Miracle-dz/UniFAR-FTUs huggingface dataset:
Machine Learning Portfolio Allocation

We find economically and statistically significant gains when using machine learning for portfolio allocation between the market index and risk-free asset. Optimal portfolio rules for time-varying expected returns and volatility are implemented with two Random Forest models. One model is employed in forecasting the sign probabilities of the excess return with payout yields. The second is used to construct an optimized volatility estimate. Reward-risk timing with machine learning provides substantial improvements over the buy-and-hold in utility, risk-adjusted returns, and maximum drawdowns. This paper presents a new theoretical basis and unifying framework for machine learning applied to both return- and volatility-timing.
Physical Layer Security: Detection of Active Eavesdropping Attacks by Support Vector Machines

This article presents a framework for converting wireless signals into structured datasets, which can be fed into machine learning algorithms for the detection of active eavesdropping attacks at the physical layer. More specifically, a wireless communication system, which consists of an access point (AP), $K$ legitimate users and an active eavesdropper, is considered. To detect the eavesdropper who breaks into the system during the authentication phase, we first build structured datasets based on different features and then apply sophisticated support vector machine (SVM) classifiers to those structured datasets. To be more specific, we first process the signals received by the AP and then define a pair of statistical features based on the post-processing of the signals. By arranging for the AP to simulate the entire process of transmission and the process of constructing features, we form the so-called artificial training data (ATD). By training SVM classifiers on the ATD, we classify the received signals associated with eavesdropping attacks and non-attacks, thereby detecting the presence of the eavesdropper. Two SVM classifiers are considered, including a classic twin-class SVM (TC-SVM) and a single-class SVM (SC-SVM). While the TC-SVM is preferred in the case of having perfect channel state information (CSI) of all channels, the SC-SVM is preferred in the realistic scenario when we have only the CSI of legitimate users. We also evaluate the accuracy of the trained models depending on the choice of kernel functions, the choice of features and on the eavesdropper’s power. Our numerical results show that careful parameter-tuning is required for exceeding an eavesdropper detection probability of 95%
Microfluidics control the ballistic energy of thermocavitation liquid jets for needle-free injections

Illuminating a water solution with a focused continuous wave laser produces a strong local heating of the liquid that leads to the nucleation of bubbles, also known as thermocavitation. During the growth of the bubble, the surrounding liquid is expelled from the constraining microfluidic channel through a nozzle, creating a jet. The characteristics of the resulting liquid jet was imaged using ultra-fast imaging techniques. Here, we provide a phenomenological description of the jet shapes and velocities, and compare them with a Boundary Integral numerical model. We define the parameter regime, varying jet speed, taper geometry and liquid volume, for optimal printing, injection and spray applications. These results are important for the design of energy-efficient needle-free jet injectors based on microfluidic thermocavitation.
Maximal Haagerup subalgebras in $L(\mathbb{Z}^2\rtimes SL_2(\mathbb{Z}))$

We prove that $L(SL_2(\textbf{k}))$ is a maximal Haagerup von Neumann subalgebra in $L(\textbf{k}^2\rtimes SL_2(\textbf{k}))$ for $\textbf{k}=\mathbb{Q}$. Then we show how to modify the proof to handle $\textbf{k}=\mathbb{Z}$. The key step for the proof is a complete description of all intermediate von Neumann subalgebras between $L(SL_2(\textbf{k}))$ and $L^{\infty}(Y)\rtimes SL_2(\textbf{k})$, where $SL_2(\textbf{k})\curvearrowright Y$ denotes the quotient of the algebraic action $SL_2(\textbf{k})\curvearrowright \widehat{\textbf{k}^2}$ by modding out the relation $\phi\sim \phi'$, where $\phi$, $\phi'\in \widehat{\textbf{k}^2}$ and $\phi'(x, y):=\phi(-x, -y)$ for all $(x, y)\in \textbf{k}^2$. As a by-product, we show $L(PSL_2(\mathbb{Q}))$ is a maximal von Neumann subalgebra in $L^{\infty}(Y)\rtimes PSL_2(\mathbb{Q})$; in particular, $PSL_2(\mathbb{Q})\curvearrowright Y$ is a prime action, i.e. it admits no non-trivial quotient actions.
Modelling techniques for biomolecular networks.

First we shortly review the different kinds of network modelling methods for systems biology with an emphasis on the different subtypes of logical models, which we review in more detail. Then we show the advantages of Boolean networks models over more mechanistic modelling types like differential equation techniques. Then follows an overlook about connections between different kinds of models and how they can be converted to each other. We also give a short overview about the mathematical frameworks for modelling of logical networks and list available software packages for logical modelling. Then we give an overview about the available standards and ontologies for storing such logical systems biology models and their results. In the end we give a short review about the difference between quantitative and qualitative models and describe the mathematics that specifically deals with qualitative modelling.
Score Engineered Logistic Regression

In several FICO studies logistic regression has been shown to be a very competitive technology for developing unrestricted scoring models, especially for performance metrics like ROC area. Application of logistic regression has been hampered by the lack of software to handle complex score engineering such as shape and pattern constraints. The purpose of this paper is to develop a sequential quadratic programming algorithm for score engineered logistic regression. This approach is based on a simple Taylor series expansion of minus log likelihood, which is locally quadratic. and fits in with the method that applies quadratic programming to B-Splines.
Policy-Aware Model Learning for Policy Gradient Methods

This paper considers the problem of learning a model in model-based reinforcement learning (MBRL). We examine how the planning module of an MBRL algorithm uses the model, and propose that the model learning module should incorporate the way the planner is going to use the model. This is in contrast to conventional model learning approaches, such as those based on maximum likelihood estimate, that learn a predictive model of the environment without explicitly considering the interaction of the model and the planner. We focus on policy gradient type of planning algorithms and derive new loss functions for model learning that incorporate how the planner uses the model. We call this approach Policy-Aware Model Learning (PAML). We theoretically analyze a generic model-based policy gradient algorithm and provide a convergence guarantee for the optimized policy. We also empirically evaluate PAML on some benchmark problems, showing promising results.
Nonlinear optics in perovskites with giant broadband optical refraction

Ludovica Falsi, 2 Luca Tartara, Fabrizio Di Mei, Mariano Flammini, Jacopo Parravicini, Davide Pierangeli, Gianbattista Parravicini, Feifei Xin, 6 Paolo Di Porto, Aharon J. Agranat, and Eugenio DelRe 8 Dipartimento di Fisica, Università di Roma “La Sapienza”, 00185 Rome, Italy Dipartimento S.B. A.I., Sezione di Fisica, “Sapienza” Università di Roma, I-00161 Roma, Italy Dipartimento di Ingegneria Industriale e dell’Informazione, Università di Pavia, I-27100 Pavia, Italy Dipartimento di Scienza dei Materiali, Università di Milano-Bicocca, I-20125 Milano, Italy Dipartimento di Fisica, Università di Pavia, I-27100 Pavia, Italy College of Physics and Materials Science, Tianjin Normal University, Tianjin, China, 300387 Applied Physics Department, Hebrew University of Jerusalem, IL-91904 Jerusalem, Israel ISC-CNR, Università di Roma “La Sapienza”, 00185 Rome, Italy (Dated: March 3, 2020)
Quantized Neural Network Inference with Precision Batching

We present PrecisionBatching, a quantized inference algorithm for speeding up neural network execution on traditional hardware platforms at low bitwidths without the need for retraining or recalibration. PrecisionBatching decomposes a neural network into individual bitlayers and accumulates them using fast 1-bit operations while maintaining activations in full precision. PrecisionBatching not only facilitates quantized inference at low bitwidths (< 8 bits) without the need for retraining/recalibration, but also 1) enables traditional hardware platforms the ability to realize inference speedups at a finer granularity of quantization (e.g: 1-16 bit execution) and 2) allows accuracy and speedup tradeoffs at runtime by exposing the number of bitlayers to accumulate as a tunable parameter. Across a variety of applications (MNIST, language modeling, natural language inference) and neural network architectures (fully connected, RNN, LSTM), PrecisionBatching yields end-to-end speedups of over 8x on a GPU within a < 1% error margin of the full precision baseline, outperforming traditional 8-bit quantized inference by over 1.5x-2x at the same error tolerance.
Computing the density of tautologies in propositional logic by solving system of quadratic equations of generating functions

In this paper, we will provide a method to compute the density of tautologies among the set of well-formed formulae consisting of $m$ variables, a negation symbol and an implication symbol, which has a possibility to be applied for other logical systems. This paper contains computational numerical values of the density of tautologies for two, three, and four variable cases. Also, for certain quadratic systems, we will introduce the $s$-cut concept to make a better approximation when we compute the ratio by brute-force counting, and discover a fundamental relation between generating functions' values on the singularity point and ratios of coefficients, which can be understood as another intepretation of the Szegő lemma for such quadratic systems. With this relation, we will provide an asymptotic lower bound $m^{-1}-(7/4)m^{-3/2}+O(m^{-2})$ of the density of tautologies as $m$ goes to the infinity.
Learning from Easy to Complex: Adaptive Multi-curricula Learning for Neural Dialogue Generation

Current state-of-the-art neural dialogue systems are mainly data-driven and are trained on human-generated responses. However, due to the subjectivity and open-ended nature of human conversations, the complexity of training dialogues varies greatly. The noise and uneven complexity of query-response pairs impede the learning efficiency and effects of the neural dialogue generation models. What is more, so far, there are no unified dialogue complexity measurements, and the dialogue complexity embodies multiple aspects of attributes—specificity, repetitiveness, relevance, etc. Inspired by human behaviors of learning to converse, where children learn from easy dialogues to complex ones and dynamically adjust their learning progress, in this paper, we first analyze five dialogue attributes to measure the dialogue complexity in multiple perspectives on three publicly available corpora. Then, we propose an adaptive multi-curricula learning framework to schedule a committee of the organized curricula. The framework is established upon the reinforcement learning paradigm, which automatically chooses different curricula at the evolving learning process according to the learning status of the neural dialogue generation model. Extensive experiments conducted on five state-of-the-art models demonstrate its learning efficiency and effectiveness with respect to 13 automatic evaluation metrics and human judgments.
Learning to Resolve Alliance Dilemmas in Many-Player Zero-Sum Games

Zero-sum games have long guided artificial intelligence research, since they possess both a rich strategy space of best-responses and a clear evaluation metric. What's more, competition is a vital mechanism in many real-world multi-agent systems capable of generating intelligent innovations: Darwinian evolution, the market economy and the AlphaZero algorithm, to name a few. In two-player zero-sum games, the challenge is usually viewed as finding Nash equilibrium strategies, safeguarding against exploitation regardless of the opponent. While this captures the intricacies of chess or Go, it avoids the notion of cooperation with co-players, a hallmark of the major transitions leading from unicellular organisms to human civilization. Beyond two players, alliance formation often confers an advantage; however this requires trust, namely the promise of mutual cooperation in the face of incentives to defect. Successful play therefore requires adaptation to co-players rather than the pursuit of non-exploitability. Here we argue that a systematic study of many-player zero-sum games is a crucial element of artificial intelligence research. Using symmetric zero-sum matrix games, we demonstrate formally that alliance formation may be seen as a social dilemma, and empirically that naive multi-agent reinforcement learning therefore fails to form alliances. We introduce a toy model of economic competition, and show how reinforcement learning may be augmented with a peer-to-peer contract mechanism to discover and enforce alliances. Finally, we generalize our agent model to incorporate temporally-extended contracts, presenting opportunities for further work.
Graph3S: A Simple, Speedy and Scalable Distributed Graph Processing System

Graph is a ubiquitous structure in many domains. The rapidly increasing data volume calls for efficient and scalable graph data processing. In recent years, designing distributed graph processing systems has been an increasingly important area to fulfil the demands of processing big graphs in a distributed environment. Though a variety of distributed graph processing systems have been developed, very little attention has been paid to achieving a good combinational system performance in terms of usage simplicity, efficiency and scalability. To contribute to the study of distributed graph processing system, this work tries to fill this gap by designing a simple, speedy and scalable system. Our observation is that enforcing the communication flexibility of a system leads to the gains of both system efficiency and scalability as well as simple usage. We realize our idea in a system Graph3S and conduct extensive experiments with diverse algorithms over big graphs from different domains to test its performance. The results show that, besides simple usage, our system has outstanding performance over various graph algorithms and can even reach up to two orders of magnitude speedup over existing in-memory systems when applying to some algorithms. Also, its scalability is competitive to disk-based systems and even better when less machines are used.
A Real-time Automatic Validation System for Optical Transients Detected by GWAC

The ground-based wide-angle camera array generates millions of single frame alerts per night. After the complicated and elaborate filters by multiple methods, a couple of dozens of candidates are still needed to be confirmed by follow-up observations in real-time. In order to free scientists from the complex and high-intensity follow-up tasks, we developed a Real-time Automatic transient Validation System (RAVS), and introduce here its system architecture, data processing flow, database schema, automatic follow-up control flow, and mobile message notification solution. This system is capable of automatically carrying out all operations in real-time without human intervention, including the validation of transient candidates, the adaptive light-curve sampling for identified targets in multi-band, and the pushing of observation results to the mobile client. The running of RAVS shows that an M-type stellar flare event can be well sampled by RAVS without a significant loss of the details, while the observing time is only less than one-third of the time coverage. Because the control logic of RAVS is designed to be independent of the telescope hardware, RAVS can be conveniently transplanted to other telescopes, especially the follow-up system of SVOM. Some future improvements are presented for the adaptive light-curve sampling, after taking into account both the brightness of sources and the evolution trends of the corresponding light-curves.
SIP-SegNet: A Deep Convolutional Encoder-Decoder Network for Joint Semantic Segmentation and Extraction of Sclera, Iris and Pupil based on Periocular Region Suppression

The current developments in the field of machine vision have opened new vistas towards deploying multimodal biometric recognition systems in various real-world applications. These systems have the ability to deal with the limitations of unimodal biometric systems which are vulnerable to spoofing, noise, non-universality and intra-class variations. In addition, the ocular traits among various biometric traits are preferably used in these recognition systems. Such systems possess high distinctiveness, permanence, and performance while, technologies based on other biometric traits (fingerprints, voice etc.) can be easily compromised. This work presents a novel deep learning framework called SIP-SegNet, which performs the joint semantic segmentation of ocular traits (sclera, iris and pupil) in unconstrained scenarios with greater accuracy. The acquired images under these scenarios exhibit purkinje reflexes, specular reflections, eye gaze, off-angle shots, low resolution, and various occlusions particularly by eyelids and eyelashes. To address these issues, SIP-SegNet begins with denoising the pristine image using denoising convolutional neural network (DnCNN), followed by reflection removal and image enhancement based on contrast limited adaptive histogram equalization (CLAHE). Our proposed framework then extracts the periocular information using adaptive thresholding and employs the fuzzy filtering technique to suppress this information. Finally, the semantic segmentation of sclera, iris and pupil is achieved using the densely connected fully convolutional encoder-decoder network. We used five CASIA datasets to evaluate the performance of SIP-SegNet based on various evaluation metrics. The simulation results validate the optimal segmentation of the proposed SIP-SegNet, with the mean f1 scores of 93.35, 95.11 and 96.69 for the sclera, iris and pupil classes respectively.
In search of biomarkers and the ideotype of barley tolerant to water scarcity

In barley plants, water shortage causes many changes on the morphological, physiological and biochemical levels resulting in the reduction of grain yield. In the present study the results of various experiments on the response of the same barley recombinant inbred lines to water shortage, including phenotypic, proteomic and metabolomic traits were integrated. Obtained results suggest that by a multi-omic approach it is possible to indicate proteomic and metabolomic traits important for reaction of barley plants to reduced water availability. Analysis of regression of drought effect (DE) for grain weight per plant on DE of proteomic and metabolomic traits allowed us to suggest ideotype of barley plants tolerant to water shortage. It was shown that grain weight under drought was determined significantly by six proteins in leaves and five in roots, the function of which were connected with defence mechanisms, ion/electron transport, carbon (in leaves) and nitrogen (in roots) metabolism, and in leaves additionally by two proteins of unknown function. Out of numerous metabolites detected in roots only Aspartic and Glutamic acids and one metabolite of unknown function, were found to have significant influence on grain weight per plant. The role of these traits as biomarkers, and especially as suggested targets of ideotype breeding, has to be further studied. One of the direction to be followed is genetic co-localization of proteomic, metabolomic and phenotypic traits in the genetic and physical maps of barley genome that can describe putative functional associations between traits; this is the next step of our analysis that is in progress.
A Wireless, Multielectrode Neural Recording System: User-Generic Ear EEG

In the past few years it has been demonstrated that electroencephalography (EEG) can be recorded from inside the ear (in-ear EEG). To open the door to low-profile earpieces as wearable brain-computer interfaces (BCIs), this work presents a practical in-ear EEG device based on multiple dry electrodes, a user-generic design, and a lightweight wireless interface for streaming data and device programming. The earpiece is designed for improved ear canal contact across a wide population of users and is fabricated in a low-cost and scalable manufacturing process based on standard techniques such as vacuum forming,plasma-treatment, and spray coating. A 2.5x2. 5 cm^2 wireless recording module is designed to record and stream data wirelessly to a host computer. Performance was evaluated on three human subjects over three months and compared with clinical-grade wet scalp EEG recordings. Recordings of spontaneous and evoked physiological signals, eye-blinks, alpha rhythm, and the auditory steady-state response (ASSR), are presented. This is the first wireless in-ear EEG to our knowledge to incorporate a dry multielectrode, user-generic design. The user-generic ear EEG recorded a mean alpha modulation of 2.17, outperforming the state-of-the-art in dry electrode in-ear EEG systems.
Rigorous bounds on dynamical response functions and time-translation symmetry breaking

Dynamical response functions are standard tools for probing local physics near the equilibrium. They provide information about relaxation properties after the equilibrium state is weakly perturbed. In this paper we focus on systems which break the assumption of thermalization by exhibiting persistent temporal oscillations. We provide rigorous bounds on the Fourier components of dynamical response functions in terms of extensive or local dynamical symmetries, i.e., extensive or local operators with periodic time dependence. Additionally, we discuss the effects of spatially inhomogeneous dynamical symmetries. The bounds are explicitly implemented on the example of an interacting Floquet system, specifically in the integrable Trotterization of the Heisenberg XXZ model.
Toward a theory of optimization for over-parameterized systems of non-linear equations: the lessons of deep learning

The success of deep learning is due, to a great extent, to the remarkable effectiveness of gradient-based optimization methods applied to large neural networks. In this work we isolate some general mathematical structures allowing for efficient optimization in over-parameterized systems of non-linear equations, a setting that includes deep neural networks. In particular, we show that optimization problems corresponding to such systems are not convex, even locally, but instead satisfy the Polyak-Lojasiewicz (PL) condition allowing for efficient optimization by gradient descent or SGD. We connect the PL condition of these systems to the condition number associated to the tangent kernel and develop a non-linear theory parallel to classical analyses of over-parameterized linear equations. We discuss how these ideas apply to training shallow and deep neural networks. Finally, we point out that tangent kernels associated to certain large system may be far from constant, even locally. Yet, our analysis still allows to demonstrate existence of solutions and convergence of gradient descent and SGD.