Miracle-dz/UniFAR-FTUs huggingface dataset:
Digital RIS (DRIS): The Future of Digital Beam Management in RIS-Assisted OWC Systems

Reconfigurable intelligent surfaces (RIS) have been recently introduced to optical wireless communication (OWC) networks to resolve skip areas and improve the signal-to-noise ratio at the user’s end. In OWC networks, RIS are based on mirrors or metasurfaces. Metasurfaces have evolved significantly over the last few years. As a result, coding, digital, programmable, and information metamaterials have been developed. The advantage of these materials is that they can enable digital signal processing (DSP) techniques. For the first time, this paper proposes the use of digital RIS (DRIS) in OWC systems. We discuss the concept of DRIS and the application of DSP methods to the physical material. In addition, we examine metamaterials for optical DRIS with liquid crystals serving as the front row material. Finally, we present a design example and discuss future research directions.
Return of the templates: Revisiting the Galactic Center excess with multimessenger observations

The Galactic center excess (GCE) remains one of the most intriguing discoveries from the Fermi Large Area Telescope (LAT) observations. We revisit the characteristics of the GCE by ﬁrst producing a new set of high-resolution galactic diﬀuse gamma-ray emission templates. This diﬀuse emission, which accounts for the bulk of the observed gamma rays, is ultimately due to cosmic-ray interactions with the interstellar medium. Using recent high-precision cosmic-ray observations, in addition to the continuing Fermi -LAT observations and observations from lower energy photons, we constrain the properties of the galactic diﬀuse emission. We describe a large set of diﬀuse gamma-ray emission templates which account for a very wide range of initial assumptions on the physical conditions in the inner galaxy. The broad properties of the GCE that we ﬁnd in this work are qual-itatively unchanged despite the introduction of this new set of templates, though its quantitative features appear mildly diﬀerent than those obtained in previous analyses. In particular, we ﬁnd a high-energy tail at higher signiﬁcance than previously reported. This tail is very prominent in the northern hemisphere, and less so in the southern hemisphere. This strongly aﬀects one prominent interpretation of the excess: known millisecond pulsars are incapable of producing this high-energy emission, even in the relatively softer southern hemisphere, and are therefore disfavored as the sole explanation of the GCE. The annihilation of dark matter particles of mass 40 +10 − 7 GeV (95 % CL) to b quarks with a cross-section of (cid:104) σ A v (cid:105) = 1 . 0 × 10 − 26 cm 3 s − 1 provides a good ﬁt to the excess especially in the relatively cleaner southern sky. Dark matter of the same mass range annihilating to b quarks or heavier dark matter particles annihilating to heavier Standard Model bosons can combine with millisecond pulsars to provide a good ﬁt to the southern hemisphere emission as well, as can a broken power-law spectrum which would be related to recent cosmic-ray burst activity. As part of this paper, we make publicly available all of our templates and the data covariance matrix we have generated to account for systematic uncertainties. I
Mind-proofing Your Phone: Navigating the Digital Minefield with GreaseTerminator

Digital harms are widespread in the mobile ecosystem. As these devices gain ever more prominence in our daily lives, so too increases the potential for malicious attacks against individuals. The last line of defense against a range of digital harms – including digital distraction, political polarisation through hate speech, and children being exposed to damaging material – is the user interface. This work introduces GreaseTerminator to enable researchers to develop, deploy, and test interventions against these harms with end-users. We demonstrate the ease of intervention development and deployment, as well as the broad range of harms potentially covered with GreaseTerminator in five in-depth case studies.
Design and Control of an Omnibot Autonomous Vehicle

A design for autonomous control of a novel omni-directional platform is presented. This platform is to be used in conjunction with a robotic arm to further research of mobile-manipulator systems. This design differs from other omni-directional platforms that use omniwheels in that its drive axes do not intersect its geometric centre. The platform can be controlled autonomously through multiple sub-systems that have been designed including a closed-loop velocity controller, a localization system, an obstacle avoidance and collision detection system, a vision system, and a data routing system. These sub-systems communicate with a remote computer which plots the path and sends data to guide the platform. The closed-loop velocity controller provides feedback which can be used to analyze and correct the path of travel.Copyright © 2008 by ASME
Spiral Language Modeling

In almost all text generation applications, word sequences are constructed in a left-to-right (L2R) or right-to-left (R2L) manner, as natural language sentences are written either L2R or R2L. However, we find that the natural language written order is not essential for text generation. In this paper, we propose Spiral Language Modeling (SLM), a general approach that enables one to construct natural language sentences beyond the L2R and R2L order. SLM allows one to form natural language text by starting from an arbitrary token inside the result text and expanding the rest tokens around the selected ones. It makes the decoding order a new optimization objective besides the language model perplexity, which further improves the diversity and quality of the generated text. Furthermore, SLM makes it possible to manipulate the text construction process by selecting a proper starting token. SLM also introduces generation orderings as additional regularization to improve model robustness in low-resource scenarios. Experiments on 8 widely studied Neural Machine Translation (NMT) tasks show that SLM is constantly effective with up to 4.7 BLEU increase comparing to the conventional L2R decoding approach.
High-Cardinality Hybrid Shaping for 4D Modulation Formats in Optical Communications Optimized via End-to-End Learning

In this paper we carry out a joint optimization of probabilistic (PS) and geometric shaping (GS) for four-dimensional (4D) modulation formats in long-haul coherent wavelength division multiplexed (WDM) optical fiber communications using an auto-encoder framework. We propose a 4D 10 bits/symbol constellation which we obtained via end-to-end deep learning over the split-step Fourier model of the fiber channel. The constellation achieved 13.6% reach increase at a data rate of approximately 400 Gbits/second in comparison to the ubiquitously employed polarization multiplexed 32-QAM format at a forward error correction overhead of 20%.
Health monitoring of reinforced concrete structures based on PZT admittance signal

Reinforced concrete (RC) structure is one of most familiar engineering structure styles in the civil engineering community, which often suffer crack damage during their service life because of some factors such as overloading, excessive use, and bad environmental conditions. Thus early detection of crack damage is of special concern for RC structures. Piezoelectric materials have direct and converse piezoelectric effects and can serve as actuators or sensors. A health monitoring method based on PZT admittance signals is addressed in this paper, which use the electromechanical coupling property of piezoelectric materials. An experimental study on health monitoring of a RC beam is implemented based on the PZT admittance signals. In this experiment, the electrical admittances of distributed PZT sheets are measured when the host beams are suffering from variable loads. From the obtained PZT admittance curves one can find that the presence of incipient crack can be captured and the cracking load of the RC beam can also generally determined. By the experimental study it is concluded that the health monitoring technique is quite effective and sensitive for RC structures, which indicates its favorable application foreground in civil engineering field.
AutoTransfer: Subject Transfer Learning with Censored Representations on Biosignals Data

We investigate a regularization framework for subject transfer learning in which we train an encoder and classifier to minimize classification loss, subject to a penalty measuring independence between the latent representation and the subject label. We introduce three notions of independence and corresponding penalty terms using mutual information or divergence as a proxy for independence. For each penalty term, we provide several concrete estimation algorithms, using analytic methods as well as neural critic functions. We propose a hands-off strategy for applying this diverse family of regularization schemes to a new dataset, which we call “Auto Transfer”. We evaluate the performance of these individual regularization strategies under our AutoTransfer framework on EEG, EMG, and ECoG datasets, showing that these approaches can improve subject transfer learning for challenging real-world datasets.
English-to-Chinese Transliteration with Phonetic Back-transliteration

Transliteration is a task of translating named entities from a language to another, based on phonetic similarity. The task has embraced deep learning approaches in recent years, yet, most ignore the phonetic features of the involved languages. In this work, we incorporate phonetic information into neural networks in two ways: we synthesize extra data using forward and back-translation but in a phonetic manner; and we pre-train models on a phonetic task before learning transliteration. Our experiments include three language pairs and six directions, namely English to and from Chinese, Hebrew and Thai. Results indicate that our proposed approach brings benefits to the model and achieves better or similar performance when compared to state of the art.
Using HYDRUS-2D Simulation Model to Evaluate Wetted Soil Volume in Subsurface Drip Irrigation Systems

Drip irrigation is considered one of the most efficient irrigation systems. Alternatively to the traditional drip irrigation systems, laterals can be installed below the soil surface. Realizing the subsurface drip irrigation (SDI), which recently has been increasing in use as a consequence of advances in plastics technology, making SDI equipment more affordable and long lasting. Due to its potential high efficiency SDI may produce benefits, especially in places where water is a limited source. As the use of SDI is relatively new, a better understanding of the infiltration process around a buried point source can contribute to increased water use efficiency and consequently the success of drip irrigation system. In addition, proper design and management of such a system needs the judicious combination of drip spacing, discharge rates, irrigation duration and time interval between consecutive irrigations. To this aim, numerical models can represent a powerful tool to analyze the evolution of the wetting pattern during the distribution and redistribution processes, in order to explore SDI management strategies, to set up the duration of irrigation, and finally to optimize water use efficiency. In the paper the suitability of the HYDRUS-2D simulation model is verified, at the scale of a single emitter, on the basis of experimental observations, with the aim to assess the axis-symmetrical infiltration process consequent to subsurface drip irrigation. The model was then applied in order to evaluate the main dimensions of the wetted soil volume surrounding the emitter during irrigation as a function of time and initial soil water content. The investigation, carried out in a sandy-loam soil, showed the suitability of the model to well simulate infiltration processes around an emitter during irrigation. Model application allowed also, for the examined soil, to evaluate the emitter spacing accounting for the maximum soil depth to irrigate.
Attacks of the Knights: Exploiting Non Uniform Cache Access Time

—Intel Knights Landing Processors have shared last level cache (LLC) across all the tiles using MESIF protocol and uses a mesh network of Caching and Homing Agents(CHA)s. Due to the structure of the network, the cache access is non uniform in nature having signiﬁcant difference in cache hit times. In this paper, we try to exploit this idea to leak secret from a victim process. First, we show a naive implementation of the attack using a gem5 simulator that achieves 100% accuracy of extracting the secret bits. Then we replicate the attack in a Intel Xeon Phi 7290@ 1.50 GHz Knight’s Landing CPU to show the efﬁcacy of the attack. In real machine we can leak the secret from a victim process at 85% accuracy and 350 kbps bandwidth. All the attacks were done on a machine without any root or sudo privileges, so this shows the strength of the attack. This can be further extended to leak secrets from different processes given the vulnerable patterns may exist in many libraries. Other processors with similar architecture (last level distributed cache in mesh networks) can also be vulnerable to similar attack strategy.
Charging of quantum battery with periodic driving

We explore the charging of a quantum battery based on spin systems through periodic modulation of a transverse-field like Ising Hamiltonian. In the integrable limit, we find that resonance tunnelling can lead to a higher transfer of energy to the battery at specific drive frequencies. When the integrability is broken in the presence of an additional longitudinal field, we find that the effective Floquet Hamiltonian contains terms which may lead to a global charging of the battery. However, we do not find any quantum advantage in the charging power, thus demonstrating that global charging is only a necessary and not sufficient condition for achieving quantum advantage.
A Streaming Volumetric Image Generation Framework for Development and Evaluation of Out-of-Core Methods

—Advances in 3D imaging technology in recent years have allowed for increasingly high resolution volumetric images of large specimen. The resulting datasets of hundreds of Gigabytes in size call for new scalable and memory efﬁcient approaches in the ﬁeld of image processing, where some progress has been made already. At the same time, quantitative evaluation of these new methods is difﬁcult both in terms of the availability of speciﬁc data sizes and in the generation of associated ground truth data. In this paper we present an algorithmic framework that can be used to efﬁciently generate test (and ground truth) volume data, optionally even in a streaming fashion. As the proposed nested sweeps algorithm is fast, it can be used to generate test data on demand. We analyze the asymptotic run time of the presented algorithm and compare it experimentally to alternative approaches as well as a hypothetical best-case baseline method. In a case study, the framework is applied to the popular VascuSynth software for vascular image generation, making it capable of efﬁciently producing larger-than-main memory volumes which is demonstrated by generating a trillion voxel (1TB) image. Implementations of the presented framework are available online in the form of the modiﬁed version of Vascusynth and the code used for the experimental evaluation. In addition, the test data generation procedure has been integrated into the popular volume rendering and processing framework Voreen.
SIDE: State Inference for Partially Observable Cooperative Multi-Agent Reinforcement Learning

As one of the solutions to the decentralized partially observable Markov decision process (Dec-POMDP) problems, the value decomposition method has achieved significant results recently. However, most value decomposition methods require the fully observable state of the environment during training, but this is not feasible in some scenarios where only incomplete and noisy observations can be obtained. Therefore, we propose a novel value decomposition framework, named State Inference for value DEcomposition (SIDE), which eliminates the need to know the global state by simultaneously seeking solutions to the two problems of optimal control and state inference. SIDE can be extended to any value decomposition method to tackle partially observable problems. By comparing with the performance of different algorithms in StarCraft II micromanagement tasks, we verified that though without accessible states, SIDE can infer the current state that contributes to the reinforcement learning process based on past local observations and even achieve superior results to many baselines in some complex scenarios.
Breaking the Communities: Characterizing community changing users using text mining and graph machine learning on Twitter

Even though the Internet and social media have increased the amount of news and information people can consume, most users are only exposed to content that reinforces their positions and isolates them from other ideological communities. This environment has real consequences with great impact on our lives like severe political polarization, easy spread of fake news, political extremism, hate groups and the lack of enriching debates, among others. Therefore, encouraging conversations between different groups of users and breaking the closed community is of importance for healthy societies. In this paper, we characterize and study users who break their community on Twitter using natural language processing techniques and graph machine learning algorithms. In particular, we collected 9 million Twitter messages from 1.5 million users and constructed the retweet networks. We identified their communities and topics of discussion associated to them. With this data, we present a machine learning framework for social media users classification which detects"community breakers", i.e. users that swing from their closed community to another one. A feature importance analysis in three Twitter polarized political datasets showed that these users have low values of PageRank, suggesting that changes are driven because their messages have no response in their communities. This methodology also allowed us to identify their specific topics of interest, providing a fully characterization of this kind of users.
Wideband spectrum sensing and signal classification for autonomous self-learning cognitive radios

In this dissertation, we develop a novel cognitive radio (CR) architecture, referred to as the Radiobot [1], whose goals go beyond dynamic spectrum access (DSA) to achieve the main features of cognition, notably, self-learning and self-reconfiguration. The proposed CR architecture is based on a sequence of signal processing and machine learning techniques that enable the Radiobot to sense a wide frequency band and act autonomously by learning from past experience. To achieve its goals, the proposed CR is equipped with the following functionalities: 1) Wideband spectrum sensing, 2) non-parametric signal classification, 3) unsupervised learning and reasoning and 4) decentralized decision-making. To this end, we implement a blind spectrum sensing method based on joint energy/cyclostationary detection. Optimal wideband energy detector is designed based on the Neyman-Pearson (NP) criterion which maximizes the detection probability of primary signals, subject to a certain false alarm rate. Cyclostationary detection is proposed as a means of extracting the underlying cyclic properties of the detected signals in order to identify the types of signals in each frequency band. Once the signal features are extracted, a Bayesian non-parametric classifier based on the Dirichlet process is applied to determine the different types of wireless systems in the surrounding radio frequency (RF) environment. In this dissertation, we extend the Dirichlet process mixture model (DPMM)-based classifier to allow for a mixture of Gaussian and non-Gaussian vector observation models, compared to existing DPMM's with scalar Gaussian observation models. We also develop a sequential DPMM classifier that can be implemented at a low processing cost, making it suitable for real-time operation. Upon identifying the RF activities in the surrounding environment, the Radiobot uses machine learning techniques for decision-making. Thus, we propose a reinforcement learning (RL) algorithm that enables the Radiobot to learn by interacting with its environment. The learning process is formulated in a decentralized partially observable Markov decision process (DEC-POMDP) framework and is shown to lead to a near-optimal policy with little knowledge about the environment. As a result, using its sensing and learning capabilities, the Radiobot can switch among multiple modes of operation to adapt to a dynamic RF environment.
Defining a Set of Common Benchmarks for Web Application Security

A recent explosion in the number of security vulnerabilities being discovered every day motivated a great deal of interest in tools that attempt to address this problem. While buffer overruns have been plaguing C programs for years, application-level vulnerabilities such as SQL injections, cross-site scripting, and path traversal attacks have become increasingly common in the last year. Looking at a daily snapshot of SecurityFocus vulnerabilities reveals that upwards of 60% of all exploits are due to application vulnerabilities such as the ones listed above.