#include <iostream>
#include <vector>
#include <string>
#include <numeric>
#include <algorithm>
#include <chrono>
#include <random>
#include <cmath>

using namespace std;

// --- パラメータ調整欄 ---

// --- 戦略パラメータ ---
// 1つの目的地への経路で試行する乱択の回数
const int NUM_TRIALS = 100;
// 序盤フェーズで訪問するターゲットの数 (全99個中)
const int INITIAL_PHASE_TARGETS = 80;
// 序盤フェーズで許容する最大マンハッタン距離
const int MAX_DIST_INITIAL_PHASE = 12;
// 後半戦略で「遠い」と判断するマンハッタン距離
const int MIN_DIST_FOR_TARGETING = 10; 

// --- 焼きなましパラメータ ---
// 実行時間制限（ミリ秒）。2000msの制限に対し、マージンを持たせる
const double TIME_LIMIT_MS = 1950.0; 
// 焼きなましの開始温度
const double SA_START_TEMP = 500000.0;
// 焼きなましの終了温度
const double SA_END_TEMP = 10.0;


// --- グローバル定数 (変更不要) ---
const int N_CONST = 10;
const int T_CONST = 1000;

// --- 乱数生成器 ---
mt19937 rng(chrono::steady_clock::now().time_since_epoch().count());

/**
 * @struct State
 * @brief 現在のゲームの状態を管理する構造体
 */
struct State {
    int r, c;
    int s;
    vector<vector<int>> grid;
    int ops_count;
    string ops_log;

    State(const vector<vector<int>>& initial_grid) {
        r = 0; c = 0; s = 0;
        grid = initial_grid;
        ops_count = 0;
        ops_log = "";
    }

    void apply_op(char op) {
        if (ops_count >= T_CONST) return;
        bool valid = true;
        if (op == 'U') { if (r > 0) r--; else valid = false; }
        else if (op == 'D') { if (r < N_CONST - 1) r++; else valid = false; }
        else if (op == 'L') { if (c > 0) c--; else valid = false; }
        else if (op == 'R') { if (c < N_CONST - 1) c++; else valid = false; }
        else if (op == 'W') { grid[r][c] ^= s; }
        else if (op == 'C') { s ^= grid[r][c]; }
        else { valid = false; }
        if (valid) { ops_count++; ops_log += op; }
    }

    void apply_ops_string(const string& ops) {
        for (char op : ops) { apply_op(op); }
    }
    
    long long calculate_total_score() const {
        long long total_score = 0;
        for(const auto& row : grid) {
            for(int cell_val : row) {
                total_score += cell_val;
            }
        }
        return total_score;
    }
};

// --- ヘルパー関数 ---
string get_simple_move_ops(int r1, int c1, int r2, int c2) {
    string path_ops = "";
    string vert_move = (r1 < r2) ? "D" : "U";
    string horz_move = (c1 < c2) ? "R" : "L";
    for (int i = 0; i < abs(r1 - r2); ++i) path_ops += vert_move;
    for (int i = 0; i < abs(c1 - c2); ++i) path_ops += horz_move;
    return path_ops;
}

pair<string, long long> find_best_ops_for_target(const State& current_state, int tr, int tc) {
    string move_ops = get_simple_move_ops(current_state.r, current_state.c, tr, tc);
    
    string best_total_ops = move_ops;
    long long best_score_increase = -1;

    for (int i = 0; i < NUM_TRIALS; ++i) {
        string trial_ops = "";
        int temp_s = current_state.s;
        vector<vector<int>> temp_grid = current_state.grid;
        long long current_increase = 0;
        int temp_r = current_state.r;
        int temp_c = current_state.c;

        for(char op : move_ops) {
            trial_ops += op;
            if (op == 'U') temp_r--; else if (op == 'D') temp_r++;
            else if (op == 'L') temp_c--; else if (op == 'R') temp_c++;

            long long potential_increase = (long long)(temp_grid[temp_r][temp_c] ^ temp_s) - temp_grid[temp_r][temp_c];
            int dist_to_target = abs(temp_r - tr) + abs(temp_c - tc);
            if (potential_increase > 0 && current_state.ops_count + trial_ops.length() + 1 + dist_to_target <= T_CONST) {
                trial_ops += 'W';
                current_increase += potential_increase;
                temp_grid[temp_r][temp_c] ^= temp_s;
            }

            if (i > 0 && uniform_int_distribution<int>(0, 1)(rng) == 1) {
                 if (current_state.ops_count + trial_ops.length() < T_CONST) {
                    trial_ops += 'C';
                    temp_s ^= temp_grid[temp_r][temp_c];
                 }
            }
        }
        
        if (current_state.ops_count + trial_ops.length() < T_CONST) {
            long long final_increase = (long long)(temp_grid[tr][tc] ^ temp_s) - temp_grid[tr][tc];
            if (final_increase > 0) {
                 trial_ops += 'W';
                 current_increase += final_increase;
            }
        }

        if (current_increase > best_score_increase) {
            best_score_increase = current_increase;
            best_total_ops = trial_ops;
        }
    }
    
    if (best_score_increase > 0) return {best_total_ops, best_score_increase};
    else return {move_ops, -1};
}

/**
 * @brief 指定された訪問順で完全なシミュレーションを実行し、最終状態を返す
 * @param visit_order 訪問するマスの順序
 * @param initial_grid 初期盤面
 * @return State シミュレーション後の最終状態
 */
State run_simulation(vector<pair<int, int>> visit_order, const vector<vector<int>>& initial_grid) {
    State current_state(initial_grid);
    vector<pair<int, int>> postponed_queue;

    // 序盤フェーズ
    int targets_visited = 0;
    while (targets_visited < INITIAL_PHASE_TARGETS && !visit_order.empty()) {
        if (current_state.ops_count >= T_CONST) break;
        pair<int, int> target_pos = visit_order.front();
        int dist = abs(current_state.r - target_pos.first) + abs(current_state.c - target_pos.second);

        if (dist > MAX_DIST_INITIAL_PHASE) {
            postponed_queue.push_back(target_pos);
            visit_order.erase(visit_order.begin());
            continue;
        }

        pair<string, long long> result = find_best_ops_for_target(current_state, target_pos.first, target_pos.second);
        current_state.apply_ops_string(result.first);
        visit_order.erase(visit_order.begin());
        targets_visited++;
    }

    // 中盤フェーズ
    visit_order.insert(visit_order.end(), postponed_queue.begin(), postponed_queue.end());
    while (!visit_order.empty()) {
        if (current_state.ops_count >= T_CONST) break;
        pair<int, int> target_pos = visit_order.front();
        pair<string, long long> result = find_best_ops_for_target(current_state, target_pos.first, target_pos.second);
        current_state.apply_ops_string(result.first);
        visit_order.erase(visit_order.begin());
    }

    // 後半戦略
    while (true) {
        int remaining_ops = T_CONST - current_state.ops_count;
        if (remaining_ops < 2) break;

        int best_target_r = -1, best_target_c = -1;
        int min_score = -1;

        for (int r = 0; r < N_CONST; ++r) {
            for (int c = 0; c < N_CONST; ++c) {
                int dist = abs(current_state.r - r) + abs(current_state.c - c);
                if (dist == 0 || dist >= remaining_ops) continue;

                bool is_target_candidate = false;
                if (remaining_ops > MIN_DIST_FOR_TARGETING + dist + 5) {
                    if (dist >= MIN_DIST_FOR_TARGETING) is_target_candidate = true;
                } else { is_target_candidate = true; }

                if (is_target_candidate) {
                    if (best_target_r == -1 || current_state.grid[r][c] < min_score) {
                        min_score = current_state.grid[r][c];
                        best_target_r = r; best_target_c = c;
                    }
                }
            }
        }
        if (best_target_r == -1) break;
        pair<string, long long> result = find_best_ops_for_target(current_state, best_target_r, best_target_c);
        current_state.apply_ops_string(result.first);
    }
    return current_state;
}


int main() {
    ios_base::sync_with_stdio(false);
    cin.tie(NULL);
    auto start_time = chrono::steady_clock::now();

    int N_in, T_in;
    cin >> N_in >> T_in;
    vector<vector<int>> initial_grid(N_CONST, vector<int>(N_CONST));
    for (int i = 0; i < N_CONST; ++i) {
        for (int j = 0; j < N_CONST; ++j) {
            cin >> initial_grid[i][j];
        }
    }

    // --- 焼きなまし法の初期化 ---
    vector<pair<int, int>> base_order;
    for (int i = 0; i < N_CONST; ++i) {
        for (int j = 0; j < N_CONST; ++j) {
            if (i == 0 && j == 0) continue;
            base_order.push_back({i, j});
        }
    }
    shuffle(base_order.begin(), base_order.end(), rng);

    vector<pair<int, int>> current_order = base_order;
    
    State current_sim_state = run_simulation(current_order, initial_grid);
    State best_state = current_sim_state; // ★ 最良の状態を保存
    
    long long current_score = current_sim_state.calculate_total_score();
    long long best_score = current_score;

    int iteration = 0;
    int accepted_count = 0;

    cerr << "[SA] Initial Score: " << best_score << endl;

    // --- 焼きなましループ ---
    while(true) {
        auto current_time = chrono::steady_clock::now();
        double elapsed_ms = chrono::duration_cast<chrono::milliseconds>(current_time - start_time).count();
        if (elapsed_ms > TIME_LIMIT_MS) break;
        iteration++;

        vector<pair<int, int>> new_order = current_order;
        
        // 近傍操作 (2-opt or 2-swap)
        if (uniform_int_distribution<int>(0, 1)(rng) == 0) { // 2-opt
            int i = uniform_int_distribution<int>(0, new_order.size() - 1)(rng);
            int j = uniform_int_distribution<int>(0, new_order.size() - 1)(rng);
            if (i > j) swap(i, j);
            if (i != j) reverse(new_order.begin() + i, new_order.begin() + j);
        } else { // 2-swap
            int i = uniform_int_distribution<int>(0, new_order.size() - 1)(rng);
            int j = uniform_int_distribution<int>(0, new_order.size() - 1)(rng);
            if (i != j) swap(new_order[i], new_order[j]);
        }
        
        State new_state = run_simulation(new_order, initial_grid);
        long long new_score = new_state.calculate_total_score();
        
        double temp = SA_START_TEMP + (SA_END_TEMP - SA_START_TEMP) * elapsed_ms / TIME_LIMIT_MS;
        double delta = new_score - current_score;

        if (delta > 0 || (temp > 0 && uniform_real_distribution<double>(0.0, 1.0)(rng) < exp(delta / temp))) {
            current_order = new_order;
            current_score = new_score;
            current_sim_state = new_state; // 現在の状態も更新
            accepted_count++;
            if (current_score > best_score) {
                best_score = current_score;
                best_state = current_sim_state; // ★ スコアが改善したら、状態を丸ごと保存
            }
        }

        if (iteration % 10 == 0) {
            cerr << "[SA] iter:" << iteration << " time:" << (int)elapsed_ms << "ms"
                 << " temp:" << (int)temp << " score:" << current_score 
                 << " best:" << best_score << " accept_rate:" << (double)accepted_count/iteration << endl;
        }
    }

    // --- 最良解の操作ログを生成して出力 ---
    // ★ 保存しておいたベストな状態から直接ログを出力する
    for (char op : best_state.ops_log) {
        cout << op << "\n";
    }

    // --- 最終デバッグ出力 ---
    cerr << "--- Final Debug Info ---" << endl;
    cerr << "SA iterations: " << iteration << endl;
    cerr << "Best score found: " << best_score << endl;
    cerr << "Final operations count: " << best_state.ops_log.length() << "/" << T_CONST << endl;
    // ★ 検算のため、最終出力のスコアも計算して表示
    cerr << "Score from final output: " << best_state.calculate_total_score() << endl;

    return 0;
}