Accelerating the training of deep reinforcement learning in autonomous driving

Received Sep 25, 2020 Revised Jun 23, 2021 Accepted Jun 25, 2021 Deep reinforcement learning has been successful in solving common autonomous driving tasks such as lane-keeping by simply using pixel data from the front view camera as input. However, raw pixel data contains a very high-dimensional observation that affects the learning quality of the agent due to the complexity imposed by a 'realistic' urban environment. Ergo, we investigate how compressing the raw pixel data from high-dimensional state to low-dimensional latent space offline using a variational autoencoder can significantly improve the training of a deep reinforcement learning agent. We evaluated our method on a simulated autonomous vehicle in CARLA and compared our results with many baselines including DDPG, PPO, and SAC. The result shows that the method greatly accelerates the training time and there was a remarkable improvement in the quality of the deep reinforcement learning agent.

INTRODUCTION
Series of automation and machine learning development has progressed through the years in order to make everyday tasks effortless. Case and point, there has been a surge of interest in self-driving cars or autonomous vehicles (AV), the ability to drive safely from one point to another with little to no human intervention. With the recent study and advancement on AV, the attention of different high-tech companies and researchers are rapidly growing. They are highlighting the need for a level five (5) full automation [1], where vehicles can be driven safely without human intervention or supervision. There is still a long way for this to be achieved along with major challenges on how to design an accurate model of an autonomous vehicle in a real-world environment.
Existing decision-making methods for autonomous driving uses a rule-based approach which requires a manually designed driving policy [2], [3]. The problem with such an approach is they lead to inaccurate decision-making, as predefined models are biased in a highly stochastic environment and suffer heavily when tasked with different scenarios. As an alternative, many researchers have used an imitation learning technique that learns a driving policy based on data obtained from an expert driver [4], [5]. Though, imitation learning suffers from several weaknesses because a policy learned from an expert driver is limited to the knowledge of the said driver and most expert drivers only show good driving behavior, which the model may never learn how to deal with a risky situation. Imitating learning is also costly and timeconsuming to implement in a real-world environment. Another method is deep reinforcement learning (RL) it achieves human-level performance in playing games like Go [6] and Atari [7]. There have been some significant breakthroughs in continuous control of deep RL methods [8], [9]. As a result, deep RL has been used to learn a driving policy with no expert data. Despite these breakthroughs, training a deep RL in a highly stochastic environment is challenging due to the high demand of computational cost it requires i.e., to achieve reasonable performance in playing Atari games deep RL required hundreds of millions of training steps [10], resulting in months of consistent training. Similarly, AlphaGo trained data from expert Go players for over 30 million training steps. The sheer amount of computational cost imposed on training a deep RL algorithm undermines its application in autonomous driving.
In this paper, we proposed using state representation learning (SRL) to retrieve relevant information from raw pixel data to accelerate the training of a deep RL agent by learning a low-dimensional state representation of an autonomous driving environment rather than directly using a front view image as input to train a deep RL agent. We used variational autoencoder (VAE) [11], a type of generative neural network model that comprises an encoder and a decoder network. The idea is to utilize backpropagation to train the network to reconstruct a high-dimensional input signal after compressing it into a low-dimensional vector, as shown in Figure 1. The goal of a VAE is to minimize reconstruction loss. To achieve this, we collected raw image data by manually driving the vehicle in the simulated environment. These images are sent as input vectors into a VAE network architecture that comprises an encoder and a decoder. The encoder compresses the raw image data into the most essential components and attempts to reconstruct the original signal by decoding the compressed representation. The pre-trained VAE images are used to train our deep RL agent, which takes the encoded low-dimensional states-input and outputs continuous control commands. Figure 1. The proposed framework: first the raw captured image is encoded with a VAE and sent as input of a pre-trained low dimensional state space into our agent that learns a driving policy to output control commands such as throttling and steering Ever since deep RL drew attention after its tremendous success in playing Atari games at a human level performance in high-dimensional state space, there has been some promising application in autonomous driving by many researchers [12]. Some of these researchers are Vitelli and Nayebi [8] which implemented a deep Q-network (DQN) agent on autonomous driving by simply using raw pixel data as input to train the policy network. They tested their method on a racing car simulator (TORCs) to evaluate the performance of the DQN agent. Pan et al. [13], acquired real-world images and fed them into an asynchronous advantage actor-critic model to train a suitable driving policy virtual environment. Sallab et al. [14] proposed a framework for an end-to-end autonomous vehicle based on a deep RL pipeline using recurrent neural networks to deal with partially observable Markov decision process scenarios in a TORCs simulator. Lillicrap et al. [12] tested their proposed continuous control deep reinforcement learning algorithm (deep deterministic policy gradient [DDPG]) on a lane following task in TORCs. Perhaps, there are many related works on autonomous driving with deep RL, however, most of the aforementioned approach is developed and tested in a racing environment or Donkey Car simulator [15] which is not practical in a real-world environment or used a raw pixel data directly from a front camera or manually designed feature representation which can be computational intensity and time-consuming to train especially in a highdimensional environment. SRL has been gaining traction in deep RL because of its ability to extract low-dimensional state space from a high-dimensional environment by reducing the complexity of the environment. Raffin et al. [16], argued that SRL improves the quality of an agent's learning by giving the agent a state space that ignores irrelevant distractors, and is disentangled in its feature representation. Kendall et al. [17], was one of the first researchers to apply deep reinforcement learning with SRL in autonomous driving. They first proposed a method of policy learning by simply mounting a single monocular image as input to observe the environment and compared it with using an online VAE. Using VAE gave a minor advantage over raw image data. However, training a VAE online i.e., simultaneously with a deep RL agent causes instabilities in the policy training [18]. We addressed this issue in our paper by opting to train the VAE offline by using a pretrained VAE with fixed feature extraction. We build on these by demonstrating how compressing the raw input image from high-dimensional observation to low-dimensional latent space before training our deep RL agent, not only reduces the training time but also improves the quality of the agent. On top of that, we trained and evaluated our agents using DDPG, proximal policy optimization (PPO), and soft actor-critic (SAC) and compared our results with the deep RL baselines.

ALGORITHM
Deep RL is simply the use of deep neural networks as a function approximator for policy and value functions in a reinforcement learning framework. The neural network is represented by a policy ( | ; ) and a value function ( ; ). We applied 3 state-of-the-art model-free deep RL algorithms to our proposed framework to learn a driving policy. This section gives a brief introduction to these algorithms.

Deep deterministic policy gradient
DDPG is a kind of actor-critic algorithm similar to an approximate deep Q network. It uses two separate neural networks for function approximators [12]: an actor and a critic. The actor is used to approximate optimal policy deterministically i.e., it always tries to output the best-believed action for any given state. The actor ( ; ) learns an ( , ) as the best action. The critic ( , ( ; ; ) learns to evaluate the optimal action-value function by using the actor's best-believed action. The algorithm samples a batch of transitions from an experience replay memory then updates the critic network using a target of: Meanwhile, the actor-network is updated using gradients of the output from the critic network: where ( , )| = , = ( ) is the action mean value of the critic network. To get the mean action, the actornetwork uses the current state as input. Then it computes the actor gradient output for the actor weights given as ( , ). The actor gradient is used to send control commands to the agent.

Proximal policy optimization
PPO is a policy gradient-based model-free reinforcement learning algorithm that employs first-order trust-region criteria to prevent divergence [19]. It combines trust region policy optimization (TRPO) [20] with gradient descent to stabilize training by creating a loss function that nearly guarantees a simultaneous improvement of the agent's policy. PPO attempts to improve the "surrogate" objective from TRPO by clipping it into a certain region and further solving it with the first-order optimization using stochastic gradient descent (SGD) [21]. The clipped "surrogate" objective is defined as: where the probability ration ( ) = ( )/ ( | ), the advantage estimate ̂ is estimated by GAE. Without the hard constraint of Kullback-Leibler (KL) divergence common in TRPO, the update rule becomes: where ∝ is the learning rate. To avoid greedy updates, PPO uses the change in probability ratio only when it makes the objective worse. Thereby providing an alternative clipped surrogate objective using a penalty on KL divergence with an adaptive coefficient.
where is the policy parameters before the update. Similar to (4), the KL penalty version is solved by applying an SGD leading to the update rule.
The computation for optimizing both versions of the objective function is much less than that of a TRPO. Therefore, the experimental results when evaluated in a MuJoCO environment, RoboSchool, and Atria games task shows that PPO performs better than TRPO.

Soft actor-critic
SAC is an off-policy algorithm that employs the best sample complexity and convergence property without the need for too many hyperparameter tuning. This makes its performance significantly better than the DDPG and PPO methods in challenging decision-making tasks. SAC attempts to balance an expected return by learning a stochastic policy * that maximize both the rewards and the entropy as: where the entropy is ( ( )) =~( .| ) [− ( ( )]. In the most actor-critic algorithm, we define the optimal policy in respect to the reward, however, SAC changes that traditional objective of the Markov decision process, by introducing a new value function called the soft value function defined as: where is the soft Q-value function. To find an optimal policy, the critic has to convert into an optimal critic by using a bellman operator such that: Given the established bellman operator, it tries to minimize the expectation over transitions of the specific temporal difference error. This process is called the policy evaluation step: where the critic parameters are learned by minimizing:

RESEARCH METHOD 3.1. Environment setup
The environment of the experiment is developed on car learning to act (CARLA) [22], an urban autonomous driving simulator built on an open-source protocol and implemented in an unreal engine 4 (UE4). It provides a variety of urban layouts, vehicle models, pedestrians, and road obstacles. making it possible for our agent to learn to drive in a 'realistic' urban traffic simulation environment. The simulation supports different sensors suits including cameras, raw images, depth maps, semantic segmentation images, and lidars. We concentrated on the raw pixel data from CARLA's built-in front-facing camera. And the VAE was trained for better state representation.

Continuous action space
Since Similar to Kendall, we reduced the action space by removing the brake action. The vehicle will drive at a low speed with no other vehicle present and with no consideration of traffic rules eliminating the need for braking. That leaves us with a steering and throttle control vector.

Latent state space
As we intend to reduce the complexity of the environment from high dimensional state space to low dimensional state space, we gathered datasets needed to train the VAE by manually driving around the CARLA environment with a keyboard, collecting images from the front-facing camera. The vehicle does not follow any traffic rules of imitation learning. Also, there is no need for labeling such as the ground truth position of the vehicle in the training dataset with exception of the raw pixel data, which is essential. We collected 10,000 images (approximately 15 minutes of driving).
A VAE model to reconstruct the collected input image from the sampled gaussian latent vector and minimize the reconstruction loss was created. The model contains an encoder and a decoder with four 2D convolution layers (conv2d) with 32, 64, 128, and 256 separate channels. Each conv2d has two (2) strides sets and a ReLu activation function. As well as the latent space layer that is fully connected to the last conv2d with a size of 64, we also used Adam optimizer [21], with a learning rate of 10 −4 and a batch size of 100. A KL-tolerance factor was applied to ensure the optimizer only applies a KL-loss once the KL-loss exceeds a tolerance factor greater than zero. In Figure 2, we explored how the encoder, , takes raw image input and outputs two vectors, and , through a parallel fully connected layer. Then we used sample z from ( , ) and passed through a decoder , producing a reconstructed signal ̂.

Network architecture
We set all networks with the same convolutional layers while the first dense layer is similar to our VAE model. To ensure the distribution of features is mobile, the policy was trained with a fixed feature extractor. The network architecture of the three deep RL algorithms (DDPG, PPO, and SAC) used in this work was adopted from implementing Stable Baseline by Hill et al. 2019 [23]. The implementation was altered by adding the optimization for the VAE in every episode. We found the following tuned hyperparameters as the most effective in the experiment. − DDPG: Defined a discount factor of 0.99, a gradient clipping of 0.005, the replay memory size of 10000, optimization steps of 300 between episodes with batch size 64, and an Ornstein-Uhlenbeck process [24] with = 0.15 and = 0.2 inspired by the work of Lillicrap et al. [12]. − PPO: A multi-layer perceptron with a discount factor of 0.99, an entropy loss of 0.0, an optimization step of 256, and a mini-batch size of 4 with a learning rate of 3e-3 for 10 epochs. − SAC: A buffer size of 30000 with an optimization step of 600 and batch size of 64. We set the learning rate to 3e-4 with gamma 0.99. SAC does not need an exploration policy; it is a component learned during training.

Reward function
As a baseline, we aimed to train our agent with a reward function similar to Kendall's. In their work, they simply defined a reward function that is proportional to the acceleration of the vehicle: where is the current speed of the vehicle in km/h, is the target speed (the recommended speed required for the agent to maximize reward), and an infraction when the agent gets off the track or collides with an obstacle. To prevent the agent from getting stuck in a bad state due to a well-known bug in CARAL, where collisions are not annotated when the vehicle is moving at a low speed of 1 km/h or less [25], we added a live penalty to punish the agent each time it gets stuck without moving or crashing at a low forward speed.

RESULTS AND DISCUSSION
To evaluate the effect better state representation has on policy learning, we trained a DDPG agent using raw pixel (baseline) and compared the result with our DDPG agent trained with a VAE (DDPG+ VAE). Similarly, we trained a SAC and PPO agent with pre-trained autoencoders and compared the result with our DDPG+ VAE agent. Finally, we evaluated our proposed method based on the cumulative reward obtained over 100 training episodes using the reward function described in section 3.5.

Comparing the baseline with our DDPG+ VAE agent
We established the baseline as a DDPG agent, which is trained directly from pixel data obtained from the vehicle's front camera. We selected the parameters of the baseline model similar to Kendall's implementation, whereas our DDPG+ VAE is the agent pre-trained with VAE as discussed in section 3.3. Figure 3 shows how the baseline fell short in performance as compared with our DDPG+ VAE agent. The baseline agent could barely learn any decent lane following skills after 100 training episodes. It keeps turning right and then goes off lane at most episodes, while only managing an average cumulative reward of 128.31. Nonetheless, our pre-trained DDPG+ VAE agent was able to learn good lane following skills after only six (6) training episodes and having an average cumulative reward of 276.13. It even learned how to take turns after a couple more episodes. This proves that our DDPG+ VAE agent not only learns faster than the baseline but also improves the overall quality of the deep RL agent.

Comparing our DDPG+ VAE agent with other model-free deep RL algorithm
To ensure the best algorithm for our method, we evaluated the effect of SRL on other deep RL algorithms in autonomous driving. We compared the performance of our proposed DDPG agent to other deep RL baselines with VAE. We trained both SAC and PPO agents with pre-trained VAE for 100 episodes and compared the results with our DDPG implementation. As observed from Figure 4, the SAC agent outperformed the other two agents by a large margin. It learned a good lane following skill on its first few episodes while being able to successfully drive around the town without any collisions accumulating an astonishing average cumulative reward of 631. Meanwhile, PPO and DDPG agents were only able to obtain an average cumulative reward of 276 and 246 responsively. We attribute the preeminence of the SAC algorithm to its ability to learn an exploration policy during training. As a result, we recommend SAC with pre-trained VAE as an alternative in speeding up deep RL training in autonomous driving. We have compiled videos showcasing the results of our experiments. This video can be found at https://youtu.be/kriYuE5z44M. 655 Figure 4. This shows that SAC has the best performance among all methods

CONCLUSION
In this paper, we explored how several aspects of deep reinforcement learning can solve a simple lane following autonomous driving tasks. From the methods used, we suggest that VAEs can accelerate the training of deep RL agents and how state representation affects the overall performance of the deep RL agent autonomous driving. This method exhibits that it is not only limited to 3D environments that are visually simple such as Prakash's Donkey Car simulator and Kendall's Custom in-house simulator but also efficient in a "realistic" urban driving simulator like CARLA. Although there were some challenges in setting up the final environment on CARLA, we have conclusively created a self-contained deep RL system that works alongside CARLA. We have shown that employing a pre-trained VAE helps improve training a deep RL agent as compared to training raw pixel data directly. It was also showcased how the VAE affects the overall result of the agent, and that a pre-trained VAE performs much better than optimizing the VAE the deep RL agent in alternate training phases. We also concur with the work of Raffin that demonstrates a more recent reinforcement learning algorithm, SAC, can work better than Kandell's DDPG in autonomous driving.


s2fieldsofstudy:
Computer Science
Linguistics

text:
A Systemic Functional Analysis of Theme in Modern Standard Arabic Texts

The concept of Theme is regarded as a functional linguistic element that exists in many languages. The main aim of this study is to explore the functions of Theme in Arabic, applying the Systemic Functional Linguistics framework adopted by Downing (1991). Methodologically, several related real examples have been selected from the written discourse of Modern Standard Arabic and then analyzed contextually. The empirical analysis has revealed that (i) Theme can provide different functions, such as Individual, Circumstantial and Subjective and Logical Frameworks for the interpretation of the Rheme, and (ii) Theme can interact dynamically with different grammatical functions (e.g. Subject, Object, etc.) and have different pragmatic functions (e.g. Topic, Given and New information). Therefore, the view that makes a necessary link between Theme on the one hand and Noun Phrase, Topic or Given information on the other hand is proven incorrect and empirically invalid. Similar results have been obtained in the context of English (Downing 1991) but not yet for Arabic? This strengthens not only the universality of the concept of Theme but also its functions.

INTRODUCTION
Across the field of linguistics, the concept of Theme has been a major concern, especially for those interested in functional linguistics 1 (Halliday 1967:218;Firbas 1992:71;Dik 1997, 2:289;Thompson 2004:142). The existence of Theme has been found in many languages in one form or another (Butler 2003, 2:147). It is also hypothesised that in every language there is a way to mark the core message of the clause (Fries 1995:15;Forey and Sampson 2017:132). The main conceptual aspect of Theme is that it "plays a fundamental part in the way the discourse is organized" (Gomez-Gonzalez 2001:98). Furthermore, this linguistic element is considered as a core and foundation-laying component for any message conveyed, since it performs certain functional tasks, such as introducing the discourse topic and creating a textual bond (Dik 1997 2:389;Ping 2004:29;Fontaine 2013:139).
This paper is concerned with Theme in the Arabic language. The main goal of this study is to show how thematic constructions function in real Modern Standard Arabic texts. This study aims to examine the applicability of the conceptual framework of Theme adopted in Downing (1991), which is based on Systemic Functional Linguistics (henceforth SFL), to Arabic. This study also aims investigates how Theme in about' (in Gomez-Gonzalez 2001:67). For instance, 'John', in the following example, is the entity that represents the 'aboutness' of the sentence, i.e., we are talking about John in this sentence. Thus, he is regarded as the Theme. 1-John gives Tom a book.
The elements that follow the Theme 'gives Tom a book' represent what is known as Rheme. Rheme, as defined by Mathesius, is what the speaker intends to state about the content of the Theme (cited in Gomez-Gonzalez 2001:64). In other words, Rheme carries the information that the speaker wishes to communicate to others (Abdul-Raof 1998:94). According to most linguists (including Mathesius), the concept of 'aboutness' is a core and central semantic feature for characterising the Theme of the sentence (Gomez-Gonzalez 2001:16). 3 This point is discussed later in Section 3.
In the view of Mathesius and other Prague linguists, Theme has to denote Given information, i.e., Theme has to be known or at least obvious (Firbas 1987:143). 4 The following dialogue is provided to illustrate this point (taken from Siewierska 1991:156). 2-A: Where did you meet Sandra? B: I met her last year in Bologna. In this sentence, the pronoun 'I' is the Theme of the sentence and is deemed to be Given because it is already stated in the preceding context (the question given by the speaker A) in the form of 'you'.
The conflation between Theme and Given information has been criticized by some linguists who believe that Givenness is not a necessary property for Theme (Prince 1981:234-235;Gomez-Gonzalez 2001:65). According to Halliday and Matthiessen (2004:93), Theme is speaker-oriented and Given is listener-oriented. Consequently, Theme can also carry New information (Fries 1995:4). This is typically realised by an indefinite element as in the following examples (Gundel 1985:89). 3-A guy I met recently collects beer cans.
In the above sentence, the entity 'guy' is the Theme of the utterance while it is indefinite and unfamiliar to the addressee, i.e. it is not Given. In terms of word order, Given information normally precedes New information. Therefore, Given-New order represents the unmarked order. (Li and Thompson 1975:165).
In Arabic, Abdul-Raof (1998:59) notes that Arab grammarians discussed the notion of 'aboutness', which is according to many linguists a central semantic feature of Theme, as noted above. They used the expression muḥaddath ʿanhu "being talked about" (see : al-Sīrāfī 1990, 2:173;Ibn al-Sarrāj 2000, 1:58;al-Jurjānī 1992:189). This is exemplified by 'Zayd' in the following sentence. 4-Zaydun munṭaliqun 5 Zayd-Nom indef-run.Sg.Msc.Nom "Zayd is running". According to classical grammar rules, the relation between the two constituents in a sentence is called isnād, which means "predication". Arab grammarians tended to refer to the person/thing that is spoken about by the term musnad ilayh "predicated to it", which can only be an NP. On the other hand, they used the term musnad "predicate" or al-muḥaddath bi-hi "the linguistic unit that is used to express the predicate", which can be either an NP or a VP, to indicate what is referred to as Rheme in functional linguistics. 6 It is relevant to state that Arabic sentences are commonly divided into two types: the verbal sentence and the nominal sentence. The verbal sentence has a verb/predicate (termed fiʿl in Arabic) in its predication while the nominal sentence does not (Aziz 1988:118-119;Ryding 2005:59). The verbal sentence consists of a verb plus a noun that acts grammatically as the subject of the sentence (termed in Arabic as fāʿil). By contrast, the nominal sentence, consists of a noun that is grammatically a subject (termed mubtadaʾ in Arabic) and an adjective or a noun complement which acts grammatically as predicate (given the Arabic term khabar). (Aziz 1988:119;Buckley 2004 209-212;Ryding 2005:58).
Structurally, Arabic is a relatively flexible word order language. It allows different structural orders in the sentence. In the verbal sentence, the VSO word order is regarded as the basic, unmarked, neutral order as exemplified in sentence (5) below (Cantarino 1975 1:41;Holes 2004:252;Buckley 2004:513). By contrast, in the nominal sentence, the subject + predicate sequence (i.e., mubtadaʾ + khabar) is the unmarked order (Holes 2004: 251) as presented in sentence (6).

5-Jāʾa
Zaydun come-3Msc.Sg.Perf Zayd-Nom "Zayd came". 6-Zaydun mustayqiẓun Zayd-Nom indef-awake.Sg.Msc.Nom "Zayd is awake". In relating English terms to Arabic ones, modern Arabic linguists typically argue for the following correspondences: in the verbal sentence, the verb acts as Rheme, while the post-verbal noun acts as Theme. In the nominal sentence case, the element that acts as mubtadaʾ is seen to express the function of Theme, while the element that is grammatically labelled as khabar is considered to express Rheme function (Moutaouakil 1989:74;Abdul-Raof 1998:99;Goldenberg 2007:308-310 Before we conclude this section, a brief review of some relevant modern studies in Theme in Arabic are provided. At the outset, it should be noted that these studies do not provide readers with a contextual reading for the examples that are analysed, except for Potter's study (2016), which has a different aim to the present study. The study conducted by Aziz (1988) shows that he builds his analysis upon the Prague School's framework in which: (i) Theme has to be Given, (a point that has been refuted above); and, (ii) the thematic element can be placed in any position in the sentence.
One can gather from the analysis of his examples that he links Theme with Topic. This point has proved controversial among linguists and will be discussed in the following section, where I will argue against such a link. Besides, there is no proper justification given in distinguishing between two types of verbal sentences. In other words, he considered the verb of the two-element sentence as Rheme, while considering the verb of the three-element sentence as Transition. This is illustrated in the following examples. 7-jāʾa Zaydun come-3Msc.Sg.Perf Zayd-Nom "Zayd came". He analysed the verb of sentence (7) jāʾa "came" as Rheme, while the verb of sentence (8) ishtarā "bought" is analysed as a Transition. He does not explain the difference between the two cases, which shows inconsistency in dealing with the functions of Arabic verbs. Abdul-Raof's (1998) study is also based on the Prague School approach. In his view, Theme must be an initial NP. Hence, as Potter points out (2016:7), the VSO order does not receive sufficient attention in his study. Bardi's (2008: 462-467) study of Theme in Arabic is both detailed and extensive. However, some of his analyses are puzzling. For example, in his analysis of initial VP sentences (i.e., that they follow VSO order), he differentiates between the freestanding subject as in sentence (9) below, and pronominal subject affixed to the verb as in sentence (10).
In his view, the subject al-muṣallūna "the worshipers" in (9) is part of the Rheme, which means that the verb kharaja "came out" alone functions as Theme. Whereas, the subject -ū "they" in (10) is regarded as part of the Theme, which is collectively composed of the verb ḥamal "took; literally: carry" and the subject -ū "they" because of the interconnection between the verb and the subject. In my opinion, this distinction is unnecessary since the subject in the two sentences, whether it is a pronoun or a freestanding noun, makes no difference to the syntactical arrangement of the Arabic sentence. In other words, in the each of the two cases, the subject is still a separate constituent that has its own syntactic and functional role. Hence, it should not be conflated with the function of the verb and thus the verbs in the two sentences function as Theme without considering the subjects as being part of it.
One recent study on Theme in Arabic was conducted by Potter (2016) in which the author has applied the SFL framework to her analysis of English and Arabic news reports. Her study aims to conduct a comparative Critical Discourse Analysis (CDA) investigation of the ideological representation of Theme-Rheme in news reports of both languages. To be more precise, the author has used the SFL framework as an instrument to set out her analysis and conduct a comparative CDA inquiry. In that regard, the scope of her study is different to the present study.
As outlined above, these studies show some limitations and inadequacies. This demonstrates that there is still a need to explore the functions of thematic structures in Arabic. Since the present study aims to analyse Theme in Arabic within the framework of the SFL theory, it is necessary to shed light on the concept of Theme in SFL before describing the framework that is applied in this study.

SYSTEMIC FUNCTIONAL LINGUISTICS AND THE ANALYSIS OF THEME
SFL is one of the dominant functional theories in linguistics. It was developed by the British linguist Michael Halliday (Butler 2003 1:43). For Halliday, language is an interactive social practice, and the main goal of linguistic theory is to describe and interpret linguistic communication between human beings (Halliday 1979: 21;Butler 2003 1:44;Fontaine 2013:5). Thus, SFL is a theory concerned with the function of the sentence and with the speaker/writer's purposes and choices. (Thompson 2004: 2;Hart 2016:19).
One crucial aspect of SFL theory is the notion of metafunctions. Halliday believes that language has three levels of function, namely: Experiential, Interpersonal and Textual. The Experiential function regards language as a means of expressing our experience in the real world. We use this function to describe what people see, hear and do. The Interpersonal function shows how we use language to interact with others in social life. It reflects interpersonal relations and attitudes through language. The Textual function exhibits how we organise and coherently construct our message. These three functions operate at different levels and are realised by different systems. The experiential is realised by Transitivity which covers a range of processes, their participants and semantic roles. The interpersonal is realised by Mood. The Textual is realised by Theme and Information structure (Halliday and Matthiessen 2004:58-59;Thompson 2004:30, 49, 141;Fontaine 2013:10).
Theme forms a major part of the Textual function. In Halliday's view, Theme acts 'as the point of departure of the message' (Halliday and Matthiessen 2004:64), and contains 'what the sentence is about' (Halliday 1967:214). Thus, Theme serves as a starting point for the message wherein aboutness is denoted. As can be seen from the definition above, Halliday proposes two features for Theme: one is structural, 'the point of departure' i.e., initial position; the other is semantic, 'what the clause is about'. For example, the constituents 'John', 'the play' and 'yesterday' in the following sentences (taken from Halliday 1967:214) are Themes because they: (i) are in the initial position; and, (ii) denote the aboutness of the sentence. 11-John saw the play yesterday. 12-The play John saw yesterday. 13-Yesterday John saw the play. This bilateral view adopted by Halliday has been criticised by various linguists. This point is discussed later in detail.
In contrast to the Prague School linguists' view as stated in Section 2, Halliday's approach is described as 'splitting' since he considers that: (i) Given is an independent notion of Theme, and it does not play any role in defining Theme; and, (ii) Theme and Given "do not necessarily coincide" (Halliday 1967:214;Fries 1995:1). Example (3) mentioned above supports Halliday's view. However, this does not mean that Theme and Given are not related and do not interact with each other. They represent the unmarked relation, i.e. Theme typically denotes Given information (Fries 1995:5;Halliday and Matthiessen 2004:93). A good example has been given in sentence (2) above.
SFL linguists (Halliday and Matthiessen 2004:79;Fontaine 2013:149-152;Forey and Sampson 2017:132-133) generally divide Theme into three types, connecting them to the three metafunctions mentioned above. They are: i. Experiential Theme. It can be a participant, circumstance or process. ii. Interpersonal Theme. It includes any element that has a thematic interpersonal role, such as an element that expresses the speaker's viewpoint (e.g. unfortunately). iii. Textual Theme. It includes an element that does not function experientially or interpersonally. Instead, they tend to signal the relationship between the clause and the neighbouring one within the text, such as connective elements that link one clause to a preceding clause.
The following examples, taken from Thompson (2004:144,158,159) respectively, clarify the differences between these types (in which Themes are underlined).
14-The Queen yesterday opened her heart to the nation. 15-Thus, disorder will tend to increase with time. 16-But surely the course does not start till next week.
The constituents "The Queen", "disorder" and "the course" in the three examples act as experiential Theme. The constituent 'surely' in (15) acts as interpersonal Theme. The constituents "Thus" and "but" in sentences (15-16) act as textual Theme.
Hence, I agree with Thompson (2007:673) who sees Theme as a region of the clause. This means Theme is not necessarily linked to one particular constituent; rather, it can also be several elements that co-exist consecutively and denote different types of 'points of departure' or 'orientation'. Now we turn to examine the bilateral definition of Theme as adopted by Halliday, as briefly shown above. This definition has been questioned by some linguists. For example, Huddleston (1988:158-162) argues that Theme, as an 'initial position constituent', must not be confused with the notion of 'Topic', which denotes 'what a clause is about'. Huddleston (1988:159) provides the following example to clarify his view: 17-She broke it.
The topic of this sentence differs depending on the context. It can be the pronoun 'She' if this sentence is an answer to: 'what did she do?' It can be the pronoun 'it' if the sentence is the answer to: 'what happened to it?' Nevertheless, the Theme of the sentence is 'She' in both contextual senses. In the first sense, both Theme and Topic are conflated in one constituent, (i.e., 'She'). In the second sense, they are not conflated. Downing (1991:122) also has examined the definition provided by Halliday for Theme. She argues that Halliday's definition leads to confusion between two different notions: Theme and Topic, and they, as she believes, must be disassociated. Theme represents the element that is located in the initial position while Topic expresses the aboutness of the sentence. Downing then concludes that 'the point of departure is not necessarily what the message is about, although the two may coincide in one wording' (ibid). She provides the following example to clarify her argument: 18-In 390 B.C. the Gauls sacked Rome. Downing (1991:123) claims that according to Halliday's view, the Theme of the sentence that represents the Topic is the circumstantial Adjunct 'In 390 B.C.'. She then presents her disagreement with this because the Adjunct cannot be seen as a constituent that expresses Topicality. Rather, she claims that the constituent that expresses the aboutness (hence, Topic) of the sentence is either 'the Gauls' or 'Rome'. In my view, Downing's argument is convincing since the above sentence can be an answer to the following questions: when did the Gauls sack Rome?
By taking the arguments of Huddleston and Downing given above into consideration, I accept that Theme and Topic have to be separated because each notion has its functional role and identification criteria. Theme is the first point at which the writer/speaker starts his sentence to set up the framework for the utterance that follows it, whereas Topic is a semantic notion which denotes 'what a sentence is about', and thus lends its identification to the context (see: Huddleston 1988:159).
As a result of the discussion above, Downing (1991:128) has dealt with the concept of Theme as 'framework' that includes several textual functions. She explicitly mentioned that she adopted this view under the influence of the works of several functionalists, namely: Chafe (1976), Thompson (1985), and Lowe (1987). For example, the 'framework' notion adopted by Chafe includes those initial elements which set 'the spatial, temporal or individual framework with which the main predication holds ' (1976:50). A similar view has been adopted by Fries (1997:232), wherein he defines Theme as an element that 'provides a framework for the interpretation of the clause'. He adds that this framework directs the addressee to what is about to be delivered in the message. Moreover, Downing benefited from the term 'situational framework' given by Lowe (1987:7) to include those Themes that are indicted by the clause complex, such as initial clauses of condition, purpose and means. One important addition that Downing has made is adopting the notion 'Subjective and Logical framework' to incorporate all functions provided by what Halliday has labelled as interpersonal and textual Themes. Having taken these procedures into account, Downing has produced a model that she claims is appropriately applicable to the analysis of the initial elements in English sentences. This model includes three types of framework and each framework has its own kinds of Theme (Downing 1991:128). They are: a. Individual Framework. b. Circumstantial framework. c. Subjective and Logical framework.
The advantage of this model is that it places the focus upon the initial position elements (hence, Theme) as being a framework which establishes the scope of the message delivered by the writer/speaker rather than denoting the Topic. However, this does not mean that Theme and Topic cannot be conflated in one entity and Downing (1991:128) has made it explicit that in English such conflations can happen. Another advantage of this model is that it helps to identify the various functions of initial elements, such as signalling 'the angle from which the speaker…. is about to project his message' (Downing 1991:129) and thereby indicating the speaker's attitude towards the content of the message and thus allows the message to develop in a coherent manner (ibid). As far as I know, this model has not been applied to the study of Theme in Arabic. Hence, this model will serve as the platform by which Arabic thematic structures will be analysed functionally.

METHODOLOGY AND DATA DESCRIPTION
This paper is based on empirical research. Hence, real texts of Modern Standard Arabic are analysed. This is crucial since real texts contain authentic, real-world uses of language and thus are helpful for identifying the communicative purposes which in turn are a desirable target for SFL theory. This is emphasised explicitly by Bloor and Bloor (1995:4) who point out that it is important for SFL linguists to study actual instances that are used by writers/speakers.
As for the data examined in this study, the following texts were consulted during the analysis: -Maqālāt fī Kalimāt "articles in a few words". This book, published in 2012, is a collection of short articles written by the well-known Syrian writer ʿAlī al-Ṭanṭāwī (referred to as al-Ṭanṭāwī in this study). These articles were written at different periods between the years 1949-1959 and were later collected into one volume in 2012. -Al-Lawn al-Ākhar "the other color". A novel written by the Egyptian novelist Iḥsān ʿAbd al-Quddūs (referred to as ʿAbd al-Quddūs in this study) that was published in 1984.

-Bayna Siyāsati Tramb wa Shakhṣiyyatih "between
Trump's policy and his personality". A political opinion article written by the Saudi writer ʿAbd al-Raḥmān al-Rāshid (referred to as al-Rāshid in the study) that was published in al-Sharq al-Awsaṭ newspaper in 2018. All these sources represent Modern Standard Arabic; a united form of Arabic that inherits its major characteristics from Classical Arabic and is used in formal settings across the present-day Arab world (Van Mol 2003:36-27;Versteegh 2014:227). The total number of the sentences analysed in this study is 70 (extracted from the three sources noted above). This study does not require a large and balanced sample but it is representative on the basis of the way Theme functions in actual Arabic usage. Nor does this study aim to deliver a statistical analysis, which normally requires a larger corpus. Instead, this study aims to explore in-depth how Theme acts in real texts and identify its possible contextual functions. Utilising authentic examples from Arabic language texts is sufficient to establish the arguments put forward in this study. To determine the actual functions of Theme in the examples requires investigation of the contextual background of each sentence. Therefore, each example included in the study was accompanied by a contextual analysis and is also represented in a diagram box to make its understanding more accessible to readers.
One final point which should be noted is that in the analysis I consider several issues related to the analysis of Theme beyond what Downing adopted in her model and its application to the English language. This includes: (i) examining the nature of the relation between Theme and Topic in each sentence analysed; and, (ii) investigating the relation between the informational units (Given-New) and the Thematic units.

Individual Framework
As defined by Downing (1991:129), this type of framework is expressed by the initial elements that refer to some participants in a given situation. In my view, this definition fits better with English because participants in English sentences are typically located in the initial position (e.g., Subject). Arabic, by contrast, is a free word-order language whereby NP and VP can be equally placed in the initial position. Therefore, I would modify the definition as follows: Individual framework is established by the initial elements that refer to some participants or processes. In other words, Theme, in Arabic, in this type can accordingly be either a noun or verb equally (participant or process in SFL terminology). This differs from English where verbs are not typically thematised since it is very rare for them to initialise sentences (Halliday 1967:218;Baker 1992:135). Given that in this paper we are concerned with Arabic, the analysis of this type of framework is divided into two structural sub-divisions: the nominal sentence and the verbal sentence.

Nominal sentence
By analysing several sentences related to this category, I found that most of the initial NPs act syntactically as mubtadaʾ "the subject in the nominal sentence". Hence, they function as Theme which is typically conflated with the notion of Topic, i.e., they express 'what the clause is about'.
The following examples (19-20) provide supportive evidence for this case.
The contextual background of this sentence is as follows: the writer of the article is talking about the policy of the President of the US Donald Trump. In this particular sentence, the writer aims to emphasise that the president, any president of the US or otherwise, is not one person who makes decisions individually. Rather, he is the primary organ of a large body of the organisation that leads the country. Therefore, the element that expresses the angle from which the writer departs his point is the constituent al-raʾīs "the president". Thus, it acts as a Theme. Also, this constituent indicates the topic that is being talked about. The constituent muʾassasah "institution", is by contrast regarded as Rheme. Furthermore, the Theme of this sentence is contextually given in the previous discourse in the article. Hence, this sentence follows the unmarked order (Given-to-New). The following box diagram visualises the analysis.

Theme Rheme
Given New Another alternative way to refer to the function of Theme as being contextually given is to use a demonstrative pronoun. This exemplified by sentence (20).
The constituent hādhā "this" refers anaphorically to a long paragraph in the article. hādhā raʾyī alladhī tasʾalīnah.

Theme Rheme
Given New In a few sentences in the data, the NPs are thematised and do not denote the topic of the sentence. This occurs when Attributes are fronted, which is a syntactic parallel to what is known as khabar muqaddam "fronted predicate" in Arabic grammar. The explanation of the following sentence will clarify this case.
The narrative background of this sentence can be described as follows: there was a man who wanted to get off from a tram that was crowded with people. Therefore, he headed to the door located at the rear of the tram. The tram officer did not allow him to get off, saying it is prohibited. Here, the sentence is thematised by the attribute mamnūʿun "prohibited", which is grammatically a predicate. This is followed by the constituent that acts grammatically as mubtadaʾ, which functionally expresses the Topic of the sentence. Hence, this case shows that Theme and Topic are not conflated in the same constituent. One of the interesting features of this sentence is that the Theme carries New information while the Rheme al-nuzūlu min al-warāʾ "to get off from the back" is already given in the previous context in the form of the imperfect verb yanzil "gets off" and in the phrases ākhir al-tram "the back of the tram". The word order followed in the sentence is grammatically and functionally 'marked' because: (i) it is a divergence from the basic grammatical order of the nominal Arabic sentence as mentioned in Section 2, i.e. the constituent al-nuzūl should be typically placed at the beginning because it is grammatically analysed as mubtadaʾ; and, (ii) and it follows New-to-Given order, a case that is described by Halliday and Matthiessen (2004:91) as 'marked'. The following box diagram visualises the correspondence between Thematic system, syntactical functions, and information structure.

Verbal sentence
Since Arabic is a free word-order language, there are a variety of word orders related to this type of sentence. Therefore, I have divided these orders into two main categories: initial NP and initial VP. The examples associated with each category are analysed in the following subsections. 1-Initial NP: In terms of syntactical arrangements, the NP element is located at the beginning of the sentence. This NP element is then followed by a verb. This category is commonly labelled by (SVO) order. This word order is regarded as 'unmarked' in English since, grammatically, it is the most common order and because, from a functional perspective, the subject is conflated with the Theme of a sentence (Comrie 1981:82;Halliday and Matthiessen 2004:73). However, in Arabic, the case is different. Grammatically, this order is seen 'marked' and alternative to (VSO) order as shown in Section 2. Yet, from a functional perspective this order is seen as 'unmarked' given that the pre-verbal subject (mubtadaʾ in Arabic terms) is conflated with the Theme. This shows a functional similarity between Arabic and English. The following examples relate to this category: In sentence (22), the subject of the sentence al-raʾīsu "president" is located initially, followed by the verb yastayqiẓu "wake". The subject represents a participant that functionally orients the readers to the Theme and at the same time to the Topic of the sentence. Hence, Theme and Topic are conflated in one word here. In terms of information structure, the content of the Theme is directly given in the title of the article and repeated several times.

Theme Rheme
Given New Similarly, sentence (23) is taken from a narrative discourse. Here, one of the novel characters is talking to her friend, expressing her honour to know someone like him. She uses the second person pronoun anta "you" as an initial element, which is followed by the main verb taʿlamu "know". The Theme of the sentence is realized by the pronoun anta that also introduces the Topic. By investigating the context, this thematic constituent is given by the previous context as it refers to her fiancé, Ḥasan. Hence, the referent of the initial pronoun is known to the readers. The remainder of the sentence represents the Rheme in which its constituents are partially given as shown in the box diagram below.

Theme Rheme
Given New Given As we have seen from the analysis of the above examples, Themes are expressed by definite NPs. Hence, those Themes are Given or familiar to the addressee. This case represents the unmarked relation between Theme and Given according to SFL theory (Halliday and Matthiessen 2004:93). However, in an unusual case, Theme can be indefinite. Therefore, it expresses New information. This is exemplified by the following sentence: The element kathīrūna "many" expresses the Theme of the sentence. This element does not refer to a particular group of people: consequently, it expresses New information which has not been communicated before in the previous discourse. This case is seen by SFL linguists as 'marked', i.e., untypical (Halliday and Matthiessen 2004:95;Bloor and Bloor 1995:72;Thompson 2007:672). The Arabic data examined in this study confirms this claim from the few examples that have been identified. Such a result runs counter to Abdul-Raof's (1998:74-81) argument stated in Section 2, where he points out that Theme in Arabic has to be a definite NP. 7 As for the Rheme of this sentence, it is better described to express Given information. Part of this information is contextually mentioned, such as the pronoun -hu "him" and the constituent siyāsatin "policy". The remaining part can be inferred i.e., it can be deduced from a word that has been given in a previous sentence, namely fahm "understanding". This word can be seen to share a similar sense to yaʿtaqidūn "they think" in some contexts.

2-Initial VP:
This is the second structural order that is related to the verbal sentence category. This order is deemed rare and highly 'marked' in English, whereas it is not seen as 'marked' in Arabic verbal sentences (Halliday 1967:218;Baker1992:135). In this pattern, the verb functions as Theme. Let us now examine the following examples (25-26): The simple sentence fājaʾa Tramb al-jamīʿa "Trump surprised everybody" follows the standard/unmarked order in Arabic (VSO). The discourse in which this sentence is given is that the writer is describing one of Trump's meetings with the NATO Secretary General. Here, the writer is narrating several actions that were performed by Trump, such as: entering the meeting room; sitting opposite the secretary; and, surprising everybody. The latter action is the one given in the sentence above. The sentence starts with the verb fājaʾa "surprised". Hence, it acts as Theme. Meanwhile, this verb carries New information since it cannot be recoverable from a previous context. The remainder of the sentence expresses the Rheme, which is Given because the constituent Trump has been repeated several times and because the constituent aljamīʿ "everybody" refers to who was in the meeting room at the time, i.e., journalists and photographers, and which are both contextually Given. Consequently, this sentence follows the marked order New-to-Given. Rhetorically, the reason for locating the verb in the initial position in this sentence seems to be because the writer aims to draw the reader's attention to the actions rather than to the person who did it. This is supported by the previous chain of actions attributed to Trump by the writer. Bardi (2008:463) states that this stylistic strategy is a feature of the newspaper article, especially when the subject refers to the same participant. Specifically, in this sentence it is Trump who executes all these actions. One remaining point that needs to be mentioned here is that the Theme in this sentence is not conflated with Topic, since the latter is realised by the post-verbal subject, namely 'Trump'.

fājaʾa Tramb al-jamīʿa
Theme Rheme New Given This sentence is given in the form of narrative discourse. Here, the writer is narrating what happened to Mirfat after she has left her country and is heading to Sudan. Similar to the previous sentence, the verb waṣalat "arrived" is placed initially and acts as the Theme of the sentence, and also denotes New information. The subject and the prepositional phrase, Mirfat ilā al-Kharṭūm, follows the verb and function as the Rheme that expresses the Topic of the sentence and denotes Given information. However, by considering this sentence from a different perspective, it may be reasonable to claim that the constituent waṣalat can be regarded as indirectly Given since the semantic content of the verb waṣalat can be deduced by the reader. In other words, since the reader knows that Mirfat has left her country to move to Sudan (a given situation in the preceding discourse), they are cognitively capable of inferring that Mirfat will have arrived at her destination anyhow. This explanation can be supported by what Halliday and Matthiessen (2004:92) have argued, namely that Given information is either already known by the listener or is accessible to them. The notion of 'accessibility' mentioned here can include the psycholinguistic inference process undertaken by the reader/addressee. This 'inference' process is, however, not a restricted notion within SFL theory since it has an equivalent notion, namely 'Sub-topic', as has been adopted by FG's linguists (Dik 1997 2:223), or 'inferrables' as has been adopted by Prince (1981:236).

Circumstantial Framework
This type of framework does not denote the aboutness of the sentence (Downing 1991:129). Rather, by borrowing from Thompson's (2004:109) definition of circumstantial Adjunct, it presents "the background against which the process takes place", i.e. it shows the description of the way how, when and where the process is executed (Fontaine 2013:79). Downing (1991:128) divides this type into three subtypes: namely, Spatial, Temporal and Situational frameworks. I will show empirically how Theme interacts with each of these subtypes in Arabic.

Spatial framework
The function of this framework is to set up the location where the processes occur. This framework is typically realised by initial circumstantial (spatial) Adjuncts that express specific places (Downing 1991:134). Consider the following (27) example: In this sentence, the writer is narrating a past event that he experienced and which can be described as follows: as he was walking one day in an area called Miskiyyah in Damascus, he came across a hemiplegic man begging people. The writer prefers to start his sentence with the circumstantial Adjunct fī awwal al-Miskiyyah "at the beginning of Miskiyyah" to establish the spatial background (spatial framework in Downing's term 1991:134) of where the action wajadtu marīḍan maflūjan "I found a hemiplegic person" occurred. Hence, the Theme of the sentence is performed by this spatial framework. The remaining part of the sentence wajadtu marīḍan maflūjan "I found a hemiplegic person" denotes the Rheme. By examining the larger discourse, the Topic of the sentence is the affixed pronoun to the verb -tu "I".

Theme Rheme
New or indirectly Given New Given New

Temporal framework
This framework marks a Theme that signals a temporal discourse span. Downing (1991:132) states that this framework denotes three types of temporal meanings: a point of time, a period, or frequency. The second of these ('a period') has not been recorded in the present data. I will, therefore, analyse pertinent examples related to the other two. Consider the following examples (28-29): The sentence is given in narrative discourse and is uttered by one of the characters who starts his sentence with a circumstantial Adjunct that denotes the temporal background. This is realised by the prepositional phrase fī al-yawmi al-tālī "the next day". This Adjunct indicates a point of time that acts as Theme since it is placed in the initial position of the sentence. What comes after the Adjunct represents the Rheme. The Topic of the sentence is the referential pronoun -tu "I", which is, grammatically, the subject of the sentence.
Grammatically, Abdul-Raof (1998:71) argues that when a circumstantial Adjunct is located in the initial position, there is actually a fronting process, i.e. there is a divergence in the clause order of the sentence. Functionally, according to SFL theory, when a circumstantial Adjunct occupies a thematic position, it is regarded as 'marked' Theme (Bloor and Bloor 1995: 76;Halliday and Matthiessen 2004:73). Thompson (2004:145) claims that marked Theme occurs when there is a contextual reason that overrules the unmarked choice. Hence, with respect to sentence (28), there appears a textual reason behind locating the circumstantial Adjunct fī al-yawmi al-tālī "the next day" in the initial place. This

ALLS 12(4):15-29
can be explained as follows: the writer may wish to keep the sentence cohesive with the previous segments of the text because the writer was narrating several interconnected actions that occurred in the past. Hence, in my view, placing this Adjunct in the final position would lead to an incohesive presentation of the text segments. A final point which should be noted here is that the Given information in this sentence is expressed by the attached pronoun -tu "I" and the prepositional phrase ilā al-maqhā "to the café." This sentence begins with the circumstantial temporal Adjunct fī kulli ṣabāḥ "every morning", which signals the frequency of the action yadhabu ilay-hi Ḥasan "Ḥasan goes to him". Here, the Theme of the sentence is realised by the initial Adjunct. According to the context, the Topic of the sentence is realised by the pronoun -hi "him" attached to the preposition ilā "to" which refers to Yāsir. In terms of information structure, the only constituents that are contextually Given are the pronoun hi-"him" and the freestanding subject Ḥasan.

Situational framework
This framework includes those initial circumstantial Adjuncts that are not classified under Individual, Spatial or Temporal frameworks. Nevertheless, they provide "a point of departure for an ensuing stretch of discourse", and can also function as Theme (Downing 1991:135). A wide range of Adjunct initiators can be included here, such as: non-finite clauses; purpose clauses; contingency clauses (e.g. conditional, concession, causal clauses, etc.); manner; means; and, exception (Ibid). 8 In the following section, I have focused on the types that their actual instances are recorded in within the present data of Arabic.

Conditional Initiators
Arabic conditional clauses are normally introduced by one of the following particles: idhā, in and law, which are regarded equivalent to 'if' in English. Let us consider the following example: This sentence is initiated by the subordinate clause idhā kāna lā yurīdūnanī bayna-hum ka-wāḥidin minhum "If they do not want me to be as one of them" which refers to a possible situation that can take place in the real world. The speaker of this sentence sets up a span of the conditional framework in which the action presented in the main clause can consequently occur, i.e. if the case 'they do not want me part of their family group' is true, the action of leaving them will be true also. Therefore, what is denoted by the initial subordinate clause acts as a circumstantial thematic framework for the semantic content of the main clause fa-lan abqā maʿahum "then, I will not stay with them". One feature of this sentence is that the initial subordinate clause expresses the Topic of the sentence, which is contextually Given previously. Specifically, the speaker has been informed that those people he is referring to in the sentence do not wish him to marry one of their relatives. This last point supports Haiman's (1978:585) argument that, from a universal perspective, subordinate clauses in conditional sentences and Topic are conflated because both share the feature of being Given. As a result, the analysis of the conditional example above indicates that Theme and Topic are conflated in the initial subordinate clause.

Theme Rheme
Given New

Causative Initiators
In normal order, the subordinate clause of a causative sentence in Arabic does not occupy an initial position. Rather, it follows the main clause (Badawi et al, 2004:610). However, this order can be reversed, as in the following (31) example. In this sentence, the writer is discussing the attitude of an anonymous group of people he refers to by the collective noun al-jamīʿ "everyone" in the previous context. Those people know that Trump is different from the former US Presidents, and because of the confusion between his personality and his policy, they warn others not to deal with him. The causative sentence is divided into clauses: an initial subordinate clause which denotes the cause bi-sababi al-khalṭi bayna shakhṣiyyatihi wa siyāsatihi "because of the confusion between his personality and his policy" and final main clause which denotes the effect yuḥadhdhirūnanā mina al-tʿāmuli maʿah "they warn us to deal with him". The writer begins his sentence with a clause that sets up the situational framework which provides the causal circumstance for what follows it. Therefore, this framework acts as a 30idhā kānū lā yurīdūnanī If-Part be-3Msc.Pl.Perf-they-Nom no want-3Msc.Pl.Imperf-they -Nom-me-Gen bayna-hum ka wāḥidin min-hum between-Prep-them-Gen as-Prep def-one.Sg.Msc.Gen from-Prep-them-Gen fa-lan abqā maʿahum then-Part-not-Part stay-1Msc.Sg.Perf with-Prep-them-Gen "If they do not want me to be amongst them as one, then I will not stay with them" (ʿAbdu al-Quddūs 1984:85).
Theme. Furthermore, the content of this framework appears to be conflated with the Topic of the sentence, i.e. it is a confusion between his personality and his policy that the writer is talking about. This Topical content can be recoverable from the preceding discourse. Hence, it denotes Given information.

Theme Rheme
Given New

Concession Initiators
The subordinate clause of this sentence (32) contains a demonstrative pronoun that refers back to the information that has been given in the previous context, which is the comfort relation between Mirfat and her father as she always freely tells him what she thinks. The Arabic word that is used in the sentence to refer to this meaning is muṣāraḥah "frankness". However, concerning this close relation between Mirfat and her father, the speaker thinks that Mirfat is more comfortable talking frankly with him, more so than her father. Here, the speaker starts his statement with the framework that establishes the situational background that can be contrasted with what comes in the main clause. Hence, the initial subordinate clause functions as Theme, which also expresses the Topic of the sentence. raghma dhālika

Theme Rheme
Given New

Subjective and Logical Framework
Downing (1991:129) did not provide an analysis of the examples related to this type of framework since she believes that an extensive analysis has already been conducted in the context of English language to cover this matter. with-Prep-him-Gen. "Because of the confusion between his personality and his policy, they warn us from dealing with him" (al-Rāshid 2018). Gen-her-Gen "Despite that, I (have) felt that Mirfat is more comfortable to talk frankly with me than her father" (ʿAbdu al-Quddūs 1984:8).

32-
The coordinator wa "and" with which the sentence (33) begins acts as a Logical framework because it provides a linking function; i.e., it links the content of this sentence with the previous context (Forey and Sampson 2017:133 about 'textual Theme'). The contextual background of this sentence is as follows: one of the Arab scholars had visited some European countries and he noticed that people were using their mother language only in verbal interactions. Following this, the writer comments with the above sentence, that this is the characteristic of all nations that are proud of their language. As can be seen, the conjunction wa "and" is logically linking between what has been said before in the previous context and the writer's comment. This is followed by a demonstrative pronoun, acting grammatically as mubtadaʾ that functions as an Individual framework/Theme. This framework expresses also the Topic of the sentence. This sentence is (34) uttered by Mirfat's uncle in reply to her when she said that she would live with her fiancé wherever he lives, even in his home country, Sudan. Her uncle begins his statement with a modal element that reveals his subjective attitude. This element is inna "indeed" which denotes a certainty meaning for what follows it. This function belongs to Downing's label 'Subjective framework'. The Subjective framework is followed by an Individual framework that is realised by the participant al-mujtamaʿa "the society".

CONCLUSION
This study aimed to explore the functions of Theme in Arabic as used in actual real-world texts from an SFL perspective. It also investigated how Theme in Arabic interacts with other grammatical functions (e.g., verb, subject and object) and pragmatic functions (e.g., Given, New). The analytical framework applied in this study was based on the work of Downing (1991) who implemented a modified SFL model in an analysis of Theme in English. As such, in this new context of usage it provides a new perspective on the functions of Theme in Arabic. The main findings of the present study can be summarised as follows.
-Theme in Arabic, as in English, acts as a framework within which Rheme is interpreted. (See: Fries 1995:4 for the case of English). This framework provides a variety of functions, such as Individual, Circumstantial and Subjective and Logical frameworks. -Since Arabic is a free word-order language, I needed to expand the scope of the Individual framework to include not only NPs, but also VPs. Consequently, the initial verb can commonly function as Theme in Arabic and, in contrast to English, it should not be regarded as 'marked' case. This point illustrates a difference with Downing who appears to restrict the Individual framework to NPs. -Another interesting result arising from the empirical analysis is that the function of Theme should not be restricted to specific grammatical functions (e.g., Verb, Subject, Object, Adjunct, etc.) or pragmatic functions (e.g., Topic, Given and New information). Theme in Arabic has the flexibility to interact with all of these functions. This result is in contrast with Aziz (1988) and Abdul-Raof (1998) who limited the expression of Theme in Arabic to NPs, overlooking VPs. This demonstrates the intrinsic flexibility that features in Theme in the usage of language in real context. -In contrast to the Prague linguists' view as reported in Section 2, the analysis found evidence from Arabic that the conflation between Theme and Given is not obligatory; i.e., Theme can be either Given or New. This sup- cherishes-3Fem.Sg.Imperf with-Prep def-language.Sg.Msc.Gen-its-Gen "And this is the habitude of every living nation that cherishes its language" (al-Ṭanṭāwī 2011:214)