arXiv subjects

Jingchao Chen

Publications and source records attributed to Jingchao Chen.

13 recordsLinked to original sources

arXiv2026

FUPareto: Bridging the Forgetting-Utility Gap in Federated Unlearning via Pareto Augmented Optimization

Federated Unlearning (FU) aims to efficiently remove the influence of specific client data from a federated model while preserving utility for the remaining clients. However, three key challenges remain: (1) existing unlearning objectives often compromise model utility or increase vulnerability to Membership Inference Attacks (MIA); (2) there is a persistent conflict between forgetting and utility, where further unlearning inevitably harms retained performance; and (3) support for concurrent multi-client unlearning is poor, as gradient conflicts among clients degrade the quality of forgetting. To address these issues, we propose FUPareto, an efficient unlearning framework via Pareto-augmented optimization. We first introduce the Minimum Boundary Shift (MBS) Loss, which enforces unlearning by suppressing the target class logit below the highest non-target class logit; this can improve the unlearning efficiency and mitigate MIA risks. During the unlearning process, FUPareto performs Pareto improvement steps to preserve model utility and executes Pareto expansion to guarantee forgetting. Specifically, during Pareto expansion, the framework integrates a Null-Space Projected Multiple Gradient Descent Algorithm (MGDA) to decouple gradient conflicts. This enables effective, fair, and concurrent unlearning for multiple clients while minimizing utility degradation. Extensive experiments across diverse scenarios demonstrate that FUPareto consistently outperforms state-of-the-art FU methods in both unlearning efficacy and retained utility.

cs.LG↗

arXiv2021

HENet: Forcing a Network to Think More for Font Recognition

Although lots of progress were made in Text Recognition/OCR in recent years, the task of font recognition is remaining challenging. The main challenge lies in the subtle difference between these similar fonts, which is hard to distinguish. This paper proposes a novel font recognizer with a pluggable module solving the font recognition task. The pluggable module hides the most discriminative accessible features and forces the network to consider other complicated features to solve the hard examples of similar fonts, called HE Block. Compared with the available public font recognition systems, our proposed method does not require any interactions at the inference stage. Extensive experiments demonstrate that HENet achieves encouraging performance, including on character-level dataset Explor_all and word-level dataset AdobeVFR

cs.CV↗

arXiv2019

Core First Unit Propagation

Unit propagation (which is called also Boolean Constraint Propagation) has been an important component of every modern CDCL SAT solver since the CDCL solver was developed. In general, unit propagation is implemented by scanning sequentially every clause over a linear watch-list. This paper presents a new unit propagation technique called core first unit propagation. The main idea is to prefer core clauses over non-core ones during unit propagation, trying to generate a shorter learnt clause. Here, the core clause is defined as one with literal block distance less than or equal to 7. Empirical results show that core first unit propagation improves the performance of the winner of the SAT Competition 2018, MapleLCMDistChronoBT.

cs.LO↗

arXiv2018

Fast Verifying Proofs of Propositional Unsatisfiability via Window Shifting

The robustness and correctness of SAT solvers are receiving more and more attention. In recent SAT competitions, a proof of unsatisfiability emitted by SAT solvers must be checked. So far, no proof checker has been efficient for every case. In the SAT competition 2016, some proofs were not verified within 20000 seconds. For this reason, we decided to develop a more efficient proof checker called TreeRat. This new checker uses a window shifting technique to improve the level of efficiency at which it verifies proofs of unsatisfiability. At the same time, we suggest that tree-search-based SAT solvers should use an equivalent relation encoding to emit proofs of subproblems. In our experiments, TreeRat was able to verify almost all proofs within 20000 seconds. On this point, TreeRat is shown to be superior to gratgen, which is an improved version of DRAT-trim. Also, in most cases, TreeRat is faster than gratgen. Like DRAT-trim, TreeRat can output also trace dependency graphs. Its output format is LRAT. The correctness of TreeRat can be ensured by checking its LRAT output.

cs.SE↗

arXiv2016

Improving abcdSAT by At-Least-One Recently Used Clause Management Strategy

We improve further the 2015 version of abcdSAT by various heuristics such as at-least-one recently used strategy, learnt clause database approximation reduction etc. Based on the requirement of different tracks at the SAT Competition 2016, we develop three versions of abcdSAT: drup, inc and lim, which participate in the competition of main (agile), incremental library and no-limit track, respectively.

cs.LO↗

arXiv2016

Two-Fold Circle-Covering of the Plane under Congruent Voronoi Polygon Conditions

The $k$-coverage problem is to find the minimum number of disks such that each point in a given plane is covered by at least $k$ disks. Under unit disk condition, when $k$=1, this problem has been solved by Kershner in 1939. However, when $k > 1$, it becomes extremely difficult. One tried to tackle this problem with different restrictions. In this paper, we restrict ourself to congruent Voronoi polygon, and prove the minimum density of the two-coverage with such a restriction. Our proof is simpler and more rigorous than that given recently by Yun et al.

math.MG↗

arXiv2016

Improving SAT Solvers via Blocked Clause Decomposition

The decision variable selection policy used by the most competitive CDCL (Conflict-Driven Clause Learning) SAT solvers is either VSIDS (Variable State Independent Decaying Sum) or its variants such as exponential version EVSIDS. The common characteristic of VSIDS and its variants is to make use of statistical information in the solving process, but ignore structure information of the problem. For this reason, this paper modifies the decision variable selection policy, and presents a SAT solving technique based on BCD (Blocked Clause Decomposition). Its basic idea is that a part of decision variables are selected by VSIDS heuristic, while another part of decision variables are selected by blocked sets that are obtained by BCD. Compared with the existing BCD-based technique, our technique is simple, and need not to reencode CNF formulas. SAT solvers for certified UNSAT track can apply also our BCD-based technique. Our experiments on application benchmarks demonstrate that the new variables selection policy based on BCD can increase the performance of SAT solvers such as abcdSAT. The solver with BCD solved an instance from the SAT Race 2015 that was not solved by any solver so far. This shows that in some cases, the heuristic based on structure information is more efficient than that based on statistical information.

cs.LO↗

arXiv2015

Fast Blocked Clause Decomposition with High Quality

Any CNF formula can be decomposed two blocked subsets such that both can be solved by BCE (Blocked Clause Elimination). To make the decomposition more useful, one hopes to have the decomposition as unbalanced as possible. It is often time consuming to achieve this goal. So far there have been several decomposition and post-processing algorithms such as PureDecompose, QuickDecompose, EagerMover etc. We found that these existing algorithms are often either inefficient or low-quality decomposition. This paper aims at improving the decomposition quality, while keeping the runtime of algorithms under control. To achieve this goal, we improve the existing BCE, and present two new variants of PureDecompose, a new heuristic decomposition called LessInterfereDecompose, and a new post-processing algorithm called RsetGuidedDecompose. Combining these new techniques results in a new algorithm called MixDecompose. In our experiments, there is no application formula where the quality of PureDecompose+EagerMover is better than MixDecompose. In terms of speed, MixDecompose is also very fast. Our average runtime is a little longer, but the worst-case runtime is shorter. In theory, our two variants of PureDecompose requires linear time in the number of clauses. By limiting the size of the touch list used by BCE, we can guarantee always that MixDecompose runs in linear time.

cs.LO↗

arXiv2012

A Dynamic Phase Selection Strategy for Satisfiability Solvers

The phase selection is an important of a SAT Solver based on conflict-driven DPLL. This paper presents a new phase selection strategy, in which the weight of each literal is defined as the sum of its implied-literals static weights. The implied literals of each literal is computed dynamically during the search. Therefore, it is call a dynamic phase selection strategy. In general, computing dynamically a weight is time-consuming. Hence, so far no SAT solver applies successfully a dynamic phase selection. Since the implied literal of our strategy conforms to that of the search process, the usual two watched-literals scheme can be applied here. Thus, the cost of our dynamic phase selection is very low. To improve Glucose 2.0 which won a Gold Medal for application category at SAT 2011 competition, we build five phase selection schemes using the dynamic phase selection policy. On application instances of SAT 2011, Glucose improved by the dynamic phase selection is significantly better than the original Glucose. We conduct also experiments on Lingeling, using the dynamic phase selection policy, and build two phase selection schemes. Experimental results show that the improved Lingeling is better than the original Lingeling.

cs.LO↗

arXiv2011

Joint Relay and Jammer Selection for Secure Two-Way Relay Networks

In this paper, we investigate joint relay and jammer selection in two-way cooperative networks, consisting of two sources, a number of intermediate nodes, and one eavesdropper, with the constraints of physical layer security. Specifically, the proposed algorithms select two or three intermediate nodes to enhance security against the malicious eavesdropper. The first selected node operates in the conventional relay mode and assists the sources to deliver their data to the corresponding destinations using an amplify-and-forward protocol. The second and third nodes are used in different communication phases as jammers in order to create intentional interference upon the eavesdropper node. Firstly, we find that in a topology where the intermediate nodes are randomly and sparsely distributed, the proposed schemes with cooperative jamming outperform the conventional non-jamming schemes within a certain transmitted power regime. We also find that, in the scenario in which the intermediate nodes gather as a close cluster, the jamming schemes may be less effective than their non-jamming counterparts. Therefore, we introduce a hybrid scheme to switch between jamming and non-jamming modes. Simulation results validate our theoretical analysis and show that the hybrid switching scheme further improves the secrecy rate.

cs.IT↗

arXiv2011

Exploiting Dynamically Propositional Logic Structures in SAT

The 32-bit hwb (hwb-n32 for short) problem is from equivalence checking that arises in combining two circuits computing the hidden weighted bit function. Since 2002, it remains still unsolvable in every SAT competition. This paper focuses on solving problems such as hwb-n32. Generally speaking, modern solvers can detect only XOR, AND, OR and ITE gates. Other non-clausal formulas (propositional logic structures) cannot be detected. To solve the hwb-n32 problem, we extract dynamically some special propositional logic structures, and then use a variant of DPLL-based solvers to solve the subproblem simplified by the extracted structure information. Using the dynamic extraction technique, we solved efficiently the hwb-n32 problem, even some of which were solved within 3000 seconds.

cs.LO↗

arXiv2011

Phase Selection Heuristics for Satisfiability Solvers

In general, a SAT Solver based on conflict-driven DPLL consists of variable selection, phase selection, Boolean Constraint Propagation, conflict analysis, clause learning and its database maintenance. Optimizing any part of these components can enhance the performance of a solver. This paper focuses on optimizing phase selection. Although the ACE (Approximation of the Combined lookahead Evaluation) weight is applied to a lookahead SAT solver such as March, so far, no conflict-driven SAT solver applies successfully the ACE weight, since computing the ACE weight is time-consuming. Here we apply the ACE weight to partial phase selection of conflict-driven SAT solvers. This can be seen as an improvement of the heuristic proposed by Jeroslow-Wang (1990). We incorporate the ACE heuristic and the existing phase selection heuristics in the new solver MPhaseSAT, and select a phase heuristic in a way similar to portfolio methods. Experimental results show that adding the ACE heuristic can improve the conflict-driven solvers. Particularly on application instances, MPhaseSAT with the ACE heuristic is significantly better than MPhaseSAT without the ACE heuristic, and even can solve a few SAT instances that remain unsolvable so far.

cs.LO↗

arXiv2011

Solving Rubik's Cube Using SAT Solvers

Rubik's Cube is an easily-understood puzzle, which is originally called the "magic cube". It is a well-known planning problem, which has been studied for a long time. Yet many simple properties remain unknown. This paper studies whether modern SAT solvers are applicable to this puzzle. To our best knowledge, we are the first to translate Rubik's Cube to a SAT problem. To reduce the number of variables and clauses needed for the encoding, we replace a naive approach of 6 Boolean variables to represent each color on each facelet with a new approach of 3 or 2 Boolean variables. In order to be able to solve quickly Rubik's Cube, we replace the direct encoding of 18 turns with the layer encoding of 18-subtype turns based on 6-type turns. To speed up the solving further, we encode some properties of two-phase algorithm as an additional constraint, and restrict some move sequences by adding some constraint clauses. Using only efficient encoding cannot solve this puzzle. For this reason, we improve the existing SAT solvers, and develop a new SAT solver based on PrecoSAT, though it is suited only for Rubik's Cube. The new SAT solver replaces the lookahead solving strategy with an ALO (\emph{at-least-one}) solving strategy, and decomposes the original problem into sub-problems. Each sub-problem is solved by PrecoSAT. The empirical results demonstrate both our SAT translation and new solving technique are efficient. Without the efficient SAT encoding and the new solving technique, Rubik's Cube will not be able to be solved still by any SAT solver. Using the improved SAT solver, we can find always a solution of length 20 in a reasonable time. Although our solver is slower than Kociemba's algorithm using lookup tables, but does not require a huge lookup table.

cs.AI↗