network resilience.
[Extended version] Rethinking Deep Neural Network Ownership Verification: Embedding Passports to Defeat Ambiguity Attacks

With substantial amount of time, resources and human (team) efforts invested to explore and develop successful deep neural networks (DNN), there emerges an urgent need to protect these inventions from being illegally copied, redistributed, or abused without respecting the intellectual properties of legitimate owners. Following recent progresses along this line, we investigate a number of watermark-based DNN ownership verification methods in the face of ambiguity attacks, which aim to cast doubts on the ownership verification by forging counterfeit watermarks. It is shown that ambiguity attacks pose serious threats to existing DNN watermarking methods. As remedies to the above-mentioned loophole, this paper proposes novel passport-based DNN ownership verification schemes which are both robust to network modifications and resilient to ambiguity attacks. The gist of embedding digital passports is to design and train DNN models in a way such that, the DNN inference performance of an original task will be significantly deteriorated due to forged passports. In other words, genuine passports are not only verified by looking for the predefined signatures, but also reasserted by the unyielding DNN model inference performances. Extensive experimental results justify the effectiveness of the proposed passport-based DNN ownership verification schemes. Code and models are available at https://github.com/kamwoh/DeepIPR
Simple Fixpoint Iteration To Solve Parity Games

A naive way to solve the model-checking problem of the mu-calculus uses fixpoint iteration. Traditionally however mu-calculus model-checking is solved by a reduction in linear time to a parity game, which is then solved using one of the many algorithms for parity games. We now consider a method of solving parity games by means of a naive fixpoint iteration. Several fixpoint algorithms for parity games have been proposed in the literature. In this work, we introduce an algorithm that relies on the notion of a distraction. The idea is that this offers a novel perspective for understanding parity games. We then show that this algorithm is in fact identical to two earlier published fixpoint algorithms for parity games and thus that these earlier algorithms are the same. Furthermore, we modify our algorithm to only partially recompute deeper fixpoints after updating a higher set and show that this modification enables a simple method to obtain winning strategies. We show that the resulting algorithm is simple to implement and offers good performance on practical parity games. We empirically demonstrate this using games derived from model-checking, equivalence checking and reactive synthesis and show that our fixpoint algorithm is the fastest solution for model-checking games.
Intrinsic Mirror Symmetry

We associate a ring R to a log Calabi-Yau pair (X,D) or a degeneration of Calabi-Yau manifolds X->B. The vector space underlying R is determined by the tropicalization of (X,D) or X->B, while the product rule is defined using punctured Gromov-Witten invariants, defined in joint work with Abramovich and Chen. In the log Calabi-Yau case, if D is maximally degenerate, then we propose that Spec R is the mirror to X\D, while in the Calabi-Yau degeneration case, if the degeneration is maximally unipotent, the mirror is expected to be Proj R. The main result in this paper is that R as defined is an associative, commutative ring with unit, with associativity the most difficult part.
Energy Budgets for Terrestrial Extrasolar Planets

The pathways through which incoming energy is distributed between the surface and atmosphere have been analyzed for the Earth. However, the effect of the spectral energy distribution of a host star on the energy budget of an orbiting planet may be significant given the wavelength-dependent absorption properties of atmospheric CO2, water vapor, surface ice, and snow. We have quantified the flow of energy on aqua planets orbiting M-, G-, and F-dwarf stars, using a 3D Global Climate Model with a static ocean. The atmosphere and surface of an M-dwarf planet receiving an instellation equal to 88% of the modern solar constant at the top of the atmosphere absorb 12% more incoming stellar radiation than those of a G-dwarf planet receiving 100% of the modern solar constant, and 17% more radiation than an F-dwarf planet receiving 108% of the modern solar constant, resulting in climates similar to that of modern-day Earth on all three planets, assuming a 24 hr rotation period and fixed CO2. At 100% instellation, a synchronously rotating M-dwarf planet exhibits smaller flux absorption in the atmosphere and on the surface of the dayside, and a dayside mean surface temperature that is 37 K colder than its rapidly rotating counterpart. Energy budget diagrams are included to illustrate the variations in global energy budgets as a function of host star spectral class, and can contribute to habitability assessments of planets as they are discovered.
DDoS Hide & Seek: On the Effectiveness of a Booter Services Takedown

Booter services continue to provide popular DDoS-as-a-service platforms and enable anyone irrespective of their technical ability, to execute DDoS attacks with devastating impact. Since booters are a serious threat to Internet operations and can cause significant financial and reputational damage, they also draw the attention of law enforcement agencies and related counter activities. In this paper, we investigate booter-based DDoS attacks in the wild and the impact of an FBI takedown targeting 15 booter websites in December 2018 from the perspective of a major IXP and two ISPs. We study and compare attack properties of multiple booter services by launching Gbps-level attacks against our own infrastructure. To understand spatial and temporal trends of the DDoS traffic originating from booters we scrutinize 5 months, worth of inter-domain traffic. We observe that the takedown only leads to a temporary reduction in attack traffic. Additionally, one booter was found to quickly continue operation by using a new domain for its website.
Pointer-based Fusion of Bilingual Lexicons into Neural Machine Translation

Neural machine translation (NMT) systems require large amounts of high quality in-domain parallel corpora for training. State-of-the-art NMT systems still face challenges related to out-of-vocabulary words and dealing with low-resource language pairs. In this paper, we propose and compare several models for fusion of bilingual lexicons with an end-to-end trained sequence-to-sequence model for machine translation. The result is a fusion model with two information sources for the decoder: a neural conditional language model and a bilingual lexicon. This fusion model learns how to combine both sources of information in order to produce higher quality translation output. Our experiments show that our proposed models work well in relatively low-resource scenarios, and also effectively reduce the parameter size and training cost for NMT without sacrificing performance.
Learning to Search for MIMO Detection

This paper proposes a novel learning to learn method, called learning to learn iterative search algorithm (LISA), for signal detection in a multi-input multi-output (MIMO) system. The idea is to regard the signal detection problem as a decision making problem over tree. The goal is to learn the optimal decision policy. In LISA, deep neural networks are used as parameterized policy function. Through training, optimal parameters of the neural networks are learned and thus optimal policy can be approximated. Different neural network-based architectures are used for fixed and varying channel models, respectively. LISA provides soft decisions and does not require any information about the additive white Gaussian noise. Simulation results show that LISA 1) obtains near maximum likelihood detection performance in both fixed and varying channel models under QPSK modulation; 2) achieves significantly better bit error rate (BER) performance than classical detectors and recently proposed deep/machine learning based detectors at various modulations and signal to noise (SNR) ratios both under i.i. d and correlated Rayleigh fading channels in the simulation experiments; 3) is robust to MIMO detection problems with imperfect channel state information; and 4) generalizes very well against channel correlation and SNRs.
A High Spectral Resolution Study of the Soft X-Ray Background with the X-Ray Quantum Calorimeter

We present here a combined analysis of four high spectral resolution observations of the Diffuse X-ray Background, made using the University of Wisconsin-Madison/Goddard Space Flight Center X-ray Quantum Calorimeter sounding rocket payload. The observed spectra support the existence of a ∼0.1 keV Local Hot Bubble and a ∼0.2 keV Hot Halo, with discrepancies between repeated observations compatible with expected contributions of time-variable emission from Solar Wind Charge Exchange. An additional component of ∼0.9 keV emission observed only at low galactic latitudes can be consistently explained by unresolved dwarf M stars.
When Are Mixed Equilibria Relevant?

Abstract Mixed strategy equilibria — Nash (NE) and maximin (MM) — are cornerstones of game theory, but their empirical relevance has always been questionable. We study in the laboratory two games, each with a unique NE and a unique (and distinct) MM in completely mixed strategies. Treatment variables include the matching protocol (pairwise random vs population mean matching), whether time is discrete or continuous, and whether players can specify explicit mixtures or only pure strategy realizations. NE mixes predict observed behavior relatively well in population mean matching treatments, and predict better than MM in all treatments. However, in most random pairwise treatments, uniform mixes predict better than NE. Regret-based and sign preserving dynamics capture regularities across all treatments.
Visualizing Movement Control Optimization Landscapes

A large body of animation research focuses on optimization of movement control, either as action sequences or policy parameters. However, as closed-form expressions of the objective functions are often not available, our understanding of the optimization problems is limited. Building on recent work on analyzing neural network training, we contribute novel visualizations of high-dimensional control optimization landscapes; this yields insights into why control optimization is hard and why common practices like early termination and spline-based action parameterizations make optimization easier. For example, our experiments show how trajectory optimization can become increasingly ill-conditioned with longer trajectories, but parameterizing control as partial target states—e.g., target angles converted to torques using a PD-controller—can act as an efficient preconditioner. Both our visualizations and quantitative empirical data also indicate that neural network policy optimization scales better than trajectory optimization for long planning horizons. Our work advances the understanding of movement optimization and our visualizations should also provide value in educational use.
Grounding learning of modifier dynamics: An application to color naming

Grounding is crucial for natural language understanding. An important subtask is to understand modified color expressions, such as “light blue”. We present a model of color modifiers that, compared with previous additive models in RGB space, learns more complex transformations. In addition, we present a model that operates in the HSV color space. We show that certain adjectives are better modeled in that space. To account for all modifiers, we train a hard ensemble model that selects a color space depending on the modifier-color pair. Experimental results show significant and consistent improvements compared to the state-of-the-art baseline model.