OpenRaiser/Intern-Atlas huggingface dataset:
Class-incremental Learning with Pre-allocated Fixed Classifiers
Venue: arXiv cs.LG 2023
Year: 2023

In class-incremental learning, a learning agent faces a stream of data with the goal of learning new classes while not forgetting previous ones. Neural networks are known to suffer under this setting, as they forget previously acquired knowledge. To address this problem, effective methods exploit past data stored in an episodic memory while expanding the final classifier nodes to accommodate the new classes. In this work, we substitute the expanding classifier with a novel fixed classifier in which a number of pre-allocated output nodes are subject to the classification loss right from the beginning of the learning phase. Contrarily to the standard expanding classifier, this allows: (a) the output nodes of future unseen classes to firstly see negative samples since the beginning of learning together with the positive samples that incrementally arrive; (b) to learn features that do not change their geometric configuration as novel classes are incorporated in the learning model. Experiments with public datasets show that the proposed approach is as effective as the expanding classifier while exhibiting novel intriguing properties of the internal feature representation that are otherwise not-existent. Our ablation study on pre-allocating a large number of classes further validates the approach.
Task Scoping: Generating Task-Specific Abstractions for Planning in Open-Scope Models
Venue: arXiv cs.AI 2023
Year: 2023

A general-purpose planning agent requires an open-scope world model: one rich enough to tackle any of the wide range of tasks it may be asked to solve over its operational lifetime. This stands in contrast with typical planning approaches, where the scope of a model is limited to a specific family of tasks that share significant structure. Unfortunately, planning to solve any specific task using an open-scope model is computationally intractable - even for state-of-the-art methods - due to the many states and actions that are necessarily present in the model but irrelevant to that problem. We propose task scoping: a method that exploits knowledge of the initial state, goal conditions, and transition system to automatically and efficiently remove provably irrelevant variables and actions from a planning problem. Our approach leverages causal link analysis and backwards reachability over state variables (rather than states) along with operator merging (when effects on relevant variables are identical). Using task scoping as a pre-planning step can shrink the search space by orders of magnitude and dramatically decrease planning time. We empirically demonstrate that these improvements occur across a variety of open-scope domains, including Minecraft, where our approach leads to a 75x reduction in search time with a state-of-the-art numeric planner, even after including the time required for task scoping itself.
BBB-Voting: 1-out-of-k Blockchain-Based Boardroom Voting
Venue: arXiv cs.CR 2023
Year: 2023

Voting is a means to agree on a collective decision based on available choices (e.g., candidates), where participants agree to abide by their outcome. To improve some features of e-voting, decentralized blockchain-based solutions can be employed, where the blockchain represents a public bulletin board that in contrast to a centralized bulletin board provides extremely high availability, censorship resistance, and correct code execution. A blockchain ensures that all entities in the voting system have the same view of the actions made by others due to its immutability and append-only features. The existing remote blockchain-based boardroom voting solution called Open Voting Network (OVN) provides the privacy of votes, universal & End-to-End verifiability, and perfect ballot secrecy; however, it supports only two choices and lacks robustness enabling recovery from stalling participants. We present BBB-Voting, an equivalent blockchain-based approach for decentralized voting such as OVN, but in contrast to it, BBB-Voting supports 1-out-of-$k$ choices and provides robustness that enables recovery from stalling participants. We make a cost-optimized implementation using an Ethereum-based environment respecting Ethereum Enterprise Alliance standards, which we compare with OVN and show that our work decreases the costs for voters by 13.5% in normalized gas consumption. Finally, we show how BBB-Voting can be extended to support the number of participants limited only by the expenses paid by the authority and the computing power to obtain the tally.
Self-supervised Geometric Features Discovery via Interpretable Attention for Vehicle Re-Identification and Beyond
Venue: arXiv cs.CV 2023
Year: 2023

To learn distinguishable patterns, most of recent works in vehicle re-identification (ReID) struggled to redevelop official benchmarks to provide various supervisions, which requires prohibitive human labors. In this paper, we seek to achieve the similar goal but do not involve more human efforts. To this end, we introduce a novel framework, which successfully encodes both geometric local features and global representations to distinguish vehicle instances, optimized only by the supervision from official ID labels. Specifically, given our insight that objects in ReID share similar geometric characteristics, we propose to borrow self-supervised representation learning to facilitate geometric features discovery. To condense these features, we introduce an interpretable attention module, with the core of local maxima aggregation instead of fully automatic learning, whose mechanism is completely understandable and whose response map is physically reasonable. To the best of our knowledge, we are the first that perform self-supervised learning to discover geometric features. We conduct comprehensive experiments on three most popular datasets for vehicle ReID, i.e., VeRi-776, CityFlow-ReID, and VehicleID. We report our state-of-the-art (SOTA) performances and promising visualization results. We also show the excellent scalability of our approach on other ReID related tasks, i.e., person ReID and multi-target multi-camera (MTMC) vehicle tracking.
Finding Cut-Offs in Leaderless Rendez-Vous Protocols is Easy
Venue: arXiv cs.LO 2023
Year: 2023

In rendez-vous protocols an arbitrarily large number of indistinguishable finite-state agents interact in pairs. The cut-off problem asks if there exists a number $B$ such that all initial configurations of the protocol with at least $B$ agents in a given initial state can reach a final configuration with all agents in a given final state. In a recent paper (Horn and Sangnier, CONCUR 2020), Horn and Sangnier proved that the cut-off problem is decidable (and at least as hard as the Petri net reachability problem) for protocols with a leader, and in EXPSPACE for leaderless protocols. Further, for the special class of symmetric protocols they reduce these bounds to PSPACE and NP, respectively. The problem of lowering these upper bounds or finding matching lower bounds was left open. We show that the cut-off problem is P-complete for leaderless protocols and in NC for leaderless symmetric protocols. Further, we also consider a variant of the cut-off problem suggested in (Horn and Sangnier, CONCUR 2020), which we call the bounded-loss cut-off problem and prove that this problem is P-complete for leaderless protocols and NL-complete for leaderless symmetric protocols. Finally, by reusing some of the techniques applied for the analysis of leaderless protocols, we show that the cut-off problem for symmetric protocols with a leader is NP-complete, thereby improving upon all the elementary upper bounds of (Horn and Sangnier, CONCUR 2020).
The Polynomial Method is Universal for Distribution-Free Correlational SQ Learning
Venue: arXiv cs.DS 2023
Year: 2023

We consider the problem of distribution-free learning for Boolean function classes in the PAC and agnostic models. Generalizing a beautiful work of Malach and Shalev-Shwartz (2022) that gave tight correlational SQ (CSQ) lower bounds for learning DNF formulas, we give new proofs that lower bounds on the threshold or approximate degree of any function class directly imply CSQ lower bounds for PAC or agnostic learning respectively. While such bounds implicitly follow by combining prior results by Feldman (2008, 2012) and Sherstov (2008, 2011), to our knowledge the precise statements we give had not appeared in this form before. Moreover, our proofs are simple and largely self-contained. These lower bounds match corresponding positive results using upper bounds on the threshold or approximate degree in the SQ model for PAC or agnostic learning, and in this sense these results show that the polynomial method is a universal, best-possible approach for distribution-free CSQ learning.
Simulation of Human and Artificial Emotion (SHArE)
Venue: arXiv cs.AI 2023
Year: 2023

The framework for Simulation of Human and Artificial Emotion (SHArE) describes the architecture of emotion in terms of parameters transferable between psychology, neuroscience, and artificial intelligence. These parameters can be defined as abstract concepts or granularized down to the voltage levels of individual neurons. This model enables emotional trajectory design for humans which may lead to novel therapeutic solutions for various mental health concerns. For artificial intelligence, this work provides a compact notation which can be applied to neural networks as a means to observe the emotions and motivations of machines.
A Nearly-Linear Time Algorithm for Linear Programs with Small Treewidth: A Multiscale Representation of Robust Central Path
Venue: arXiv cs.DS 2023
Year: 2023

Arising from structural graph theory, treewidth has become a focus of study in fixed-parameter tractable algorithms in various communities including combinatorics, integer-linear programming, and numerical analysis. Many NP-hard problems are known to be solvable in $\widetilde{O}(n \cdot 2^{O(\mathrm{tw})})$ time, where $\mathrm{tw}$ is the treewidth of the input graph. Analogously, many problems in P should be solvable in $\widetilde{O}(n \cdot \mathrm{tw}^{O(1)})$ time; however, due to the lack of appropriate tools, only a few such results are currently known. [Fom+18] conjectured this to hold as broadly as all linear programs; in our paper, we show this is true: Given a linear program of the form $\min_{Ax=b,\ell \leq x\leq u} c^{\top} x$, and a width-$τ$ tree decomposition of a graph $G_A$ related to $A$, we show how to solve it in time $$\widetilde{O}(n \cdot τ^2 \log (1/\varepsilon)),$$ where $n$ is the number of variables and $\varepsilon$ is the relative accuracy. Combined with recent techniques in vertex-capacitated flow [BGS21], this leads to an algorithm with $\widetilde{O}(n^{1+o(1)} \cdot \mathrm{tw}^2 \log (1/\varepsilon))$ run-time. Besides being the first of its kind, our algorithm has run-time nearly matching the fastest run-time for solving the sub-problem $Ax=b$ (under the assumption that no fast matrix multiplication is used). We obtain these results by combining recent techniques in interior-point methods (IPMs), sketching, and a novel representation of the solution under a multiscale basis similar to the wavelet basis.
Recoverable, Abortable, and Adaptive Mutual Exclusion with Sublogarithmic RMR Complexity
Venue: arXiv cs.DC 2023
Year: 2023

We present the first recoverable mutual exclusion (RME) algorithm that is simultaneously abortable, adaptive to point contention, and with sublogarithmic RMR complexity. Our algorithm has $O(\min(K,\log_W N))$ RMR passage complexity and $O(F + \min(K,\log_W N))$ RMR super-passage complexity, where $K$ is the number of concurrent processes (point contention), $W$ is the size (in bits) of registers, and $F$ is the number of crashes in a super-passage. Under the standard assumption that $W=Θ(\log N)$, these bounds translate to worst-case $O(\frac{\log N}{\log \log N})$ passage complexity and $O(F + \frac{\log N}{\log \log N})$ super-passage complexity. Our key building blocks are: * A $D$-process abortable RME algorithm, for $D \leq W$, with $O(1)$ passage complexity and $O(1+F)$ super-passage complexity. We obtain this algorithm by using the Fetch-And-Add (FAA) primitive, unlike prior work on RME that uses Fetch-And-Store (FAS/SWAP). * A generic transformation that transforms any abortable RME algorithm with passage complexity of $B < W$, into an abortable RME lock with passage complexity of $O(\min(K,B))$.
SeekNet: Improved Human Instance Segmentation and Tracking via Reinforcement Learning Based Optimized Robot Relocation
Venue: arXiv cs.CV 2023
Year: 2023

Amodal recognition is the ability of the system to detect occluded objects. Most SOTA Visual Recognition systems lack the ability to perform amodal recognition. Few studies have achieved amodal recognition through passive prediction or embodied recognition approaches. However, these approaches suffer from challenges in real-world applications, such as dynamic obstacles. We propose SeekNet, an improved optimization method for amodal recognition through embodied visual recognition. Additionally, we implement SeekNet for social robots, where there are multiple interactions with crowded pedestrians. We also demonstrate the benefits of our algorithm on occluded human detection and tracking over other baselines. Additionally, we set up a multi-robot environment with SeekNet to identify and track visual disease markers for airborne disease in crowded areas. We conduct our experiments in a simulated indoor environment and show that our method enhances the overall accuracy of the amodal recognition task and achieves the largest improvement in detection accuracy over time in comparison to the baseline approaches.
Near-Optimal Multi-Robot Motion Planning with Finite Sampling
Venue: arXiv cs.RO 2023
Year: 2023

An underlying structure in several sampling-based methods for continuous multi-robot motion planning (MRMP) is the tensor roadmap (TR), which emerges from combining multiple PRM graphs constructed for the individual robots via a tensor product. We study the conditions under which the TR encodes a near-optimal solution for MRMP -- satisfying these conditions implies near optimality for a variety of popular planners, including dRRT*, and the discrete methods M* and CBS when applied to the continuous domain. We develop the first finite-sample analysis of this kind, which specifies the number of samples, their deterministic distribution, and magnitude of the connection radii that should be used by each individual PRM graph, to guarantee near-optimality using the TR. This significantly improves upon a previous asymptotic analysis, wherein the number of samples tends to infinity. Our new finite sample-size analysis supports guaranteed high-quality solutions in practice within finite time. To achieve our new result, we first develop a sampling scheme, which we call the staggered grid, for finite-sample motion planning for individual robots, which requires significantly fewer samples than previous work. We then extend it to the much more involved MRMP setting which requires to account for interactions among multiple robots. Finally, we report on a few experiments that serve as a verification of our theoretical findings and raise interesting questions for further investigation.
Conjecturing-Based Discovery of Patterns in Data
Venue: arXiv cs.LG 2023
Year: 2023

We propose the use of a conjecturing machine that suggests feature relationships in the form of bounds involving nonlinear terms for numerical features and boolean expressions for categorical features. The proposed Conjecturing framework recovers known nonlinear and boolean relationships among features from data. In both settings, true underlying relationships are revealed. We then compare the method to a previously-proposed framework for symbolic regression on the ability to recover equations that are satisfied among features in a dataset. The framework is then applied to patient-level data regarding COVID-19 outcomes to suggest possible risk factors that are confirmed in the medical literature.
Margin of Victory in Tournaments: Structural and Experimental Results
Venue: arXiv cs.GT 2023
Year: 2023

Tournament solutions are standard tools for identifying winners based on pairwise comparisons between competing alternatives. The recently studied notion of margin of victory (MoV) offers a general method for refining the winner set of any given tournament solution, thereby increasing the discriminative power of the solution. In this paper, we reveal a number of structural insights on the MoV by investigating fundamental properties such as monotonicity and consistency with respect to the covering relation. Furthermore, we provide experimental evidence on the extent to which the MoV notion refines winner sets in tournaments generated according to various stochastic models.
MeLPUF: Memory-in-Logic PUF Structures for Low-Overhead IC Authentication
Venue: arXiv cs.CR 2023
Year: 2023

Physically Unclonable Functions (PUFs) are used for securing electronic devices across the implementation spectrum ranging from Field Programmable Gate Array (FPGA) to system on chips (SoCs). However, existing PUF implementations often suffer from one or more significant deficiencies: (1) significant design overhead; (2) difficulty to configure and integrate based on application-specific requirements; (3) vulnerability to model-building attacks; and (4) spatial locality to a specific region of a chip. These factors limit their application in the authentication of designs used in diverse applications. In this work, we propose MeLPUF: Memory-in-Logic PUF; a low-overhead, distributed PUF that leverages the existing logic gates in a design to create cross-coupled inverters (i.e., memory cells) in a logic circuit as an entropy source. It exploits these memory cells' power-up states as the entropy source to generate device-specific unique fingerprints. A dedicated control signal governs these on-demand memory cells. They can be dispersed across the combinational logic of a design to achieve distributed authentication. They can also be synthesized with a standard logic synthesis tool to meet the target area, power, and performance constraints. We evaluate the quality of MeLPUF signatures with circuit-level simulations and experimental measurements using FPGA silicon (TSMC 55nm process). Our analysis shows the high quality of the PUF in terms of uniqueness, randomness, and robustness while incurring modest overhead. We further demonstrate the scalability of MeLPUF by aggregating power-up states from multiple memory cells, thus creating PUF signatures or digital identifiers of varying lengths. Additionally, we suggest optimization techniques that can be leveraged to boost the performance of MeLPUF further.
Learning to extrapolate using continued fractions: Predicting the critical temperature of superconductor materials
Venue: arXiv cs.LG 2023
Year: 2023

In the field of Artificial Intelligence (AI) and Machine Learning (ML), the approximation of unknown target functions $y=f(\mathbf{x})$ using limited instances $S={(\mathbf{x^{(i)}},y^{(i)})}$, where $\mathbf{x^{(i)}} \in D$ and $D$ represents the domain of interest, is a common objective. We refer to $S$ as the training set and aim to identify a low-complexity mathematical model that can effectively approximate this target function for new instances $\mathbf{x}$. Consequently, the model's generalization ability is evaluated on a separate set $T=\{\mathbf{x^{(j)}}\} \subset D$, where $T \neq S$, frequently with $T \cap S = \emptyset$, to assess its performance beyond the training set. However, certain applications require accurate approximation not only within the original domain $D$ but also in an extended domain $D'$ that encompasses $D$. This becomes particularly relevant in scenarios involving the design of new structures, where minimizing errors in approximations is crucial. For example, when developing new materials through data-driven approaches, the AI/ML system can provide valuable insights to guide the design process by serving as a surrogate function. Consequently, the learned model can be employed to facilitate the design of new laboratory experiments. In this paper, we propose a method for multivariate regression based on iterative fitting of a continued fraction, incorporating additive spline models. We compare the performance of our method with established techniques, including AdaBoost, Kernel Ridge, Linear Regression, Lasso Lars, Linear Support Vector Regression, Multi-Layer Perceptrons, Random Forests, Stochastic Gradient Descent, and XGBoost. To evaluate these methods, we focus on an important problem in the field: predicting the critical temperature of superconductors based on physical-chemical characteristics.
Simplify Node-RED For End User Development in SeismoCloud
Venue: arXiv cs.HC 2023
Year: 2023

Networks of IoT devices often require configuration and definition of behavior by the final user. Node-RED is a flow-based programming platform commonly used for End User Development, but it requires networking and protocols skills in order to be efficiently used. We add a level of abstraction to Node-RED nodes in order to allow non-skilled users to configure and control networks of IoT devices and online services. We applied such abstractions to the SeismoCloud application for earthquake monitoring.
Linear codes and incidence structures of bent functions and their generalizations
Venue: arXiv cs.IT 2023
Year: 2023

In this paper we consider further applications of $(n,m)$-functions for the construction of 2-designs. For instance, we provide a new application of the extended Assmus-Mattson theorem, by showing that linear codes of APN functions with the classical Walsh spectrum support 2-designs. On the other hand, we use linear codes and combinatorial designs in order to study important properties of $(n,m)$-functions. In particular, we give a new design-theoretic characterization of $(n,m)$-plateaued and $(n,m)$-bent functions and provide a coding-theoretic as well as a design-theoretic interpretation of the extendability problem for $(n,m)$-bent functions.
Book Embeddings of k-Map Graphs
Venue: arXiv cs.CG 2023
Year: 2023

A map is a partition of the sphere into regions that are labeled as countries or holes. The vertices of a map graph are the countries of a map. There is an edge if and only if the countries are adjacent and meet in at least one point. For a k-map graph, at most k countries meet in a point. A graph is k-planar if it can be drawn in the plane with at most k crossings per edge. A p-page book embedding of a graph is a linear ordering of the vertices and an assignment of the edges to p pages, so that there is no conflict for edges assigned to the same page. The minimum number of pages is the book thickness of a graph, also known as stack number or page number. We show that every k-map graph has a book embedding in $6\lfloor k/2 \rfloor+5$ pages, which, for n-vertex graphs, can be computed in O(kn) time from its map. Our result improves the best known upper bound. Towards a lower bound, it is shown that some k-map graphs need $\lfloor 3k/4 \rfloor$ pages. In passing, we obtain an improved upper bound of eleven pages for 1-planar graphs, which are subgraphs of 4-map graphs, and of 17 pages for optimal 2-planar graphs.