prompt_front: Now, answer the following question based on the text below, only give me the answer and do not output any other words.

Question: Where can users go for troubleshooting and support?

Read the following text and answer briefly.

Context 1: 'Quectel_QuecPython_BC25 开发板使用说明 版本：Quectel_QuecPython_BC25 开发板使用说明_V1 1日期：2021-11-30 状态：临时文件
Quectel_QuecPython_BC25 开发板使用说明一、基本概述BC25_QuecPython_EVB_V1 1 开发板（本文简称“V1 1 开发板”）是专门针对 BC25 制造，是一款小巧便携的“口袋型”开发板。体型虽小，但是功能丰富，拥 有 SIM 卡座、板载天线、磁开关、LED 等元件。开发者仅需一条 USB Type-C 数据线即可轻松玩转开发板。二、开发板资源Quectel 移远 BC25 通信模组NANO SIM 自弹卡座USB Type-C 数据接口开机按键，唤醒按键磁开关单色灯GPIO 排针上海市闵行区田林路 1016 号科技绿洲 3 期（B 区）5 号楼 200233 邮箱: info@quectel com 网址: www quectel com 1 / 6
三、开发板介绍Quectel_QuecPython_BC25 开发板使用说明开发板是为方便开发者使用 QuecPython，而设计的一款基于 BC25 通信模块 的开发板，其上集成了开发常用的配置，可以满足开发者的开发需求。V1 1 开发板正面接口V1 1 开发板配置开发板配备了多种外设。明细如下：序 号名称型号是否支持接口类 型1磁开关KTH1601SL-ST3是GPIO2LED 灯S3528UG6W9TLC2G- 是GPIOTJ- 34微动按键GPIOA5--------是是---------上海市闵行区田林路 1016 号科技绿洲 3 期（B 区）5 号楼 200233 邮箱: info@quectel com 网址: www quectel com 2 / 6
Quectel_QuecPython_BC25 开发板使用说明四、功能详解4 1 磁开关开发板集成了一个磁开关。使用磁铁靠近，可使磁开关输出引脚变为低电平， 默认为高电平。4 2 LED 灯开发板集成了一颗高亮度灯珠，可以用来做显著指示灯。上海市闵行区田林路 1016 号科技绿洲 3 期（B 区）5 号楼 200233 邮箱: info@quectel com 网址: www quectel com 3 / 6
4 3 按键开发板集成了 2 个微动按键，其功能是 S1 为开机键，S2 为睡眠唤醒按键。Quectel_QuecPython_BC25 开发板使用说明五、调试步骤1 拿到开发板 V1 1 先插上 USB 安装串口驱动，在官方 QQ 群文件搜 CP210 或者自 行百度下载 CP210x 的串口芯片驱动进行安装。2 使用串口工具（例如 QCOM_V1 6）连接 BC25 的主串口（硬件 17、18 脚）。V1 1 选择 Enhanced COM 口，波特率选择 9600,打开串口，按下 PWK 键约一秒松开进 行开机，串口工具收到消息则代表开机成 功，然后按下 EINT 键串口显示 +QATWAKEUP 表示模组唤醒了。3 从 https://python quectel com/download 下载 BC25QuecPython 版本固件， 使用 Qflash（群文件下载）选择 BC25 的调试串口（硬件 38、39 脚），波特率选 择 921600，选择 lod 后缀的固件，按下 EINT 键串口工具显示模组已经唤醒串口 工具发 AT+QSCLK=0 可关闭睡眠（不会发 AT 则多按几次 EINT 键），点击 Start 开 始下载固件,下载进度条开始下载，等待下载完成。关闭以上所有工具，并给板 子断电重新上电。4 从 https://python quectel com/download 下载 QPYCOM 工具，直接解压运行 工具，选择主串口（同第 2 步），选择 57600 波特率，打开串口。再按 PWK 按键 进行开机，会看到 QPYCOM 有打印 mount Type "help()" for more information 然后就可以进行 QuecPython 的交互调 试了。上海市闵行区田林路 1016 号科技绿洲 3 期（B 区）5 号楼 200233 邮箱: info@quectel com 网址: www quectel com 4 / 6
六、常见问题解决Quectel_QuecPython_BC25 开发板使用说明Q：模块的固件在哪？A：请登录 QuecPython 网站下载：http://python quectel com/downloadQ：哪里有开发板和其他常用资料？A：请登录 QuecPython 网站下载：http://python quectel com/downloadP S  如果您遇到任何问题，请参照本官网在线文档进行解决或访问 QuecPython 社区进行搜索、交流、提问：QuecPython 社区或者联系我们的在线支持：QQ 群 445121768获取 QuecPython 开发固件及加入官方交流群官网主页：https://python quectel com官网文件下载（各类资料、工具）：https://python quectel com/download官网 wiki（常用于视频教程、手把手教程下载、API 库）：https://python quectel com/wiki/#/官网文档中心(拥有从入门到精通的各种文档介绍、必看)：https://python quectel com/doc/工单系统：https://workorder quectel com/QuecPython 社区：https://forumschinese quectel com/c/function-subjects/quectpython/43QuecPython 官方 QQ 开发交流群：445121768微信公众号：QuecPython移远 OTA 升级平台： https://cloudota quectel com/移远 IoT 管理平台：https://python quectel com/doc/doc/Advanced_development/zh/QuecPython Cloud/QuecCloud html上海市闵行区田林路 1016 号科技绿洲 3 期（B 区）5 号楼 200233 邮箱: info@quectel com 网址: www quectel com 5 / 6
附录 1 V1 1 开发板丝印图Quectel_QuecPython_BC25 开发板使用说明附录 2 V1 1 开发板原理图上海市闵行区田林路 1016 号科技绿洲 3 期（B 区）5 号楼 200233 邮箱: info@quectel com 网址: www quectel com 6 / 6
PIU101 PIU102 PIU103 PIU104 PIU105 PIU106 PIU107 PIU108 COR9 PIR902 PIR901 PIU109 PIU1010 PIU1011 PIU1012 PIU1013 PIU1044 PIU1043 PIU1042 PIU1041 PIU1040 PIU1039 PIU1038 PIU1037 PIU1036 COU1A PIU1014 PIU1015 PIU1016 PIU1017 PIU1018 PIU1019 PIU1020 PIU1021 PIU1022 PIU1035 PIU1034 PIU1033 PIU1032 PIU1031 PIU1030 PIU1029 PIU1028 PIU1027 PIU1026 PIU1025 PIU1024 PIU1023 COJ1 PIJ101 COC1 PIC101 PIC102 COC2 PIC201 PIC202 PIC602 COC6 PIC601 COR22 PIR2201 PIR2301 COR23 COR24 PIR2401 PIR2501 COR25 COR14 PIR1401 PIR1601 COR16 PIR2202 PIR2302 PIR2402 PIR2502 PIR1402 PIR1602 COC14 PIC1402 PIC1401 COR33 PIR3301 PIR3302 COU2 PIU201 PIU202 PIU203 PIR3002 COR30 COD6 PIR3001 PID601 PID602 COR31 PIR3102 PIR3101 PIQ201 PIQ203 COQ2 COR32 PIQ202 PIR3201 PIR3202 COR1 COD1 PIR102 PIR101 PID101 PID102 PIQ103 COQ1 PIQ102 COR13 PIR1302 PIR1301 PIQ101 PIR1501 COR15 PIR1502 PIR302 COR3 PIR301 PIR402 COR4 PIR401 PIU1045 PIU1046 PIU1047 PIU1048 PIU1049 PIU1050 PIU1051 PIU1052 PIU1053 COR19 PIR1902 PIR1901 COR20 PIR2002 PIR2001 PIU1072 PIU1071 PIU1070 PIU1069 PIU1068 COU1B PIU1054 PIU1055 PIU1056 PIU1057 PIU1058 PIU1067 PIU1066 PIU1065 PIU1064 PIU1063 PIU1062 PIU1061 PIU1060 PIU1059 PIU1073 PIU1074 PIU1075 PIU1076 PIU1077 PIU1078 PIU1079 PIU1080 COU1C PIU1088 PIU1087 PIU1086 PIU1085 PIU1084 PIU1083 PIU1082 PIU1081 PIU1089 PIU1090 PIU1091 COU1D PIU1094 PIU1093 PIU1092 COM2 COM1 1122334455667788DDCCBBAATitleNumberRevisionSizeA3Date:2021/11/1Sheet ofFile:E:\\\\  \\1 BC25 SchDocDrawn By:1J1ADCR44 7KR34 7KADC_INGNDQUECTEL_LOGOQuecPythonGNDC1100uF 6 3VGNDAUX_TXD_1V8AUX_RXD_1V8GNDUSIM1_VDDRESETNETLIGHTM_RXD_1V8M_TXD_1V8PIN19PIN20VDD_EXTPIN23PIN22PIN21R234 7KR224 7KVDD_EXTR254 7KR244 7KPIN20PIN21PIN23PIN22PIN25PIN30PIN31PIN32PIN33GNDR14 7K312Q1D1蓝 LEDNETR130RR15NCNETLIGHTC61uFGNDGND1RESERVED2MIC_P3MIC_N4SPK_P5SPK_N6PWRKEY7RESERVED8RESERVED9GND10USIM_DATA11USIM_RST12USIM_CLK13USIM_VDD14RESET_N15NET_STATUS16MAIN_RXD17MAIN_TXD18MAIN_DTR19MAIN_RI20MAIN_DCD21MAIN_CTS22MAIN_RTS23VDD_EXT24STATUS25RESERVED26GND27AUX_RXD28AUX_TXD29PCM_CLK30PCM_SYNC31PCM_DIN32PCM_DOUT33GND34ANT_MAIN35GND36GND37DBG_RXD38DBG_TXD39GND40GND41VBAT42VBAT43RESERVED44U1ABC25/EC800NGND45GND46GND47GND48RESERVED49RESERVED50RESERVED51RESERVED52RESERVED53RESERVED54RESERVED55RESERVED56RESERVED57RESERVED58USB_DP59USB_DM60USB_VBUS61RESERVED62RESERVED63RESERVED64RESERVED65I2C_SDA66I2C_SCL67RESERVED68RESERVED69GND70GND71GND72U1BEC800NGND73RESERVED74RESERVED75RESERVED76RESERVED77RESERVED78USIM_DET79RESERVED80RESERVED81USB_BOOT82RESERVED83RESERVED84RESERVED85RESERVED86RESERVED87GND88U1CEC800NGND89GND90GND91GND92GND93GND94U1DEC800NGNDGNDUSIM_DETUSB_BOOTGNDVBUSDM_EC800NDP_EC800NGNDGNDPIN3POWRKEYUSIM1_CLKUSIM1_RSTUSIM1_DATAGNDGNDD_RXD_1V8D_TXD_1V8C2100uF 6 3V+3 8VR90RADCI2C_SCL_EC800NI2C_SDA_EC800NR164 7KR144 7KI2C_SDA_EC800NI2C_SCL_EC800N+3 8VPIN4PIN5PIN6R190RR200RDM_EC800NDP_EC800NUSB_DMUSB_DP+3 8VGNDR304 7K312Q2D6翠绿灯珠NETR310RR32NCPIN30+3 8VGND3OUTPUT2VCC1U2KTH1601SL-ST3VCC_1V8C141uFGNDGNDR3310KVCC_1V8PIN31磁性开关灯珠EC800N焊接R19、R20电源部分请参考官方设计BC25不焊接
COC9 PIC902 PIC901 COU3 PIU301 PIU302 PIU303 PIU306 PIU305 PIU304 COR7 PIR702 PIR701 COL1 PIL101 PIL102 PIC701 PIC702 COC7 COD2 PID202 PID201 COC10 PIC1001 PIC1002 PIC1201 PIC1202 COC12 COR8 PIR802 PIR801 PID501 PID502 COD5 COU6 PIU601 PIU602 PIU603 PIU605 PIU604 PIC1102 COC11 PIC1101 PIC802 COC8 PIC801 COR21 PIR2101 PIR2102 COUSBC1 PIUSBC100 PIUSBC10A12 PIUSBC10A9 PIUSBC10A8 PIUSBC10A7 PIUSBC10A6 PIUSBC10A5 PIUSBC10A4 PIUSBC10A1 PIUSBC10B1 PIUSBC10B4 PIUSBC10B5 PIUSBC10B6 PIUSBC10B7 PIUSBC10B8 PIUSBC10B9 PIUSBC10B12 COR10 PIR1002 PIR1001 COR11 PIC1301 PIC1302 COC13 PIR1101 PIR1102 COD4 PID401 PID402 COD3 PID301 PID302 COD7 PID701 PID702 11223344DDCCBBAATitleNumberRevisionSizeA4Date:2021/11/1Sheet ofFile:E:\\\\  \\2 POWER SchDocDrawn By:type-CDCDCGNDGNDB1VBUSB4CC2B5DP2B6DN2B7SBU2B8VBUSB9GNDB1200000000GNDA1VBUSA4CC1A5DP1A6DN1A7SBU1A8VBUSA9GNDA12USBC1USB3 1C16PFSMTGNDGNDUSB_DMUSB_DMUSB_DPUSB_DPVBUSVBUSVBUSVBUSD3ESD9L5 0ST5GD4ESD9L5 0ST5GD2SMBJ6 5CAGND1SW2VIN3VFB4EN5VBST6U3TPS563201DDCRGND2 2uHL1WPN4020H2R2MTC90 1uFR710KC120 1uFGNDGND+5V+3 8V+5VR1110KR1040 2KC130 1uFGND+3 8VD54 7KR8GND+3 8VC722uF 10VC1022uF 10VVCC_1V8C84 7uFR2110KVIN1GND2EN3NC4VOUT5U6ME6212C18M5GGNDGNDC114 7uFGND+5VD7SS34VBUS+5V
COC3 PIC301 PIC302 COCARD1 PICARD10C1 PICARD10C2 PICARD10C3 PICARD108 PICARD109 PICARD1010 PICARD1011 PICARD10C5 PICARD10C6 PICARD10C7 PICARD10CD PIR1202 COR12 PIR1201 PIU501 PIU503 PIU504 PIU505 PIU506 COU5 PIU502 11223344DDCCBBAATitleNumberRevisionSizeA4Date:2021/11/1Sheet ofFile:E:\\\\  \\4 SIM-CARD SchDocDrawn By:123456U5USIM1_VDDUSIM1_RSTUSIM1_CLKUSIM1_DATAGND10KR12USIMGNDVCCC1RSTC2CLKC3I/OC7VPPC6GNDC5CDCDEP8EP9EP10EP11CARD1SMN-303GNDC30 1uFUSIM_DET
COJ5 PIJ501 PIJ502 PIJ503 PIJ504 PIJ505 PIJ506 PIJ507 PIJ508 PIJ509 PIJ5010 PIJ5011 PIJ5012 PIJ5013 PIJ5014 PIJ5015 COJ6 PIJ601 PIJ602 PIJ603 PIJ604 PIJ605 PIJ606 PIJ607 PIJ608 PIJ609 PIJ6010 PIJ6011 PIJ6012 PIJ6013 PIJ6014 PIJ6015 COU4 PIU409 PIR501 PIR502 COR5 COS1 PIS101 PIS102 COR17 COS2 PIR1702 PIR1701 PIS201 PIS202 COR18 PIR1802 PIR1801 COR2 PIR201 PIR202 PIC402 COC4 PIC401 PIC502 COC5 PIC501 PIR602 COR6 PIU405 PIR601 PIU406 PIC1702 PIC1701 COC17 PIU407 PIU402 PIU408 PIU403 PIU404 PIU401 PIU4024 PIU4023 PIU4022 PIU4021 PIU4020 PIU4019 PIU4018 PIU4017 PIU4016 PIU4015 PIU4014 PIU4013 PIU4012 PIU4011 PIU4010 PIU400 COR26 COR27 PIR2602 PIR2702 PIR2601 PIR2701 COR28 COR29 PIR2802 PIR2902 PIR2801 PIR2901 11223344DDCCBBAATitleNumberRevisionSizeA4Date:2021/11/1Sheet ofFile:E:\\\\  \\6 GPIO+UART SchDocDrawn By:GPIOAUX_RXD_1V8AUX_TXD_1V8GNDD_TXD_1V8D_RXD_1V8S1S2GNDVDD_EXTPOWRKEYPIN19VBUSRI_SCI1GND2D+3D-4VIO5VDD6REGIN7VBUS8-RST9CTS_ECI10RTS_ECI11RXD_ECI12TXD_ECI13GPIO 1_ECI14GPIO 0_ECI15NC16RI_ECI17CTS_SCI18RTS_SCI19RXD_SCI20TXD_SCI21GPIO 2_SCI22GPIO 1_SCI23GPIO 0_SCI24GND0U4CP2105GND1uFC17R5NC1uFC4C50 1uFGNDGNDR6NCR20RADC_INM_TXD_1V8M_RXD_1V8PIN19PIN25PIN33PIN30PIN31PIN32USB_DMUSB_DPM_RXD_1V8M_TXD_1V8R260RR270RR280RR290RD_RXD_1V8D_TXD_1V8PIN3PIN4PIN5PIN6PIN20PIN21PIN22PIN23123456789101112131415J5Header 15123456789101112131415J6Header 15R170RR180RUSB_BOOTI2C_SCL_EC800NI2C_SDA_EC800N+3 8VRESETGNDVCC_1V8VCC_1V8+5VEC800N不焊接CP2105
'
Context 2: Paper Info

Title: Bistability between π-diradical open-shell and closed-shell states in indeno[1,2-a]fluorene
Publish Date: Unkown
Author List: Shantanu Mishra (from IBM Research Europe -Zurich), Manuel Vilas-Varela (from Department of Organic Chemistry, Center for Research in Biological Chemistry and Molecular Materials (CiQUS), University of Santiago de Compostela), Leonard-Alexander Lieske (from IBM Research Europe -Zurich), Ricardo Ortiz (from Donostia International Physics Center (DIPC)), Igor Rončević (from Department of Chemistry, University of Oxford), Florian Albrecht (from IBM Research Europe -Zurich), Diego Peña (from Department of Organic Chemistry, Center for Research in Biological Chemistry and Molecular Materials (CiQUS), University of Santiago de Compostela), Leo Gross (from IBM Research Europe -Zurich)

Figure

Fig  1 | Non-benzenoid non-alternant polycyclic conjugated hydrocarbons a, Classical nonbenzenoid non-alternant polycyclic conjugated hydrocarbons: pentalene, azulene and heptalene b, Generation of indacenes and indenoindenes through benzinterposition and benzannelation of pentalene, respectively Gray filled rings represent Clar sextets c, Closed-shell Kekulé (left) and openshell non-Kekulé (right) resonance structures of QDMs Note that meta-QDM is a non-Kekulé molecule All indenofluorene isomers, being derived through benzannelation of indacenes, contain a central QDM moiety d, Closed-shell Kekulé (top) and open-shell non-Kekulé (bottom) resonance structures of indenofluorenes Compared to their closed-shell structures, 1 and 5 gain two Clar sextets in the openshell structure, while 2-4 gain only one Clar sextet in the open-shell structure Colored bonds in d highlight the ortho-and para-QDM moieties in the two closed-shell Kekulé structures of 5  e, Scheme of on-surface generation of 5 by voltage pulse-induced dehydrogenation of 6 (C20H14) Structures 7 and 8 represent the two monoradical species (C20H13) 
Fig  2 | Characterization of open-shell indeno[1,2-a]fluorene on bilayer NaCl/Au(111) a, DFTcalculated wave functions of the frontier orbitals of 5OS in the triplet configuration for the spin up (occupied) level (isovalue: 0 002 e -Å -3 ) Blue and red colors represent opposite phases of the wave function b, Corresponding DFT-calculated spin density of 5OS (isovalue: 0 01 e -Å -3) Blue and orange colors represent spin up and spin down densities, respectively c, Probability density of the SOMOs of 5OS (isovalue: 0 001 e -Å -3 ) d, DFT-calculated bond lengths of 5OS e, Constant-height I(V) spectra acquired on a species of 5 assigned as 5OS, along with the corresponding dI/dV(V) spectra Open feedback parameters: V = -2 V, I = 0 17 pA (negative bias side) and V = 2 V, I = 0 17 pA (positive bias side) Acquisition position of the spectra is shown in Supplementary Fig 7 f, Scheme of many-body transitions associated to the measured ionic resonances of 5OS Also shown are STM images of assigned 5OS at biases where the corresponding transitions become accessible Scanning parameters: I = 0 3 pA (V = -1 2V and -1 5 V) and 0 2 pA (V = 1 3 V and 1 6 V)  g, Laplace-filtered AFM image of assigned 5OS STM set point: V = 0 2 V, I = 0 5 pA on bilayer NaCl, Δz = -0 3Å The tip-height offset Δz for each panel is provided with respect to the STM setpoint, and positive (negative) values of Δz denote tip approach (retraction) from the STM setpoint f and g show the same molecule at the same adsorption site, which is next to a trilayer NaCl island The bright and dark features in the trilayer NaCl island in g correspond to Cl -and Na + ions, respectively Scale bars: 10 Å (f) and 5 Å (g) 
Fig  3 | Characterization of closed-shell indeno[1,2-a]fluorene on bilayer NaCl/Au(111) a, DFTcalculated wave functions of the frontier orbitals of closed-shell 5 0 (isovalue: 0 002 e -Å -3 ) The wave functions shown here are calculated for the 5para geometry b, DFT-calculated bond lengths of 5ortho (top) and 5para (bottom) c, Constant-height I(V) spectra acquired on a species of 5 assigned as 5para, along with the corresponding dI/dV(V) spectra Open feedback parameters: V = -2 V, I = 0 15 pA (negative bias side) and V = 2 2 V, I = 0 15 pA (positive bias side) Acquisition position of the spectra is shown in Supplementary Fig  7  d, Scheme of many-body transitions associated to the measured ionic resonances of 5para Also shown are STM images of assigned 5para at biases where the corresponding transitions become accessible Scanning parameters: I = 0 15 pA (V = -1 5 V) and 0 2 pA (V = 1 7 V)  e, Laplace-filtered AFM image of assigned 5para STM set point: V = 0 2 V, I = 0 5 pA on bilayer NaCl, Δz = -0 7 Å  f, Selected bonds labeled for highlighting bond order differences between 5para and 5ortho For the bond pairs a/b, c/d and e/f, the bonds labeled in bold exhibit a higher bond order than their neighboring labeled bonds in 5para g, Laplace-filtered AFM images of 5 on bilayer NaCl/Cu(111) showing switching between 5OS and 5para as the molecule changes its adsorption position The faint protrusion adjacent to 5 is a defect that stabilizes the adsorption of 5  STM set point: V = 0 2 V, I = 0 5 pA on bilayer NaCl, Δz = -0 3Å  STM and STS data in c and d are acquired on the same species, while the AFM data in e is acquired on a different species Scale bars: 10 Å (d) and 5 Å (e,g) 
NMR (300 MHz, CDCl3) δ: 7 51 (m, 2H), 7 40 -7 28 (m, 5H), 7 27 -7 20 (m, 2H), 7 13 (d, J = 7 7 Hz, 1H), 2 07 (s, 3H), 1 77 (s, 3H) ppm  13C NMR-DEPT (75 MHz, CDCl3, 1:1 mixture of atropisomers) δ: 141 2 (C), 141 1 (C), 140 0 (C), 139 4 (2C), 137 5 (C), 137 4 (C), 136 0 (3C), 134 8 (C), 134 5 (C), 134 1 (C), 134 0 (C), 133 7 (C), 133 6 (C), 131 6 (CH), 131 2 (CH), 131 1 (CH), 130 7 (CH), 129 8 (CH), 129 7 (CH), 129 5 (CH), 129 4 (CH), 129 0 (CH), 128 9 (CH), 128 7 (2CH), 128 6 (2CH), 127 2 (CH), 127 1 (CH), 127 0 (CH), 126 9 (CH), 126 7 (CH), 126 6 (CH), 20 6 (CH3), 20 5 (CH3), 17 7 (CH3), 17 5 (CH3) ppm MS (APCI) m/z (%): 327 (M+1, 100) HRMS: C20H16Cl2; calculated: 327 0702, found: 327 0709 
NMR (500 MHz, CDCl3) δ: 7 93 (d, J = 7 6 Hz, 1H), 7 85 (d, J = 7 5 Hz, 1H), 7 78 (d, J = 7 7 Hz, 1H), 7 65 (d, J = 7 4 Hz, 1H), 7 61 (d, J = 7 5 Hz, 1H), 7 59 (d, J = 7 7 Hz, 1H), 7 47 (ddd, J = 8 4, 7 2, 1 1 Hz, 1H), 7 42 (dd, J = 8 1, 7 0 Hz, 1H), 7 35 (m, 2H), 4 22 (s, 3H), 4 02 (s, 3H) ppm  13C NMR-DEPT (125 MHz, CDCl3) δ: 144 1 (C), 143 3 (C), 142 3 (C), 141 9 (C), 141 8 (C), 141 2 (C), 138 2 (C), 136 5 (C), 127 0 (CH), 126 9 (CH), 126 7 (CH), 126 6 (CH), 125 3 (CH), 125 2 (CH), 123 6 (CH), 122 2 (CH), 119 9 (CH), 118 4 (CH), 37 4 (CH2), 36 3 (CH2) ppm MS (APCI) m/z (%): 254 (M+, 88) HRMS: C20H14; calculated: 254 1090, found: 254 1090 

abstract

Indenofluorenes are non-benzenoid conjugated hydrocarbons that have received great interest owing to their unusual electronic structure and potential applications in nonlinear optics and photovoltaics  Here, we report the generation of unsubstituted indeno[1,2-a]fluorene, the final and yet unreported parent indenofluorene regioisomer, on various surfaces by cleavage of two C-H bonds in 7,12-dihydro indeno[1,2-a]fluorene through voltage pulses applied by the tip of a combined scanning tunneling microscope and atomic force microscope 
On bilayer NaCl on Au(111), indeno[1,2a]fluorene is in the neutral charge state, while it exhibits charge bistability between neutral and anionic states on the lower work function surfaces of bilayer NaCl on Ag(111) and Cu(111)  In the neutral state, indeno[1,2-a]fluorene exhibits either of two ground states: an open-shell π-diradical state, predicted to be a triplet by density functional and multireference many-body perturbation theory calculations, or a closedshell state with a para-quinodimethane moiety in the as-indacene core 
Switching between open-and closed-shell states of a single molecule is observed by changing its adsorption site on NaCl  The inclusion of non-benzenoid carbocyclic rings is a viable route to tune the physicochemical properties of polycyclic conjugated hydrocarbons (PCHs)   Non-benzenoid polycycles may lead to local changes in strain, conjugation, aromaticity, and, relevant to the context of the present work, induce an open-shell ground state of the corresponding PCHs  
Many nonbenzenoid PCHs are also non-alternant, where the presence of odd-membered polycycles breaks the bipartite symmetry of the molecular network   Figure shows classical examples of non-benzenoid non-alternant PCHs, namely, pentalene, azulene and heptalene  Whereas azulene is a stable PCH exhibiting Hückel aromaticity ([4n+2] π-electrons, n = 2), pentalene and heptalene are unstable Hückel antiaromatic compounds with [4n] π-electrons, n = 2 (pentalene) and n = 3 (heptalene) 
Benzinterposition of pentalene generates indacenes, consisting of two isomers s-indacene and as-indacene (Fig  )  Apart from being antiaromatic, indacenes also contain proaromatic quinodimethane (QDM) moieties (Fig  ) , which endows them with potential open-shell character  While the parent s-indacene and asindacene have never been isolated, stable derivatives of s-indacene bearing bulky substituents have been synthesized  
A feasible strategy to isolate congeners of otherwise unstable non-benzenoid non-alternant PCHs is through fusion of benzenoid rings at the ends of the π-system, that is, benzannelation  For example, while the parent pentalene is unstable, the benzannelated congener indeno[2,1-a]indene is stable under ambient conditions (Fig  )  
However, the position of benzannelation is crucial for stability: although indeno[2,1a]indene is stable, its regioisomer indeno[1,2-a]indene (Fig  ) oxidizes under ambient conditions   Similarly, benzannelation of indacenes gives rise to the family of PCHs known as indenofluorenes (Fig  ), which constitute the topic of the present work 
Depending on the benzannelation position and the indacene core, five regioisomers can be constructed, namely, indeno [ Practical interest in indenofluorenes stems from their low frontier orbital gap and excellent electrochemical characteristics that render them as useful components in organic electronic devices  
The potential open-shell character of indenofluorenes has led to several theoretical studies on their use as non-linear optical materials and as candidates for singlet fission in organic photovoltaics   Recent theoretical work has also shown that indenofluorene-based ladder polymers may exhibit fractionalized excitations 
Fundamentally, indenofluorenes represent model systems to study the interplay between aromaticity and magnetism at the molecular scale   Motivated by many of these prospects, the last decade has witnessed intensive synthetic efforts toward the realization of indenofluorenes  Derivatives of 1-4 have been realized in solution , while 1-3 have also been synthesized on surfaces and characterized using scanning tunneling microscopy (STM) and atomic force microscopy (AFM), which provide information on molecular orbital densities , molecular structure and oxidation state  
With regards to the open-shell character of indenofluorenes, 2-4 are theoretically and experimentally interpreted to be closed-shell, while calculations indicate that 1 and 5 should exhibit open-shell ground states   Bulk characterization of mesitylsubstituted 1, including X-ray crystallography, temperature-dependent NMR, and electron spin resonance spectroscopy, provided indications of its open-shell ground state  
Electronic characterization of 1 on Au(111) surface using scanning tunneling spectroscopy (STS) revealed a low electronic gap of 0 4 eV (ref  )  However, no experimental proof of an openshell ground state of 1 on Au(111), such as detection of singly occupied molecular orbitals (SOMOs) or spin excitations and correlations due to unpaired electrons , was shown 
In this work, we report the generation and characterization of unsubstituted 5  Our research is motivated by theoretical calculations that indicate 5 to exhibit the largest diradical character among all indenofluorene isomers   The same calculations also predict that 5 should possess a triplet ground state 
Therefore, 5 would qualify as a Kekulé triplet, of which only a handful of examples exist   However, definitive synthesis of 5 has never been reported so far  Previously, Dressler et al  reported transient isolation of mesityl-substituted 5, where it decomposed both in the solution and in solid state , and only the structural proof of the corresponding dianion was obtained 
On-surface generation of a derivative of 5, starting from truxene as a precursor, was recently reported   STM data on this compound, containing the indeno[1,2-a]fluorene moiety as part of a larger PCH, was interpreted to indicate its open-shell ground state  However, the results did not imply the ground state of unsubstituted 5  Here, we show that on insulating surfaces 5 can exhibit either of two ground states: an open-shell or a closed-shell 
We infer the existence of these two ground states based on high-resolution AFM imaging with bond-order discrimination and STM imaging of molecular orbital densities   AFM imaging reveals molecules with two different geometries  Characteristic bond-order differences in the two geometries concur with the geometry of either an open-or a closed-shell state 
Concurrently, STM images at ionic resonances show molecular orbital densities corresponding to SOMOs for the open-shell geometry, but orbital densities of the highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) for the closed-shell geometry  Our experimental results are in good agreement with density functional theory (DFT) and multireference perturbation theory calculations 
Finally, we observe switching between open-and closed-shell states of a single molecule by changing its adsorption site on the surface  Synthetic strategy toward indeno[1,2-a]fluorene  The generation of 5 relies on the solution-phase synthesis of the precursor 7,12-dihydro indeno[1,2-a]fluorene (6)  Details on synthesis and characterization of 6 are reported in Supplementary Figs 
  Single molecules of 6 are deposited on coinage metal (Au(111), Ag(111) and Cu(111)) or insulator surfaces  In our work, insulating surfaces correspond to two monolayer-thick (denoted as bilayer) NaCl on coinage metal surfaces
Context 3: Paper Info

Title: Two-stage Pipeline for Multilingual Dialect Detection
Publish Date: Unkown
Author List: Ankit Vaidya (from Pune Institute of Computer Technology), Aditya Kane (from Pune Institute of Computer Technology)

Figure

Figure 1: Class distribution of dialects
Figure 2: System diagram for dialect classification The LID classifies the input into one of 3 languages The sample is then further classified into dialects by language specific models 
Figure 3: Confusion matrix of 9-way classification Note that rows are normalized according to the number of samples is that class 
Our complete results for Track-1 using the two-stage dialect detection pipeline Model-* denotes the language of the models used for the experiments 
Performance on Track-1 validation dataset of individual models used in the two-stage pipeline "Lg" stands for language of the model used 
Comparative results of two-way classification using the finetuned (F T ) predictions and predictions adapted from three-way classification models 

abstract

Dialect Identification is a crucial task for localizing various Large Language Models  This paper outlines our approach to the VarDial 2023 DSL-TL shared task  Here we have to identify three or two dialects from three languages each which results in a 9-way classification for Track-1 and 6-way classification for Track-2 respectively 
Our proposed approach consists of a two-stage system and outperforms other participants' systems and previous works in this domain  We achieve a score of 58 54% for Track-1 and 85 61% for Track-2  Our codebase is available publicly 1  

Introduction

Language has been the primary mode of communication for humans since the pre-historic ages  Studies have explored the evolution of language and outlined mathematical models that govern the intricacies of natural language   Inevitably, as humans established civilization in various parts of the world, this language was modified by, and for the group of people occupied by that particular geographical region 
This gave rise to multiple national dialects of the same language  The VarDial workshop (colocated with EACL 2023) explores various dialects and variations of the same language  We participated in the Discriminating Between Similar Languages -True Labels (DSL-TL) shared task  In this task, the participants were provided with data from three languages, with each language having three varieties 
This shared task consisted of two tracks -Track-1 featuring nine-way classification and Track-2 featuring six-way classification  The second track included two particular national dialects of each language (eg  American English and British English), and the first track had one general  We ranked 1 st in both of the tracks 
Moreover, we beat the next best submission by a margin of 4 5% in the first task and 5 6% in the second task We were the only team to surpass the organizer baseline scores  We present our winning solution in this paper  We used an end-to-end deep learning pipeline which consisted of a language identification model and three language-specific models, one for each language 
We converged upon the best combination by doing an elaborate analysis of various models available  Furthermore, in this work we also analyze the performance of the pipeline as a whole and also provide an ablation study  Lastly, we provide some future directions in this area of research 

Related Work

The present literature encompasses various aspects of dialect identification  We study this from three perspectives: large language models, language identification and dialect classification problems 

Large Language Models

The success of transformers and BERT based models was inevitable since the initial boom of the transformer  2017) model  In recent years, many other architectures like RoBERTa and ELECTRA have further pushed the state-of-the-art in this domain  Moreover, autoregressive models like GPT and GPT-2 have also shown their prowess 
Multilingual versions of RoBERTA, namely XLM-RoBERTa are also available  Lastly, language specific models like Spanish BERT (la Rosa y Eduardo G  Ponferrada y Manu Romero y Paulo Villegas y Pablo González de Prado Salas y María Grandury, 2022) and Portuguese BERT are available as well  Our winning solution makes use of these large language models trained on specific languages 

Language Identification Models

Many multilingual language identification models have been developed in order to classify the language of the input sentence beforehand  Even though the initial works used n-gram models and generative mixture models or even conditional random fields and other classical machine learning methods like naive bayes , modern methods have shifted to the use of deep learning for language identification 



ground_truth: Online documentation, QuecPython community, online support: QQ group 445121768.

prompt_front: Now, answer the following question based on the text below, only give me the answer and do not output any other words.

Question: Can individual molecules of indeno[1,2-a]fluorene switch between open-shell and closed-shell states?

Read the following text and answer briefly.

Context 2:  Voltage pulses ranging between 4-6 V are applied by the tip of a combined STM/AFM system, which result in cleavage of one C-H bond at each of the pentagonal apices of 6, thereby leading to the generation of 5 (Fig  ) 
In the main text, we focus on the generation and characterization of 5 on insulating surfaces  Generation and characterization of 5 on coinage metal surfaces is shown in Supplementary Fig    )  Blue and orange colors represent spin up and spin down densities, respectively  c, Probability density of the SOMOs of 5OS (isovalue: 0 001 e -Å -3 ) 
d, DFT-calculated bond lengths of 5OS  e, Constant-height I(V) spectra acquired on a species of 5 assigned as 5OS, along with the corresponding dI/dV(V) spectra  Open feedback parameters: V = -2 V, I = 0 17 pA (negative bias side) and V = 2 V, I = 0 17 pA (positive bias side)  Acquisition position of the spectra is shown in Supplementary Fig    f, Scheme of many-body transitions associated to the measured ionic resonances of 5OS 
Also shown are STM images of assigned 5OS at biases where the corresponding transitions become accessible  Scanning parameters: I = 0 3 pA (V = -1 2 V and -1 5 V) and 0 2 pA (V = 1 3 V and 1 6 V)  g, Laplace-filtered AFM image of assigned 5OS  STM set point: V = 0 2 V, I = 0 5 pA on bilayer NaCl, Δz = -0 3
Å  The tip-height offset Δz for each panel is provided with respect to the STM setpoint, and positive (negative) values of Δz denote tip approach (retraction) from the STM setpoint  f and g show the same molecule at the same adsorption site, which is next to a trilayer NaCl island  The bright and dark features in the trilayer NaCl island in g correspond to Cl -and Na + ions, respectively 
Scale bars: 10 Å (f) and 5 Å (g)  To experimentally explore the electronic structure of 5, we used bilayer NaCl films on coinage metal surfaces to electronically decouple the molecule from the metal surfaces  Before presenting the experimental findings, we summarize the results of our theoretical calculations performed on 5 in the neutral charge state (denoted as 5 0 ) 
We start by performing DFT calculations on 5 0 in the gas phase  Geometry optimization performed at the spin-unrestricted UB3LYP/6-31G level of theory leads to one local minimum, 5OS, the geometry of which corresponds to the open-shell resonance structure of 5 (Fig  , the label OS denotes open-shell) 
The triplet electronic configuration of 5OS is the lowest-energy state, with the openshell singlet configuration 90 meV higher in energy  Geometry optimization performed at the restricted closed-shell RB3LYP/6-31G level reveals two local minima, 5para and 5ortho, the geometries of which (Fig  ) exhibit bond length alternations in line with the presence of a para-or an ortho-QDM moiety, respectively, in the as-indacene core of the closed-shell resonance structures of 5 (Fig  )  
Relative to 5OS in the triplet configuration, 5para and 5ortho are 0 40 and 0 43 eV higher in energy, respectively  Additional DFT results are shown in Supplementary Fig    To gain more accurate insights into the theoretical electronic structure of 5, we performed multireference perturbation theory calculations (Supplementary Fig  ) based on quasi-degenerate second-order n-electron valence state perturbation theory (QD-NEVPT2) 
In so far as the order of the ground and excited states are concerned, the results of QD-NEVPT2 calculations qualitatively match with DFT calculations  For 5OS, the triplet configuration remains the lowest-energy state, with the open-shell singlet configuration 60 meV higher in energy  The energy differences between the open-and closed-shell states are substantially reduced in QD-NEVPT2 calculations, with 5para and 5ortho only 0 11 and 0 21 eV higher in energy, respectively, compared to 5OS in the triplet configuration 
We also performed nucleus-independent chemical shift calculations to probe local aromaticity of 5 in the openand closed-shell states  While 5OS in the triplet configuration exhibits local aromaticity at the terminal benzenoid rings, 5OS in the open-shell singlet configuration, 5para and 5ortho all display antiaromaticity (Supplementary Fig  ) 
The choice of the insulating surface determines the charge state of 5: while 5 adopts neutral charge state on the high work function bilayer NaCl/Au(111) surface (irrespective of its openor closed-shell state, Supplementary Fig  ), 5 exhibits charge bistability between 5 0 and the anionic state 5 -1 on the lower work function bilayer NaCl/Ag(111) and Cu(111) surfaces (Supplementary Figs  ) 
In the main text, we focus on the characterization of 5 on bilayer NaCl/Au(111)  Characterization of charge bistable 5 is reported in Supplementary Figs    We first describe experiments on 5 on bilayer NaCl/Au(111), where 5 exhibits a geometry corresponding to the calculated 5OS geometry, and an open-shell electronic configuration 
We compare the experimental data on this species to calculations on 5OS with a triplet configuration, as theory predicts a triplet ground state for 5OS  For 5OS, the calculated frontier orbitals correspond to the SOMOs ψ1 and ψ2 (Fig  ), whose spin up levels are occupied and the spin down levels are empty 
Figure shows the DFT-calculated bond lengths of 5OS, where the two salient features, namely, the small difference in the bond lengths within each ring and the notably longer bond lengths in the pentagonal rings, agree with the open-shell resonance structure of 5 (Fig  )  Figure shows an AFM image of 5 adsorbed on bilayer NaCl/Au(111) that we assign as 5OS, where the bond-order differences qualitatively correspond to the calculated 5OS geometry (discussed and compared to the closed-shell state below) 
Differential conductance spectra (dI/dV(V), where I and V denote the tunneling current and bias voltage, respectively) acquired on assigned 5OS exhibit two peaks centered at -1 5 V and 1 6 V (Fig  ), which we assign to the positive and negative ion resonances (PIR and NIR), respectively  Figure shows the corresponding STM images acquired at the onset (V = -1 2
V/1 3 V) and the peak (V = -1 5 V/1 6 V) of the ionic resonances  To draw a correspondence between the STM images and the molecular orbital densities, we consider tunneling events as many-body electronic transitions between different charge states of 5OS (Fig  )  Within this framework, the PIR corresponds to transitions between 5 0 and the cationic state 5  
At the onset of the PIR at -1 2 V, an electron can only be detached from the SOMO ψ1 and the corresponding STM image at -1 2 V shows the orbital density of ψ1  Increasing the bias to the peak of the PIR at -1 5 V, it becomes possible to also empty the SOMO ψ2, such that the corresponding STM image shows the superposition of ψ1 and ψ2, that is, |ψ1| 2 + |ψ2| 2 (ref 
)  Similarly, the NIR corresponds to transitions between 5 0 and 5 -1   At the NIR onset of 1 3 V, only electron attachment to ψ2 is energetically possible  At 1 6 V, electron attachment to ψ1 also becomes possible, and the corresponding STM image shows the superposition of ψ1 and ψ2  The observation of the orbital densities of SOMOs, and not the hybridized HOMO and LUMO, proves the open-shell ground state of assigned 5OS 
Measurements of the monoradical species with a doublet ground state are shown in Supplementary Fig    Unexpectedly, another species of 5 was also experimentally observed that exhibited a closedshell ground state  In contrast to 5OS, where the frontier orbitals correspond to the SOMOs ψ1 and ψ2, DFT calculations predict orbitals of different shapes and symmetries for 5para and 5ortho, denoted as α and β and shown in Fig   
For 5ortho, α and β correspond to HOMO and LUMO, respectively  The orbitals are inverted in energy and occupation for 5para, where β is the HOMO and α is the LUMO  Fig  shows an AFM image of 5 that we assign as 5para  We experimentally infer its closed-shell state first by using qualitative bond order discrimination by AFM 
In high-resolution AFM imaging, chemical bonds with higher bond order are imaged brighter (that is, with higher frequency shift Δf) due to stronger repulsive forces, and they appear shorter   In Fig  , we label seven bonds whose bond orders show significant qualitative differences in the calculated 5ortho, 5para (Fig  ) and 5OS (Fig  ) geometries 
In 5para, the bonds b and d exhibit a higher bond order than a and c, respectively  This pattern is reversed for 5ortho, while the bond orders of the bonds a-d are all similar and small for 5OS  Furthermore, in 5para bond f exhibits a higher bond order than e, while in 5ortho and 5OS bonds e and f exhibit similar bond order (because they belong to Clar sextets) 
Finally, the bond labeled g shows a higher bond order in 5para than in 5ortho and 5OS  The AFM image of assigned 5para shown in Fig  indicates higher bond orders of the bonds b, d and f compared to a, c and e, respectively  In addition, the bond g appears almost point-like and with enhanced Δf contrast compared to its neighboring bonds, indicative of a high bond order (see Supplementary Fig  for height-dependent measurements) 
These observations concur with the calculated 5para geometry (Fig  )  Importantly, all these distinguishing bond-order differences are distinctly different in the AFM image of 5OS shown in Fig  , which is consistent with the calculated 5OS geometry (Fig  )  In the AFM images of 5OS (Fig  and Supplementary Fig  ), the bonds a-d at the pentagon apices appear with similar contrast and apparent bond length 
The bonds e and f at one of the terminal benzenoid rings also exhibit similar contrast and apparent bond length, while the central bond g appears longer compared to assigned 5para  Further compelling evidence for the closed-shell state of assigned 5para is obtained by STM and STS  dI/dV(V) spectra acquired on an assigned 5para species exhibit two peaks centered at -1 4 V (PIR) and 1 6 V (NIR) (Fig  ) 
STM images acquired at these biases (Fig  ) show the orbital densities of β (-1 4 V) and α (1 6 V)  First, the observation of α and β as the frontier orbitals of this species, and not the SOMOs, strongly indicates its closed-shell state  Second, consistent with AFM measurements that indicate good correspondence to the calculated 5para geometry, we observe β as the HOMO and α as the LUMO 
For 5ortho, α should be observed as the HOMO and β as the LUMO  We did not observe molecules with the signatures of 5ortho in our experiments  We observed molecules in open-(5OS, Fig  ) and closed-shell (5para, Fig  ) states in similar occurrence after their generation from 6 on the surface  We could also switch individual molecules between open-and closed-shell states as shown in Fig  and Supplementary Fig   
To this end, a change in the adsorption site of a molecule was induced by STM imaging at ionic resonances, which often resulted in movement of the molecule  The example presented in Fig  shows a molecule that was switched from 5para to 5OS and back to 5para  The switching is not directed, that is, we cannot choose which of the two species will be formed when changing the adsorption site, and we observed 5OS and 5para in approximately equal yields upon changing the adsorption site 
The molecule in Fig  is adsorbed on top of a defect that stabilizes its adsorption geometry on bilayer NaCl  At defect-free adsorption sites on bilayer NaCl, that is, without a third layer NaCl island or atomic defects in the vicinity of the molecule, 5 could be stably imaged neither by AFM nor by STM at ionic resonances (Supplementary Fig  ) 
Without changing the adsorption site, the state of 5 (open-or closedshell) never changed, including the experiments on bilayer NaCl/Ag(111) and Cu(111), on which the charge state of 5 could be switched (Supplementary Figs  )  Also on these lower work function surfaces, both open-and closed-shell species were observed for 5 0 and both showed charge bistability between 5 0 (5OS or 5para) and 5 -1 (Supplementary Figs  ) 
The geometrical structure of 5 -1 probed by AFM, and its electronic structure probed by STM imaging at the NIR (corresponding to transitions between 5 -1 and the dianionic state 5 -2 ), are identical within the measurement accuracy for the charged species of both 5OS and 5para  When cycling the charge state of 5 between 5 0 and 5 -1 several times, we always observed the same state (5OS or 5para) when returning to 5 0 , provided the molecule did not move during the charging/discharging process 
Based on our experimental observations we conclude that indeno[1,2-a]fluorene (5), the last unknown indenofluorene isomer, can be stabilized in and switched between an open-shell (5OS) and a closed-shell (5para) state on NaCl  For the former, both DFT and QD-NEVPT2 calculations predict a triplet electronic configuration 
Therefore, 5 can be considered to exhibit the spin-crossover effect, involving magnetic switching between high-spin (5OS) and low-spin (5para) states, coupled with a reversible structural transformation  So far, the spin-crossover effect has mainly only been observed in transition-metal-based coordination compounds with a near-octahedral geometry  
The observation that the switching between open-and closedshell states is related to changes in the adsorption site but is not achieved by charge-state cycling alone, indicates that the NaCl surface and local defects facilitate different electronic configurations of 5 depending on the adsorption site 
Gas-phase QD-NEVPT2 calculations predict that 5OS is the ground state, and the closed-shell 5para and 5ortho states are 0 11 and 0 21 eV higher in energy  The experiments, showing bidirectional switching between 5OS and 5para, indicate that a change in the adsorption site can induce sufficient change in the geometry of 5 (leading to a corresponding change in the ground state electronic configuration) and thus induce switching 
Switching between open-and closed-shell states in 5 does not require the breaking or formation of covalent bonds , but a change of adsorption site on NaCl where the molecule is physisorbed  Our results should have implications for single-molecule devices, capitalizing on the altered electronic and chemical properties of a system in π-diradical open-shell and closed-shell states such as frontier orbital and singlet-triplet gaps, and chemical reactivity 
For possible future applications as a single-molecule switch, it might be possible to also switch between open-and closed-shell states by changing the local electric field, such as by using chargeable adsorbates   Scanning probe microscopy measurements and sample preparation  STM and AFM measurements were performed in a home-built system operating at base pressures below 1×10 -10 mbar and a base temperature of 5 K  Bias voltages are provided with respect to the sample 
All STM, AFM and spectroscopy measurements were performed with carbon monoxide (CO) functionalized tips  AFM measurements were performed in non-contact mode with a qPlus sensor   The sensor was operated in frequency modulation mode with a constant oscillation amplitude of 0 5 Å  STM measurements were performed in constantcurrent mode, AFM measurements were performed in constant-height mode with V = 0 V, and I(V) and Δf(V) spectra were acquired in constant-height mode 
Positive (negative) values of the tip-height offset Δz represent tip approach (retraction) from the STM setpoint  All dI/dV(V) spectra are obtained by numerical differentiation of the corresponding I(V) spectra  STM and AFM images, and spectroscopy curves, were post-processed using Gaussian low-pass filters 
Au(111), Ag(111) and Cu(111) surfaces were cleaned by iterative cycles of sputtering with Ne + ions and annealing up to 800 K  NaCl was thermally evaporated on Au(111), Ag(111) and Cu(111) surfaces held at 323 K, 303 K and 283 K, respectively  This protocol results in the growth of predominantly bilayer (100)-terminated islands, with a minority of trilayer islands 
Sub-monolayer coverage of 6 on surfaces was obtained by flashing an oxidized silicon wafer containing the precursor molecules in front of the cold sample in the microscope  CO molecules for tip functionalization were dosed from the gas phase on the cold sample  Density functional theory calculations  DFT was employed using the PSI4 program package  
All molecules with different charge (neutral and anionic) and electronic (open-and closed-shell) states were independently investigated in the gas phase  The B3LYP exchangecorrelation functional with 6-31G basis set was employed for structural relaxation and singlepoint energy calculations  The convergence criteria were set to 10 −4 eV Å −1 for the total forces and 10 −6 eV for the total energies 
Multireference calculations  Multireference calculations were performed on the DFToptimized geometries using the QD-NEVPT2 level of theory , with three singlet roots and one triplet root included in the state-averaged calculation  A (10,10) active space (that is, 10 electrons in 10 orbitals) was used along with the def2-TZVP basis set  
Increasing either the active space size or expanding the basis set resulted in changes of about 50 meV for relative energies of the singlet and triplet states  These calculations were performed using the ORCA program package   Nucleus-independent chemical shift (NICS) calculations  Isotropic nucleus-independent chemical shift values were evaluated at the centre of each ring using the B3LYP exchangecorrelation functional with def2-TZVP basis set using the Gaussian 16 software package  
Starting materials (reagent grade) were purchased from TCI and Sigma-Aldrich and used without further purification  Reactions were carried out in flame-dried glassware and under an inert atmosphere of purified Ar using Schlenk techniques  Thin-layer chromatography (TLC) was performed on Silica Gel 60 F-254 plates (Merck) 
Column chromatography was performed on silica gel (40-60 µm)  Nuclear magnetic resonance (NMR) spectra were recorded on a Bruker Varian Mercury 300 or Bruker Varian Inova 500 spectrometers  Mass spectrometry (MS) data were recorded in a Bruker Micro-TOF spectrometer  The synthesis of compound 6 was developed following the two-step synthetic route shown in Supplementary Fig  , which is based on the preparation of methylene-bridge polyarenes by means of Pd-catalyzed activation of benzylic C-H bonds  
Supplementary Figure | Synthetic route to obtain compound 6  The complex Pd2(dba)3 (20 mg, 0 02 mmol) was added over a deoxygenated mixture of 1,3-dibromo-2,4-dimethylbenzene (9, 100 mg, 0 38 mmol), boronic acid 10 (178 mg, 1 14 mmol), K2CO3 (314 mg, 2 28 mmol) and XPhos (35 mg, 0 08 mmol) in toluene (1:1, 10 mL), and the resulting mixture was heated at 90 °C for 2 h 
After cooling to room temperature, the solvents were evaporated under reduced pressure  The reaction crude was purified by column chromatography (SiO2; hexane:CH2Cl2 9:1) affording 11 (94 mg, 76%) as a colorless oil  The complex Pd(OAc)2 (7 mg, 0 03 mmol) was added over a deoxygenated mixture of terphenyl 11 (90 mg, 0 27 mmol), K2CO3 (114 mg, 0 83 mmol) and ligand L (26 mg, 0 06 mmol) in NMP (2 mL) 
The resulting mixture was heated at 160 °C for 4 h  After cooling to room temperature, H2O (30 mL) was added, and the mixture was extracted with EtOAc (3x15 mL)  The combined organic extracts were dried over anhydrous Na2SO4, filtered, and evaporated under reduced pressure  The reaction crude was purified by column chromatography (SiO2; hexane:CH2Cl2 9:1) affording compound 6 (8 mg, 11%) as a white solid  in AFM imaging due to their reduced adsorption height compared to the rest of the carbon atoms 
We attribute this observation to the significantly different lattice parameter of Cu(111) (2 57 Å) compared to Au(111) and Ag(111) (2 95 Å and 2 94 Å, respectively) , such that the apical carbon atoms of the pentagonal rings of 5 adsorb on the on-top atomic sites on Au(111) and Ag(111), but not on Cu(111) 
Our speculation is based on a previous study of polymers of 1 on Au(111) by Di Giovannantonio et al  , where both tilted and planar individual units of 1 were observed depending on whether the apical carbon atoms of the pentagonal rings in 1 adsorbed on the on-top or hollow sites of the surface, respectively 
Given the strong molecule-metal interaction, we found no electronic state signatures of 5 on all three metal surfaces  STM set point for AFM images: V = 0  e, Frontier orbital spectrum of 5 -1   In the anionic state, ψ2 becomes doubly occupied and ψ1 is the SOMO  Filled and empty circles denote occupied and empty orbitals, respectively 
For each panel, zero of the energy axis has been aligned to the respective highest-energy occupied orbital 
Context 3: 
Recent works have mainly focused on deep learning based language identification, where handling codemixed data is a big challenge in the domain  For our experiments, we use a version of XLM-RoBERTa finetuned on a language identification dataset 2   This model has near-perfect test accuracy of 99 6% 

Dialect Classification

Dialect classification has been previously solved using statistical methods like Gaussian Mixture Models and Frame Selection Decoding or Support Vector Machines (SVM)   It has been explored relatively sparsely, mostly in the case for local languages   Deep learning approaches have been explored in previous editions of the VarDial workshop shared tasks and otherwise  
Dialect classification was also explored previously as a part of other shared tasks   We want to stress that given the multilingual nature of the dataset, using the present methods directly was not an option  In our work, although we take inspiration from the previous works, we propose a novel system that surpasses the performance of the previous systems by a large margin 

Data

The dataset  We observed that the class PT-BR had the most number of samples (2,724) and the class EN had the least number of samples (349), and thus the imbalance ratio was almost 1:8  We have illustrated the data distribution in Figure   We tried to mitigate this imbalance using over-sampling and weighted sampling methods 
However, the improved data sampling method did not affect the performance 

System Description

This was a problem of multi-class classification having 9 classes for Track-1 and 6 classes for Track-2  The samples were belonging to 3 languages having 3 varieties each, so the classification pipeline was made in 2 stages  The Language Identification (LID) model which is the first stage classifies the sentence into 3 languages: English (EN), Spanish (ES) and Portuguese (PT) 
The LID is a pretrained XLM-RoBERTa that is fine-tuned for the task of language identification  It is able to classify the input sentence into 20 languages  We classify and separate the samples according to their language  The samples corresponding to the specific languages are then fed into the language specific models for dialect identification 
For dialect identification we have used models like BERT and RoBERTa with a linear layer connected to the pooler output of the models  Then fine-tuning is done on the models for dialect identification   using the samples corresponding to the specific languages  For the task of dialect identification we experimented with several pretrained models like XLM-RoBERTa, BERT, ELECTRA, GPT-2 and RoBERTa 
All models were fine-tuned for 20 epochs with a learning rate of 1e-6 and weight decay 1e-6 with a batch size of 8  The best performing model checkpoint was chosen according to the epoch-wise validation macro-F1 score  5 Experiments and Results

Experiments using Large Language Models

For the task of Dialect Identification we have tried various language specific models like XLM-RoBERTa, BERT, ELECTRA, RoBERTa and GPT- 2  The base variant of all these models were used and all the models were used through the Hugging-Face library  The pooler output of these models was passed through a linear layer and the models were fine-tuned 
First, we experimented with different models for Track-1  All the models were trained for 20 epochs with learning rate 1e-6, weight decay 1e-6 and a batch size of 8  We used XLM-RoBERTa as the baseline for all 3 languages  The best performing models for the English language were RoBERTa and BERT whereas GPT-2 was the worst performing 
Similarly the language specific versions of RoBERTa and BERT performed well for the Spanish and Portuguese respectively  Overall the worst performing model was GPT-2 across all 3 languages  The validation F1 scores are present in Table   The two best-performing models for every language were chosen for Track-2 
The same procedure as specified above was used and the F1 scores are present in Table   The train and validation F1 scores for 2-class classification are higher for all models as compared to the F1 score of the same models for 3-class classification  This was mainly due to the poor representation and accuracy of classification of the third class 
We observed symptoms of overfitting in all models after 12-15 epochs and the best validation F1 score was obtained in the range of 4-8 epochs 

LID experiments

The pipeline for dialect identification is divided into two parts as the sentences in the dataset belong to different languages  The stages are described in Section 4  The XLM-RoBERTa we have used for language classification has a test accuracy of 99 6% meaning it correctly classifies all input sentences and hence, can be considered as a perfect classifier 
For the final pipeline we experimented using the two best performing models for each language in Track-1 and Track-2  For both the tracks we experimented with all 8 (2 3 ) possible combinations of models and calculated the validation F1 score for the combined validation dataset which had sentences belonging to all languages 
The validation scores for Track-1 and Track-2 are shown in Table and Table respectively  For both the tracks, the three pipelines with the best validation F1 scores were chosen for submission 

Using 3-way classifier as a 2-way classifier

In Track-1, participants are expected to train a classifier which classifies amongst 9 classes, and in Track-2, participants are expected to train a classifier which classifies amongst 6 classes



ground_truth: Yes, individual molecules of indeno[1,2-a]fluorene can switch between open-shell and closed-shell states by changing their adsorption site on the surface.

prompt_front: Now, answer the following question based on the text below, only give me the answer and do not output any other words.

Question: What models were used for dialect identification?

Read the following text and answer briefly.

Context 3:  These 6 classes are a proper subset of the 9 classes from Track-1  Thus, an intuitive baseline for Track-2 is to use the model finetuned for Track-1, whilst considering only the relevant classes for the latter task 
The classes EN , ES and P T , i e  the classes without any national dialect associated with them are not included in Track-2 as compared to Track-1  Thus, we calculate the predictions for the Track-2 validation dataset using the models for Track-1 and exclude the metrics for Track-1 specific classes to get the metrics for this "adapted" 2-way classification 
We show the results of this experiment in Table and observe that, as expected, the adapted 2-way classification performs worse compared to the explicitly finetuned variant 

Results for Track-1 and Track-2

We now present our experiments and their performance for both tracks  Our experiments for Track-1 are described in Table and our experiments for Track-2 are described in Table   The participants were allowed three submissions for evaluation on the test set, so we submitted predictions using the three systems which performed the best on the validation set 
As mentioned in Section 5 2, we performed 2 3 , i e  a total of 8 experiments using the two best models for each language  We observed that RoBERTa base on English, Spanish BERT base on Spanish and Portuguese BERT base performed the best on the testing set for Track-1  The same combination, with RoBERTa base for English, worked best for Track-2 
All of our submissions were the top submissions for each track, which surpassed the next best competitors by a margin of 4 5% and 5 6% for Track-1 and Track-2 respectively 

Ablation of best submissions

We hereby make some observations of our submissions and other experiments  To assist this, we plot the confusion matrices of our best submissions for Track-1 and Track-2 in Figures respectively  Note that these confusion matrices have their rows (i e  true labels axes) normalized according to the number of samples in the class 
Here are observations from our experiments: 1  BERT-based models outperform other models across all languages: We observe that BERT-based models outperform ELECTRA-based and GPT-2-based models, as shown in Table   We speculate this is because of the inherent architecture of BERT, which combines semantic learning with knowledge retention 
This combination of traits is particularly useful for this task  2  Common labels perform the worst across all languages: We observe that the common labels EN , ES and P T perform the worst, both in the individual as well as the two-stage setup  We hypothesize this is because of the absence of dialect specific words, or words that are specific to the geographical origin of the national dialect (for example, "Yankees" for EN-US and "Oxford" for EN-GB) 
3  English models work better than models of other languages: It can be noted from Figures 4 and 3 that the English models have the best performance across all classes  This can be attributed to two reasons: absence of national dialect specific words and lesser pretraining data in the case of Portuguese 
4  British English is most correctly classified class: We can observe that the Spanish or Portuguese models make equal number of mistakes in the case of either national dialect, in the case of Track-2 (see Figure )  However, in the case of English, the label EN − GB is correctly classified for more than 95% of the cases 
We speculate this is because British English involves slightly distinctive grammar and semantics, which help the model separate it from other classes  5  The proposed 2-step method is scalable for multiple language dialect classification: We can strongly assert that the novel 2-step deep learning method for multilingual dialect classification is a scalable method for the task due to two specific reasons: firstly, the multilingual models (like XLM-RoBERTa) might not have the vocabulary as well as the learning capabilities to learn the minute differences between individual dialects 
Secondly, this system can be quickly expanded for a new language by simply adding a language specific dialect classifier, provided the language identification model supports that particular language 

Conclusion

In this paper we propose a two-stage classification pipeline for dialect identification for multilingual corpora  We conduct thorough ablations on this setup and provide valuable insights  We foresee multiple future directions for this work  The first is to expand this work to many languages and dialects 
Secondly, it is a worthwhile research direction to distill this multi-model setup into a single model with multiple prediction heads  The obvious limitation of this system is the excessive memory consumption due to the usage of language specific models  For low resource languages this system is difficult to train and scale 
We hope that these problems will be addressed by researchers in future works 



ground_truth: BERT, RoBERTa, ELECTRA, GPT-2, and XLM-RoBERTa.