While the arrival times of our robot were longer than the results of #REFR , we note that their robot was traveling at a speed (1.5m/s) about 7 times faster than our robot (0.22m/s).

Likewise, higher crowd density (more dangerous) resulted in longer arrival time.

Exceptions were seen in the ahead crowd behavior, where the arrival times with fast moving obstacles ahead were shorter than with slow moving obstacles.

The fast moving obstacles were traveling at a speed close to the speed of the robot.

In this case, when the obstacles were moving fast ahead of the robot, there was very little chance of the robot being confronted by the obstacles. The ahead environments were quite safe.

Consequently, there were very few safety violations as seen from the high ego and social scores in the results of ahead crowd behavior in Fig. 4 .

Taking into account the speed difference, our approach has performed relatively faster and with higher success rate than the approach of #OTHEREFR .

In addition, we have observed an interesting policy learned by our model.

In cases where there were too dense obstacles in the way of the robot, the robot would take a detour and avoid the crowd cluster to reach the goal.

However, in most cases, the robot navigated through the crowd. Fig.  5 illustrates the robot's behaviors during navigation.

Egosafety and social-safety violations do not necessary result in collisions.

Deep Reinforcement Learning-Based Mapless Crowd Navigation With Perceived Risk Of The Moving Crowd For Mobile Robots

Abstract: Classical map-based navigation methods are commonly used for robot navigation, but they often struggle in crowded environments due to the Frozen Robot Problem (FRP). Deep reinforcement learning-based methods address the FRP problem, however, suffer from the issues of generalization and scalability. To overcome these challenges, we propose a method that uses Collision Probability (CP) to help the robot navigate safely through crowds. The inclusion of CP in the observation space gives the robot a sense of the level of danger of the moving crowd. The robot will navigate through the crowd when it appears safe but will take a detour when the crowd is moving aggressively. By focusing on the most dangerous obstacle, the robot will not be confused when the crowd density is high, ensuring scalability of the model. Our approach was developed using deep reinforcement learning (DRL) and trained using the Gazebo simulator in a non-cooperative crowd environment with obstacles moving at randomized speeds and directions. We then evaluated our model on four different crowd-behavior scenarios with varying densities of crowds. The results shown that our method achieved a 100% success rate in all test settings. We compared our approach with a current state-of-the-art DRLbased approach, and our approach has performed significantly better. Importantly, our method is highly generalizable and requires no fine-tuning after being trained once. We further demonstrated the crowd navigation capability of our model in real-world tests.

Mapless Navigation Among Dynamics With Social-Safety-Awareness: A Reinforcement Learning Approach From 2D Laser Scans

Abstract: We propose a method to tackle the problem of mapless collision-avoidance navigation where humans are present using 2D laser scans. Our proposed method uses ego-safety to measure collision from the robot's perspective while social-safety to measure the impact of our robot's actions on surrounding pedestrians. Specifically, the social-safety part predicts the intrusion impact of our robot's action into the interaction area with surrounding humans. We train the policy using reinforcement learning on a simple simulator and directly evaluate the learned policy in Gazebo and real robot tests. Experiments show the learned policy can be smoothly transferred without any fine tuning. We observe that our method demonstrates time-efficient path planning behavior with high success rate in mapless navigation tasks. Furthermore, we test our method in a navigation among dynamic crowds task considering both low and high volume traffic. Our learned policy demonstrates cooperative behavior that actively drives our robot into traffic flows while showing respect to nearby pedestrians. Evaluation videos are at this https URL


First, we can notice that experimental and simulation results are almost similar, meaning that we can guide our LFI with high reliability and confidence #REFR .

For that purpose, we need to put to the test that LFI reach (near) identical performance than what expected by simulations.

We ran a BSCA simulation (in Python) over all the weight columns and bit lines that pointed out the MSB of the second column weight as the most sensitive.

Therefore, contrary to the previous experiments, the laser source was triggered only when the 20 most sensitive weights were read from the Flash.

The laser location was set accordingly (X = 760 µm) and the power increased to 360 mW to ensure a higher success rate on weights stored in distant addresses.

The blue curve in Fig.5b represents our experimental results (mean accuracy over 100 inferences) while the red one is the BSCA simulations for the MSB.

For an adversarial budget of only 5 bit-sets (0.1% faulted bits) the embedded model accuracy drops to 39% which represents a significant loss and a strong integrity impact compared to the nominal performance of 92%.

Moreover, after 10 bit-sets (accuracy to 25%), the most effective faults have been injected and the accuracy did not decrease anymore.

In a security evaluation context, this observation positions the level of robustness of the model according to the adversarial budget.

Evaluation Of Parameter-Based Attacks Against Embedded Neural Networks With Laser Injection

Abstract: Upcoming certification actions related to the security of machine learning (ML) based systems raise major evaluation challenges that are amplified by the large-scale deployment of models in many hardware platforms. Until recently, most of research works focused on API-based attacks that consider a ML model as a pure algorithmic abstraction. However, new implementation-based threats have been revealed, emphasizing the urgency to propose both practical and simulation-based methods to properly evaluate the robustness of models. A major concern is parameter-based attacks (such as the Bit-Flip Attack-BFA) that highlight the lack of robustness of typical deep neural network models when confronted by accurate and optimal alterations of their internal parameters stored in memory. Setting in a security testing purpose, this work practically reports, for the first time, a successful variant of the BFA on a 32-bit Cortex-M microcontroller using laser fault injection. It is a standard fault injection means for security evaluation, that enables to inject spatially and temporally accurate faults. To avoid unrealistic brute-force strategies, we show how simulations help selecting the most sensitive set of bits from the parameters taking into account the laser fault model.

On Evaluating Adversarial Robustness

Abstract: Correctly evaluating defenses against adversarial examples has proven to be extremely difficult. Despite the significant amount of recent work attempting to design defenses that withstand adaptive attacks, few have succeeded; most papers that propose defenses are quickly shown to be incorrect. ::: We believe a large contributing factor is the difficulty of performing security evaluations. In this paper, we discuss the methodological foundations, review commonly accepted best practices, and suggest new methods for evaluating defenses to adversarial examples. We hope that both researchers developing defenses as well as readers and reviewers who wish to understand the completeness of an evaluation consider our advice in order to avoid common pitfalls.


State-of-the-Art commercial platforms integrate Intel Optane DC Persistent Memory (DCPM) modules along with DRAM, leading to heterogeneous memory systems #REFR .

The memory system is one of the main components that limit the scalability and contribute to the energy consumption of supercomputers #OTHEREFR .

The integration of more DRAM modules to enable more complex simulations, analytics and effective in-memory processing has negative impact on the sustainability and maintenance costs of supercomputing centres.

In particular, despite the low access latency of traditional DRAM technologies, the increased leakage and refresh power requirements limit DRAM scalability and introduce a significant challenge towards reaching exascale performance.

In order to overcome DRAM limitations, non-volatile memory (NVM) technologies have been introduced, such as the 3D-XPoint, which is a subclass of the Phase-Change Memories (PCM) #OTHEREFR , Spin-Transfer Torque RAM (STT-RAM) #OTHEREFR and Resistive RAM (ReRAM) #OTHEREFR .

For instance, the upcoming Aurora exascale supercomputer employs the DAOS storage architecture, which integrates a complex memory and storage hierarchy, including Intel Optane DCPM modules #OTHEREFR .

This work has been partially funded by EU Horizon 2020 program under grant agreement No 101015922 AI@EDGE (https://aiatedge.eu/).

These emerging memory technologies provide higher density than DRAM, enabling increased aggregate memory capacities with fewer nodes, having positive impact on the energy consumption, resilience and sustainability.

Additionally, the data persistence features of the NVM technologies can be used to provide fault tolerance support to applications.

On the other hand, the Optane DCPM provides, in general, higher access latency and lower bandwidth compared to DRAM #OTHEREFR , #OTHEREFR , #OTHEREFR .

Energy Consumption Evaluation Of Optane Dc Persistent Memory For Indexing Data Structures

Abstract: The Intel Optane DC Persistent Memory (DCPM) is an attractive novel technology for building storage systems for data intensive HPC applications, as it provides lower cost per byte, low standby power and larger capacities than DRAM, with comparable latency. This work provides an in-depth evaluation of the energy consumption of the Optane DCPM, using wellestablished indexes specifically designed to address the challenges and constraints of the persistent memories. We study the energy efficiency of the Optane DCPM for several indexing data structures and for the LevelDB key-value store, under different types of YCSB workloads. By integrating an Optane DCPM in a memory system, the energy drops by 71.2% and the throughput increases by 37.3% for the LevelDB experiments, compared to a typical SSD storage solution.

Basic Performance Measurements Of The Intel Optane Dc Persistent Memory Module

Abstract: Scalable nonvolatile memory DIMMs will finally be commercially available with the release of the Intel Optane DC Persistent Memory Module (or just "Optane DC PMM"). This new nonvolatile DIMM supports byte-granularity accesses with access times on the order of DRAM, while also providing data storage that survives power outages. This work comprises the first in-depth, scholarly, performance review of Intel's Optane DC PMM, exploring its capabilities as a main memory device, and as persistent, byte-addressable memory exposed to user-space applications. This report details the technologies performance under a number of modes and scenarios, and across a wide variety of macro-scale benchmarks. Optane DC PMMs can be used as large memory devices with a DRAM cache to hide their lower bandwidth and higher latency. When used in this Memory (or cached) mode, Optane DC memory has little impact on applications with small memory footprints. Applications with larger memory footprints may experience some slow-down relative to DRAM, but are now able to keep much more data in memory. When used under a file system, Optane DC PMMs can result in significant performance gains, especially when the file system is optimized to use the load/store interface of the Optane DC PMM and the application uses many small, persistent writes. For instance, using the NOVA-relaxed NVMM file system, we can improve the performance of Kyoto Cabinet by almost 2x. Optane DC PMMs can also enable user-space persistence where the application explicitly controls its writes into persistent Optane DC media. In our experiments, modified applications that used user-space Optane DC persistence generally outperformed their file system counterparts. For instance, the persistent version of RocksDB performed almost 2x faster than the equivalent program utilizing an NVMM-aware file system.


In addition, Figure 5 shows that NLI-SAN achieves similar performance with KGAT , while having a simpler architecture for the application, and outperforms GEAR #REFR Table 1 : Document retrieval on the PANACEA dataset.

Fact-Checking We investigate the performance of our system in document retrieval and veracity assessment in (Arana-Catania et al., 2022) .

Table 1 shows that combining BM25 and MonoT5 is the most effective approach for document retrieval of the selected techniques.

Rumour Detection As shown in Figure 6 , our comparison #OTHEREFR among various models, including branchLSTM #OTHEREFR , TD-RvNN #OTHEREFR , BiGCN #OTHEREFR , SAVED (Dougrez-Lewis et al., 2021) and BERT #OTHEREFR for rumour detection evaluated on Twitter15, Twitter16 and PHEME #OTHEREFR , reveals there is no model that always performs the best.

Although state-of-the-art models can achieve high accuracy on their training datasets, such performance drops quickly while evaluating on a different dataset #OTHEREFR .

Due to the limitation of existing models in generalisation, users should interpret this result with caution as the system cannot guarantee output correctness.

This paper introduces a web-based system on factchecking and rumour detection based on novel natural language processing models for COVID-19 misinformation detection.

Going forward, we will keep updating the data and explore other methods for misinformation identification to improve the current system and introduce more functions to the system as part of our continuing efforts to support the general public to identify misinformation.

Panacea: An Automated Misinformation Detection System On Covid-19

Abstract: In this demo, we introduce a web-based misinformation detection system PANACEA on COVID-19 related claims, which has two modules, fact-checking and rumour detection. Our fact-checking module, which is supported by novel natural language inference methods with a self-attention network, outperforms state-ofthe-art approaches. It is also able to give automated veracity assessment and ranked supporting evidence with the stance towards the claim to be checked. In addition, PANACEA adapts the bi-directional graph convolutional networks model, which is able to detect rumours based on comment networks of related tweets, instead of relying on the knowledge base. This rumour detection module assists by warning the users in the early stages when a knowledge base may not be available.

Gear: Graph-Based Evidence Aggregating And Reasoning For Fact Verification

Abstract: Fact verification (FV) is a challenging task which requires to retrieve relevant evidence from plain text and use the evidence to verify given claims. Many claims require to simultaneously integrate and reason over several pieces of evidence for verification. However, previous work employs simple models to extract information from evidence without letting evidence communicate with each other, e.g., merely concatenate the evidence for processing. Therefore, these methods are unable to grasp sufficient relational and logical information among the evidence. To alleviate this issue, we propose a graph-based evidence aggregating and reasoning (GEAR) framework which enables information to transfer on a fully-connected evidence graph and then utilizes different aggregators to collect multi-evidence information. We further employ BERT, an effective pre-trained language representation model, to improve the performance. Experimental results on a large-scale benchmark dataset FEVER have demonstrated that GEAR could leverage multi-evidence information for FV and thus achieves the promising result with a test FEVER score of 67.10%. Our code is available at this https URL.


To address this problem, many approaches have been designed to minimize the representation divergence using the alignment annotations #REFR .

In real-world applications, objects can always be represented with multiple modalities.

For example, articles are with image and text modalities, videos are with image and audio modalities, etc.

To relate information from multiple modalities, an important task in multimodal machine learning is cross-modal retrieval, which aims to search one modal instances for given other modal instances.

In this paper, we focus primarily, but not exclusively, on two modalities: visual signals and natural language.

Actually, the main challenge of vision-language retrieval is the semantic divergence of heterogeneous data.

Initial approaches are always dual stream models, which typically build independent embedding network for each modality and constrain the consistency of cross-modal output representations with various similarity measures.

For example, #OTHEREFR constrained the consistency of global representations between two modalities.

Furthermore, to consider the fine-grained similarity, #OTHEREFR turned to measure the consistency of regional representations, #OTHEREFR developed the graph-level consistency by considering both regions and edges.

It is notable that vision and language encoders can adopt either shallow or deep models depending on the design.

With the development of vision-language Transformer, single-stream approaches are proposed #OTHEREFR , in which the two modalities interact from the input level.

Covlr: Coordinating Cross-Modal Consistency And Intra-Modal Structure For Vision-Language Retrieval

Abstract: Current vision-language retrieval aims to perform cross-modal instance search, in which the core idea is to learn the consistent visionlanguage representations. Although the performance of cross-modal retrieval has greatly improved with the development of deep models, we unfortunately find that traditional hard consistency may destroy the original relationships among single-modal instances, leading the performance degradation for single-modal retrieval. To address this challenge, in this paper, we experimentally observe that the vision-language divergence may cause the existence of strong and weak modalities, and the hard cross-modal consistency cannot guarantee that strong modal instances' relationships are not affected by weak modality, resulting in the strong modal instances' relationships perturbed despite learned consistent representations. To this end, we propose a novel and directly Coordinated Vision-Language Retrieval method (dubbed CoVLR), which aims to study and alleviate the desynchrony problem between the cross-modal alignment and single-modal cluster-preserving tasks. CoVLR addresses this challenge by developing an effective meta-optimization based strategy, in which the cross-modal consistency objective and the intra-modal relation preserving objective are acted as the meta-train and meta-test tasks, thereby CoVLR encourages both tasks to be optimized in a coordinated way. Consequently, we can simultaneously insure cross-modal consistency and intra-modal structure. Experiments on different datasets validate CoVLR can improve single-modal retrieval accuracy whilst preserving crossmodal retrieval capacity compared with the baselines. CCS CONCEPTS • Computing methodologies → Supervised learning by classification; • Information systems → Retrieval tasks and goals.

A Comprehensive Survey On Cross-Modal Retrieval

Abstract: In recent years, cross-modal retrieval has drawn much attention due to the rapid growth of multimodal data. It takes one type of data as the query to retrieve relevant data of another type. For example, a user can use a text to retrieve relevant pictures or videos. Since the query and its retrieved results can be of different modalities, how to measure the content similarity between different modalities of data remains a challenge. Various methods have been proposed to deal with such a problem. In this paper, we first review a number of representative methods for cross-modal retrieval and classify them into two main groups: 1) real-valued representation learning, and 2) binary representation learning. Real-valued representation learning methods aim to learn real-valued common representations for different modalities of data. To speed up the cross-modal retrieval, a number of binary representation learning methods are proposed to map different modalities of data into a common Hamming space. Then, we introduce several multimodal datasets in the community, and show the experimental results on two commonly used multimodal datasets. The comparison reveals the characteristic of different kinds of cross-modal retrieval methods, which is expected to benefit both practical applications and future research. Finally, we discuss open problems and future research directions.


As with any type of planar straight-line graph, the number of linear forests on O(k) points is singly exponential in k #REFR , so P [K] can be encoded with O(k) bits of information.

Q can be encoded by specifying its size and the subset of L ∩ S of that size, out of |Q| possibilities, so by Lemma 11 the number of bits needed to specify it is O log n + k + k log(n/k) .

(Both the log n term and the +1 in the statement of the lemma are included to handle the case when k = 0 but |Q| > 0.

Lemma 11 applies only when k ≤ n/2 but for larger k the bound to be proven is superlinear and the result is immediate.)

• For each point in Q, a specification of whether it has a neighbor in L, and if so in which direction. This takes O(k) bits of information.

• The induced subgraph P [K ∪ Q], a linear forest using only the points in K ∪ Q, and omitting the edges of P that lie entirely within L.

Then P may be recovered by combining the induced subgraph P [K ∪ Q] with segments of L starting and ending at points of Q and continuing in the specified direction from each of these points.

All pieces of this encoding add up to the stated bound on the number of bits needed to encode the entire path.

Non-Crossing Hamiltonian Paths And Cycles In Output-Polynomial Time

Abstract: We show that, for planar point sets, the number of non-crossing Hamiltonian paths is polynomially bounded in the number of non-crossing paths, and the number of non-crossing Hamiltonian cycles (polygonalizations) is polynomially bounded in the number of surrounding cycles. As a consequence, we can list the non-crossing Hamiltonian paths or the polygonalizations, in time polynomial in the output size, by filtering the output of simple backtracking algorithms for non-crossing paths or surrounding cycles respectively. To prove these results we relate the numbers of non-crossing structures to two easily-computed parameters of the point set: the minimum number of points whose removal results in a collinear set, and the number of points interior to the convex hull. These relations also lead to polynomial-time approximation algorithms for the numbers of structures of all four types, accurate to within a constant factor of the logarithm of these numbers.

Counting Plane Graphs: Cross-Graph Charging Schemes

Abstract: We study cross-graph charging schemes for graphs drawn in the plane. These are charging schemes where charge is moved across vertices of different graphs. Such methods have been recently applied to obtain various properties of triangulations that are embedded over a fixed set of points in the plane. We show how this method can be generalized to obtain results for various other types of graphs that are embedded in the plane. Specifically, we obtain a new bound of $O^*(187.53^N)$ (where the $O^*()$ notation hides polynomial factors) for the maximum number of crossing-free straight-edge graphs that can be embedded over any specific set of $N$ points in the plane (improving upon the previous best upper bound $207.85^N$ in Hoffmann et al.). We also derive upper bounds for numbers of several other types of plane graphs (such as connected and bi-connected plane graphs), and obtain various bounds on expected vertex-degrees in graphs that are uniformly chosen from the set of all crossing-free straight-edge graphs that can be embedded over a specific point set. We then show how to apply the cross-graph charging-scheme method for graphs that allow certain types of crossings. Specifically, we consider graphs with no set of $k$ pairwise-crossing edges (more commonly known as $k$-quasi-planar graphs). For $k=3$ and $k=4$, we prove that, for any set $S$ of $N$ points in the plane, the number of graphs that have a straight-edge $k$-quasi-planar embedding over $S$ is only exponential in $N$.

linear forests
planar straight-line graph


As a result, RIGA has the similar vulnerability of #REFR as their watermark extraction procedures only differ into the type of extractor, which is also inexecutable due to the incompatible input dimension of the trained extractor for RIGA. Evaluation Results.

Meanwhile, they replace the watermark extractor, which has been previously implemented with a predefined linear transformation #OTHEREFR , with a learnable fully-connected neural network (FCN), for boosting the encoding capacity of watermarking messages. Similar to Uchida et al.

#OTHEREFR , the watermark-related weights are first selected from the target model and then projected to a binary string s via the FCN-based extractor during the ownership verification procedure.

Simply replacing the linear transformation matrix in Uchida et al.

#OTHEREFR to a learnable extractor can not completely eliminate the removal threats from our attack based on model structural obfuscation.

We follow their evaluation settings to watermark Inception-V3 trained on CelebA, which achieves 95.90% accuracy and 0% BER [74] .

We employ the default setups that the watermark is embedded into the third convolutional layer of the target model and the extractor is a multiple layer perceptron with one hidden layer.

With our attack framework, we successfully inhibit the ownership verification of RIGA without any loss to the utility of victim model.

Even applying the error-handling mechanisms, the BER of extracted message is increased to an unacceptable level.

For example, when we utilize Max-First error-handling to obtain the embedded watermark, the BER is increased to 76.04% when we inject the dummy neurons generated via NeuronSplit. Passport-aware Normalization. Zhang et al.

Rethinking White-Box Watermarks On Deep Learning Models Under Neural Structural Obfuscation

Abstract: Copyright protection for deep neural networks (DNNs) is an urgent need for AI corporations. To trace illegally distributed model copies, DNN watermarking is an emerging technique for embedding and verifying secret identity messages in the prediction behaviors or the model internals. Sacrificing less functionality and involving more knowledge about the target DNN, the latter branch called white-box DNN watermarking is believed to be accurate, credible and secure against most known watermark removal attacks, with emerging research efforts in both the academy and the industry. In this paper, we present the first systematic study on how the mainstream white-box DNN watermarks are commonly vulnerable to neural structural obfuscation with dummy neurons, a group of neurons which can be added to a target model but leave the model behavior invariant. Devising a comprehensive framework to automatically generate and inject dummy neurons with high stealthiness, our novel attack intensively modifies the architecture of the target model to inhibit the success of watermark verification. With extensive evaluation, our work for the first time shows that nine published watermarking schemes require amendments to their verification procedures.

Embedding Watermarks Into Deep Neural Networks

Abstract: Significant progress has been made with deep neural networks recently. Sharing trained models of deep neural networks has been a very important in the rapid progress of research and development of these systems. At the same time, it is necessary to protect the rights to shared trained models. To this end, we propose to use digital watermarking technology to protect intellectual property and detect intellectual property infringement in the use of trained models. First, we formulate a new problem: embedding watermarks into deep neural networks. Second, we propose a general framework for embedding a watermark in model parameters, using a parameter regularizer. Our approach does not impair the performance of networks into which a watermark is placed because the watermark is embedded while training the host network. Finally, we perform comprehensive experiments to reveal the potential of watermarking deep neural networks as the basis of this new research effort. We show that our framework can embed a watermark during the training of a deep neural network from scratch, and during fine-tuning and distilling, without impairing its performance. The embedded watermark does not disappear even after fine-tuning or parameter pruning; the watermark remains complete even after 65% of parameters are pruned.


In #REFR , a decentralized adaptive routing scheme has been developed, in which the imperfection of quantum memories is taken into account.

Quantum entanglement routing has been the focus of many works in the past few years.

A design of an optimal routing scheme has been presented in #OTHEREFR , with the end-toend entanglement rate was set as the optimality figure of merit.

In #OTHEREFR , the authors have presented a remote entanglement distribution scheme for linear repeater chains, where an optimal route maximizing the end-to-end entanglement is determined alongside with the optimal sequence of the entanglement swaps.

Distributed entanglement routing algorithms, with latency taken into account has been the purpose of #OTHEREFR .

The problem of optimizing end-to-end entanglement in a many source-destination scenario has been studied in details as a multicommodity flow problem in #OTHEREFR .

A multipath routing approach for multiple end-to-end entanglement has been thoroughly investigated in #OTHEREFR .

In #OTHEREFR , the authors handled the case where quantum repeaters are allowed to perform quantum encoding, where it has been shown that the later increases drastically the end-toend entanglement rates with respect to usual protocols.

This has been recently experimentally investigated in NV centers in #OTHEREFR .

The effect of this type of intermediate encoding on end-to-end key rate generation in QKD has been studied in #OTHEREFR .

For Multipartite entanglement generation and distribution in quantum networks relying on a central node connected by EPR pairs to the remote clients has been the focus of #OTHEREFR .

The Quantum Internet: An Efficient Stabilizer States Distribution Scheme

Abstract: Quantum networks constitute a major part of quantum technologies. They will boost distributed quantum computing drastically by providing a scalable modular architecture of quantum chips, or by establishing an infrastructure for measurement based quantum computing. Moreover, they will provide the backbone of the future quantum internet, allowing for high margins of security. Interestingly, the advantages that the quantum networks would provide for communications, rely on entanglement distribution, which suffers from high latency in protocols based on Bell pair distribution and bipartite entanglement swapping. Moreover, the designed algorithms for multipartite entanglement routing suffer from intractability issues making them unsolvable exactly in polynomial time. In this paper, we investigate a new approach for graph states distribution in quantum networks relying inherently on local quantum coding-LQC-isometries and on multipartite states transfer. Additionally, single-shot bounds for stabilizer states distribution are provided. Analogously to network coding, these bounds are shown to be achievable if appropriate isometries/stabilizer codes in relay nodes are chosen, which induces a lower latency entanglement distribution. As a matter of fact, the advantages of the protocol for different figures of merit of the network are provided.

Opportunistic Entanglement Distribution For The Quantum Internet

Abstract: Quantum entanglement is a building block of the entangled quantum networks of the quantum Internet. A fundamental problem of the quantum Internet is entanglement distribution. Since quantum entanglement will be fundamental to any future quantum networking scenarios, the distribution mechanism of quantum entanglement is a critical and emerging issue in quantum networks. Here we define the method of opportunistic entanglement distribution for the quantum Internet. The opportunistic model defines distribution sets that are aimed to select those quantum nodes for which the cost function picks up a local minimum. The cost function utilizes the error patterns of the local quantum memories and the predictability of the evolution of the entanglement fidelities. Our method provides efficient entanglement distributing with respect to the actual statuses of the local quantum memories of the node pairs. The model provides an easily-applicable, moderate-complexity solution for high-fidelity entanglement distribution in experimental quantum Internet scenarios.


In #REFR , the robust stabilization of the FM algorithm when uncertainty exists in the disturbance is given by the linear matrix inequality (LMI).

It is widely used in various fields, such as the optimization design in the chemical industry #OTHEREFR , power control #OTHEREFR , and resource allocation #OTHEREFR , #OTHEREFR .

Since GP contains and optimizes only positive parameters, it plays a crucial role in the positive system #OTHEREFR . GP-based algorithms generally are robust and time-efficient in solving.

As shown in #OTHEREFR , the optimization framework for parameter tuning problems constrained by H 2 norm, H ∞ norm, Hankel norm or Schattern p-norm can be established and solved efficiently via GP.

For the uncertainty of the log-quantized feedback errors in the FM algorithm, the stabilization problem for bounded-input bounded-output systems is investigated in #OTHEREFR .

The refinement of the quantization level enables the cellular network to obtain a better QoS.

Compared with LMI, GP allows greater scalability since it can solve more complex and larger-scale networks with higher accuracy.

Therefore, the robust stabilization and resource allocation problems of the FM algorithm with structured uncertainties are further investigated through GP in #OTHEREFR .

In this work, we propose a convex optimization framework, specifically GP, for the robust stabilization problem under structured uncertainties of the discrete-time FM algorithm.

The GP formulation is extended to an iterative algorithm to address the resilient stabilization problem of cellular network QoS under the threat of addingedge attacks, that is, to determine the precise GNE of the two subnetwork policymakers.

The attacker can be regarded as the worst-case structured uncertainty in this GP framework with norm constraints.

Optimal Resource Allocation Between Two Nonfully Cooperative Wireless Networks Under Malicious Attacks: A Gestalt Game Perspective

Abstract: In this paper, the problem of seeking optimal distributed resource allocation (DRA) policies on cellular networks in the presence of an unknown malicious adding-edge attacker is investigated. This problem is described as the games of games (GoG) model. Specifically, two subnetwork policymakers constitute a Nash game, while the confrontation between each subnetwork policymaker and the attacker is captured by a Stackelberg game. First, we show that the communication resource allocation of cellular networks based on the Foschini-Miljanic (FM) algorithm can be transformed into a geometric program and be efficiently solved via convex optimization. Second, the upper limit of attack magnitude that can be tolerated by the network is calculated by the corresponding theory, and it is proved that the above geometric programming (GP) framework is solvable within the attack bound, that is, there exists a Gestalt Nash equilibrium (GNE) in our GoG. Third, a heuristic algorithm that iteratively uses GP is proposed to identify the optimal policy profiles of both subnetworks, for which asymptotic convergence is also confirmed. Fourth, a greedy heuristic adding-edge strategy is developed for the attacker to determine the set of the most vulnerable edges. Finally, simulation examples illustrate that the proposed theoretical results are robust and can achieve the GNE. It is verified that the transmission gains and interference gains of all channels are well tuned within a limited budget, despite the existence of malicious attacks.

A Convex Characterization Of Robust Stability For Positive And Positively Dominated Linear Systems

Abstract: We provide convex necessary and sufficient conditions for the robust stability of linear positively dominated systems. In particular we show that the structured singular value is always equal to its convex upper bound for nonnegative matrices and we use this result to derive necessary and sufficient Linear Matrix Inequality (LMI) conditions for robust stability that involve only the system's static gain. We show how this approach can be applied to test the robust stability of the Foschini-Miljanic algorithm for power control in wireless networks in presence of uncertain interference.


For DMControl, we collect the offline dataset in a similar way to the procedure to collect the Mixed dataset using SAC #REFR .

The Weak dataset is collected from the first 1M transitions generated by DQN.

The Mixed dataset is obtained by concatenating multiple checkpoints evenly throughout the training of DQN.

The quality of the dataset increases from Random to Mixed.

We also evaluate the algorithms on datasets of different sizes.

Larger datasets can be obtained by running the above procedure multiple times with different random seeds.

In our experiments, we compare our algorithm with a wide range of previous algorithms including both sample-efficient RL algorithms and pretraining algorithms for RL.

For Atari games, we incorporate the representation pretrained by RePreM with Rainbow #OTHEREFR in downstream tasks (except for dynamic prediction).

The baseline algorithms include not only sample-efficient online RL algorithms (such as Rainbow, SimPLe #OTHEREFR

2019] , data-effecient Rainbow/DER #OTHEREFR , DrQ #OTHEREFR , and SPR #OTHEREFR

Reprem: Representation Pre-Training With Masked Model For Reinforcement Learning

Abstract: Inspired by the recent success of sequence modeling in RL and the use of masked language model for pre-training, we propose a masked model for pre-training in RL, RePreM (Representation Pre-training with Masked Model), which trains the encoder combined with transformer blocks to predict the masked states or actions in a trajectory. RePreM is simple but effective compared to existing representation pretraining methods in RL. It avoids algorithmic sophistication (such as data augmentation or estimating multiple models) with sequence modeling and generates a representation that captures long-term dynamics well. Empirically, we demonstrate the effectiveness of RePreM in various tasks, including dynamic prediction, transfer learning, and sample-efficient RL with both value-based and actor-critic methods. Moreover, we show that RePreM scales well with dataset size, dataset quality, and the scale of the encoder, which indicates its potential towards big RL models.

Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning With A Stochastic Actor

Abstract: Model-free deep reinforcement learning (RL) algorithms have been demonstrated on a range of challenging decision making and control tasks. However, these methods typically suffer from two major challenges: very high sample complexity and brittle convergence properties, which necessitate meticulous hyperparameter tuning. Both of these challenges severely limit the applicability of such methods to complex, real-world domains. In this paper, we propose soft actor-critic, an off-policy actor-critic deep RL algorithm based on the maximum entropy reinforcement learning framework. In this framework, the actor aims to maximize expected reward while also maximizing entropy - that is, succeed at the task while acting as randomly as possible. Prior deep RL methods based on this framework have been formulated as Q-learning methods. By combining off-policy updates with a stable stochastic actor-critic formulation, our method achieves state-of-the-art performance on a range of continuous control benchmark tasks, outperforming prior on-policy and off-policy methods. Furthermore, we demonstrate that, in contrast to other off-policy algorithms, our approach is very stable, achieving very similar performance across different random seeds.


First, the data from the BCI Competition IV 2a are preprocessed using the code from #REFR available at https://github.com/MultiScale-BCI/IV-2a.

For both the optimization over particles and over transformations, we use geoopt #OTHEREFR with the Riemannian gradient descent. We now detail the hyperparameters and the procedure.

We applied a band-pass filter between 8 and 30 Hz.

With these hyper-parameters, we get one regularized covariance matrix per subject.

For all experiments, we report the results averaged over 5 runs.

For the sliced discrepancies, we always use L = 500 projections which we draw only once a the beginning.

When optimizing over particles, we used a learning rate of 1000 for the sliced methods and of 10 for Wasserstein and Sinkhorn.

Sliced-Wasserstein On Symmetric Positive Definite Matrices For M/Eeg Signals

Abstract: When dealing with electro or magnetoencephalography records, many supervised prediction tasks are solved by working with covariance matrices to summarize the signals. Learning with these matrices requires using Riemanian geometry to account for their structure. In this paper, we propose a new method to deal with distributions of covariance matrices and demonstrate its computational efficiency on M/EEG multivariate time series. More specifically, we define a Sliced-Wasserstein distance between measures of symmetric positive definite matrices that comes with strong theoretical guarantees. Then, we take advantage of its properties and kernel methods to apply this distance to brain-age prediction from MEG data and compare it to state-of-the-art algorithms based on Riemannian geometry. Finally, we show that it is an efficient surrogate to the Wasserstein distance in domain adaptation for Brain Computer Interface applications.

Fast And Accurate Multiclass Inference For Mi-Bcis Using Large Multiscale Temporal And Spectral Features

Abstract: Accurate, fast, and reliable multiclass classification of electroencephalography (EEG) signals is a challenging task towards the development of motor imagery brain-computer interface (MI-BCI) systems. We propose enhancements to different feature extractors, along with a support vector machine (SVM) classifier, to simultaneously improve classification accuracy and execution time during training and testing. We focus on the well-known common spatial pattern (CSP) and Riemannian covariance methods, and significantly extend these two feature extractors to multiscale temporal and spectral cases. The multiscale CSP features achieve 73.70$\pm$15.90% (mean$\pm$ standard deviation across 9 subjects) classification accuracy that surpasses the state-of-the-art method [1], 70.6$\pm$14.70%, on the 4-class BCI competition IV-2a dataset. The Riemannian covariance features outperform the CSP by achieving 74.27$\pm$15.5% accuracy and executing 9x faster in training and 4x faster in testing. Using more temporal windows for Riemannian features results in 75.47$\pm$12.8% accuracy with 1.6x faster testing than CSP.


We apply generative models watermarked with 48-bit signatures on prompts of the MS-COCO #REFR validation set.

This section shows the potential of our method for detection and identification or images generated by a Stable-Diffusion-like model #OTHEREFR 2 .

We evaluate detection and identification on the outputs, as illustrated in Figure 1 .

We evaluate their robustness to different transformations applied to generated images: strong cropping (10% of the image remaining), brightness shift (strength factor 2.0), as well as a combination of crop 50%, brightness shift 1.5 and JPEG 80. This covers typical geometric and photometric edits (see Fig. 5 for visual examples).

The performance is partly obtained from experiments and partly by extrapolating small-scale measurements.

The Stable Signature: Rooting Watermarks In Latent Diffusion Models

Abstract: Generative image modeling enables a wide range of applications but raises ethical concerns about responsible deployment. This paper introduces an active strategy combining image watermarking and Latent Diffusion Models. The goal is for all generated images to conceal an invisible watermark allowing for future detection and/or identification. The method quickly fine-tunes the latent decoder of the image generator, conditioned on a binary signature. A pre-trained watermark extractor recovers the hidden signature from any generated image and a statistical test then determines whether it comes from the generative model. We evaluate the invisibility and robustness of the watermarks on a variety of generation tasks, showing that Stable Signature works even after the images are modified. For instance, it detects the origin of an image generated from a text prompt, then cropped to keep 10% of the content, with 90+% accuracy at a false positive rate below 10 −6. 1. Introduction Recent progress in generative modeling and natural language processing has made it easy to create and manipulate images in a photorealistic manner. For instance, DALL•E 2 [60] or Stable Diffusion [64] generate images from text, which are often indistinguishable from real artworks. They have given birth to many image edition tools like ControlNet [100], Instruct-Pix2Pix [7], and others [13, 27, 67]. They are establishing themselves as creative tools for artists, designers, and the general public. While this is a great step forward for generative AI, it raises new ethical concerns. Indeed, their sophistication is such that it will soon be impossible to distinguish AI generations from real pictures. For example, a generated picture recently won an art competition [28]. Not being able to identify that images are generated by AI makes it difficult to remove them from certain platforms and to ensure their compliance with ethical standards. The lack of traceability also opens the door to new threats such as deep fakes, impersonation or copyright usurpation [8, 17].

Microsoft Coco: Common Objects In Context

Abstract: We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding. This is achieved by gathering images of complex everyday scenes containing common objects in their natural context. Objects are labeled using per-instance segmentations to aid in precise object localization. Our dataset contains photos of 91 objects types that would be easily recognizable by a 4 year old. With a total of 2.5 million labeled instances in 328k images, the creation of our dataset drew upon extensive crowd worker involvement via novel user interfaces for category detection, instance spotting and instance segmentation. We present a detailed statistical analysis of the dataset in comparison to PASCAL, ImageNet, and SUN. Finally, we provide baseline performance analysis for bounding box and segmentation detection results using a Deformable Parts Model.

generative models
MS-COCO validation set


Pre-trained language models (PTLMs) are neural networks that aim to learn the statistical patterns in programming languages #REFR .

PTLMs are first pre-trained with the next token prediction objective on a large-scale unlabeled code corpus.

Given a code file, PTLMs are trained to predict the next token given some previous write a function to remove first and last occurrence of a given character from the string

Towards Enhancing In-Context Learning For Code Generation

Abstract: In-context learning (ICL) with pre-trained language models (PTLMs) has shown great success in code generation. ICL does not require training. PTLMs take as the input a prompt consisting of a few requirement-code examples and a new requirement, and output a new program. However, existing studies simply reuse ICL techniques for natural language generation and ignore unique features of code generation. We refer to these studies as standard ICL. Inspired by observations of the human coding process, we propose a novel ICL approach for code generation named AceCoder. Compared to standard ICL, AceCoder has two novelties. (1) Example retrieval. It retrieves similar programs as examples and learns programming skills (e.g., algorithms, APIs) from them. (2) Guided Code Generation. It encourages PTLMs to output an intermediate preliminary (e.g., test cases, APIs) before generating programs. The preliminary can help PTLMs understand requirements and guide the next code generation. We apply AceCoder to six PTLMs (e.g., Codex) and evaluate it on three public benchmarks using the Pass@k. Results show that AceCoder can significantly improve the performance of PTLMs on code generation. (1) In terms of Pass@1, AceCoder outperforms standard ICL by up to 79.7% and fine-tuned models by up to 171%. (2) AceCoder is effective in PTLMs with different sizes (e.g., 1B to 175B) and different languages (e.g., Python, Java, and JavaScript). (3) We investigate multiple choices of the intermediate preliminary. (4) We manually evaluate generated programs in three aspects and prove the superiority of AceCoder. (5) Finally, we discuss some insights about ICL for practitioners.

A Survey Of Machine Learning For Big Code And Naturalness

Abstract: Research at the intersection of machine learning, programming languages, and software engineering has recently taken important steps in proposing learnable probabilistic models of source code that exploit the abundance of patterns of code. In this article, we survey this work. We contrast programming languages against natural languages and discuss how these similarities and differences drive the design of probabilistic models. We present a taxonomy based on the underlying design principles of each model and use it to navigate the literature. Then, we review how researchers have adapted these models to application areas and discuss cross-cutting and application-specific challenges and opportunities.

Pre-trained language models
programming languages


In particular, for pedestrian, we outperform the baseline model PV-RCNN++ #REFR by 2.77%/2.56% in terms of both L1 and L2 mAP, which indicates the effectiveness of the proposed model in handling hard examples.

The results on the validation set are displayed in Tab.

1, and it can be seen that we achieve new state-of-the-art performance on all the three classes.

In comparison with other Transformer-based models, we focus on vehicle since the counterparts only report the performance on it. As Tab.

2 shows, our OcTr achieves the best mAP among all these convolution-and Transformer-based backbones.

It also outperforms the Transformer-based detection head network CT3D #OTHEREFR by 2.52% and 1.46% in L1 and L2 mAP.

Regarding the accuracies at different distances, OcTr ranks the first place in the range of 30m-50m and 50m-inf, which surpasses the previous best by 0.45%, 2.66% in L1 mAP and 1.18%, 2.26% in L2 mAP respectively.

It clearly illustrates that OcTr has the advantage in capturing long-range fine-grained context, which facilitates dealing with objects far away.

Octr: Octree-Based Transformer For 3D Object Detection

Abstract: A key challenge for LiDAR-based 3D object detection is to capture sufficient features from large scale 3D scenes especially for distant or/and occluded objects. Albeit recent efforts made by Transformers with the long sequence modeling capability, they fail to properly balance the accuracy and efficiency, suffering from inadequate receptive fields or coarse-grained holistic correlations. In this paper, we propose an Octree-based Transformer, named OcTr, to address this issue. It first constructs a dynamic octree on the hierarchical feature pyramid through conducting self-attention on the top level and then recursively propagates to the level below restricted by the octants, which captures rich global context in a coarse-to-fine manner while maintaining the computational complexity under control. Furthermore, for enhanced foreground perception, we propose a hybrid positional embedding, composed of the semantic-aware positional embedding and attention mask, to fully exploit semantic and geometry clues. Extensive experiments are conducted on the Waymo Open Dataset and KITTI Dataset, and OcTr reaches newly state-of-the-art results.

Pv-Rcnn: Point-Voxel Feature Set Abstraction For 3D Object Detection

Abstract: We present a novel and high-performance 3D object detection framework, named PointVoxel-RCNN (PV-RCNN), for accurate 3D object detection from point clouds. Our proposed method deeply integrates both 3D voxel Convolutional Neural Network (CNN) and PointNet-based set abstraction to learn more discriminative point cloud features. It takes advantages of efficient learning and high-quality proposals of the 3D voxel CNN and the flexible receptive fields of the PointNet-based networks. Specifically, the proposed framework summarizes the 3D scene with a 3D voxel CNN into a small set of keypoints via a novel voxel set abstraction module to save follow-up computations and also to encode representative scene features. Given the high-quality 3D proposals generated by the voxel CNN, the RoI-grid pooling is proposed to abstract proposal-specific features from the keypoints to the RoI-grid points via keypoint set abstraction with multiple receptive fields. Compared with conventional pooling operations, the RoI-grid feature points encode much richer context information for accurately estimating object confidences and locations. Extensive experiments on both the KITTI dataset and the Waymo Open dataset show that our proposed PV-RCNN surpasses state-of-the-art 3D detection methods with remarkable margins by using only point clouds.


C. Current Defense Strategy And Limitation

High success had been achieved by successful defense of Randomization-based defense technique against both blackbox and gray-box based attacks, but it is still vulnerable whitebox based attack, for example, the EoT method #REFR can be easily attacked and compromised simply by considering the randomization process during attack.

The approach is similar to a typical antivirus software, which is constantly being updated on a regular basis.

As effective as adversarial training may be in defense against adversarial attack, it still requires continuous maintenance or update in order to be effective in combating new threats and it is still suffering from the fundamental problem of the fact that it can only successfully defend against threats or attack that has already happened and is already trained against.

2) Randomization: Several adversarial defense methods relied on randomization as a technique for mitigating the effects of adversarial Perturbations in the input and/or feature domain #OTHEREFR .

The idea behind this defense technique is the robustness of deep neural network model to random perturbation.

The aim of randomization-based defense is to randomize the adversarial effects of the adversarial sampling into several random effects which is a very ok and normal thing for varieties of deep neural network models.

Adversarial Sampling For Fairness Testing In Deep Neural Network

Abstract: In this research, we focus on the usage of adversarial sampling to test for the fairness in the prediction of deep neural network model across different classes of image in a given dataset. While several framework had been proposed to ensure robustness of machine learning model against adversarial attack, some of which includes adversarial training algorithm. There is still the pitfall that adversarial training algorithm tends to cause disparity in accuracy and robustness among different group. Our research is aimed at using adversarial sampling to test for fairness in the prediction of deep neural network model across different classes or categories of image in a given dataset. We successfully demonstrated a new method of ensuring fairness across various group of input in deep neural network classifier. We trained our neural network model on the original image, and without training our model on the perturbed or attacked image. When we feed the adversarial samplings to our model, it was able to predict the original category/ class of the image the adversarial sample belongs to. We also introduced and used the separation of concern concept from software engineering whereby there is an additional standalone filter layer that filters perturbed image by heavily removing the noise or attack before automatically passing it to the network for classification, we were able to have accuracy of 93.3%. Cifar-10 dataset have ten categories of dataset, and so, in order to account for fairness, we applied our hypothesis across each categories of dataset and were able to get a consistent result and accuracy.

Synthesizing Robust Adversarial Examples

Abstract: Standard methods for generating adversarial examples for neural networks do not consistently fool neural network classifiers in the physical world due to a combination of viewpoint shifts, camera noise, and other natural transformations, limiting their relevance to real-world systems. We demonstrate the existence of robust 3D adversarial objects, and we present the first algorithm for synthesizing examples that are adversarial over a chosen distribution of transformations. We synthesize two-dimensional adversarial images that are robust to noise, distortion, and affine transformation. We apply our algorithm to complex three-dimensional objects, using 3D-printing to manufacture the first physical adversarial objects. Our results demonstrate the existence of 3D adversarial objects in the physical world.

Randomization-based defense technique
gray-box based attacks


Bonnabel and Sepulchre #REFR proposed a metric for S + (p, n) that is invariant with respect to all transformations that preserve angles and derived the geometric mean.

In contrast to SPD matrices, symmetric positive semidefinite (SPSD) matrices are singular and do not have matrix logarithms.

There are two main approaches to define metrics on SPSD matrices.

The first approach #OTHEREFR , #OTHEREFR exploits S + (p, n), the manifold of rank-p SPSDs of size n, which can be identified with the quotient manifold R

is the set of full-rank n × p matrices and O p is the orthogonal group of order p.

The second approach involves adding a regularization term to transform the SPSD matrix to a PSD or truncating the spectrum. Dodero et al.

#OTHEREFR regularized the graph Laplacian to become positive definite by adding a regularization term and used the Log-Euclidean metric for downstream classification tasks. Shnitzer et al.

#OTHEREFR truncated the full spectrum of diffusion operators to a fixed length and proved that the spectrum truncation preserves the lower bound of the Log-Euclidean metric.

(2) Fréchet Analysis of Graph Laplacians.

The Fréchet mean #OTHEREFR is a concept in statistics that provides a representative center of a set of data objects in a metric space.

Fréchet Statistics Based Change Point Detection In Dynamic Social Networks

Abstract: This paper proposes a method to detect change points in dynamic social networks using Fréchet statistics. We address two main questions: (1) what metric can quantify the distances between graph Laplacians in a dynamic network and enable efficient computation, and (2) how can the Fréchet statistics be extended to detect multiple change points while maintaining the significance level of the hypothesis test? Our solution defines a metric space for graph Laplacians using the Log-Euclidean metric, enabling a closed-form formula for Fréchet mean and variance. We present a framework for change point detection using Fréchet statistics and extend it to multiple change points with binary segmentation. The proposed algorithm uses incremental computation for Fréchet mean and variance to improve efficiency and is validated on simulated and two real-world datasets, namely the UCI message dataset and the Enron email dataset.

Riemannian Metric And Geometric Mean For Positive Semidefinite Matrices Of Fixed Rank

Abstract: This paper introduces a new metric and mean on the set of positive semidefinite matrices of fixed-rank. The proposed metric is derived from a well-chosen Riemannian quotient geometry that generalizes the reductive geometry of the positive cone and the associated natural metric. The resulting Riemannian space has strong geometrical properties: it is geodesically complete, and the metric is invariant with respect to all transformations that preserve angles (orthogonal transformations, scalings, and pseudoinversion). A meaningful approximation of the associated Riemannian distance is proposed, that can be efficiently numerically computed via a simple algorithm based on SVD. The induced mean preserves the rank, possesses the most desirable characteristics of a geometric mean, and is easy to compute.


In contrast to the existing literature #REFR on PO for the LQR problem, the cost J(G) here is projected gradient dominated, meaning that G is optimal if the projected gradient Π X− ∇J(G) is equal to zero.

V ∈ N (X − ) with V F = 1 in the descent cone of S G (a) such that J (G)[V ] ≤ −c(α(a)(J(G) − J * ) 1/2 ), where J (G)[V ] denotes the derivative along the direction V .

Let V = Π X− ∇J(G)/ Π X− ∇J(G) F be the normalized projected gradient.

Then, we have J (G)[V ] ≤ J (G)[V ] since both V and V are in N (X − )

, and V is the direction of the projection of the gradient.

The proof is completed by letting µ(a) = 1/(cα(a)) #OTHEREFR .

It is usually regarded as a weaker condition than strong convexity in nonconvex optimization theory.

Under Lemma 4, one can show global convergence of projected gradient update #OTHEREFR .

To further show a linear convergence rate, we require the smoothness of J(G).

However, since J(K) tends extremely to infinity as G approaches the boundary ∂S G , we can only show that J(G) is locally smooth over any sublevel set. Define the Hessian acting on the direction Z ∈

R T ×n as ∇ 2 J(G)[Z, Z] := d 2 dt 2 J(G + tZ) t=0

Data-Enabled Policy Optimization For The Linear Quadratic Regulator

Abstract: Policy optimization (PO), an essential approach of reinforcement learning for a broad range of system classes, requires significantly more system data than indirect (identification-followed-by-control) methods or behavioralbased direct methods even in the simplest linear quadratic regulator (LQR) problem. In this paper, we take an initial step towards bridging this gap by proposing the data-enabled policy optimization (DeePO) method, which requires only a finite number of sufficiently exciting data to iteratively solve the LQR via PO. Based on a data-driven closed-loop parameterization, we are able to directly compute the policy gradient from a bath of persistently exciting data. Next, we show that the nonconvex PO problem satisfies a projected gradient dominance property by relating it to an equivalent convex program, leading to the global convergence of DeePO. Moreover, we apply regularization methods to enhance certainty-equivalence and robustness of the resulting controller and show an implicit regularization property. Finally, we perform simulations to validate our results.

Global Convergence Of Policy Gradient Methods For The Linear Quadratic Regulator

Abstract: Direct policy gradient methods for reinforcement learning and continuous control problems are a popular approach for a variety of reasons: 1) they are easy to implement without explicit knowledge of the underlying model 2) they are an "end-to-end" approach, directly optimizing the performance metric of interest 3) they inherently allow for richly parameterized policies. A notable drawback is that even in the most basic continuous control problem (that of linear quadratic regulators), these methods must solve a non-convex optimization problem, where little is understood about their efficiency from both computational and statistical perspectives. In contrast, system identification and model based planning in optimal control theory have a much more solid theoretical footing, where much is known with regards to their computational and statistical properties. This work bridges this gap showing that (model free) policy gradient methods globally converge to the optimal solution and are efficient (polynomially so in relevant problem dependent quantities) with regards to their sample and computational complexities.


We train our model on the Faceforensics++ dataset and evaluate its performance on the test sets of Celeb-DF #REFR and DFDC [11] .

We ran the public code of methods marked with "*" to produce results under identical settings (HQ for training and single frames for testing). mum improvement of 10.25% (F2F →FSW).

Meanwhile, our model remains effective under the four intra-domain settings, which are shown in gray.

The method tends to slightly underperform when trained on NeuralTextures, likely because its manipulation patterns only exist in certain small regions, and may be neglected during our block sampling.

Nevertheless, compared to existing methods, our deepfake detector yields much better overall performances.

Specifically, following the previous practice in #OTHEREFR , we validate the model on Celeb-DF and use the selected model to test on DFDC.

We adopt the HQ version of FF for training, and only use one frame every video for testing.

Under the same setting, we ran the public code of RECCE #OTHEREFR , UIA-ViT #OTHEREFR and SBI #OTHEREFR to produce corresponding results.

In Table 2 , we show a competitive performance with existing image-based methods, signaling satisfying adaptability of RFFR to different datasets, especially high quality datasets like Celeb-DF.

SBI #OTHEREFR is a recent powerful deepfake detection method.

Real Face Foundation Representation Learning For Generalized Deepfake Detection

Abstract: The emergence of deepfake technologies has become a matter of social concern as they pose threats to individual privacy and public security. It is now of great significance to develop reliable deepfake detectors. However, with numerous face manipulation algorithms present, it is almost impossible to collect sufficient representative fake faces, and it is hard for existing detectors to generalize to all types of manipulation. Therefore, we turn to learn the distribution of real faces, and indirectly identify fake images that deviate from the real face distribution. In this study, we propose Real Face Foundation Representation Learning (RFFR), which aims to learn a general representation from large-scale real face datasets and detect potential artifacts outside the distribution of RFFR. Specifically, we train a model on real face datasets by masked image modeling (MIM), which results in a discrepancy between input faces and the reconstructed ones when applying the model on fake samples. This discrepancy reveals the low-level artifacts not contained in RFFR, making it easier to build a deepfake detector sensitive to all kinds of potential artifacts outside the distribution of RFFR. Extensive experiments demonstrate that our method brings about better generalization performance, as it significantly outperforms the state-of-the-art methods in crossmanipulation evaluations, and has the potential to further improve by introducing extra real faces for training RFFR.

Celeb-Df: A Large-Scale Challenging Dataset For Deepfake Forensics

Abstract: AI-synthesized face-swapping videos, commonly known as DeepFakes, is an emerging problem threatening the trustworthiness of online information. The need to develop and evaluate DeepFake detection algorithms calls for large-scale datasets. However, current DeepFake datasets suffer from low visual quality and do not resemble DeepFake videos circulated on the Internet. We present a new large-scale challenging DeepFake video dataset, Celeb-DF, which contains 5,639 high-quality DeepFake videos of celebrities generated using improved synthesis process. We conduct a comprehensive evaluation of DeepFake detection methods and datasets to demonstrate the escalated level of challenges posed by Celeb-DF.


DistilBERT is on par with or exceeding ELMo on a binary text classification task #REFR .

And this would enable a fairer comparison.

Recently, it was shown that ELMo and BERT make no significant difference in semantic analysis #OTHEREFR .

Here it is observed that although they are close-by in the null context, DistilBERT is more robust than ELMo in the cross-context in text classification.

The findings of this study are in line with prior work.

The fairly comparable scores of ELMo and the traditional baselines in the null context supports the observation of #OTHEREFR that is, when it comes to contextual embeddings, there is only a small improvement in learning semantics over traditional ML methods.

DistilBERT, as a transformerbased model, is better in capturing long-term dependencies in an input sequence #OTHEREFR .

DistilBERT is lighter than ELMo and has a shorter training time #OTHEREFR .

Here it should be noted that the experimental settings of the previous work and In this study, ELMo and DistilBERT are compared on their fine-tuning performance on two binary text classification tasks.

The main focus was to see how much can these models be benefited in a practical way without any modification to the pretraining outputs.

But the models were actually pretrained on entirely different corpora (ELMo on One Billion Words Benchmark #OTHEREFR , DistilBERT on English Wikipedia and Toronto BookCorpus #OTHEREFR ).

Abstract: I am grateful to my family for their unconditional love and patience. I am grateful to ArzucanÖzgür, for being such an inspiring figure by her selfless devotion to research the most righteous way with the passion to contribute to the community. I am grateful to Ali Hürriyetoglu, for being such a role model, who could somehow always find a way to turn the mist of research questions into a structured path to create practical solutions by combining creativity and technique. I cannot thank enough my dear friends who put up with my whims throughout this journey. I thank fellows from TabiLAB for inspiring me with their brilliance, invaluable insights and recommendations. I thank Koç University EMW research team for their generosity in sharing the data which was created with blood, sweat and tears. I feel lucky that I got to meet fellows in EMW project engineering team who invested their precious time and energy to support me in this study from the very beginning. Lastly, I owe the deepest gratitude to our professors and staff members in our department who taught us how to form such a great community and made it feel like the dearest home from the day one.

To Tune Or Not To Tune? Adapting Pretrained Representations To Diverse Tasks

Abstract: While most previous work has focused on different pretraining objectives and architectures for transfer learning, we ask how to best adapt the pretrained model to a given target task. We focus on the two most common forms of adaptation, feature extraction (where the pretrained weights are frozen), and directly fine-tuning the pretrained model. Our empirical results across diverse NLP tasks with two state-of-the-art models show that the relative performance of fine-tuning vs. feature extraction depends on the similarity of the pretraining and target tasks. We explore possible explanations for this finding and provide a set of adaptation guidelines for the NLP practitioner.


It is consistent with the amplitude of the DP noise in a Gaussian noise perturbation mechanism developed in #REFR .

given , δ, and M , we adjust the DP noise variance to balance privacy preservation and the convergence of FL training.

Given a privacy budget for M global aggregations, more clients involved in the model updates, i.e., a larger q in (8), lead to requirements of stronger perturbation noises being added to the local model of each involved client.

This indicates less privacy leakage for each client, which is consistent with the conclusion drawn in #OTHEREFR .

m = 1, • • • , M , since ϑ−ϑ 1−M ϑ−1 ϑ→1 − −− → M .

Amplitude-Varying Perturbation For Balancing Privacy And Utility In Federated Learning

Abstract: While preserving the privacy of federated learning (FL), differential privacy (DP) inevitably degrades the utility (i.e., accuracy) of FL due to model perturbations caused by DP noise added to model updates. Existing studies have considered exclusively noise with persistent root-mean-square amplitude and overlooked an opportunity of adjusting the amplitudes to alleviate the adverse effects of the noise. This paper presents a new DP perturbation mechanism with a time-varying noise amplitude to protect the privacy of FL and retain the capability of adjusting the learning performance. Specifically, we propose a geometric series form for the noise amplitude and reveal analytically the dependence of the series on the number of global aggregations and the (, δ)-DP requirement. We derive an online refinement of the series to prevent FL from premature convergence resulting from excessive perturbation noise. Another important aspect is an upper bound developed for the loss function of a multi-layer perceptron (MLP) trained by FL running the new DP mechanism. Accordingly, the optimal number of global aggregations is obtained, balancing the learning and privacy. Extensive experiments are conducted using MLP, supporting vector machine, and convolutional neural network models on four public datasets. The contribution of the new DP mechanism to the convergence and accuracy of privacy-preserving FL is corroborated, compared to the state-of-the-art Gaussian noise mechanism with a persistent noise amplitude.

Federated Learning With Differential Privacy: Algorithms And Performance Analysis

Abstract: In this paper, to effectively prevent information leakage, we propose a novel framework based on the concept of differential privacy (DP), in which artificial noises are added to the parameters at the clients side before aggregating, namely, noising before model aggregation FL (NbAFL). First, we prove that the NbAFL can satisfy DP under distinct protection levels by properly adapting different variances of artificial noises. Then we develop a theoretical convergence bound of the loss function of the trained FL model in the NbAFL. Specifically, the theoretical bound reveals the following three key properties: 1) There is a tradeoff between the convergence performance and privacy protection levels, i.e., a better convergence performance leads to a lower protection level; 2) Given a fixed privacy protection level, increasing the number $N$ of overall clients participating in FL can improve the convergence performance; 3) There is an optimal number of maximum aggregation times (communication rounds) in terms of convergence performance for a given protection level. Furthermore, we propose a $K$-random scheduling strategy, where $K$ ($1<K<N$) clients are randomly selected from the $N$ overall clients to participate in each aggregation. We also develop the corresponding convergence bound of the loss function in this case and the $K$-random scheduling strategy can also retain the above three properties. Moreover, we find that there is an optimal $K$ that achieves the best convergence performance at a fixed privacy level. Evaluations demonstrate that our theoretical results are consistent with simulations, thereby facilitating the designs on various privacy-preserving FL algorithms with different tradeoff requirements on convergence performance and privacy levels.


The expectation and variance of CBC k+i|k are given in #REFR , and Var CBC k+i|k ≥ 2σ 4 tr(W W).

The nice properties of deterministic MPC-CBF, such as avoiding obstacles at an early stage, are maintained in a stochastic scenario because the chance-constrained approach still considers the CBF constraints in the optimization problem.

The chance constraint only adds an additional constraint on the probability of violating the CBF constraint.

, then introducing more randomness to the system by increasing the standard deviation of the noise σ may lead to (18) becoming infeasible.

Meantime, we recall the trace inequality tr(W W) ≤ tr(W) with W being a symmetric positive definite matrix.

By substituting #OTHEREFR and the two inequality conditions into #OTHEREFR

D = f (x k+i|k ) + g(x k+i|k )u k+i|k − ξ(o k+i|k ) 2 W − (1 − γ)h x k+i|k , o k+i|k − 1 is deterministic if

Moving Obstacle Collision Avoidance Via Chance-Constrained Mpc With Cbf

Abstract: Model predictive control (MPC) with control barrier functions (CBF) is a promising solution to address the moving obstacle collision avoidance (MOCA) problem. Unlike MPC with distance constraints (MPC-DC), this approach facilitates early obstacle avoidance without the need to increase prediction horizons. However, the existing MPC-CBF method is deterministic and fails to account for perception uncertainties. This paper proposes a generalized MPC-CBF approach for stochastic scenarios, which maintains the advantages of the deterministic method for addressing the MOCA problem. Specifically, the chanceconstrained MPC-CBF (CC-MPC-CBF) technique is introduced to ensure that a user-defined collision avoidance probability is met by utilizing probabilistic CBFs. However, due to the potential empty intersection between the reachable set and the safe region confined by CBF constraints, the CC-MPC-CBF problem can pose challenges in achieving feasibility. To address this issue, we propose a sequential implementation approach that involves solving a standard MPC optimization problem followed by a predictive safety filter optimization, which leads to improved feasibility. Furthermore, we introduce an iterative convex optimization scheme to further expedite the resolution of the predictive safety filter, which results in an efficient approach to tackling the non-convex CC-MPC-CBF problem. We apply our proposed algorithm to a 2-D integrator system for MOCA, and we showcase its resilience to obstacle measurement uncertainties and favorable feasibility properties.

Safety-Aware Adaptive Reinforcement Learning With Applications To Brushbot Navigation

Abstract: This paper presents a safety-aware learning framework that employs an adaptive model learning method together with barrier certificates for systems with possibly nonstationary agent dynamics. To extract the dynamic structure of the model, we use a sparse optimization technique, and the resulting model will be used in combination with control barrier certificates which constrain feedback controllers only when safety is about to be violated. Under some mild assumptions, solutions to the constrained feedback-controller optimization are guaranteed to be globally optimal, and the monotonic improvement of a feedback controller is thus ensured. In addition, we reformulate the (action-)value function approximation to make any kernel-based nonlinear function estimation method applicable. We then employ a state-of-the-art kernel adaptive filtering technique for the (action-)value function approximation. The resulting framework is verified experimentally on a brushbot, whose dynamics is unknown and highly complex.


We use Kaiming initialization #REFR for the NeRF model and initialize all cameras to the origin, looking in the −z direction, with the focal length (f ) set to the image width.

Our implementation is based on the framework provided by Khalid et al.

#OTHEREFR , with a few modifications for enhanced computation efficiency.

Specifically, we do not include the effect of the dynamic component in our calculations, as the static representation is used in the analysis, ee keep the hidden layer dimension at 256, and we sample only 4096 pixels from each input image and 128 points along each ray.

To optimize the NeRF, camera poses, and focal lengths, we employ three separate Adam optimizers, all with an initial learning rate of 0.001.

The learning rate of the NeRF model decays every 100 epochs by multiplying it by 0.997 (equivalent to stair-cased exponential decay), while the learning rates of the pose and focal length parameters decay every 10 epochs with a multiplier of 0.9.

Unless otherwise specified, all models are trained for 10,000 epochs. Further technical details are provided in the supplementary material.

Refinerf: Modelling Dynamic Neural Radiance Fields With Inconsistent Or Missing Camera Parameters

Abstract: Figure 1: Our proposed approach learns camera parameters using a simple photometric loss using a learning scheduler and is easy to incorporate in both static and dynamic frameworks.

Delving Deep Into Rectifiers: Surpassing Human-Level Performance On Imagenet Classification

Abstract: Rectified activation units (rectifiers) are essential for state-of-the-art neural networks. In this work, we study rectifier neural networks for image classification from two aspects. First, we propose a Parametric Rectified Linear Unit (PReLU) that generalizes the traditional rectified unit. PReLU improves model fitting with nearly zero extra computational cost and little overfitting risk. Second, we derive a robust initialization method that particularly considers the rectifier nonlinearities. This method enables us to train extremely deep rectified models directly from scratch and to investigate deeper or wider network architectures. Based on the learnable activation and advanced initialization, we achieve 4.94% top-5 test error on the ImageNet 2012 classification dataset. This is a 26% relative improvement over the ILSVRC 2014 winner (GoogLeNet, 6.66% [33]). To our knowledge, our result is the first to surpass the reported human-level performance (5.1%, [26]) on this dataset.


The input text sequence is first converted into a phoneme sequence using grapheme-to-phoneme conversion #REFR and then aligned with speech using our internal alignment tool to obtain the phoneme-level duration.

Training Dataset To train the neural audio codec and the diffusion model, we use the English subset of Multilingual LibriSpeech (MLS) #OTHEREFR as the training data, which contains 44K hours of transcribed speech data derived from LibriVox audiobooks.

The number of distinct speakers is 2742 for males and 2748 for females respectively. The sample rate is 16KHz for all speech data.

The frame-level pitch sequence is extracted from the speech using PyWorld 3 .

Evaluation Dataset We employ two benchmark datasets for evaluation: 1) LibriSpeech #OTHEREFR test-clean, which contains 40 distinct speakers and 5.4 hours of annotated speech data.

2) VCTK dataset #OTHEREFR , which contains 108 distinct speakers.

For LibriSpeech test-clean, we randomly sample 15 utterances for each speaker and form a subset of 600 utterances for evaluation.

For VCTK, we randomly sample 5 utterances for each speaker, resulting in a subset of 540 utterances for evaluation.

Naturalspeech 2: Latent Diffusion Models Are Natural And Zero-Shot Speech And Singing Synthesizers

Abstract: Scaling text-to-speech (TTS) to large-scale, multi-speaker, and in-the-wild datasets is important to capture the diversity in human speech such as speaker identities, prosodies, and styles (e.g., singing). Current large TTS systems usually quantize speech into discrete tokens and use language models to generate these tokens one by one, which suffer from unstable prosody, word skipping/repeating issue, and poor voice quality. In this paper, we develop NaturalSpeech 2, a TTS system that leverages a neural audio codec with residual vector quantizers to get the quantized latent vectors and uses a diffusion model to generate these latent vectors conditioned on text input. To enhance the zero-shot capability that is important to achieve diverse speech synthesis, we design a speech prompting mechanism to facilitate in-context learning in the diffusion model and the duration/pitch predictor. We scale NaturalSpeech 2 to large-scale datasets with 44K hours of speech and singing data and evaluate its voice quality on unseen speakers. NaturalSpeech 2 outperforms previous TTS systems by a large margin in terms of prosody/timbre similarity, robustness, and voice quality in a zero-shot setting, and performs novel zero-shot singing synthesis with only a speech prompt. Audio samples are available at https://speechresearch.github.io/naturalspeech2.

Token-Level Ensemble Distillation For Grapheme-To-Phoneme Conversion

Abstract: Grapheme-to-phoneme (G2P) conversion is an important task in automatic speech recognition and text-to-speech systems. Recently, G2P conversion is viewed as a sequence to sequence task and modeled by RNN or CNN based encoder-decoder framework. However, previous works do not consider the practical issues when deploying G2P model in the production system, such as how to leverage additional unlabeled data to boost the accuracy, as well as reduce model size for online deployment. In this work, we propose token-level ensemble distillation for G2P conversion, which can (1) boost the accuracy by distilling the knowledge from additional unlabeled data, and (2) reduce the model size but maintain the high accuracy, both of which are very practical and helpful in the online production system. We use token-level knowledge distillation, which results in better accuracy than the sequence-level counterpart. What is more, we adopt the Transformer instead of RNN or CNN based models to further boost the accuracy of G2P conversion. Experiments on the publicly available CMUDict dataset and an internal English dataset demonstrate the effectiveness of our proposed method. Particularly, our method achieves 19.88% WER on CMUDict dataset, outperforming the previous works by more than 4.22% WER, and setting the new state-of-the-art results.


In #REFR , the authors proposed a joint sensing and transmission protocol to enable UAV-to-Device communication to improve UAV sensing services. Kurt et al.

#OTHEREFR proposed a differentially private AFL-based anomaly detection scheme for IoT infrastructure.

However, in the literature on anomaly detection methods for IoT environments, there is a lack of discussion about the issue of continually moving sensing targets.

Yet this is an issue that increases the difficulty of data sensing and collection.

UAVs and HAPSs can provide wireless sensing support from the sky to track moving sensing targets #OTHEREFR , #OTHEREFR , #OTHEREFR .

In #OTHEREFR , a cooperative internet of UAVs was established to execute integrated sensing and transmission tasks for ground sensing targets under energy and communication constraints.

#OTHEREFR leveraged caching, com-munication, computing, and sensing capabilities of a HAPS to serve autonomous devices for future aerial delivery networks.

In addition to providing sensing capabilities, UAV-enabled and HAPS-enabled wireless technologies also play key roles in enhancing wireless connectivity and computation capability for ubiquitous IoT #OTHEREFR - #OTHEREFR .

Considering the constrained energy capacity in UAVs, #OTHEREFR - #OTHEREFR studied the UAV-enabled energy-efficient computation offloading, data uploading, and coverage enhancement systems, respectively.

Compared to UAVs, HAPSs can provide wider coverage and stronger computational capabilities in a sustainable manner. Ren et al.

#OTHEREFR proposed a HAPS-assisted caching and computation offloading framework for intelligent transportation systems, where a HAPS played the dual role of a powerful computing server and a data library.

A Vhetnet-Enabled Asynchronous Federated Learning-Based Anomaly Detection Framework For Ubiquitous Iot

Abstract: Anomaly detection for the Internet of Things (IoT) is a major intelligent service required by many fields, including intrusion detection, state monitoring, device-activity analysis, and security supervision. However, the heterogeneous distribution of data and resource-constrained end nodes in ubiquitous IoT systems present challenges for existing anomaly detection models. Due to the advantages of flexible deployment and multidimensional resources, high altitude platform stations (HAPSs) and unmanned aerial vehicles (UAVs), which are important components of vertical heterogeneous networks (VHetNets), have significant potential for sensing, computing, storage, and communication applications in ubiquitous IoT systems. In this paper, we propose a novel VHetNet-enabled asynchronous federated learning (AFL) framework to enable decentralized UAVs to collaboratively train a global anomaly detection model based on their local sensory data from ubiquitous IoT devices. In the VHetNet-enabled AFL framework, a HAPS operates as a central aerial server, and the local models trained in UAVs are uploaded to the HAPS for global aggregation due to its wide coverage and strong storage and computation capabilities. We also introduce a UAV selection strategy into the AFL framework to prevent UAVs with low local model quality and large energy consumption from affecting the learning efficiency and detection accuracy of the global model. To ensure the security of transmissions between UAVs and the HAPS via wireless links, we add designed noise to local model parameters in UAVs to achieve differential privacy during the information exchange process. Moreover, we propose a compound-action actor-critic (CA2C)-based joint device association, UAV selection, and UAV trajectory planning algorithm to further enhance the overall federated execution efficiency and detection model accuracy under the UAV energy constraints. Extensive experimental evaluation on a real-world dataset demonstrates that the proposed algorithm can achieve high detection accuracy with short federated execution time and low energy consumption. Index Terms-Anomaly detection, ubiquitous Internet of Things (IoT), vertical heterogeneous network (VHetNet), asynchronous federated learning (AFL), differential privacy.

Uav-To-Device Underlay Communications: Age Of Information Minimization By Multi-Agent Deep Reinforcement Learning

Abstract: In recent years, unmanned aerial vehicles (UAVs) have found numerous sensing applications, which are expected to add billions of dollars to the world economy in the next decade. To further improve the Quality-of-Service (QoS) in such applications, the 3rd Generation Partnership Project (3GPP) has considered the adoption of terrestrial cellular networks to support UAV sensing services, also known as the cellular Internet of UAVs. In this paper, we consider a cellular Internet of UAVs, where the sensory data can be transmitted either to base station (BS) via cellular links, or to mobile devices by underlay UAV-to-Device (U2D) communications. To evaluate the freshness of data, the age of information (AoI) is adopted, in which a lower AoI implies fresher data. Since UAVs' AoIs are determined by their trajectories during sensing and transmission, we investigate the AoI minimization problem for UAVs by designing their trajectories. This problem is a Markov decision problem (MDP) with an infinite state-action space, and thus we utilize multi-agent deep reinforcement learning (DRL) to approximate the state-action space. Then, we propose a multi-UAV trajectory design algorithm to solve this problem. Simulation results show that our algorithm achieves a lower AoI than greedy algorithm and policy gradient algorithm.


This observation is in line with what has been established in classical models as well #REFR .

5 , we see that an EBM can easily be learned for states that possess paramagnetic order.

But as expected, the sample complexity of the learning procedure increases when the states come close to the ground state of the classical model.

Therefore, the observed effect of g on learning is much more pronounced for ground states when compared to thermal states.

The results of using NeurISE for an anti-ferromagnetic TIM on a 50 qubit 1D lattice with open boundaries at the critical point are given in Fig. 3d .

We see that this method has no problems learning states that possess long-range order.

Similarly to learning representations of thermal states, our experiments with ground states also show that the error in the learning procedure scales favorably with the system size.

In SI, section H, we give scaling results for learning ground states, and discuss learning EBM representations of ground states for the Heisenberg model in 2D.

Another important aspect of quantum states that can greatly aid the learning process is the presence of symmetries.

Symmetries can significantly reduce the size of the hypothesis space that a machine-learning method must optimize over.

Learning Energy Based Representations Of Quantum Many-Body States

Abstract: Efficient representation of quantum many-body states on classical computers is a problem of enormous practical interest. An ideal representation of a quantum state combines a succinct characterization informed by the system's structure and symmetries, along with the ability to predict the physical observables of interest. A number of machine learning approaches have been recently used to construct such classical representations [1-6] which enable predictions of observables [7] and accounts for physical symmetries [8]. However, the structure of a quantum state gets typically lost unless a specialized ansatz is employed based on prior knowledge of the system [9-12]. Moreover, most such approaches give no information about what states are easier to learn in comparison to others. Here, we propose a new generative energy-based representation of quantum many-body states derived from Gibbs distributions used for modeling the thermal states of classical spin systems. Based on the prior information on a family of quantum states, the energy function can be specified by a small number of parameters using an explicit low-degree polynomial or a generic parametric family such as neural nets, and can naturally include the known symmetries of the system. Our results show that such a representation can be efficiently learned from data using exact algorithms in a form that enables the prediction of expectation values of physical observables. Importantly, the structure of the learned energy function provides a natural explanation for the hardness of learning for a given class of quantum states.

Optimal Structure And Parameter Learning Of Ising Models

Abstract: Reconstruction of structure and parameters of an Ising model from binary samples is a problem of practical importance in a variety of disciplines, ranging from statistical physics and computational biology to image processing and machine learning. The focus of the research community shifted towards developing universal reconstruction algorithms which are both computationally efficient and require the minimal amount of expensive data. We introduce a new method, Interaction Screening, which accurately estimates the model parameters using local optimization problems. The algorithm provably achieves perfect graph structure recovery with an information-theoretically optimal number of samples, notably in the low-temperature regime which is known to be the hardest for learning. The efficacy of Interaction Screening is assessed through extensive numerical tests on synthetic Ising models of various topologies with different types of interactions, as well as on a real data produced by a D-Wave quantum computer. This study shows that the Interaction Screening method is an exact, tractable and optimal technique universally solving the inverse Ising problem.