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

// コンパイルオプション推奨: g++ -std=c++17 -O3 -march=native -o a.out main.cpp

using namespace std;

// --- xoshiro256++ 高速乱数生成器 ---
// see: https://prng.di.unimi.it/
class Xoshiro256 {
public:
    using result_type = uint64_t;
    static constexpr result_type min() { return 0; }
    static constexpr result_type max() { return UINT64_MAX; }

    Xoshiro256() : Xoshiro256(chrono::steady_clock::now().time_since_epoch().count()) {}
    explicit Xoshiro256(uint64_t seed) {
        s[0] = split_mix64(seed);
        s[1] = split_mix64(seed);
        s[2] = split_mix64(seed);
        s[3] = split_mix64(seed);
    }

    result_type operator()() {
        const uint64_t result = rotl(s[0] + s[3], 23) + s[0];
        const uint64_t t = s[1] << 17;
        s[2] ^= s[0];
        s[3] ^= s[1];
        s[1] ^= s[2];
        s[0] ^= s[3];
        s[2] ^= t;
        s[3] = rotl(s[3], 45);
        return result;
    }

    // [0, upper_bound) の整数乱数を生成
    inline uint64_t next_int(uint64_t upper_bound) {
        return operator()() % upper_bound;
    }

    // [0.0, 1.0) の浮動小数点数乱数を生成
    inline double next_double() {
        return (operator()() >> 11) * (1.0 / (1ULL << 53));
    }

private:
    array<uint64_t, 4> s;
    static inline uint64_t rotl(const uint64_t x, int k) {
        return (x << k) | (x >> (64 - k));
    }
    static uint64_t split_mix64(uint64_t& x) {
        x += 0x9e3779b97f4a7c15;
        uint64_t z = x;
        z = (z ^ (z >> 30)) * 0xbf58476d1ce4e5b9;
        z = (z ^ (z >> 27)) * 0x94d049bb133111eb;
        return z ^ (z >> 31);
    }
};

// グローバル乱数生成器
Xoshiro256 rng;

// --- パラメータ調整欄 ---

// --- 戦略パラメータ ---
const int INITIAL_PHASE_TARGETS = 80;
const int MAX_DIST_INITIAL_PHASE = 12;
const int MIN_DIST_FOR_TARGETING = 10;

// --- 焼きなましパラメータ ---
const double TIME_LIMIT_MS = 1980.0;
const double SA_START_TEMP = 100000.0;
const double SA_END_TEMP = 1.0;

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


/**
 * @struct State
 * @brief 現在のゲームの状態を管理する構造体
 * @note スコアを差分更新するように変更
 */
struct State {
    int r, c;
    int s;
    vector<vector<int>> grid;
    int ops_count;
    string ops_log;
    long long score; // ★ スコアを差分更新

    State(const vector<vector<int>>& initial_grid) {
        r = 0; c = 0; s = 0;
        grid = initial_grid;
        ops_count = 0;
        ops_log.reserve(T_CONST + 50); // ★ 事前にメモリ確保
        score = 0;
        for(const auto& row : grid) {
            for(int cell_val : row) {
                score += cell_val;
            }
        }
    }

    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') {
            long long old_val = grid[r][c];
            grid[r][c] ^= s;
            score += (long long)grid[r][c] - old_val; // ★ スコアの差分更新
        }
        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);
        }
    }
};

// --- 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;
}

/**
 * @brief 特定のターゲット(tr, tc)への最適な操作列を見つける（修正版）
 * @param current_state 現在の状態
 * @param tr ターゲットの行
 * @param tc ターゲットの列
 * @return pair<string, long long> {操作列, スコア増加量}
 */
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 total_ops = "";
    long long total_increase = 0;

    int temp_s = current_state.s;
    vector<vector<int>> temp_grid = current_state.grid;
    int temp_r = current_state.r;
    int temp_c = current_state.c;

    // --- 経路上での操作シミュレーション ---
    for (char op : move_ops) {
        if (current_state.ops_count + total_ops.length() >= T_CONST) break;
        
        total_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_w_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_w_increase > 0 && current_state.ops_count + total_ops.length() + 1 + dist_to_target <= T_CONST) {
            total_ops += 'W';
            total_increase += potential_w_increase;
            temp_grid[temp_r][temp_c] ^= temp_s;
        }

        if (current_state.ops_count + total_ops.length() < T_CONST) {
            int s_after_copy = temp_s ^ temp_grid[temp_r][temp_c];
            int target_value = temp_grid[tr][tc];

            long long score_at_target_if_no_copy = (long long)(target_value ^ temp_s);
            long long score_at_target_if_copy = (long long)(target_value ^ s_after_copy);

            if (score_at_target_if_copy > score_at_target_if_no_copy) {
                total_ops += 'C';
                temp_s = s_after_copy;
            }
        }
    }
    
    // --- 目的地での最終的な書き込み('W') ---
    if (current_state.ops_count + total_ops.length() < T_CONST) {
        long long final_increase = (long long)(temp_grid[tr][tc] ^ temp_s) - temp_grid[tr][tc];
        if (final_increase > 0) {
            total_ops += 'W';
            total_increase += final_increase;
        }
    }

    if (total_increase > 0) {
        return {total_ops, total_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
 * @note vector::eraseの代わりにインデックスを使うように変更
 */
State run_simulation(const vector<pair<int, int>>& visit_order, const vector<vector<int>>& initial_grid) {
    State current_state(initial_grid);
    vector<pair<int, int>> postponed_queue;
    postponed_queue.reserve(N_CONST * N_CONST);

    size_t visit_idx = 0;

    // 序盤フェーズ
    int targets_visited = 0;
    while (targets_visited < INITIAL_PHASE_TARGETS && visit_idx < visit_order.size()) {
        if (current_state.ops_count >= T_CONST) break;
        pair<int, int> target_pos = visit_order[visit_idx];
        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_idx++;
            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_idx++;
        targets_visited++;
    }

    // 中盤フェーズ (残りのターゲットと後回しにしたターゲットを処理)
    vector<pair<int, int>> mid_game_order;
    mid_game_order.reserve(N_CONST * N_CONST);
    for(size_t i = visit_idx; i < visit_order.size(); ++i) mid_game_order.push_back(visit_order[i]);
    mid_game_order.insert(mid_game_order.end(), postponed_queue.begin(), postponed_queue.end());
    
    for(const auto& target_pos : mid_game_order) {
        if (current_state.ops_count >= T_CONST) break;
        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);
    }

    // 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;
    base_order.reserve(N_CONST * N_CONST - 1);
    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 best_state = run_simulation(current_order, initial_grid);
    long long best_score = best_state.score;
    long long current_score = best_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 (rng.next_int(10) == 0) { // 2-opt
            int i = rng.next_int(new_order.size());
            int j = rng.next_int(new_order.size());
            if (i == j) continue;
            if (i > j) swap(i, j);
            reverse(new_order.begin() + i, new_order.begin() + j);
        } else { // 2-swap
            int i = rng.next_int(new_order.size());
            int j = rng.next_int(new_order.size());
            if (i == j) continue;
            swap(new_order[i], new_order[j]);
        }
        
        State new_state = run_simulation(new_order, initial_grid);
        long long new_score = new_state.score; // ★ 差分更新されたスコアを使用
        
        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 && rng.next_double() < exp(delta / temp))) {
            current_order = move(new_order); // ★ moveで効率化
            current_score = new_score;
            if (current_score > best_score) {
                best_score = current_score;
                best_state = move(new_state); // ★ moveで効率化
            }
            accepted_count++;
        }

        if (iteration % 100 == 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出力 ---
    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
    State final_check_state(initial_grid);
    final_check_state.apply_ops_string(best_state.ops_log);
    cerr << "Score from final output (re-calculated): " << final_check_state.score << endl;

    return 0;
}