Miracle-dz/UniFAR-FTUs huggingface dataset:
Hybrid Quantum Neural Network for Drug Response Prediction

Simple Summary This work successfully employs a novel approach in processing patient and drug data to predict the drug response for cancer patients. The approach uses a deep quantum computing circuit as part of a machine learning architecture to simultaneously consider the cell line and the chemical and predict its effect. The resultant hybrid quantum architecture predicted the drug response with 15% better effectiveness than its classical counterpart. This result presents a step towards designing personalized drugs using the abilities of quantum computers. Abstract Cancer is one of the leading causes of death worldwide. It is caused by various genetic mutations, which makes every instance of the disease unique. Since chemotherapy can have extremely severe side effects, each patient requires a personalized treatment plan. Finding the dosages that maximize the beneficial effects of the drugs and minimize their adverse side effects is vital. Deep neural networks automate and improve drug selection. However, they require a lot of data to be trained on. Therefore, there is a need for machine-learning approaches that require less data. Hybrid quantum neural networks were shown to provide a potential advantage in problems where training data availability is limited. We propose a novel hybrid quantum neural network for drug response prediction based on a combination of convolutional, graph convolutional, and deep quantum neural layers of 8 qubits with 363 layers. We test our model on the reduced Genomics of Drug Sensitivity in Cancer dataset and show that the hybrid quantum model outperforms its classical analog by 15% in predicting IC50 drug effectiveness values. The proposed hybrid quantum machine learning model is a step towards deep quantum data-efficient algorithms with thousands of quantum gates for solving problems in personalized medicine, where data collection is a challenge.
A metaheuristic multi-objective interaction-aware feature selection method

- Multi-objective feature selection is one of the most significant issues in the field of pattern recognition. It is challenging because it maximizes the classification performance and, at the same time, minimizes the number of selected features, and the mentioned two objectives are usually conflicting. To achieve a better Pareto optimal solution, metaheuristic optimization methods are widely used in many studies. However, the main drawback is the exploration of a large search space. Another problem with multi-objective feature selection approaches is the interaction between features. Selecting correlated features has negative effect on classification performance. To tackle these problems, we present a novel multi-objective feature selection method that has several advantages. Firstly, it considers the interaction between features using an advanced probability scheme. Secondly, it is based on the Pareto Archived Evolution Strategy (PAES) method that has several advantages such as simplicity and its speed in exploring the solution space. However, we improve the structure of PAES in such a way that generates the offsprings, intelligently. Thus, the proposed method utilizes the introduced probability scheme to produce more promising offsprings. Finally, it is equipped with a novel strategy that guides it to find the optimum number of features through the process of evolution. The experimental results show a significant improvement in finding the optimal Pareto front compared to state-of-the-art methods on different real-world datasets.
Relation of a unified quantum field theory of spinors to the structure of general relativity

Based on a unified quantum field theory of spinors assumed to describe all matter fields and their interactions we construct the space-time structure of general relativity according to a general connection within the corresponding spinor space. The tetrad field and the corresponding metric field are composed from a space-time dependent basis of spinors within the internal space of the fundamental matter field. Similar to twistor theory the Minkowski signature of the space-time metric is related to this spinor nature of elementary matter, if we assume the spinor space to be endowed with a symplectic structure. The equivalence principle and the property of background independence arise from the fact that all elementary fields are composed from the fundamental spinor field. This means that the structure of space-time according to general relativity seems to be a consequence of a fundamental theory of matter fields and not a presupposition as in the usual setting of relativistic quantum field theories.
A Modular 3-Degree-of-Freedom Force Sensor for Robot-assisted Minimally Invasive Surgery Research

Effective force modulation during tissue manipulation is important for ensuring safe robot-assisted minimally invasive surgery (RMIS). Strict requirements for in-vivo distal force sensing have led to prior sensor designs that trade off ease of manufacture and integration against force measurement accuracy along the tool axis. These limitations have made collecting high-quality 3-degree-of-freedom (3-DoF) bimanual force data in RMIS inaccessible to researchers. We present a modular and manufacturable 3-DoF force sensor that integrates easily with an existing RMIS tool. We achieve this by relaxing biocompatibility and sterilizability requirements while utilizing commercial load cells and common electromechanical fabrication techniques. The sensor has a range of +-5 N axially and +-3 N laterally with average root mean square errors(RMSEs) of below 0.15 N in all directions. During teleoperated mock tissue manipulation tasks, a pair of jaw-mounted sensors achieved average RMSEs of below 0.15 N in all directions. For grip force, it achieved an RMSE of 0.156 N. The sensor has sufficient accuracy within the range of forces found in delicate manipulation tasks, with potential use in bimanual haptic feedback and robotic force control. As an open-source design, the sensors can be adapted to suit additional robotic applications outside of RMIS.
Reconstruction of the Real Quantum Channel via Convex Optimization

Quantum process tomography is often used to completely characterize an unknown quantum process. However, it may lead to an unphysical process matrix, which will cause the loss of information respect to the tomography result. Convex optimization, widely used in machine learning, is able to generate a global optimal model that best fits the raw data while keeping the process tomography in a legitimate region. Only by correctly revealing the original action of the process can we seek deeper into its properties like its phase transition and its Hamiltonian. Thus, we reconstruct the real quantum channel using convex optimization from our experimental result obtained in free-space seawater. In addition, we also put forward a criteria, state deviation, to evaluate how well the reconstructed process fits the tomography result. We believe that the crossover between quantum process tomography and convex optimization may help us move forward to machine learning of quantum channels.
Desire Backpropagation: A Lightweight Training Algorithm for Multi-Layer Spiking Neural Networks based on Spike-Timing-Dependent Plasticity

Spiking neural networks (SNNs) are a viable alternative to conventional artificial neural networks when resource efficiency and computational complexity are of importance. A major advantage of SNNs is their binary information transfer through spike trains which eliminates multiplication operations. The training of SNNs has, however, been a challenge, since neuron models are non-differentiable and traditional gradient-based backpropagation algorithms cannot be applied directly. Furthermore, spike-timing-dependent plasticity (STDP), albeit being a spike-based learning rule, updates weights locally and does not optimize for the output error of the network. We present desire backpropagation, a method to derive the desired spike activity of all neurons, including the hidden ones, from the output error. By incorporating this desire value into the local STDP weight update, we can efficiently capture the neuron dynamics while minimizing the global error and attaining a high classification accuracy. That makes desire backpropagation a spike-based supervised learning rule. We trained three-layer networks to classify MNIST and Fashion-MNIST images and reached an accuracy of 98.41% and 87.56%, respectively. In addition, by eliminating a multiplication during the backward pass, we reduce computational complexity and balance arithmetic resources between forward and backward pass, making desire backpropagation a candidate for training on low-resource devices.
Galactoseismology in the GAIA Era

Abstract The GAIA satellite will provide unprecedented phase-space information for our Galaxy and enable a new era of Galactic dynamics. We may soon see successful realizations of Galactoseismology, i.e., inferring the characteristics of the Galactic potential and sub-structure from a dynamical analysis of observed perturbations in the gas or stellar disk of the Milky Way. Here, we argue that to maximally take advantage of the GAIA data and other complementary surveys, it is necessary to build comprehensive models for both the stars and the gas. We outline several key morphological puzzles of the Galactic disk and proposed solutions that may soon be tested.
Entanglement steering in adaptive circuits with feedback

The intensely studied measurement-induced entanglement phase transition has become a hallmark of non-unitary quantum many-body dynamics. Usually, such a transition only shows up at the level of each individual quantum trajectory, and is absent for the density matrix averaged over measurement outcomes. In this work, we introduce a class of adaptive random circuit models with feedback that exhibit transitions in both settings. After each measurement, a unitary operation is either applied or not depending on the measurement outcome, which steers the averaged density matrix towards a unique state above a certain measurement threshold. Interestingly, the transition for the density matrix and the entanglement transition in the individual quantum trajectory in general happen at \textit{different} critical measurement rates. We demonstrate that the former transition belongs to the parity-conserving universality class by an explicit mapping to a classical branching-annihilating random walk process.
Orbital angular momentum based intra- and interparticle entangled states generated via a quantum dot source

Abstract. Engineering single-photon states endowed with orbital angular momentum (OAM) is a powerful tool for quantum information photonic implementations. Indeed, due to its unbounded nature, OAM is suitable for encoding qudits, allowing a single carrier to transport a large amount of information. Most of the experimental platforms employ spontaneous parametric down-conversion processes to generate single photons, even if this approach is intrinsically probabilistic, leading to scalability issues for an increasing number of qudits. Semiconductor quantum dots (QDs) have been used to get over these limitations by producing on-demand pure and indistinguishable single-photon states, although only recently they have been exploited to create OAM modes. Our work employs a bright QD single-photon source to generate a complete set of quantum states for information processing with OAM-endowed photons. We first study hybrid intraparticle entanglement between OAM and polarization degrees of freedom of a single photon whose preparation was certified by means of Hong–Ou–Mandel visibility. Then, we investigate hybrid interparticle OAM-based entanglement by exploiting a probabilistic entangling gate. The performance of our approach is assessed by performing quantum state tomography and violating Bell inequalities. Our results pave the way for the use of deterministic sources for the on-demand generation of photonic high-dimensional quantum states.
Speech Separation with Large-Scale Self-Supervised Learning

Self-supervised learning (SSL) methods such as WavLM have shown promising speech separation (SS) results in small-scale simulation-based experiments. In this work, we extend the exploration of the SSL-based SS by massively scaling up both the pre-training data (more than 300K hours) and fine-tuning data (10K hours). We also investigate various techniques to efficiently integrate the pre-trained model with the SS network under a limited computation budget, including a low frame rate SSL model training setup and a fine-tuning scheme using only the part of the pre-trained model. Compared with a supervised baseline and the WavLM-based SS model using feature embeddings obtained with the previously released 94K hours trained WavLM, our proposed model obtains 15.9% and 11.2% of relative word error rate (WER) reductions, respectively, for a simulated far-field speech mixture test set. For conversation transcription on real meeting recordings using continuous speech separation, the proposed model achieves 6.8% and 10.6% of relative WER reductions over the purely supervised baseline on AMI and ICSI evaluation sets, respectively, while reducing the computational cost by 38%.
Power Grid Congestion Management via Topology Optimization with AlphaZero

The energy sector is facing rapid changes in the transition towards clean renewable sources. However, the growing share of volatile, fluctuating renewable generation such as wind or solar energy has already led to an increase in power grid congestion and network security concerns. Grid operators mitigate these by modifying either generation or demand (redispatching, curtailment, flexible loads). Unfortunately, redispatching of fossil generators leads to excessive grid operation costs and higher emissions, which is in direct opposition to the decarbonization of the energy sector. In this paper, we propose an AlphaZero-based grid topology optimization agent as a non-costly, carbon-free congestion management alternative. Our experimental evaluation confirms the potential of topology optimization for power grid operation, achieves a reduction of the average amount of required redispatching by 60%, and shows the interoperability with traditional congestion management methods. Our approach also ranked 1st in the WCCI 2022 Learning to Run a Power Network (L2RPN) competition. Based on our findings, we identify and discuss open research problems as well as technical challenges for a productive system on a real power grid.
Phase diagram of a one-dimensional Ising model with an anomalous Z_2 symmetry

Anomalous global symmetries, which can be realized on the boundary of symmetry-protected topological phases, brings new phases and phase transitions to condensed matter physics. In this work, we study a one dimensional model with an anomalous Z2 symmetry, using the density-matrix renormalization group method. Besides a symmetry-breaking ferromagnetic phase, we find a gapless phase described by the SU(2)_1 conformal field theory, despite the existence of only discrete Z_2 symmetry in the Hamiltonian. The phase transition between the ferromagnetic phase and the gapless phase is continuous and has the same critical scaling as in the gapless phase. Our numerical finding is compatible of theoretical constraints on possible phases resulting from the symmetry anomaly.
When is Realizability Sufficient for Off-Policy Reinforcement Learning?

Model-free algorithms for reinforcement learning typically require a condition called Bellman completeness in order to successfully operate off-policy with function approximation, unless additional conditions are met. However, Bellman completeness is a requirement that is much stronger than realizability and that is deemed to be too strong to hold in practice. In this work, we relax this structural assumption and analyze the statistical complexity of off-policy reinforcement learning when only realizability holds for the prescribed function class. We establish finite-sample guarantees for off-policy reinforcement learning that are free of the approximation error term known as inherent Bellman error, and that depend on the interplay of three factors. The first two are well known: they are the metric entropy of the function class and the concentrability coefficient that represents the cost of learning off-policy. The third factor is new, and it measures the violation of Bellman completeness, namely the mis-alignment between the chosen function class and its image through the Bellman operator. In essence, these error bounds establish that off-policy reinforcement learning remains statistically viable even in absence of Bellman completeness, and characterize the intermediate situation between the favorable Bellman complete setting and the worst-case scenario where exponential lower bounds are in force. Our analysis directly applies to the solution found by temporal difference algorithms when they converge.
Examining the Rotation Period Distribution of the 40 Myr Tucana–Horologium Association with TESS

The Tucana–Horologium association (Tuc-Hor) is a 40 Myr old moving group in the southern sky. In this work, we measure the rotation periods of 313 Tuc-Hor objects with TESS light curves derived from TESS full-frame images and membership lists driven by Gaia EDR3 kinematics and known youth indicators. We recover a period for 81.4% of the sample and report 255 rotation periods for Tuc-Hor objects. From these objects we identify 11 candidate binaries based on multiple periodic signals or outlier Gaia DR2 and EDR3 renormalized unit weight error values. We also identify three new complex rotators (rapidly rotating M dwarf objects with intricate light-curve morphology) within our sample. Along with the six previously known complex rotators that belong to Tuc-Hor, we compare their light-curve morphology between TESS Cycle 1 and Cycle 3 and find that they change substantially. Furthermore, we provide context for the entire Tuc-Hor rotation sample by describing the rotation period distributions alongside other youth indicators such as Hα and Li equivalent width, as well as near-ultraviolet and X-ray flux. We find that measuring rotation periods with TESS is a fast and effective means to confirm members in young moving groups.
Causal Counterfactuals for Improving the Robustness of Reinforcement Learning

Reinforcement learning (RL) is used in various robotic applications. RL enables agents to learn tasks autonomously by interacting with the environment. The more critical the tasks are, the higher the demand for the robustness of the RL systems. Causal RL combines RL and causal inference to make RL more robust. Causal RL agents use a causal representation to capture the invariant causal mechanisms that can be transferred from one task to another. Currently, there is limited research in Causal RL, and existing solutions are usually not complete or feasible for real-world applications. In this work, we propose CausalCF, the first complete Causal RL solution incorporating ideas from Causal Curiosity and CoPhy. Causal Curiosity provides an approach for using interventions, and CoPhy is modified to enable the RL agent to perform counterfactuals. Causal Curiosity has been applied to robotic grasping and manipulation tasks in CausalWorld. CausalWorld provides a realistic simulation environment based on the TriFinger robot. We apply CausalCF to complex robotic tasks and show that it improves the RL agent's robustness using CausalWorld.
Real energies and Berry phases in all PT-regimes in time-dependent non-Hermitian theories

We demonstrate that the existence of a Hermitian time-dependent intertwining operator that maps the non-Hermitian time-dependent energy operator to its Hermitian conjugate and its right to its left eigenstates guarantees the reality of the instantaneous energies. This property holds throughout all three PT -regimes, in the time-independent scenario referred to as the PT -symmetric regime, the exceptional point and the spontaneously broken PT -regime. We also propose a modified adiabatic approximation consisting of an expansion of the wavefunctions in terms the instantaneous eigenstates of the energy operator, instead of the usually used eigenfunctions of the Hamiltonian. We show that this proposal always leads to real Berry phases. We illustrate the working of our general proposals with two explicit examples for a time-dependent non-Hermitian spin model.
Towards High-Power Microwaves

In this paper, we review and compare HPM sources operating without a magnetic ﬁeld to guide the electron beam that are capable of producing high-power microwave (HPM) pulses with a duration of about 100 ns. The proposed analysis summarizes multi-year research carried with three types of HPM sources: a split-cavity oscillator (SCO); an axial vircator; and a virtual cathode oscillator in Reﬂex Triode geometry. These options were simulated for electron beam energy ∼ 400 keV and for pulsers with demonstrated capability to provide high power microwave pulses with the required pulse duration. Designed sources were experimentally tested, and their advantages and weaknesses are discussed with respect to high output power, long pulse duration, and good operating stability.
Vision-based navigation and obstacle avoidance via deep reinforcement learning

Development of navigation algorithms is essential for the successful deployment of robots in rapidly changing hazardous environments for which prior knowledge of configuration is often limited or unavailable. Use of traditional path-planning algorithms, which are based on localization and require detailed obstacle maps with goal locations, is not possible. In this regard, vision-based algorithms hold great promise, as visual information can be readily acquired by a robot's onboard sensors and provides a much richer source of information from which deep neural networks can extract complex patterns. Deep reinforcement learning has been used to achieve vision-based robot navigation. However, the efficacy of these algorithms in environments with dynamic obstacles and high variation in the configuration space has not been thoroughly investigated. In this paper, we employ a deep Dyna-Q learning algorithm for room evacuation and obstacle avoidance in partially observable environments based on low-resolution raw image data from an onboard camera. We explore the performance of a robotic agent in environments containing no obstacles, convex obstacles, and concave obstacles, both static and dynamic. Obstacles and the exit are initialized in random positions at the start of each episode of reinforcement learning. Overall, we show that our algorithm and training approach can generalize learning for collision-free evacuation of environments with complex obstacle configurations. It is evident that the agent can navigate to a goal location while avoiding multiple static and dynamic obstacles, and can escape from a concave obstacle while searching for and navigating to the exit.