Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigations of the non-adiabatic photophysics of Cu(I)-phenanthroline complexes

Cu(I)-phenanthrolines are an important class of metal-organic molecules that exhibits much promise for solar energy harvesting and solar-driven catalysis applications. Although many experimental studies have been performed calling for high-level simulations to elucidate their photophysics, a complete picture is still missing. This is the goal of the present thesis. On the ultrafast (femtosecond) timescale we studied the non-adiabatic relaxation of a prototypical Cu(I)-phenanthroline, [Cu(dmp)2]+, by performing excited state simulations using two approaches: quantum dynamics and trajectory surface hopping. These simulations help to identify several mechanisms, internal conversion, pseudo Jahn-Teller distortion, intersystem crossing, occurring in the subpicosecond time scale. Surprisingly, we have found that intersystem crossing does not take place between the lowest singlet and triplet excited states, as previously proposed, but between the lowest singlet and higher triplet states. Moreover, we observed the initial stages (< 100 fs) of the solvent reorganization due to the electronic density changes in the excited state. This leads to an energy stabilization of the excited states that is associated with an increase of the non-radiative decay rate. The quantum dynamics simulations allowed us to provide indications for performing additional spectroscopy measurements by using the recently developed X-ray Free Electron Lasers (X-FELs). This technology can monitor both electronic and structural changes with an unprecedented time resolution of tens of femtoseconds and, therefore, is capable of revealing the aforementioned processes. In addition, we questioned the feasibility of such experiments and calculated the signal strengths for XAS and XES transient spectra. Finally, we analyzed the luminescence quenching, which has been observed for all Cu(I)-phenanthroline complexes when they are dissolved in strongly donating solvents. By performing Molecular Dynamics calculations we showed that, in contrast with the previously accepted model based on the formation of an exciplex (a species formed by two molecules, one in the excited state and one in the ground state), no stable exciplex is formed and that quenching is due to electrostatic solute-solvent interactions. In addition, we investigated how the geometry configuration can affect the luminescence lifetime in these molecules. We found a correlation between rigidity of the copper complex - inhibition of the pseudo Jahn-Teller distortion - and lifetime of the emission. The more the metal complex retains the ground state structure (large substituents), the longer its lifetime. This effect is attributed to a higher energy gap (excited state minus ground state energy) due to the reduction of relaxation. Our research reveals important insights into the relaxation mechanism and the complex interplay between geometry and electronic structure in Cu(I)-phenanthroline. These results can be exploited for guiding the synthesis of complexes with the desired physical properties.
Do Cognitive Biases Impact M&A Performance in Emerging Markets? Evidence from Russian Firms

In this paper we investigate cognitive biases as a potential reason for the varied results of M&amp;A in emerging capital markets. We focus on two cognitive biases, CEO overconfidence and availability bias, which significantly influence CEO behavior, encouraging them to be irrational in M&amp;A deals. <br>Based on 237 M&amp;A deals closed by Russian firms during the period 2005–2019 we empirically prove that CEO overconfidence destroys value, and availability bias creates value in M&amp;A deals in the Russian market. We show that due to the low level of corporate governance in emerging capital markets, all corporate governance mechanisms can mitigate CEO irrationalities in M&amp;A. <br>
Rapid Feature Evolution Accelerates Learning in Neural Networks

Neural network (NN) training and generalization in the inﬁnite-width limit are well-characterized by kernel methods with a neural tangent kernel (NTK) that is stationary in time. However, ﬁnite-width NNs consistently outperform corresponding kernel methods, suggesting the importance of feature learning, which manifests as the time evolution of NTKs. Here, we analyze the phenomenon of kernel alignment of the NTK with the target functions during gradient descent. We ﬁrst provide a mechanistic explanation for why alignment between task and kernel occurs in deep linear networks. We then show that this behavior occurs more generally if one optimizes the feature map over time to accelerate learning while constraining how quickly the features evolve. Empirically, gradient descent undergoes a feature learning phase, during which top eigenfunctions of the NTK quickly align with the target function and the loss decreases faster than power law in time; it then enters a kernel gradient descent (KGD) phase where the alignment does not improve signiﬁcantly and the training loss decreases in power law. We show that feature evolution is faster and more dramatic in deeper networks. We also found that networks with multiple output nodes develop separate, specialized kernels for each output channel, a phenomenon we termed kernel specialization. We show that this class-speciﬁc alignment does not occur in linear networks.
On the conjectural decomposition of symmetric powers of automorphic representations for GL(3) and GL(4)

Given a cuspidal automorphic representation π for GL(3) over a number field and a positive integer k, assume that the symmetric mth power lifts of π are isobaric automorphic for m ≤ k, cuspidal for m ≤ k− 1, and that certain associated Rankin–Selberg products are isobaric automorphic. Then the number of cuspidal isobaric summands in the kth symmetric power lift is bounded above by 3 when k ≥ 7, and bounded above by 2 when k ≥ 19 with k ≡ 1 (mod 3). We then investigate the analogous problem for GL(4).
Approximate MRAM: High-Performance and Power-Efficient Computing With MRAM Chips for Error-Tolerant Applications

Approximate computing (AC) leverages the inherent error resilience and is used in many big-data applications from various domains such as multimedia, computer vision, signal processing, and machine learning to improve systems performance and power consumption. Like many other approximate circuits and algorithms, the memory subsystem can also be used to enhance performance and save power significantly. This paper proposes an efficient and effective systematic methodology to construct an approximate non-volatile magneto-resistive RAM (MRAM) framework using consumer-off-the-shelf (COTS) MRAM chips. In the proposed scheme, an extensive experimental characterization of memory errors is performed by manipulating the write latency of MRAM chips which exploits the inherent (intrinsic/extrinsic process variation) stochastic switching behavior of magnetic tunnel junctions (MTJs). The experimental results, involving error-resilient image compression and machine learning applications, reveal that the proposed AC framework provides a significant performance improvement and demonstrates a reduction in MRAM write energy of <inline-formula><tex-math notation="LaTeX">${\sim }47.5\%$</tex-math><alternatives><mml:math><mml:mrow><mml:mo>∼</mml:mo><mml:mn>47</mml:mn><mml:mo>. </mml:mo><mml:mn>5</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="ferdaus-ieq1-3174584.gif"/></alternatives></inline-formula> on average with negligible or no loss in output quality.
Accelerating Neural ODEs Using Model Order Reduction

Embedding nonlinear dynamical systems into artificial neural networks is a powerful new formalism for machine learning. By parameterizing ordinary differential equations (ODEs) as neural network layers, these Neural ODEs are memory-efficient to train, process time series naturally, and incorporate knowledge of physical systems into deep learning (DL) models. However, the practical applications of Neural ODEs are limited due to long inference times because the outputs of the embedded ODE layers are computed numerically with differential equation solvers that can be computationally demanding. Here, we show that mathematical model order reduction (MOR) methods can be used for compressing and accelerating Neural ODEs by accurately simulating the continuous nonlinear dynamics in low-dimensional subspaces. We implement our novel compression method by developing Neural ODEs that integrate the necessary subspace-projection and interpolation operations as layers of the neural network. We validate our approach by comparing it to neuron pruning and singular value decomposition (SVD)-based weight truncation methods from the literature in image and time-series classification tasks. The methods are evaluated by acceleration versus accuracy when adjusting the level of compression. On this spectrum, we achieve a favorable balance over existing methods by using MOR when compressing a convolutional Neural ODE. In compressing a recurrent Neural ODE, SVD-based weight truncation yields good performance. Based on our results, our integration of MOR with Neural ODEs can facilitate efficient, dynamical system-driven DL in resource-constrained applications.
Crowdsourcing Learning as Domain Adaptation: A Case Study on Named Entity Recognition

Crowdsourcing is regarded as one prospective solution for effective supervised learning, aiming to build large-scale annotated training data by crowd workers. Previous studies focus on reducing the influences from the noises of the crowdsourced annotations for supervised models. We take a different point in this work, regarding all crowdsourced annotations as gold-standard with respect to the individual annotators. In this way, we find that crowdsourcing could be highly similar to domain adaptation, and then the recent advances of cross-domain methods can be almost directly applied to crowdsourcing. Here we take named entity recognition (NER) as a study case, suggesting an annotator-aware representation learning model that inspired by the domain adaptation methods which attempt to capture effective domain-aware features. We investigate both unsupervised and supervised crowdsourcing learning, assuming that no or only small-scale expert annotations are available. Experimental results on a benchmark crowdsourced NER dataset show that our method is highly effective, leading to a new state-of-the-art performance. In addition, under the supervised setting, we can achieve impressive performance gains with only a very small scale of expert annotations.
Clustering Coeﬃcients in Weighted Undirected Multilayer Networks

Complex systems in the real world are frequently characterized by entities connected by relationships of diﬀerent nature. These systems can be appropriately described by multilayer networks where the diﬀerent relations between nodes can be conveniently expressed structuring the network through diﬀerent layers. To extend in a multilayer context the classical network indicators proposed for monolayer networks is an important issue. In this work we study the incidence of triangular patterns expressed through the local clustering coeﬃcient in a weighted undirected multilayer network. We provide new local clustering coeﬃcients for multilayer networks, looking at the network from diﬀerent perspectives depending on the node’s position, as well as a global clustering coeﬃcient for the whole network. We also prove that all the well-known expressions for clustering coeﬃcients existing in the literature, suitably extended to the multilayer framework, may be uniﬁed into our proposal, both in terms of tensors and supradjacency matrix. To show the eﬀective-ness of the proposed measures, we study an application to the multilayer temporal ﬁnancial network based on the returns of the S&P100 assets.
MixerGAN: An MLP-Based Architecture for Unpaired Image-to-Image Translation

While attention-based transformer networks achieve unparalleled success in nearly all language tasks, the large number of tokens (pixels) found in images coupled with the quadratic activation memory usage makes them prohibitive for problems in computer vision. As such, while language-to-language translation has been revolutionized by the transformer model, convolutional networks remain the de facto solution for image-to-image translation. The recently proposed MLP-Mixer architecture alleviates some of the computational issues associated with attention-based networks while still retaining the long-range connections that make transformer models desirable. Leveraging this memory-efficient alternative to self-attention, we propose a new exploratory model in unpaired image-to-image translation called MixerGAN: a simpler MLP-based architecture that considers long-distance relationships between pixels without the need for expensive attention mechanisms. Quantitative and qualitative analysis shows that MixerGAN achieves competitive results when compared to prior convolutional-based methods.
FoveaTer: Foveated Transformer for Image Classification

Many animals and humans process the visual field with a varying spatial resolution (foveated vision) and use peripheral processing to make eye movements and point the fovea to acquire high-resolution information about objects of interest. This architecture results in computationally efficient rapid scene exploration. Recent progress in self-attention-based Vision Transformers, an alternative to the traditionally convolution-reliant computer vision systems. However, the Transformer models do not explicitly model the foveated properties of the visual system nor the interaction between eye movements and the classification task. We propose Foveated Transformer (FoveaTer) model, which uses pooling regions and eye movements to perform object classification tasks using a Vision Transformer architecture. Using square pooling regions or biologically-inspired radial-polar pooling regions, our proposed model pools the image features from the convolution backbone and uses the pooled features as an input to transformer layers. It decides on subsequent fixation location based on the attention assigned by the Transformer to various locations from past and present fixations. It dynamically allocates more fixation/computational resources to more challenging images before making the final image category decision. Using five ablation studies, we evaluate the contribution of different components of the Foveated model. We perform a psychophysics scene categorization task and use the experimental data to find a suitable radial-polar pooling region combination. We also show that the Foveated model better explains the human decisions in a scene categorization task than a Baseline model. We demonstrate our model's robustness against PGD adversarial attacks with both types of pooling regions, where we see the Foveated model outperform the Baseline model.
Re-evaluating Word Mover's Distance

The word mover's distance (WMD) is a fundamental technique for measuring the similarity of two documents. As the crux of WMD, it can take advantage of the underlying geometry of the word space by employing an optimal transport formulation. The original study on WMD reported that WMD outperforms classical baselines such as bag-of-words (BOW) and TF-IDF by significant margins in various datasets. In this paper, we point out that the evaluation in the original study could be misleading. We re-evaluate the performances of WMD and the classical baselines and find that the classical baselines are competitive with WMD if we employ an appropriate preprocessing, i.e., L1 normalization. In addition, we introduce an analogy between WMD and L1-normalized BOW and find that not only the performance of WMD but also the distance values resemble those of BOW in high dimensional spaces.
A Measurement Study on the (In)security of End-of-Life (EoL) Embedded Devices

Embedded devices are becoming popular. Meanwhile, researchers are actively working on improving the security of embedded devices. However, previous work ignores the insecurity caused by a special category of devices, i.e., the End-of-Life (EoL in short) devices. Once a product becomes End-of-Life, vendors tend to no longer maintain its firmware or software, including providing bug fixes and security patches. This makes EoL devices susceptible to attacks. For instance, a report showed that an EoL model with thousands of active devices was exploited to redirect web traffic for malicious purposes. In this paper, we conduct the first measurement study to shed light on the (in)security of EoL devices. To this end, our study performs two types of analysis, including the aliveness analysis and the vulnerability analysis. The first one aims to detect the scale of EoL devices that are still alive. The second one is to evaluate the vulnerabilities existing in (active) EoL devices. We have applied our approach to a large number of EoL models from three vendors (i.e., D-Link, Tp-Link, and Netgear) and detect the alive devices in a time period of ten months. Our study reveals some worrisome facts that were unknown by the community. For instance, there exist more than 2 million active EoL devices. Nearly 300,000 of them are still alive even after five years since they became EoL. Although vendors may release security patches after the EoL date, however, the process is ad hoc and incomplete. As a result, more than 1 million active EoL devices are vulnerable, and nearly half of them are threatened by high-risk vulnerabilities. Attackers can achieve a minimum of 2.79 Tbps DDoS attack by compromising a large number of active EoL devices. We believe these facts pose a clear call for more attention to deal with the security issues of EoL devices.
Controllable Person Image Synthesis with Spatially-Adaptive Warped Normalization

Controllable person image generation aims to produce realistic human images with desirable attributes such as a given pose, cloth textures, or hairstyles. However, the large spatial misalignment between source and target images makes the standard image-to-image translation architectures unsuitable for this task. Most state-of-the-art methods focus on alignment for global pose-transfer tasks. However, they fail to deal with region-specific texture-transfer tasks, especially for person images with complex textures. To solve this problem, we propose a novel Spatially-Adaptive Warped Normalization (SAWN) which integrates a learned flow-field to warp modulation parameters. It allows us to efficiently align person spatially-adaptive styles with pose features. Moreover, we propose a novel Self-Training Part Replacement (STPR) strategy to refine the model for the texture-transfer task, which improves the quality of the generated clothes and the preservation ability of non-target regions. Our experimental results on the widely used DeepFashion dataset demonstrate a significant improvement of the proposed method over the state-of-the-art methods on pose-transfer and texture-transfer tasks. The code is available at https://github.com/zhangqianhui/Sawn.
IoTAthena: Unveiling IoT Device Activities From Network Traffic

The recent spate of cyber attacks towards Internet of Things (IoT) devices in smart homes calls for effective techniques to understand, characterize, and unveil IoT device activities. In this paper, we present a new system, named IoTAthena, to unveil IoT device activities from raw network traffic consisting of timestamped IP packets. IoTAthena characterizes each IoT device activity using an activity signature consisting of an ordered sequence of IP packets with inter-packet time intervals. IoTAthena has two novel polynomial time algorithms, sigMatch and actExtract. For any given signature, sigMatch can capture all matches of the signature in the raw network traffic. Using sigMatch as a subfunction, actExtract can accurately unveil the sequence of various IoT device activities from the raw network traffic. Using the network traffic of heterogeneous IoT devices collected at the router of a real-world smart home testbed and a public IoT dataset, we demonstrate that IoTAthena is able to characterize and generate activity signatures of IoT device activities and accurately unveil the sequence of IoT device activities from raw network traffic.
Numerical investigation of three-dimensional partial cavitation in a Venturi geometry

Sheet cavitation appears in many hydraulic applicationss and can lead to technical issues. Some fundamental outcomes such as the complex topology of 3-Dimensional cavitation pockets and their associated dynamics need to be carefully visited.In the present paper, the dynamics of partial cavitation developing in a 3-D Venturi geometry and the interaction with sidewalls are numerically investigated. The simulations are performed usinf a one-fluid compressible Reynolds-Averaged Navier-Stokes (RANS solver associated to a non-linear turbulence model and a void transport-equation model. A detailed analysis of this cavitating flow is carried out using innovative tools such as Spectral Proper Orthogonal Decompositions. Particular attenttion is paid in the study of 3-D effects by comparing numerical results obtained with sidewalls and periodic conditions. A three-dimensional dynamics of the sheet cavitation, unrelated to the presence of sidewalls, is identified and discussed.
A Minimax Lower Bound for Low-Rank Matrix-Variate Logistic Regression

This paper considers the problem of matrix-variate logistic regression. This paper derives the fundamental error threshold on estimating low-rank coefficient matrices in the logistic regression problem by deriving a lower bound on the minimax risk. The bound depends explicitly on the dimension and distribution of the covariates, the rank and energy of the coefficient matrix, and the number of samples. The resulting bound is proportional to the intrinsic degrees of freedom in the problem, which suggests the sample complexity of the low-rank matrix logistic regression problem can be lower than that for vectorized logistic regression. The proof techniques utilized in this work also set the stage for development of minimax lower bounds for tensor-variate logistic regression problems.
The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collider

We describe the outcome of a data challenge conducted as part of the Dark Machines (https://www.darkmachines.org) initiative and the Les Houches 2019 workshop on Physics at TeV colliders. The challenged aims to detect signals of new physics at the Large Hadron Collider (LHC) using unsupervised machine learning algorithms. First, we propose how an anomaly score could be implemented to define model-independent signal regions in LHC searches. We define and describe a large benchmark dataset, consisting of >1 billion simulated LHC events corresponding to 10\, fb^{-1}10fb−1 of proton-proton collisions at a center-of-mass energy of 13 TeV. We then review a wide range of anomaly detection and density estimation algorithms, developed in the context of the data challenge, and we measure their performance in a set of realistic analysis environments. We draw a number of useful conclusions that will aid the development of unsupervised new physics searches during the third run of the LHC, and provide our benchmark dataset for future studies at https://www.phenoMLdata. org. Code to reproduce the analysis is provided at https://github.com/bostdiek/DarkMachines-UnsupervisedChallenge.