Miracle-dz/UniFAR-FTUs huggingface dataset:
DeepScalper: A Risk-Aware Deep Reinforcement Learning Framework for Intraday Trading with Micro-level Market Embedding

Reinforcement learning (RL) techniques have shown great success in quantitative investment tasks, such as portfolio management and algorithmic trading. Especially, intraday trading is one of the most proﬁtable and risky tasks because of the intraday behaviors of the ﬁnancial market that re-ﬂect billions of rapidly ﬂuctuating values. However, it is hard to apply existing RL methods to intraday trading due to the following three limitations: 1) overlooking micro-level market information (e.g., limit order book); 2) only focusing on local price ﬂuctuation and failing to capture the overall trend of the whole trading day; 3) neglecting the impact of market risk. To tackle these limitations, we propose DeepScalper, a deep re-inforcement learning framework for intraday trading. Speciﬁcally, we adopt an encoder-decoder architecture to learn robust market embedding incorporating both macro-level and micro-level market information. Moreover, a novel hindsight reward function is designed to provide the agent a long-term horizon for capturing the overall price trend. In addition, we propose a risk-aware auxiliary task by predicting future volatility, which helps the agent take market risk into consideration while maximizing proﬁt. Finally, extensive experiments on two stock index futures and four treasury bond futures demonstrate that DeepScalper achieves signiﬁcant improvement against many state-of-the-art approaches.
Keeping Deep Lithography Simulators Updated: Global–Local Shape-Based Novelty Detection and Active Learning

Learning-based presimulation (i.e., layout-to-fabrication) models have been proposed to predict the fabrication-induced shape deformation from an IC layout to its fabricated circuit. Such models are usually driven by pairwise learning, involving a training set of layout patterns and their reference shape images after fabrication. However, it is expensive and time consuming to collect the reference shape images of all layout clips for model training and updating. To address the problem, we propose a deep-learning-based layout novelty detection scheme to identify novel (unseen) layout patterns, which cannot be well predicted by a pretrained presimulation model. We devise a global–local novelty scoring mechanism to assess the potential novelty of a layout by exploiting two subnetworks: 1) an autoencoder and 2) a pretrained presimulation model. The former characterizes the global structural dissimilarity between a given layout and training samples, whereas the latter extracts a latent code representing the fabrication-induced local deformation. By integrating the global dissimilarity with the local deformation boosted by a self-attention mechanism, our model can accurately detect novelties without the ground-truth circuit shapes of test samples. Based on the detected novelties, we further propose two active-learning strategies to sample a reduced amount of representative layouts most worthy to be fabricated for acquiring their ground-truth circuit shapes. Experimental results demonstrate: 1) the effectiveness of our layout novelty detection algorithm and 2) the ability of our active-learning strategies in selecting representative novel layouts for keeping a learning-based presimulation model updated.
Multi-Agent Adversarial Attacks for Multi-Channel Communications

Recently Reinforcement Learning (RL) has been applied as an anti-adversarial remedy in wireless communication networks. However, studying the RL-based approaches from the adversary's perspective has received little attention. Additionally, RL-based approaches in an anti-adversary or adversarial paradigm mostly consider single-channel communication (either channel selection or single channel power control), while multi-channel communication is more common in practice. In this paper, we propose a multi-agent adversary system (MAAS) for modeling and analyzing adversaries in a wireless communication scenario by careful design of the reward function under realistic communication scenarios. In particular, by modeling the adversaries as learning agents, we show that the proposed MAAS is able to successfully choose the transmitted channel(s) and their respective allocated power(s) without any prior knowledge of the sender strategy. Compared to the single-agent adversary (SAA), multi-agents in MAAS can achieve significant reduction in signal-to-noise ratio (SINR) under the same power constraints and partial observability, while providing improved stability and a more efficient learning process. Moreover, through empirical studies we show that the results in simulation are close to the ones in communication in reality, a conclusion that is pivotal to the validity of performance of agents evaluated in simulations.
Hatred by Hated People: Xenophobia in Israel

Israel is a state with a large (some 80 percent) Jewish majority. Jews were victims of prejudice and xenophobia for two millennia. They constituted a minority in their countries of residence, and were subject to discrimination and persecution. The founders of Israel pledged, in the state's Declaration of Independence, to establish a society free from hatred, a society where all citizens enjoyed equal rights. The horrors suffered by the Jewish people during World War II consolidated Israel's pledge to erase hatred. Yet the country is imbued with hatred towards three distinct groups: Arabs, foreign workers, and Ethiopian Jews. The Arabs are hated in the setting of the ArabIsraeli conflict; foreign workers may be resented on account of economic rivalry; Ethiopian Jews are probably shunned because they are different in skin color and have customs from the majority of Israelis. This article attempts to examine hatred in contemporary Israel, addressing the following questions: (a) What is the scope of hatred? (.
On the Robustness of Counterfactual Explanations to Adverse Perturbations

Counterfactual explanations (CEs) are a powerful means for understanding how decisions made by algorithms can be changed. Researchers have proposed a number of desiderata that CEs should meet to be practically useful, such as requiring minimal effort to enact, or complying with causal models. We consider a further aspect to improve the usability of CEs: robustness to adverse perturbations,whichmaynaturallyhappenduetounfortunatecircumstances.SinceCEstypicallyprescribea sparse formofintervention (i.e., only a subset of the features should be changed), we provide two definitions of robustness, which concern, respectively, the features to change and to keep as they are. These definitions are workable in that they can be incorporated as penalty terms in the loss functions that are used for discovering CEs. To experiment with the proposed definitions of robustness, we create and release code where five data sets (commonly used in the field of fair and explainable machine learning) have been enriched with feature-specific annotations that can be used to sample meaningful perturbations. Our experiments show that CEs are often not robust and, if adverse perturbations take place, the intervention they prescribe may require a much larger cost than anticipated, or even become impossible. However, accounting for robustness in the search process, which can be done rather easily, allows discovering robust CEs systematically. Robust CEs are resilient to adverse perturbations: additional intervention to contrast perturbations is much less costly than for non-robust CEs. Our code is available at: https://github.com/marcovirgolin/robust-counterfactuals
Dynamic Channel Access via Meta-Reinforcement Learning

In this paper, we address the channel access problem in a dynamic wireless environment via meta-reinforcement learning. Spectrum is a scarce resource in wireless communications, especially with the dramatic increase in the number of devices in networks. Recently, inspired by the success of deep reinforcement learning (DRL), extensive studies have been conducted in addressing wireless resource allocation problems via DRL. However, training DRL algorithms usually requires a massive amount of data collected from the environment for each specific task and the well-trained model may fail if there is a small variation in the environment. In this work, in order to address these challenges, we propose a meta-DRL framework that incorporates the method of Model-Agnostic Meta-Learning (MAML). In the proposed framework, we train a common initialization for similar channel selection tasks. From the initialization, we show that only a few gradient descents are required for adapting to different tasks drawn from the same distribution. We demonstrate the performance improvements via simulation results.
Data and knowledge-driven approaches for multilingual training to improve the performance of speech recognition systems of Indian languages

We propose data and knowledge-driven approaches for multilingual training of the automated speech recognition (ASR) system for a target language by pooling speech data from multiple source languages. Exploiting the acoustic similarities between Indian languages, we implement two approaches. In phone/senone mapping, deep neural network (DNN) learns to map senones or phones from one language to the others, and the transcriptions of the source languages are modified such that they can be used along with the target language data to train and fine-tune the target language ASR system. In the other approach, we model the acoustic information for all the languages simultaneously by training a multitask DNN (MTDNN) to predict the senones of each language in different output layers. The cross-entropy loss and the weight update procedure are modified such that only the shared layers and the output layer responsible for predicting the senone classes of a language are updated during training, if the feature vector belongs to that particular language. In the low-resource setting (LRS), 40 hours of transcribed data each for Tamil, Telugu and Gujarati languages are used for training. The DNN based senone mapping technique gives relative improvements in word error rates (WER) of 9.66%, 7.2% and 15.21% over the baseline system for Tamil, Gujarati and Telugu languages, respectively. In medium-resourced setting (MRS), 160, 275 and 135 hours of data for Tamil, Kannada and Hindi languages are used, where, the same technique gives better relative improvements of 13.94%, 10.28% and 27.24% for Tamil, Kannada and Hindi, respectively. The MTDNN with senone mapping based training in LRS, gives higher relative WER improvements of 15.0%, 17.54% and 16.06%, respectively for Tamil, Gujarati and Telugu, whereas in MRS, we see improvements of 21.24% 21.05% and 30.17% for Tamil, Kannada and Hindi languages, respectively.
Deep Reinforcement Learning with Spiking Q-learning

With the help of special neuromorphic hardware, spiking neural networks (SNNs) are expected to realize artificial intelligence (AI) with less energy consumption. It provides a promising energy-efficient way for realistic control tasks by combining SNNs with deep reinforcement learning (RL). There are only a few existing SNN-based RL methods at present. Most of them either lack generalization ability or employ Artificial Neural Networks (ANNs) to estimate value function in training. The former needs to tune numerous hyper-parameters for each scenario, and the latter limits the application of different types of RL algorithm and ignores the large energy consumption in training. To develop a robust spike-based RL method, we draw inspiration from non-spiking interneurons found in insects and propose the deep spiking Q-network (DSQN), using the membrane voltage of non-spiking neurons as the representation of Q-value, which can directly learn robust policies from high-dimensional sensory inputs using end-to-end RL. Experiments conducted on 17 Atari games demonstrate the DSQN is effective and even outperforms the ANN-based deep Q-network (DQN) in most games. Moreover, the experiments show superior learning stability and robustness to adversarial attacks of DSQN.
Are Your Sensitive Attributes Private? Novel Model Inversion Attribute Inference Attacks on Classification Models

Increasing use of machine learning (ML) technologies in privacy-sensitive domains such as medical diagnoses, lifestyle predictions, and business decisions highlights the need to better understand if these ML technologies are introducing leakage of sensitive and proprietary training data. In this paper, we focus on model inversion attacks where the adversary knows non-sensitive attributes about records in the training data and aims to infer the value of a sensitive attribute unknown to the adversary, using only black-box access to the target classification model. We first devise a novel confidence score-based model inversion attribute inference attack that significantly outperforms the state-of-the-art. We then introduce a label-only model inversion attack that relies only on the model's predicted labels but still matches our confidence score-based attack in terms of attack effectiveness. We also extend our attacks to the scenario where some of the other (non-sensitive) attributes of a target record are unknown to the adversary. We evaluate our attacks on two types of machine learning models, decision tree and deep neural network, trained on three real datasets. Moreover, we empirically demonstrate the disparate vulnerability of model inversion attacks, i.e., specific groups in the training dataset (grouped by gender, race, etc.) could be more vulnerable to model inversion attacks.
Development of a 500-kW Modular Multilevel Cascade Converter for Battery Energy Storage Systems

Renewable energy sources such as wind turbine and photovoltaic power generators may make the power grid unstable due to their output fluctuations. Battery energy storage systems (BESSs) are being considered as a countermeasure for this issue. A modular multilevel cascade converter (MMCC) is expected as a power conversion circuit for BESSs because each bridge cell can control the state of charge of a battery unit, independent of that of another one, and the harmonic current generated is low enough to eliminate the ac filter, normally installed on conventional two-level converters, from the MMCC. This paper describes the development of a real-scale (500 kW) single-star-bridge-cell-based MMCC for BESSs and reports successful test results obtained from a downscaled grid model and a 6.6-kV real-scale distribution line.
Single and Multi-objective Test Cases Prioritization for Self-driving Cars in Virtual Environments

Testing with simulation environments helps to identify critical failing scenarios for self-driving cars (SDCs). Simulation-based tests are safer than in-field operational tests and allow detecting software defects before deployment. However, these tests are very expensive and are too many to be run frequently within limited time constraints. In this article, we investigate test case prioritization techniques to increase the ability to detect SDC regression faults with virtual tests earlier. Our approach, called SDC-Prioritizer, prioritizes virtual tests for SDCs according to static features of the roads we designed to be used within the driving scenarios. These features can be collected without running the tests, which means that they do not require past execution results. We introduce two evolutionary approaches to prioritize the test cases using diversity metrics (black-box heuristics) computed on these static features. These two approaches, called SO-SDC-Prioritizer and MO-SDC-Prioritizer, use single-objective and multi-objective genetic algorithms (GA), respectively, to find trade-offs between executing the less expensive tests and the most diverse test cases earlier. Our empirical study conducted in the SDC domain shows that MO-SDC-Prioritizer significantly (P- value <=0.1e-10) improves the ability to detect safety-critical failures at the same level of execution time compared to baselines: random and greedy-based test case orderings. Besides, our study indicates that multi-objective meta-heuristics outperform single-objective approaches when prioritizing simulation-based tests for SDCs. MO-SDC-Prioritizer prioritizes test cases with a large improvement in fault detection while its overhead (up to 0.45% of the test execution cost) is negligible.
Black and White holes in four-dimensional Chern-Simons gravity

We discuss a four-dimensional gravitational action which was obtained replacing a RandallSundrum type metric in the so called five-dimensional Einstein-Chern-Simons gravity action. We studied black hole solutions of the corresponding 4-dimensional gravitational field equations. It is found that for a spherically symmetric metric such equations lead to a spacetime with a cosmological constant inversely proportional to the square of the compactification radius and to one solution dependent on an arbitrary constant C. If this constant is negative, we find a Schwarzschid-de Sitter black hole. If C is positive, the solution can be understood as a white hole solution which is obtained applying to the solution with C < 0 the discrete coordinate transformation PT accompanied by the transformation C −→ −C̃, with C̃ > 0, corresponding to a transformation known as mass reversal. a samuel.lepe@pucv.cl b ceriquelme@udec.cl c patsalgado@unap.cl
Profitable Strategy Design for Trades on Cryptocurrency Markets with Machine Learning Techniques

— AI and data driven solutions have been applied to different fields and achieved outperforming and promising results. In this research work we apply k-Nearest Neighbours, eXtreme Gradient Boosting and Random Forest classifiers for detecting the trend problem of three cryptocurrency markets. We use these classifiers to design a strategy to trade in those markets. Our input data in the experiments include price data with and without technical indicators in separate tests to see the effect of using them. Our test results on unseen data are very promising and show a great potential for this approach in helping investors with an expert system to exploit the market and gain profit. Our highest profit factor for an unseen 66 day span is 1.60. We also discuss limitations of these approaches and their potential impact on Efficient Market Hypothesis.
An In-Module Disturbance Barrier for Mitigating Write Disturbance in Phase-Change Memory

Write disturbance error (WDE) appears as a serious reliability problem preventing phase-change memory (PCM) from general commercialization, and therefore several studies have been proposed to mitigate WDEs. Verify-and-correction (VnC) eliminates WDEs by always verifying the data correctness on neighbors after programming, but incurs significant performance overhead. Encoding-based schemes mitigate WDEs by reducing the number of WDE-vulnerable data patterns; however, mitigation performance notably fluctuates with applications. Moreover, encoding-based schemes still rely on VnC-based schemes. Cache-based schemes lower WDEs by storing data in a write cache, but it requires several megabytes of SRAM to significantly mitigate WDEs. Despite the efforts of previous studies, these methods incur either significant performance or area overhead. Therefore, a new approach, which does not rely on VnC-based schemes or application data patterns, is highly necessary. Furthermore, the new approach should be transparent to processors (i.e., in-module), because the characteristic of WDEs is determined by manufacturers of PCM products. In this paper, we present an in-module disturbance barrier (IMDB) that mitigates WDEs on demand. IMDB includes a two-level hierarchy comprising two SRAM-based tables, whose entries are managed with a dedicated replacement policy that sufficiently utilizes the characteristics of WDEs. The naive implementation of the replacement policy requires hundreds of read ports on SRAM, which is infeasible in real hardware; hence, an approximate comparator is also designed. We also conduct a rigorous exploration of architecture parameters to obtain a cost-effective design. The proposed method significantly reduces WDEs without noticeable speed degradation or additional energy consumption compared to previous methods.
Fisher Task Distance and Its Applications in Neural Architecture Search and Transfer Learning

We formulate an asymmetric (or non-commutative) distance between tasks based on Fisher Information Matrices. We provide a proof of consistency for our distance through theorems and experiments on various classiﬁcation tasks. We then apply our proposed measure of task distance in transfer learning on visual tasks in the Taskonomy dataset. Additionally, we show how the proposed distance between a target task and a set of baseline tasks can be used to reduce the neural architecture search space for the target task. The complexity reduction in search space for task-speciﬁc architectures is achieved by building on the optimized architectures for similar tasks instead of doing a full search and without using this side information. Experimental results demonstrate the efﬁcacy of the proposed approach and its improvements over other methods.
Revenue Attribution on iOS 14 using Conversion Values in F2P Games

Mobile app developers use paid advertising campaigns to acquire new users. Marketing managers decide where to spend and how much to spend based on the campaigns' performance. Apple's new privacy mechanisms have a profound impact on how performance marketing is measured. Starting iOS 14.5, all apps must get system permission for tracking explicitly via the new App Tracking Transparency Framework, which shows the users a pop-up asking if they give the app permission to track. If a user does not allow tracking, the required identifier to deterministically find the online advertising campaign that brought the user to install the app is not shared. Instead of relying on individual identifiers, Apple proposed a new performance mechanism called conversion value, which is an integer set by the apps for each user, and the developers can get the number of installs per conversion value for each campaign. However, interpreting how conversion values are used to measure the campaigns performance is not obvious because it requires a method to translate the conversion values to revenue. This paper investigates the task of attributing revenue to advertising campaigns using the reported conversion values per campaign. Our contributions are to formalize the problem, find the theoretically optimal revenue attribution function for any conversion value schema, and show empirical results on past data of a free-to-play mobile game using different conversion value schemas.
Vulnerability, Risk, and Mitigation Assessment of Water Supply Systems for Insufficient Fire Flows

Water supply systems' vulnerability toward physical, chemical/biological, and cyber threats has drawn significant attention to their potential failure. These systems are critical not only because they must provide sufficient water for everyday municipal users but they also must deliver significant flows and volumes of water with high reliability during emergencies such as pipe failures, power outage, and fires. In this study, a methodology is developed to determine the vulnerable components of a water distribution system with respect to a fire event by utilizing a risk based optimization procedure. Using this methodology, vulnerability analysis is performed for a water supply system under three scenarios: (1) accidental failure due to soil-pipe interaction, (2) accidental failure due to a seismic event, and (3) malevolent attack. Several mitigation strategies based on hardening of specific sets of water mains are evaluated through further simulation. Finally, the mitigation strategies are assessed with a benefit-cost analysis to complete the system risk analysis.